Archives des data catalog

Data mapping, the key to regulatory compliance

by Zeenea Software | Feb 23, 2021 | Data Catalog, Data Compliance

Regardless of the business sector, data management is a key strategic asset for companies. This information is key to innovate on tomorrow’s products and services. In addition, with the rise of new technologies such as Big Data, IoT or artificial intelligence, organizations are collecting exponential volumes of data, from different sources and in a variety of formats.

In addition, with increasingly strict data regulations such as the GDPR, data processing now requires the implementation of appropriate security measures to protect against information leaks and abusive processing.

The challenge lies in re-appropriating its data assets. In other words, companies are looking for solutions to maintain data mapping that reflects their operational reality.

What is data mapping?

Let’s go back to the basics: data mapping allows users to evaluate and graphically visualize data entry points as well as their processes. There are several types of information to be mapped, such as:

The information on data
The data processes themselves

Information on data

The idea of data mapping is to work on data semantics (the analysis of word meanings and relations between them).

This work is not done on data itself, but rather on metadata. Metadata gives meaning and context to data, which in term enables a better understanding of it. It can represent the data’s “business” name, its technical name, its location, when it was stored, by whom, etc…

By setting up semantic rules and a common data language through a business glossary, companies can identify and locate their data, and thus facilitate access to data for all employees.

On data processes

Concerning data processing, it is important to identify :

data flows: with their sources and destinations,
data transformations: all the transformations applied to the data during its processing.

A powerful tool : Data Lineage

Data lineage is defined as the data’s life cycle and shows all of the transformations that took place between its initial state and its final state.

Data lineage is strongly linked to data mapping and processing; it is essential to see which data are concerned by these processes and be able to analyze the impacts very quickly. For example, if a process anomaly has caused a corruption, it is possible to know which data is potentially affected.

In another case, the mapping from a data point of view must be able to tell which data sets the data comes from. Thus, one can quickly analyze the impacts of a change in the source data set by quickly finding the related data.

The benefits of implementing data mapping

With a mapping solution, companies can therefore respond to data regulations, in particular the GDPR, by answering these questions:

Who? Who is responsible for the data or a processing operation? Data protection? Who are the possible subcontractors?
What? What is the nature of the data collected? Is it sensitive data?
Why? Can we justify the purpose of collecting and processing the information?
Where? Where is the data stored? In what database?
Until when? What is the retention period for each category of data?
How? How is it stored? What is the framework and what security measures are in place for the secure collection and storage of personal data?

By answering these questions, IT Managers, Data Lab Managers, Business Analysts and Data Scientists are able to make their work on data relevant and efficient.

These highlighted questions allow companies to comply with regulations but also to :

Improve data quality: Providing as much information as possible to allow users to know if the data is suitable for use.
Make employees more efficient and autonomous in understanding data through graphical and ergonomic data mapping.
Analyze data in depth, so that better decisions can be made based on the data and ultimately become a data-driven organization.

Conclusion

It is by having properly mapped information that an enterprise will be able to leverage its data. Quality data analysis is only possible with data that is properly documented, tracked, and accessible to all.

Are you looking for a data mapping tool?

You can learn more about our data catalog solution by visiting the links below:

Zeenea Data Catalog
Zeenea Studio – the solution for data managers
Zeenea Explorer – making your data teams’ daily life easier

or directly schedule an appointment for a demo of our solution

IoT in manufacturing: why your enterprise needs a data catalog

by Zeenea Software | Jan 12, 2021 | Data Catalog, Data Inspiration

Digital transformation has become a priority in organizations’ business strategies and manufacturing industries are no exception to the rule! With stronger customer expectations, increased customization demands, and the complexity of the global supply chain, manufacturers are in need to find new, more innovative products and services. In response to these challenges, manufacturing companies are increasingly investing in IoT (Internet of Things).

In fact, the IoT market has boosted exponentially over the past few years. IDC reports the IoT footprint is expected to grow up to $1.2 trillion in 2022, and Statista, by way of contrast, is confident its economic impact may be between $3.9 and $11.1 trillion by 2025.

In this article, we define what IoT is and some manufacturing-specific use cases as well as explain why a Zeenea Data Catalog is an essential tool for manufacturers to advance in their IoT implementations.

What is IoT?

A quick definition

According to Tech Target, the internet of things, (IoT), “is a system of interrelated computing devices, mechanical and digital machines, objects, or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.”

A “thing” in the IoT can therefore be a person with a heart monitor implant, an automobile that has built-in sensors to alert the driver when tire pressure is low or any other object that can be assigned an ID and is able to transfer data over a network.

From a manufacturing point of view, IoT is a way to digitize industry processes. Industrial IoT employs a network of sensors to collect critical production data and uses various software to turn this data into valuable insights about the efficiency of the manufacturing operations.

IoT use cases in manufacturing industries

Currently, many IoT projects deal with facility and asset management, security and operations, logistics, customer servicing, etc. Here is a list of examples of IoT use cases in manufacturing:

Predictive maintenance

For industries, unexpected downtime and breakdowns are the biggest issues. Hence manufacturing companies realize the importance of identifying the potential failures, their occurrences and consequences. To overcome these potential issues, organizations now use machine learning for faster and smarter data-driven decisions.

With machine learning, it becomes easy to identify patterns in available data and predict machine outcomes. This works by identifying the correct data set, combining it with a machine to feed real-time data.This kind of information allows manufacturers to estimate the current condition of machinery, determine warning signs, transmit alerts and activate corresponding repair processes.

With Predictive maintenance through the use of IoT, manufacturers can lower the maintenance costs, lessen the downtime and extend equipment life, thereby enhancing quality of production by attending to problems before equipment fails.

For instance, Medivators, one of the leading medical equipment manufacturers, successfully integrated IoT solutions across their service and experienced an impressive 78% boost of the service events that could be easily diagnosed and resolved without any additional human resources.

Asset tracking

IoT asset tracking is one of the fastest growing phenomena across manufacturing industries. It is expected that by 2027, there will be 267 million active asset trackers in use worldwide for agriculture, supply chain, construction, mining, and other markets.

While in the past manufacturers would spend a lot of time manually tracking and checking their products, IoT uses sensors and asset management software to track things automatically. These sensors continuously or periodically broadcast their location information over the internet and the software then displays that information for you to see. This therefore allows manufacturing companies to reduce the amount of time they spend locating materials, tools, and equipment.

A striking example of this can be found in the automotive industry, where IoT has helped significantly in the tracking of data for individual vehicles. For example, Volvo Trucks introduced connected-fleet services that include smart navigation with real-time road conditions based on information from other local Volvo trucks. In the future, more real-time data from vehicles will help weather analytics work faster and more accurately; for example, windshield wiper and headlight use during the day indicate weather conditions. These updates can help maximize asset usage by rerouting vehicles in response to weather conditions.

Another tracking example is seen at Amazon. They are using WiFi robots to scan QR codes on its products to track and triage its orders. Imagine being able to track your inventory—including the supplies you have in stock for future manufacturing—at the click of a button. You’d never miss a deadline again! And again, all that data can be used to find trends to make manufacturing schedules even more efficient.

Driving innovation

By collecting and audit-trailing manufacturing data, companies can better track production processes and collect exponential amounts of data. That knowledge helps develop innovative products, services, and new business models. For example, JCDecaux Asia has developed their displaying strategy thanks to data and IoT. Their objective was to have a precise idea of the interest of the people for the campaigns they carried out, and to attract their attention more and more via animations. “On some screens, we have installed small cameras, which allow us to measure whether people slow down in front of the advertisement or not.”, explains Emmanuel Bastide, Managing Director for Asia at JCDecaux.

In the future, will displaying advertising be tailored to individual profiles? JCDecaux says that in airports, for example, it is possible to better target advertising according to the time of day or the landing of a plane coming from a particular country! By being connected to the airport’s arrival systems, the generated data can send the information to the displaying terminals, which can then display a specific advertisement for the arriving passengers.

Data catalog: one way to rule data for any manufacturer

To enable advanced analytics, collect data from sensors, guarantee digital security and use machine learning and artificial intelligence, manufacturing industries need to “unlock data,” which means centralizing in a smart and easy-to-use corporate “Yellow Pages” of the data landscape. For industrial companies, extracting meaningful insights from data is made simpler and more accessible with a data catalog.

A data catalog is a central repository of metadata enabling anyone in the company to have access, understand and trust any necessary data to achieve a particular goal.

Zeenea data catalog x IoT: the perfect match

Zeenea helps industries build an end-to-end information value chain. Our data catalog allows you to manage a 360° knowledge base using the full potential of the metadata of your business assets.

Zeenea success story in the manufacturing industry

In 2017, Renault Digital was born with the aim of transforming the Renault Group into a data-driven company. Today, this entity is made up of a community of experts in terms of digital practices, capable of innovating while delivering agile delivery and maximum value to the company’s business IT projects. In a conference in Zeenea’s Data Centric Exchange (French), Jean-Pierre Huchet, Head of Renault’s Data Lake, states that their main data challenges were:

Data was too siloed,
Complicated data access,
No clear and shared definitions of data terms,
Lack of visibility on personal / sensitive data,
Weak data literacy.

By choosing Zeenea Data Catalog as their data catalog software, they were able to overcome these challenges and more. Zeenea today has become an essential brick in Renault Digital’s data projects. Its success can be translated into :

Its integration into Renault Digital’s onboarding: mastering the data catalog is part of their training program.
Resilient documentation processes & rules implemented via Zeenea.
Hundreds of active users.

Now, Zeenea is their main data catalog, with Renault Digital’s objectives of having a clear vision of the data upstream and downstream of the hybrid data lake, a 360 degree view on the use of their data, as well as the creation of several thousands of Data Explorers.

Zeenea’s unique features for manufacturing companies

At Zeenea, our data catalog has the following features to solve your problematics :

Universal connectivity to all technologies used by leading manufacturers
Flexible metamodel templates adapted to manufacturers’ use-cases
Compliance to specific manufacturing standards through automatic data lineage
A smooth transition in becoming data literate through compelling user experiences
An affordable platform with a fast return on investment (ROI)

Are you interested in unlocking data access for your company?

Are you in the manufacturing industry? Get the keys to unlocking data access for your company by downloading our new white paper “Unlock data for the manufacturing industry”

Download our white paper

Machine Learning Data Catalogs: good but not good enough!

by Zeenea Software | Nov 10, 2020 | Data Catalog

How can you benefit from a Machine Learning Data Catalog?

You can use Machine Learning Data Catalogs (MLDCs) to interpret data, accelerate the use of data in your organization, and link data to business results.

We provide real-world examples of the smart features of a data catalog in our previous articles:

It is clear that this data catalog specificity is a cornerstone in choosing the right data cataloguing solution. In fact, Forrester highlights exactly that in their latest report: “Now Tech: Machine Learning Data Catalogs, Q4 2020.”

In this document, they cite Zeenea Data Catalog as one of the key Machine Learning Data Catalog vendors on the market! However, as data professionals, you are aware that the “intelligent” aspect of a data catalog is a good solution, but not enough for you to achieve your data democratization mission.

Machine Learning Data Catalog vs Smart Data Catalogs: what’s the difference?

The term “smart data catalog” has become a buzzword over the past few months. However, when referring to something being “smart” most people automatically think, and rightly so, of a data catalog with only Machine Learning capabilities.

We at Zeenea, do not believe that a smart data catalog is reduced to only having ML features! In fact, there are different ways to be “smart”. We like to refer to machine learning as an aspect, among others, of a Smart Data Catalog.

The 5 pillars of a smart data catalog can be found in its :

Design: the way users explore the catalog and consume information,

User experience: how it adapts to different user profiles,

Inventory: provides an intelligent and automatic way to inventory,

Search engine: meets different expectations and gives intelligent suggestions,

Metadata management: a catalog that marks up and links data together using ML features.

This conviction is detailed in our article: “A smart data catalog, a must-have for data leaders” which was also given last September at the Data Innovation 2020 by Guillaume Bodet, CEO of Zeenea.

Watch the conference

What is a knowledge graph and how can it empower data catalog capabilities?

by Zeenea Software | Nov 4, 2020 | Data Catalog

Knowledge graphs have been interacting with us for quite some time. Whether it be through personalized shopping experiences via online recommendations on websites such as Amazon, Zalando, or through our favorite search engine Google.

However, this concept is still often a challenge for most data and analytics managers who struggle to aggregate and link their business assets in order to take advantage of them as do these web giants.

In fact, to support this claim, Gartner stated in their article “How to Build Knowledge Graphs That Enable AI-Driven Enterprise Applications” that:

“Data and analytics leaders are encountering increased hype around knowledge graphs, but struggle to find meaningful use cases that can secure business buy-in.”.

In this article, we will define the concept of a knowledge graph by illustrating it with the example of Google and highlight how it can empower a data catalog.

What is a knowledge graph exactly?

According to GitHub, a knowledge graph is a type of ontology that depicts knowledge in terms of entities and their relationships in a dynamic and data-driven way. Contrary to static ontologies, who are very hard to maintain.

Here are other definitions of a knowledge graph by various experts:

A “means of storing and using data, which allows people and machines to better tap into the connections in their datasets.” (Datanami)
A “database which stores information in a graphical format – and, importantly, can be used to generate a graphical representation of the relationships between any of its data points.” (Forbes)
“Encyclopedias of the Semantic World.” (Forbes)

Through machine learning algorithms, it provides structure for all your data and enables the creation of multilateral relations throughout your data sources. The fluidity of this structure grows more as new data is introduced, allowing more relations to be created and more context to be added which helps your data teams to make informed decisions with connections you may have never found.

The idea of a knowledge graph is to build a network of objects, and more importantly, create semantic or functional relationships between the different assets.

Within a data catalog, a knowledge graph is therefore what represents different concepts and what links objects together through semantic or static links.

Google example

Google’s algorithm uses this system to gather and provide end users with information relevant to their queries.

Google’s knowledge graph contains more than 500 million objects, as well as more than 3.5 billion facts about and relationships between these different objects.

Their knowledge graph enhances Google Search in three main ways:

Find the right thing: Search not only based on keywords but on their meanings.
Get the best summary: Collect the most relevant information from various sources based on the intent.
Go deeper and broader: Discover more than you expected thanks to relevant suggestions.

How do knowledge graphs empower data catalog usages ?

Rich and in-depth search results

Today, many search engines use multiple knowledge graphs in order to go beyond basic keyword-based searching. Knowledge graphs allow these search engines to understand concepts, entities and the relationships between them. Benefits include:

The ability to provide deeper and more relevant results, including facts and relationships, rather than just documents,

The ability to form searches as questions or sentences — rather than a list of words,

The ability to understand complex searches that refer to knowledge found in multiple items using the relationships defined in the graph.

Optimized data discovery

Enterprise data moves from one location to another in the speed of light, and is being stored in various data sources and storage applications. Employees and partners are accessing this data from anywhere and anytime, so identifying, locating and classifying your data in order to protect it and gain insights from it should be the priority!

The benefits of knowledge graphs for data discovery include:

A better understanding of enterprise data, where it is, who can access it and where, and how it will be transmitted,
Automatic data classification based on context,
Risk management and regulatory compliance,
Complete data visibility,
Identification, classification, and tracking of sensitive data,
The ability to apply protective controls to data in real time based on predefined policies and contextual factors
Adequately assess the full data picture.

On one hand it helps implement the appropriate security measures to prevent the loss of sensitive data and avoid devastating financial and reputational consequences for the enterprise. On the other, it enables teams to dig deeper into the data context to identify the specific items that reveal the answers and find ways to answer your questions.

Smart recommendations

As mentioned in the introduction, recommendation services are now a familiar component of many online stores, personal assistants and digital platforms.

The recommendations need to take a content-based approach. Within a data catalog, machine learning capabilities combined with a knowledge graph, will be able to detect certain types of data, apply tags, or statistical rules on data to run effective and smart asset suggestions.

This capacity is also known as data pattern recognition. It refers to being able to identify similar assets and rely on statistical algorithms and ML capabilities that are derived from other pattern recognition systems.

This data pattern recognition system helps data stewards maintain their metadata management :

Identify duplicates and copy metadata
Detect logical data types (emails, city, addresses, and so on)
Suggest attribute values (recognize documentation patterns to apply to a similar object or a new one)
Suggest links – semantic or lineage links
Detect potential errors to help improve the catalog’s quality and relevance

The idea is to use some techniques that are derived from content-based recommendations found in general-purpose catalogs. When the user has found something, the catalog will suggest alternatives based both on their profile and pattern recognition.

Some data catalog use cases empowered by knowledge graphs

Gathering assets that have been used or related to causes of failure in digital projects.
Finding assets with particular interests aligned with new products for the marketing department.
Generating complete 360° views of people and companies in the sales department.
Matching enterprise needs to people and projects for HRs.
Finding regulations relating to specific contracts and investments assets in the finance department.

Conclusion

With the never ending increase of data in enterprises, organizing your information without a strategy means not being able to stay competitive and relevant in the digital age. Ensuring that your data catalog has an enterprise Knowledge Graph is essential for avoiding the dreaded ‘black box’ effect.

Through a knowledge graph in combination with AI and machine learning algorithms, your data will have more context and will enable you to not only discover deeper and more subtle patterns but also to make smarter decisions.

For more insights on what is a knowledge graph, here is a great article by BARC Analyst Timm Grosser “Linked Data for Analytics?“

Start your data catalog journey with Zeenea

Zeenea is a 100% cloud-based solution, available anywhere in the world with just a few clicks. By choosing Zeenea Data Catalog, control the costs associated with implementing and maintaining a data catalog while simplifying access for your teams.

The automatic feeding mechanisms, as well as the suggestion and correction algorithms, reduce the overall costs of a catalog, and guarantee your data teams with quality information in record time.

Get a free demo

BARC, the consulting firm, states Zeenea is The Adaptive Data Catalog

by Zeenea Software | Oct 5, 2020 | News & events, Zeenea Product

Last week, we had the pleasure to read this statement done by BARC in their last briefing research: “Zeenea is the Adaptive Data Catalog”. To understand the significance of this research, let’s explain who BARC is.

Who is BARC?

The Business Application Research Center (BARC) is an industry analyst and consulting firm for business software with a focus on Business Intelligence/Analytics, Data Management, Enterprise Content Management (ECM), Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP).

BARC analysts have been supporting companies in strategy, organization, architecture and software evaluations for more than 20 years.

An established, continuous program of market research and product comparison studies forms the basis of BARC’s comprehensive knowledge of all the leading software providers and products, best practices and the latest market trends and developments.

BARC events offer a focused overview of leading software solutions, trendsetting developments and current requirements as well as market developments in the various areas of enterprise applications.
In short, they want to transform companies – more specifically based in DACH countries – into Digital Leaders!

What did BARC say about Zeenea Data Catalog?

The research was written by Timm Grosser, BARC’s Senior Analyst for Data Management. He views Zeenea as a young and growing data catalog software provider on the market.

Timm Grosser BARC

He stated:

“Zeenea is a fairly young product and has already demonstrated quite a lot considering it has just three years of market experience and 50 customers. The company is concentrating on the core of data cataloging, thereby setting itself apart from the competition, which is becoming broader and broader from my point of view. The tool is clearly arranged for me.
[…]
The roadmap features many ML-based, innovative functions, such as improved similarity detection, pattern recognition and identifying relevant attribute values.
I think the idea and approaches have succeeded in making the data catalog grow adaptively with the company. I am of the opinion that we do not need another battleship catalog. Instead, it is important to take the company and users on a journey towards a data-centric enterprise. And this journey must be feasible. It requires consideration of the organization, the capabilities within the company, and a flexible, simple structure for catalog content. The idea is there and the first technological precautions have been taken. I’m curious to see what Zeenea will make of it.”

Read researches about Zeenea x BARC

Discover everything about BARC’s research on Zeenea via this link 👉 The Adaptive Data Catalog
Or If you prefer knowing how to choose a data catalog as a Data Leader, please read this other research 👉 The Data Catalog – The “Yellow Pages” for Business-Relevant Data

A smart data catalog, a must-have for data leaders

by Zeenea Software | Aug 26, 2020 | Data Catalog

The term “smart data catalog” has become a buzzword over the past few months. However, when referring to something being “smart” most people automatically think, and rightly so, of a data catalog with only Machine Learning capabilities.

We at Zeenea, do not believe that a smart data catalog is reduced to only having ML features!

In fact, there are many different ways to be “smart”.

This article focuses on the conference that Guillaume Bodet, co-founder and CEO of Zeenea, gave at the Data Innovation Summit 2020: “Smart Data Catalogs, A must-have for leaders”.

watch the conference

A quick definition of data catalog

We define a data catalog as being:

A detailed inventory of all data assets in an organization and their metadata, designed to help data professionals quickly find the most appropriate data for any analytical business purpose.

A data catalog is meant to serve different people, or end-users. All of these end-users have different expectations, needs, profiles, and ways to understand data. These end-users consist of data analysts, data stewards, data scientists, business analysts, and so much more. As more and more people are using and working with data, a data catalog must be smart for all end-users.

Click here for a more in-depth article on what is a data catalog

What does a “data asset” refer to?

An asset, financially speaking, typically appears in the balance sheet with an estimation of value. When referring to data assets, it is just as important, even more important in some cases, than other enterprise assets. The issue is that the value for data assets aren’t always known.

However, there are many ways to tap the value of your data. There is the possibility for enterprises to directly use their data’s value, like for example selling or trading their data. Many organizations do this; they clean the data, structure it, and then proceed to sell it.

Enterprises can also make value indirectly from their data. Data assets enable organizations to:

Innovate for new products/services
Improve overall performance
Improve product positioning
Better understand markets/customers
Increase operational efficiency

High performing enterprises are those that master their data landscape and exploit their data assets in every aspect of their activity

The hard things about data catalogs…

When your enterprise deals with thousands of data, that usually means you are dealing with possibly

100s of systems that store internal data (data warehouses, applications, data lakes, datastores, APIs, etc) as well as external data from partners.
1,000s of datasets, models, and visualizations (data assets) that are composed of thousands of fields.
And these fields contain millions of attributes (or metadata)!

Not to mention the hundreds of users using them…

This raises two different questions.

How can I build, maintain, and enforce the quality of my information for my end-users to trust in my catalog?

How can I quickly find data assets for specific use cases?

The answer is in smart data catalogs!

At Zeenea, we believe that are five core areas of “smartness” for a data catalog. It must be smart in its:

Design: the way users explore the catalog and consume information,
User experience: how it adapts to different profiles,
Inventories: provides a smart and automatic way of inventorying,
Search engine: supports the different expectations and gives smart suggestions,
Metadata management: a catalog that tags and links data together through ML features.

Let’s go into detail for each of these areas.

A smart design

Knowledge graph

A data catalog with smart design uses knowledge graphs rather than static ontologies (a way to classify information, most of the time built as a hierarchy). The problem with ontologies is that they are very hard to build and maintain, and usually only certain types of profiles truly understand the various classifications.

A knowledge graph on the other hand, is what represents different concepts in a data catalog and what links objects together through semantic or static links. The idea of a knowledge graph is to build a network of objects, and more importantly, create semantic or functional relationships between the different assets in your catalog.

Basically, a smart data catalog provides users with a way to find and understand related objects.

Adaptive metamodels

In a data catalog, users will find hundreds of different properties, to which aren’t relevant to some users. Typically, two types of information are managed:

Entities: plain objects, glossary entries, definitions, models, policies, descriptions, etc.
Properties: the attributes that you put on the entities (any additional information such as create date, last updated date, etc.)

The design of the metamodel must serve the data consumer. It needs to be adapted to new business cases and must be simple enough to manage for users to maintain and understand it. Bonus points if it is easy to create new types of objects and sets of attributes!

Semantic attributes

Most of the time, in a data catalog, the metamodel’s attributes are technical properties. Some of the attributes on an object include generic types such as text, number, date, list of values, and so on. As this information is necessary to have, it is not completely sufficient because they do not have information on the semantics, or meaning. The reason this is important is because with this information, the catalog can adapt the visualization of the attribute and improve suggestions to users.

In conclusion, there is one size fits all to a data catalog’s design, and it must evolve in time to support new data areas and use cases.

A smart user experience

As stated above, a data catalog holds a lot of information and end-users often struggle to find the information of interest to them. Expectations differ between profiles! A data scientist will expect statistical information, whereas a compliance officer expects information on various regulatory policies.

With smart and adaptive user experience, a data catalog will present the most relevant information to specific end-users. Information hierarchy and adjusted search results in a smart data catalog is based on:

Static preferences: already known in the data catalog if the profile is more focused on data science, IT, etc.
Dynamic profiling: to learn what the end-user usually searches, their interests, and how they’ve used the catalog in the past.

A smart inventory system

A data catalog’s adoption is built on trust – and trust can only come if its content is accurate. As the data landscape moves at a fast pace, it must be connected to operational systems to maintain the first level of information on metadata on your data assets.

The catalog must synchronize its content with the actual content of the operational systems.

A catalog’s typical architecture is to have scanners that scan your operational systems and bring and synchronize information from various sources (Big Data, noSQL, Cloud, Data Warehouse, etc.). The idea is to have universal connectivity so enterprises can scan any type of system automatically and set them in the knowledge graph.

In Zeenea, there is an automation layer to bring back the information from the systems to the catalog. It can:

Update assets to reflect physical changes
Detect deleted or moved assets
Resolve links between objects
Apply rules to select the appropriate set of attributes and define attribute values

A smart search engine

In a data catalog, the search engine is one of the most important features. We distinguish between two kinds of searches:

High intent search: the end-user already knows what they are looking for and has precise information on their query. They either already have the name of the dataset or already know where it is found. Low intent searches are commonly used by more data savvy people.
Low intent search: the end-user isn’t exactly sure what they are looking for, but want to discover what they could use for their context. Searches are made through keywords and users expect the most relevant results to appear.

A smart data catalog must support both types of searches!

It must also provide smart filtering. It is a necessary complement to the user’s search experience (especially low intent research), allowing them to narrow their search results by excluding attributes that aren’t relevant. Just like many big companies like Google, Booking.com, and Amazon, the filtering options must be adapted to the content of the search and the user’s profile in order for the most pertinent results to appear.

Smart metadata management

Smart metadata management is usually what we call the “augmented data catalog”, the catalog that has machine learning capabilities that will enable it to detect certain types of data, apply tags, or statistical rules on data.

A way to make metadata management smart is to apply data pattern recognition. Data pattern recognition refers to being able to identify similar assets and rely on statistical algorithms and ML capabilities that are derived from other pattern recognition systems.

This data pattern recognition system helps data stewards set their metadata:

Identify duplicates and copy metadata
Detect logical data types (emails, city, addresses, and so on)
Suggest attribute values (recognize documentation patterns to apply to a similar object or a new one)
Suggest links – semantic or lineage links
Detect potential errors to help improve the catalog’s quality and relevance

It also helps data consumers find their assets. The idea is to use some techniques that are derived from content-based recommendations found in general-purpose catalogs. When the user has found something, the catalog will suggest alternatives based both on their profile and pattern recognition.

Start your data catalog journey with Zeenea

Zeenea is a 100% cloud-based solution, available anywhere in the world with just a few clicks. By choosing Zeenea Data Catalog, control the costs associated with implementing and maintaining a data catalog while simplifying access for your teams.

The automatic feeding mechanisms, as well as the suggestion and correction algorithms, reduce the overall costs of a catalog, and guarantee your data teams with quality information in record time.

Get a free demo

Data science: accelerate your data lake initiatives with metadata

by Zeenea Software | Jun 15, 2020 | Data Catalog, Metadata Management

Data lakes offer an unlimited storage for data and present lots of potential benefits for data scientists in the exploration and creation of new analytical models. However, this structured, unstructured and semi-structured data are mashed together and the business insights they contain are often overlooked or misunderstood by data users.

The reason for this is that many technologies used to implement data lakes lack the necessary information capabilities that organizations usually take for granted. It is therefore necessary for these enterprises to manage their data lakes by putting in place effective metadata management which considers metadata discovery, data cataloguing, and overall enterprise metadata management applied to the company’s data lake.

2020 is the year that most data and analytics use cases will require connecting to distributed data sources, leading enterprises to double their investments in metadata management. – Gartner 2019.

How to leverage your data lake with metadata management

To get value from your data lake, it is essential for companies to have both skilled users (such as data scientists or citizen data scientists) and effective metadata management for their data science initiatives. To begin with, an organization could focus on a specific dataset and its related metadata. Then, leverage this metadata as more data is added into the data lake. Setting up metadata management can make it easier for data lake users to initiate this task.

Here are the areas of focus for successful metadata management in your data lake:

Creating a metadata repository

Semantic tagging is essential for discovering enterprise metadata. Metadata discovery is defined as the process of using solutions to discover the semantics of data elements in datasets. This process usually results in a set of mappings between different data elements in a centralized metadata repository. This allows data science users to understand their data and have visibility on whether or not they are clean, up-to-date, trustworthy, etc.

Automating metadata discovery

As numerous and diverse data gets added to a data lake on a daily basis, maintaining ingestion can be quite a challenge! By using automated solutions not only does it make it easier for data scientists or CDS to find their information but it also supports metadata discovery.

Data cataloguing

A data catalog consists of metadata in which various data objects, categories, properties and fields are stored. Data cataloguing is both used for internal and external data (from partners or suppliers for example). In a data lake, it is used for capturing a robust set of attributes for every piece of content within the lake and enriches the metadata catalog by leveraging these information assets. This enables data science users to have a view into the flow of the data, perform impact analysis, have a common business vocabulary and accountability and an audit trail for compliance.

Data & Analytics Governance

Data & analytics governance is an important use case when it comes to metadata management. Applied to data lakes, the question “could it be exposed?” must become an essential part of the organization’s governance model. Enterprises must therefore extend their existing information governance models to specifically address business analytics and data science use cases that are built on the data lakes. Enterprise metadata management helps in providing the means to better understand the current governance rules that relate to strategic types of information assets.

Contrary to traditional approaches, the key objective of metadata management is to drive a consistent approach to the management of information assets. The more metadata semantics are consistent across all assets, the greater the consistency and understanding, allowing the leveraging of information knowledge across the company. When investing in data lakes, organizations need to consider an effective metadata strategy for those information assets to be leveraged from the data lake.

Start metadata management with Zeenea

As mentioned above, implementing metadata management into your organization’s data strategy is not only beneficial, but essential for enterprises looking to create business value with their data. Data science teams working with various amounts of data in a data lake need the right solutions to be able to trust and understand their information assets. To support this emerging discipline, Zeenea gives you everything you need to collect, update and leverage your metadata through its next generation platform!

Check out our metadata management platform

DataOps: How data catalogs enable better data discovery in a Big Data project

by Zeenea Software | May 6, 2020 | Data Catalog

In today’s world, Big Data environments are more and more complex and difficult to manage. We believe that Big Data architectures should, among other things:

Retrieve information on a wide spectrum of data,
Use advanced analytics techniques such as statistical algorithms, machine learning and artificial intelligence,
Enable the development of data oriented applications such as a recommendation system on a website.

In order to put in place a successful Big Data architecture, enterprise data are stored in a centralized data lake, destined to serve various purposes. However, the massive & continuous amount of diverse & varied data from different sources transforms a data lake into a data swamp. So as business functions are increasingly working with data, how can we help them find their way?

In order for your Big Data to be exploited at their full potential, your data must be well documented.

Data documentation is key here. However, documenting data such as their business name, description, owner, tags, level of confidentiality, etc can be an extremely time consuming task, especially with millions of data available in your lake!

With a DataOps approach, an agile framework focused on improving communication, integration and automation of data flows between data managers and data consumers across an organization, enterprises are able to carry out their projects in an incremental manner. Supported by a data catalog solution, enterprises are able to easily map and leverage their data assets, in an agile, collaborative and intelligent manner.

How does a data catalog support a DataOps approach in your Big Data project?

Let’s go back to the basics… what is a data catalog?

A data catalog automatically captures and updates technical and operation metadata from an enterprise’s data sources and stores them in a unique source of truth. It’s purpose is to democratize data understanding: to allow your collaborators to find the data they need via one easy-to-use platform above data systems. Data catalogs don’t require technical expertise to actually discover what is new and seize opportunities!

Effective data lake documentation for your Big Data

Think of Legos. Legos can be created and built into anything you want, but at its core, Legos are still just a set of bricks. Theses blocks can be shaped to any need, desire or resource!

In your quest to facilitate your data lake journey, it is important to create effective documentation through the following:

Customizable layouts,
Interactive components,
A set of pre-created templates.

By offering modular templates, Data Stewards can simply and efficiently configure documentation templates according to their business users’ data lake search queries.

Monitor Big Data with automated capabilities

Through an innovative architecture and connectors, data catalogs can connect to your Big Data sources, where the IT department can monitor their data lake. They are able to map new incoming datasets, be notified of any deleted or modified datasets or even report errors to referring contacts for example.

Users are able to access to up-to-date information in real time!

These automated capabilities allow users to be notified of when new datasets appear, when they are deleted, when there are errors, when they were last updated, etc.

Support Big Data documentation with augmented capabilities

Intelligent data catalogs are essential for data documentation. They rest on artificial intelligence and machine learning techniques, one being “fingerprinting” technology. This feature offers data users that are responsible for a particular data set some suggestions as for its documentation. These recommendations can, for example, be associated with tags, contacts, or even business terms of other data sets based on:

The analysis on the data itself (statistical analysis),
The schema resembling other data sets,
The links on the other data set’s fields.

An intelligent data catalog also detects personal/private data in any given data set and report it on its interface. This feature helps enterprises respond to the different GDPR demands put into place in May 2018, as well as alert potential users on a data’s sensitivity level.

Enrich your Big Data documentation with Zeenea Data Catalog

Enrich your data’s documentation with Zeenea! Our metadata management platform was designed for Data Stewards, and centralizes all data knowledge in a single and easy-to-use interface.

Automatically imported, generated, or added by the administrator, data stewards are able to efficiently document their data directly within our data catalog.

Give meaning to your data with metadata!

How you’re going to fail your data catalog project (or not…)

by Zeenea Software | Mar 25, 2020 | Data Catalog

There are many solutions on the data catalog market that offer an overview of all enterprise data all thanks to the efforts conducted by data teams.

However, after a short period of use, due to the approaches undertaken by enterprises and the solutions that were chosen, data catalog projects often fall into disuse.

Here are some of the things that can make a data catalog project fail… or not!

Your objectives were not defined

Many data catalog projects are launched under a Big Bang approach, with the aim of documenting assets, but without truly knowing what their objectives are.

Fear not! In order to avoid bad project implementation, we advocate a model based on iteration and value generation. Conversely, this approach allows for better risk control and the possibility of a faster return on investment.

The first effects should be observable at the end of each iteration. In other words, the objective must be set to produce concrete value for the company, especially for your data users.

For example, if your goal is data compliance, start documentation focused on these properties and target a particular domain, geographic area, business unit, or business process.

Your troops’ motivation will wear off over time…

While it is possible to gain adherence and support regarding your company’s data inventory efforts in its early stages, it is impossible to maintain this support and commitment over time without automation capabilities.

We believe that descriptive documentation work should be kept to a minimum to keep your teams motivated. The implementation of a data catalog must be a progressive project and will only last if the effort required by each individual is greater than the value they will get in the near future.

You won’t have the critical mass of information needed

For a data catalog to bring value to your organization, it must be richly populated.

In other words, when a user searches for information in a data catalog, they must be able to find it for the most part.

At the start of your data catalog implementation project, the chances that the information requested by a user is not available are quite high.

However, this transition period should be as short as possible so that your users can quickly see the value generated by the data catalog. By choosing a tactical solution, based on its technology and connectivity to information sources, a pre-filled data catalog will be available as soon as it is implemented.

Does not reflect your operational reality

In addition to these challenges, data catalogs must have a set of automated features that are useful and effective over time. Surprisingly, many solutions do not have offer these minimum requirements for a viable project, and are unfortunately destined for a slow and painful death.

Connecting data catalogs to your sources will ensure that your data consumers :

Reliability as to the information made available in the data catalog for analysis and use in their projects.
Fresh information: are they up to date, in real time?

How does Zeenea Data Catalog empower your data teams?

by Zeenea Software | Feb 26, 2020 | Data Catalog, Zeenea Product

Data has become one of the main drivers for innovation for many sectors.

And as data continues to rapidly multiply, companies need to evolve and grasp new technologies to succeed in their data & analytics strategy. And this is where Zeenea Data Catalog comes in!

First of all, what are the problems of companies regarding their data?

As a leading data catalog solution for data-driven companies such as LCL, Société Générale, Renault, we always come across, among others, three main issues:

Lack of visibility: With many different data sources such as various data warehouses, data lakes, cloud data, etc, it becomes complicated for employees to find the relevant data and thus, transforming Big Data into Big Chaos! It becomes confusing and demotivating for data teams to work with their data, as they end up spending most of their time wondering where their data actually is, and if it is still reliable.

Lack of knowledge: Most enterprises today have specific people or teams that handle data since data is usually a very technical, complicated subject for employees. This lack of sharing and communicating of data reduces the enterprise’s potential to produce more value, at a local level where any employee becomes a valuable asset and data silos disappear.

Lack of culture: As many companies have understood, it is essential to implement data culture within the organization to truly become data driven. A good change management is sought out with the right people, processes, and solutions that offer a way for companies to create data literacy and facilitate their data journey.

However, do not forget: with great data comes great responsibility! This refers to what we call a data democracy culture.

And there is an answer for all of these issues: a modern and smart data catalog software.

The choice of a data catalog in your company

As mentioned above, many leading companies have trusted Zeenea in their quest for implementing a data catalog solution. Choosing Zeenea is choosing:

An overview of all of an enterprise’s data assets through our connectors,

A Google-esque search engine that enables employees to intuitively search for a dataset, business term, or even a field from just a single keyword. Narrow your search with various personalized filters (reliability score, popularity, type of document, etc.).

A collaborative application that allows enterprises to become acculturated to data thanks to collective knowledge, discussions, feeds, etc,

A machine learning technology that notifies you and gives suggestions as to your catalogued data’s documentation,

A dedicated user experience that allows data leaders to empower their data explorers to become autonomous in their data journeys.

Learn more about our data catalog

Contact us for more information on our data catalog for teams

If you are interested in getting more information, getting a free personalized demo, or just want to say hi, do not hesitate to contact our team who will get back to you as soon as we’ve received your request 🙂

How to evaluate your future Data Catalog?

by Zeenea Software | Dec 4, 2019 | Data Catalog

The explosion of data sources in organizations, the heterogeneity of data or even the new demands related to data makes it essential to maintain the documentation of your information! However, enterprises continue to use older, more “traditional” methods to inventory and understand these new assets.

It is for this reason that Data Catalog solutions appeared in the market.

As you’ve probably noticed… there are many Data Catalog solutions available in the market! This profusion of offers leaves companies uncertain about which Data Catalog will best meet their expectations.

That said, on which specificities should you evaluate your future Data Catalog? We believe that you should keep in mind in your shortlist of solutions, at a minimum, these five founding principles:

1. One Data Catalog for all and all for one Data Catalog

Implementing a data catalog means having a metadata management strategy at the enterprise level.

In other words, acquiring a Data Catalog at a data storage level would lead to recreating well-known “data silos,” but this time, relative to metadata. It makes it, therefore, difficult and complicated to manage and integrate them in other systems.

A Data Catalog must become a reference point within your enterprise. The solution must connect to all your data storage or information systems from the most cutting-edge to those more traditional.

2. From declaration to automation

When evaluating your solutions, think of choosing an automated data catalog. An essential brick in metadata management, this feature will simplify and automate your information’s inventory as well as update them from your different databases in your future data catalog.

This is a simple way to make available accurate information to your data users.

3. Simple!

Simple does not mean complete!

In this case, we think of the word simple for future data catalog users. A “well-made” interface thought for non-technical users will allow the organization to better adapt and retain to a solution.

4. Progressively deploy a solution with the right support

To best convince your users of using such a tool, not only evaluate its capacity, but also its support system offered by the software editor (or its partners) to help you put into place a metadata management strategy within your Data Catalog.

For example, at Zeenea, we work with our clients each step of the way, and with each metadata source to maximize the value of our solution (automation, search engine, collaboration, etc.) alongside a pilot population, growing over time.

5. From passive to active metadata

Your future Data Catalog should not be a simple inventory of information. Think about the range possibilities today with this raw material! By providing a solution offering machine learning, thanks to research, profiling metadata and / or data from the tool, enrich day-to-day documentation as well as the meaning and uses of your data assets.

Thus, transform your metadata into enterprise assets

What is the difference between a data dictionary and a business glossary?

by Zeenea Software | Nov 18, 2019 | Data Catalog, Metadata Management, Zeenea Product

In metadata management, we often talk about data dictionaries and business glossaries. Although they might sound similar, they’re actually quite different! Let’s take a look at their differences and relations.

What is a data dictionary?

A data dictionary is a collection of descriptions of data objects or items in a data model.

These descriptions can include attributes, fields, or even properties on their data such as their types, transformations, relations, etc.

Data dictionaries help data explorers better understand their data and metadata. Usually in the form of tables or spreadsheets, data dictionaries are a must have IT knowledge for technical users such as developers, data analysts, data scientists, etc.

What is a business glossary?

While data dictionaries are useful to technical users, a business glossary is meant to bring meaning and context to data in all departments of the enterprise.

A business glossary is therefore a place where business and/or data terms are defined. It may sound simple, however, it is rare that all employees in an organization share a common understanding of even basic terms such as “contact” and “customer”.

Example of a business glossary:

The main differences between data dictionaries and business glossaries are:

Data dictionaries deal with database and system specifications, mostly used by IT teams. Business glossaries are more accessible and standardize definitions for everyone in the organization.

Data dictionaries usually come in the form of schemas, tables, columns, etc. whereas a business glossary provides a unique definition for business terms in textual form.

A business glossary cross references terms and their relationships whereas data dictionaries do not.

What is the relation between a data dictionary and a business glossary?

The answer is simple: a business glossary provides meaning to the data dictionary.

For example, a US social security number (SSN) will be defined as “a unique number assigned by the US government for the purpose of identifying individuals within the US Social Security System” in the business glossary. In the data dictionary, the term SSN is defined as “a nine character string typically displayed with hyphens”.

If a data citizen ever has a doubt on what the term “SSN” means in the context of their data dictionary, they can always search for the associated business term inside the business glossary.

Interested in automating a data dictionary and building a business glossary for your enterprise?

Create a central metadata repository for all corporate data sources with our data catalog thanks to our connectors and APIs.

Our tool also provides a user friendly and intuitive way to build and import your business glossaries in order to link these definitions with any Zeenea’s concepts.

Create a single source of truth in your enterprise!

Discover our Business Glossary

Data Revolutions: Towards a Business Vision of Data

by Zeenea Software | Aug 19, 2019 | Data Catalog

The use of massive data by the internet giants in the 2000s was a wake-up call for enterprises: Big Data is a lever for growth and competitiveness that encourages innovation. Today, enterprises are re-organizing themselves around their data in order to adopt a “data-driven” approach. It’s a story constituting several twists and turns that tends to finally find a solution.

This article discusses the different enterprise data revolutions undertaken in recent years up to now, in an attempt to maximize the business value of data.

Siloed architectures

In the 80s, Information Systems developed immensely. Business applications were created, advanced programming language emerged, and relational databases appeared. All these applications stayed on their owners’ platforms, isolated from the rest of the IT ecosystem.

For these historical and technological reasons, an enterprise’s internal data were distributed in various technologies and in heterogeneous formats. In addition to organizational problems, we then speak of a tribal effect. Each IT department have their own tools and implicitly, manage their own data for their own uses. We are witnessing a type of data hoarding within organizations. To back these suggestions, we frequently recall Conway’s law: “All architecture reflects the organization that created it.” Thus, this organization, called silos, makes for very complex and onerous cross-referencing of data originating from two different systems.

The search for a centralized and comprehensive vision of an enterprise’s data will lead Information Systems to a new revolution.

The concept of a Data Warehouse

By the end of the 90s, Business Intelligence was in full swing. For analytical purposes and with the goal of responding to all strategic questions, the concept of a data warehouse appeared.

To make this, we will recover the data from mainframes or relational databases and transfer them to an ETL (Extract Transform Loader). Projected in a so-called pivot format, analysts and decision-makers can access data collected and formatted to answer pre-established questions and specific cases of reflection. From the question, we get a data model!

This revolution always comes with some problems…Using ETL tools has a certain cost, not to mention the hardware that comes with it. The elapsed time between the formalization of the need and the receipt of the report is time-consuming. It’s a revolution that is costly for perfectible efficiency.

The new revolution of a data lake…

The arrival of data lakes reverses the previous reasoning. A data lake enables organizations to centralize all useful data storages, regardless of their source or format, for a very low cost. . We stock an enterprise’s data without presuming their usage in the treatment of a future use case. It is only according to a specific use where we will select these raw data and transform them into strategic information.

We are moving from an “a priori” to an “a posteriori” logic. This revolution of a data lake focuses on new skills and knowledge: data scientists and data engineers are capable of launching the treatment of data, producing results much faster than the time spent using data warehouses.

Another advantage of this Promised Land is its’ price. Often offered in an open-source way, data lakes are cheap, including the hardware that comes with them. We often speak of community hardware.

… or rather a data swamp

Certain advantages are present with the data lake revolution but come along with new challenges. The expertise needed to instantiate and to maintain these data lakes are rare and thus, are costly for enterprises. Additionally, pouring data in a data lake day after day without efficient management or organization brings on the serious risk of rendering the infrastructure unusable. Data are then inevitably lost in the mass.

This data management is accompanied by new issues related to data regulation (GDPR, Cnil, etc.) and data security: already existing topics in the data warehouse world. Finding the right data for the right use is not yet an easy thing to do.

The settlement: constructing Data Governance

The internet giants understood that centralizing these data is the first step, however insufficient. The last brick necessary to go towards a “data-driven” approach is to construct data governance. Innovating through data requires greater knowledge of these data. Where are my data stored? Who uses them? With which goal in mind? How are they being used?

To help data professionals chart and visualize the data life cycle, new tools have appeared: we call them, “Data Catalogs.” Located above data infrastructures, they allow you to create a searchable metadata directory. They make it possible to acquire a business vision and data techniques by centralizing all collected information. In the same way that Google doesn’t store web pages but rather, their metadata to reference them, companies must also store their data’s metadata in order to facilitate the exploitation of and discovery of them. Gartner confirms this in their study, “Data Catalog is the New Black”: if your data lake’s data is without metadata management and governance, it will be considered inefficient.

Thanks to these new tools, data becomes an asset for all employees. The easy-to-use interface doesn’t require technical skills, becoming a simple way to know, organize, and manage these data. The data catalog becomes the reference collaborative tool in the enterprise.

Acquiring an all-round view of these data and to start data governance to drive ideations thus becomes possible.

What is a Chief Data Officer

by Zeenea Software | Jun 17, 2019 | Data Catalog, Data governance

According to a Gartner study presented at the Data & Analytics conference in London 2019, 90% of large companies will have a CDO by 2020!

With the arrival of Big Data, many companies find themselves with colossal amounts of data without knowing how to exploit them. In response to this challenge, a new function is emerging within these large companies: the Chief Data Officer.

The Chief Data Officer’s role

Considered as data “gurus”, Chief Data Officers (CDO) play a key role in an enterprise’s data strategy. They are in charge of improving the organization’s overall efficiency and the capacity to create value around their data.

In order for CDOs to fulfill their missions, they must reflect on providing high-quality, managed, and secure data assets. In other words, they must find the right balance between an offensive and defensive data governance strategy that matches the enterprise’s needs.

According to the Gartner study, presented at their annual Data & Analytics event in London in March 2019, the CDO, among other things, has several important responsibilities within a company:

Define a data & analytics strategy

What are the short, medium, and long-term data objectives? How can I implement a data culture within my enterprise? How can I democratize data access? How can I measure my data assets quality? How can I attain internal and/or legal regulatory compliance? How can I empower my data users?

There are so many questions that CDOs must ask themselves in order to implement a data & analytics strategy in their organization.

Once the issues have been identified, it is time for operational initiatives. A CDO acts as a supervisor so that the efforts made in providing data information are trustworthy and valuable.

Their role takes shape over time. They must become the new “Data Democracy” leaders within their companies and maintain the investment provided for its infrastructure and organization.

Build Data Governance

Implementing data governance must successfully combine compliance with increasingly demanding regulatory requirements and the exploitation of as much data as possible in all areas of an enterprise. To achieve this goal, a CDO must first ask themselves a few questions:

What data do I have in my organization?
Are these data sufficiently documented to be understood and managed by my collaborators?
Where do they come from?
Are they secure?
What rules or restrictions apply to my data?
Who is responsible for them?
Who uses my data? And how?
How can my collaborators access them?

It’s by building agile data governance in the most offensive way possible that CDOs will be able to facilitate data access and ensure their quality in order to add value to them.

Evangelize a “Data Democracy” culture

Data Democracy refers to the idea that if each employee, with full awareness, can easily access as much data as possible, an enterprise as a whole will reap the benefits. This right to access data comes with duties and responsibilities, including contributing to maintaining the highest level of data quality and documentation. Therefore, governance is no longer the sole preserve of a few, but becomes everyone’s business.

To achieve this mission, Zeenea connects and federates teams around data through a common language. Our data catalog allows anyone – with the allotted allowances – to discover and trust in an enterprise’s data assets.

Are you a Chief Data Officer looking for a Data Governance tool?

In order for Chief Data Officers achieve their objectives, they need to be equipped with the right tools. With Zeenea’s data catalog, CDOs can identify their data assets, make them accessible and usable by their collaborators in order to be valorized.

Easy to use and intuitive, our data catalog is the CDO’s indispensable tool for implementing agile data governance. Contact us for more information.

How Artificial Intelligence enhances data catalogs

by Zeenea Software | Jun 5, 2019 | Data Catalog

Can machines think? We are talking about artificial intelligence, “the biggest myth of our time”!

A simple definition for AI could be: “a set of applied theories and techniques to create machines capable of simulating intelligence.” Among those AI functions, there is deep learning, an automated learning method used to process data.

Data must be understood and accessible. It’s with the help of an intelligent data catalog that data users, such as data scientists, can easily research and efficiently choose the right datasets for their machine learning algorithms.

Let’s see how.

Search engine: facilitation dataset research

By connecting to all of an enterprise’s data sources, a data catalog can efficiently pull up a maximum amount of documentation (otherwise known as metadata) from its storage systems.

This information, indexed and filterable in Zeenea’s search engine, allows for data users to quickly attain the data sets needed for their information systems.

Recommendation system

Guiding Data Scientists in their choices

An intelligent data catalog is a tool that rests on “fingerprinting” technology. This intelligent feature gives recommendations to data users as to what data sets are the most relevant for their projects based on, among others:

How the data is used,
The quality and scoring of the documentation,
Its previous searches,
What other users search for.
Give more meaning to their datasets

This feature offers data users that are responsible for a particular data set some suggestions as for its documentation. These recommendations can, for example, be associated with tags, contacts, or even business terms of other data sets based on:

The analysis on the data itself (statistical analysis),
The schema resembling other data sets,
The links on the other data set’s fields.
Automatically contextualizing data sets in a data catalog allows for any data user to work with data that is understood and appropriate for their use cases.

Automatic dataset linking: visualizing your data’s life cycle

As mentioned above, with fingerprinting technology, a data catalog can recognize and connect to other data sets. We are talking about data lineage: a visual representation of data life cycles.

Automatic error detection: be aware of errors in datasets

In order to overcome potential data interpretation problems, an intelligent data catalog must be able to automatically detect errors or misunderstandings in the quality and documentation of any data.

This key feature, based on the analysis of data or its documentation, must alert data users of its integrity.

GDPR notification: be notified of sensitive information

An intelligent data catalog must be able to detect personal/private data in any given data set and report it on its interface. This feature helps enterprises respond to the different GDPR demands put into place in May 2018, and also to alert potential users on the sensitivity level as well as the use of their data.

Data catalog: a self-service data platform

by Zeenea Software | Feb 19, 2019 | Data Catalog

A data catalog is a portal that brings metadata on collected data sets together by the enterprise. This classified and organized information lets data users to re(find) relevant data sets for their work.

A new wave of data catalogs appeared on the market. Their purpose is signing up an enterprise in a data-driven approach. Any authorized person in the enterprise must have the capability of accessing, understanding, and contributing to data documentation and moreover, without technical skills. What we are talking about is self-service data.

Zeenea identified the 4 characteristics that the new generation of a data catalog must respect. It must be:

An enterprise’s data catalog. A data catalog must be connected to all of the enterprise’s data sources to collect and regroup all metadata in a single centralized location to avoid the multiplication of tools.

A catalog of connected data. We believe that a data catalog must always be up to date and accurate on the information it provides in order to be useful for its users. By being connected to data sources, the data catalog can import the documentation from storage systems and ensure an automatic update of metadata in the two structures (storages and data catalog).

A collaborative data catalog. In a user-centric approach, a data catalog must be the reference data tool of an enterprise. By involving employees through collaborative features, the enterprise benefits from collective intelligence. To share, to assign, to comment, and to qualify within the same data catalog, increasing productivity and knowledge among all of your collaborators.

An intelligent data catalog. By choosing a data catalog equipped with artifical intelligence for the auto-population of metadata, for example, it allows your data managers to become more efficient.

These characteristics will be the subject of more in-depth articles.

What are the different types of metadata?

by Zeenea Software | Feb 19, 2019 | Data Catalog, Metadata Management

Dealing with large volumes of data is essential to any organization’s success. But knowing what kind of data it is, where it comes from, and how it can be used is just as important. This is the role of metadata. So how can companies optimize and enhance it? Follow this guide.

Data is essential to have in-depth knowledge of an organization’s market, industry, customers, or products. But to exploit the full potential of this data, it is essential to focus on its metadata. This data on data is a prerequisite for knowing how to best use it. By having a precise vision of what made it possible to generate the data, at what time, via which source, it is possible to contextualize this information. Metadata is, in a way, structured information that describes, explains, locates, or facilitates access, use, or management of an information source.

However, the role of metadata is not limited to understanding the origin of data.

Properly managed and structured, metadata will also allow organizations to know how to get the most out of the information they have according to the objectives they’ve set.

How is metadata useful?

Metadata is everywhere. Not just in client files or in website archives. When taking a picture with a smartphone, metadata is instantly attached to the image: date, time, location… All this information can be valuable when wanting to create a virtual photo album for example.

It’s the same in the context of a company’s data project!

While metadata is necessary to truly understand where data comes from and how it can be used, it is not the only thing it is used for. In fact, when properly managed, metadata is a major lever for organizations seeking to structure and enhance their information on a daily basis. Optimal metadata management is therefore the foundation of a data-driven transformation project.

The different types of metadata

If the use of the generic term for metadata is to qualify the information relative to data, it is important to know that they can be classified into different types.

Thus, it is important to distinguish between descriptive metadata, which presents a resource in a way that facilitates the identification of the available data, and structural metadata. The latter provides information on the composition or organization of a data resource. To describe a data portfolio, there is also administrative metadata, which provides information on the date of creation or acquisition of the data, but also on its associated permissions, lifespan, and use.

Alongside this generic metadata is a wide range of other types of metadata. They provide context on the application and business uses of information, on technical aspects, or even reinforce an information’s descriptive dimension.

The larger the volume of data you have, the more varied the sources of data acquisition and collection are, and the more companies will benefit from fine-tuned metadata management.

What tools manage metadata?

To organize and optimize metadata use for all employees, it is essential to use a Data Catalog. Through this metadata management solution, organizations are able to index their data and metadata as well as quickly identify the sources of information that are available to data teams. But a Data Catalog’s mission goes even further: it will enable companies to reference all their data assets, facilitate data access when needed, and perform thematic searches.

Indeed, the quality of this metadata conditions the quality of a data description, with a direct impact on its visibility and ease of use.

At Zeenea, we’ve identified three types of metadata within our data catalog:

Technical metadata: they describe the structure of a dataset and the information related to storage systems.

Business metadata: it applies business context to datasets: descriptions (context and usage), owners and referents, tags, and properties in order to create a taxonomy over the datasets that will be indexed by our search engine. Business metadata are also present at the schema level of a dataset: descriptions, tags, or data confidentiality level per column.

Operational metadata: this allows us to understand when and how the data was created or transformed: statistical analysis of the data, date of update, origin (lineage), volume, cardinality, the identifier of the processes that created or transformed the data, status of the processes on the data, etc.

What is a Data Steward?

by Zeenea Software | Feb 18, 2019 | Data Catalog, Data governance

Data stewards are the first point of reference for data and serve as an entry point for data access. They have the technical and business knowledge of data, which is why they are often called the “masters of data” within an organization! As the true guardians of data, let’s discover their role, missions & responsibilities.

Faced with the challenges of data exploitation and optimization, organizations are in need of specialists who can combine their actions with their knowledge of data.

In a recent article, we discussed the prerogatives and differences between Data Engineers and Data Architects. We also deciphered the missions of a Data Analyst, a Data Product Manager, and a Chief Data Officer. All of these specialists have the mission of making data speak, of giving it life, either by organizing it, by defining a strategy, or by manipulating it. To do so, they all have a common requirement: to work with quality data.

This is the essential mission of the Data Steward, which is responsible for the quality of their data, which ultimately conditions all of the processes and decisions from a company’s data strategy.

The Data Steward’s multiple skills

To do so, a Data Steward must have strong communication skills and be able to distinguish the different types and formats of data.

Acting as a point of convergence for all the data generated and used in the company, they must also ensure constant vigilance over the quality of their data in order to identify the priority data that needs to be cleaned or standardized.

Versatile and multi-skilled, the Data Steward is considered the key contact for an organization in terms of data. So much so that they are often called the “master of data”. In order to live up to Data Stewardship requirements, this expert must be present on all fronts, as he or she plays a central role in the proper implementation of a data strategy.

What is the role of the Data Steward in the company?

Companies are reorganizing around their data to produce value and finally innovate from this raw material. Data Stewards are there to orchestrate the data in the company’s information systems. They must ensure the proper documentation of the data and facilitate their availability to their users such as Data Scientists or Project Managers, for example.

The essential role of the Data Steward is to supervise the life cycle of all available data, to ensure that its quality remains optimal. Behind the notion of data quality, there is also that of availability. The Data Steward, through his data quality missions, also contributes to ensuring that business teams can easily access the data they need.

To give the notion of Data Stewardship its full meaning and scope, the “master of data” must be able to play the role of the bridge between the data and business teams. Working closely with the business lines and in constant partnership with the IT teams, the Data Steward not only helps to identify and collect data but also to validate and structure it. Their communication skills enable them to identify the people responsible for and knowledgeable about the data, to collect the associated information in order to centralize it and perpetuate this knowledge within the company. More specifically, Data Stewards provide metadata knowledge; a structured set of information describing a dataset. They transform this abstract data into concrete assets for the business.

Although there is no specific training for the Data Steward profession, the most commonly sought-after profile is that of an expert business user, familiar with data management techniques and data processing.

What are the Data Steward’s responsibilities?

The Data Steward must fulfill a wide range of missions. In particular, they must deal with the day-to-day management of data in the broadest sense of the term, ensuring that the processes for collecting and processing information are fluid. Finding and knowing the data, imposing a certain discipline in the management of metadata, and facilitating their availability to employees – These are just some of the issues that Data Stewards must address.

Once the data is collected, it is the Data Steward who is responsible for optimizing its storage and transmission to the business teams, after having created the conditions for indexing the data. As one of the key players in ensuring data quality, the Data Steward has another critical task: cleaning up the data by removing duplicates and eliminating useless information. To accomplish this, the Data Steward must ensure that the documentation of the data they manage is up-to-date.

Finally, as the Data Steward is also responsible for providing data access for all of your teams, they are constantly monitoring the security of their data assets, both with regard to external threats and internal dangers (particularly to the blunders of certain employees). Operational supervision of data, coordination of data documentation, compliance, and risk management, the Data Steward is a multi-faceted player who contributes to optimized data governance.

What is a Data Catalog?

by Zeenea Software | Feb 12, 2019 | Data Catalog

It is no secret that the enormous volumes of information that companies generate require the right tools in order to correctly manage them. Indeed, with great data comes great responsibility! For organizations to truly profit off of their data, it is essential to be equipped with a solution that enables data-driven people to easily find, discover, manage, and above all, trust in their information assets.

And this, with a data catalog! Created to unify all enterprise data, a data catalog enables data managers and data users to improve productivity and efficiency when working with their data.

In fact, in 2017, Gartner declared data catalogs as “the new black in data management and analytics”. In “Augmented Data Catalogs: Now an Enterprise Must-Have for Data and Analytics Leaders” they state:

“The demand for data catalogs is soaring as organizations continue to struggle with finding, inventorying and analyzing vastly distributed and diverse data assets.”

In this article, we will share everything there is to know about data catalogs for companies seeking to truly become data-driven.

What exactly is a data catalog?

Before getting into the subject of data cataloging, it is important to understand the concept of metadata management. A data catalog uses metadata – data on data – to create a searchable repository of all enterprise information assets. This metadata collected by various data sources (Big Data, Cloud services, Excel sheets, etc.) is automatically scanned to enable users of the catalog to search for their data and get information such as the availability, freshness, and quality of a data asset.

Therefore, by definition, a data catalog has become a standard for efficient metadata management. At Zeenea, we broadly define a data catalog as being:

“A detailed inventory of all data assets in an organization and their metadata, designed to help data professionals quickly find the most appropriate data for any analytical business purpose.”

What is the purpose of a data catalog?

Topics on data are still considered to be an extremely technical domain. However, data innovation is only possible if it is shared by as many people as possible. This is the very purpose of a data catalog: to democratize data access.

A data catalog is meant to serve different people or end-users. All of these end-users – data analysts, data stewards, data scientists, business analysts, and so much more – have different expectations, needs, profiles, and ways to understand data. As more and more people are using and working with data, a data catalog must adapt to all end-users. In fact, data catalogs don’t require technical expertise to search for, discover, and understand a company’s data landscape.

What are the benefits of a data catalog?

As mentioned above, a data catalog centralizes and unifies the metadata collected so that it can be shared with IT teams and business functions. This unified view of data allows organizations to:

Accelerate data discovery

As thousands of datasets and assets are being created each day, enterprises find themselves struggling to understand and gain insights from their information to create value. Many recent surveys still state that data science teams spend 80% of their time preparing and tidying their data instead of analyzing and reporting it. By deploying a data catalog, the speed of data discovery can increase up to 5 times. This way, data teams can focus on what’s important: delivering their data projects on time.

Sustain a data culture

Just like organizational or corporate culture, data culture refers to a workplace environment where decisions are made through emphatic and empirical data proof. A data catalog allows for data knowledge to no longer be limited to a group of experts: it enables organizations to better collaborate on their information assets.

Build Agile Data Governance

Instead of deploying overly complex processes too difficult to maintain on assumed information, data catalogs enable a bottom-up, agile data governance approach. A data catalog enables data users to create a data process registry, document legal obligations, track the lifecycle of data, as well as identify sensitive information. All this is in a single centralized repository.

Maximize the value of data

By collecting all the data of an enterprise on a reference data tool, it becomes possible to cross-reference these assets and get value from them more easily. The collaboration of technical and professional teams within the data catalog enables innovations that meet proven market needs.

Produce better and faster

More than 70% of the dedicated time to data analysis is invested in “data quarrels” activities. Cataloging simplifies data retrieval, the identification of associated contacts, and therefore, data-driven decision-making.

Ensure good control over data

Misinterpreted or erroneous, enterprises expose themselves to the risk of basing their decision on incorrect information. Connected data catalogs permit access to always up-to-date data. Data users can ensure that data and their information are correct and usable.

What are a data catalog’s key features to look out for?

A flexible & adaptable metamodel template

A data catalog should automatically capture and update metadata from an enterprise’s data sources. Through a flexible metamodel template, it should be possible to add, configure – at the hand of the data catalog’s administrator – and overlay documentation properties on cataloged datasets. Via this approach, the catalog offers a simple and modular way to configure documentation templates according to the enterprise’s objectives and priorities.

A smart search engine

One of the core features of a data catalog is a search engine. All indexed metadata should be searchable via a search bar. Through simple keyword searches, a data catalog should be able to show the most accurate results to a query. It should also enable users to filter their search results. A smart search engine also optimized results based on the user’s profile and preferences. A smart search engine thus, enables users to be able to quickly find their information assets.

A knowledge graph

The presence of a knowledge graph is essential to any data cataloging project. The knowledge graph is what represents different concepts and what links objects together through semantic or static links. A data catalog’s knowledge graph, therefore, provides users with rich and in-depth search results, optimized data discovery, smart recommendations, and more.

Data lineage

With data lineage, it is possible to visualize in whole the origin and the transformations of one specific data over time. This allows users to understand where the data originate from, when and where they separate and fuse with other data. These transformations and treatments carried out by the data are indispensable for conforming to the GDPR and other data regulations.

A Business Glossary

A business glossary enables data consumers to manage a common business vocabulary and make it available across the entire organization. This must-have feature provides a clear meaning and context to data terms.

what-is-a-data-catalog-business-glossary

What are a Data Catalog’s use cases? And for whom?

For Chief Data Officers

The Chief Data Officer plays a key role in the overall data strategy of an enterprise; their purpose is to master their data and facilitate their access in order to become data-driven. A data catalog helps them:

Ensure data reliability and value
Create a data literate organization
Valorize a data set’s context for data explorers
Evangelize a data culture with rights and duties
Start a compliance process with the European regulation (GDPR).

For Data Stewards

Known as the main contact for data inquiries thanks to their technical and operational knowledge, the Data Steward is most commonly nicknamed the “Master of data”! A data catalog enables data stewards to:

Centralize data knowledge in a single platform
Enrich data documentation
Establish communication between them and data explorers
Qualify the value of data.

For Data Scientists

To achieve their missions, end-users must be able to quickly find, discover, and understand the right data asset for their use-cases. A data catalog helps them:

Easily find data through a search engine
View the history of their information: date of creation and the actions carried out on it
Understand the context of their data
Identify the associated people
Easily collaborate with peers.

A representative data catalog journey

A data catalog becomes extremely handy in the different phases of your projects:

A data catalog in the deployment phase

Connect to your data sources – A data catalog plugs into all your data sources. Connect your data integration, data preparation, data visualization, CRM solutions, etc in order to fully integrate all your technologies into a single source of truth.

A data catalog in the documentation phase

Create a metamodel – A data catalog captures and updates technical and operational metadata from an enterprise’s data sources. It allows you to add and configure – at the hand of the data catalog’s administrator – or overlay information (information that can be mandatory or not) on its cataloged datasets.

A data catalog in the discovery phase

Understand your data – With a data catalog, data citizens – with technical capabilities or not – are able to fully understand their enterprise data. A data catalog allows users to have access to and easily search for any information within the catalog.

Define your data – A data catalog allows data leaders, such as data stewards or chief data officers, to correctly define the pertinent data to be used. Through metadata, data managers can easily document their datasets, allowing their data teams to access contextualized data.

Explore your data – Discover and collect available data in a data catalog. By cataloging all enterprise data in a central repository, data citizens are able to ensure that their data is reliable and usable.

A data catalog in the collaboration phase

Communicate with data – A data catalog allows users to become data fluent. Both the IT & business departments are able to understand and communicate around different data projects. Through collaborative features such as discussions, data becomes a topic for all to share across the enterprise.

Start your cataloging journey with Zeenea

Zeenea is a 100% cloud-based solution, available anywhere in the world with just a few clicks. By choosing Zeenea, you give your data teams the best next-generation environment to find, understand and use your data assets.

Check out our two applications:

Zeenea Studio – enable your data management teams to manage, maintain and enrich the documentation of their company’s data assets.

Zeenea Explorer – provide your data teams with a user-friendly interface and customized exploration paths to make their data discovery more efficient.

For a product demo or for more information on our data catalog:

get a demo

Data mapping: The challenges in an organization

by Zeenea Software | Jul 3, 2018 | Data Catalog, Data governance, Metadata Management

The arrival of Big Data did not simplify how enterprises work with data. The volume, the variety, and the various data storage systems are exploding.

To prove this, Matt Turck published what we call the Big Data Landscape. Updated every year, this infographic shows the different key players in various sub-domains of the Big Data landscape.

Thus, with the Big Data revolution, it is even more difficult to answer “primary” questions related to data mapping:

What are the most pertinent datasets and tables for my use cases and my organization?
Do I have sensitive data? How are they used?
Where does my data come from? How have they been transformed?
What will be the impacts on my datasets if they are transformed?

>> Download our toolkit: Metamodel template <<

So many questions that information systems managers, Data Lab managers, Data Analysts or even Data Scientists ask themselves to be able to deliver efficient and pertinent data analysis.

Among others, these questions allow enterprises to:

Improve data quality: Providing as much information as possible allows users to know if the data is suitable for use.
Comply with European regulations (GDPR): mark personal data and the carried out processes.
Make employees more efficient and autonomous in understanding data through graphical and ergonomic data mapping.

To put these into action, companies must build what is called data lineage.

How to map your information system’s data?

by Zeenea Software | Jun 27, 2018 | Data Catalog

Data lineage is defined as the life cycle of data: its origin, movements, and impacts over time. It offers greater visibility and simplifies data analysis in case of errors.

With the emergence of Big Data and information systems becoming more complex, data lineage becomes an essential tool for data-driven enterprises. How can we represent the life cycle of data in an intelligible way, maintainable with a certain granularity in the information provided?

We are witnessing a paradigm shift in the representation and formalization of data mapping.

View the video (FRENCH)

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What is data mapping?

Information on data

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The benefits of implementing data mapping

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What is IoT?

A quick definition

IoT use cases in manufacturing industries

Data catalog: one way to rule data for any manufacturer

Zeenea data catalog x IoT: the perfect match

Zeenea success story in the manufacturing industry

Zeenea’s unique features for manufacturing companies

Are you interested in unlocking data access for your company?

How can you benefit from a Machine Learning Data Catalog?

Machine Learning Data Catalog vs Smart Data Catalogs: what’s the difference?

What is a knowledge graph exactly?

Google example

How do knowledge graphs empower data catalog usages ?

Rich and in-depth search results

Optimized data discovery

Smart recommendations

Some data catalog use cases empowered by knowledge graphs

Conclusion

Start your data catalog journey with Zeenea

Who is BARC?

What did BARC say about Zeenea Data Catalog?

Read researches about Zeenea x BARC

A quick definition of data catalog

What does a “data asset” refer to?

The hard things about data catalogs…

The answer is in smart data catalogs!

A smart design

Adaptive metamodels

Semantic attributes

A smart user experience

A smart inventory system

A smart search engine

Smart metadata management

Start your data catalog journey with Zeenea

How to leverage your data lake with metadata management

Creating a metadata repository

Automating metadata discovery

Data cataloguing

Data & Analytics Governance

Start metadata management with Zeenea

How does a data catalog support a DataOps approach in your Big Data project?

Effective data lake documentation for your Big Data

Monitor Big Data with automated capabilities

Support Big Data documentation with augmented capabilities

Enrich your Big Data documentation with Zeenea Data Catalog

Your objectives were not defined

Your troops’ motivation will wear off over time…

You won’t have the critical mass of information needed

Does not reflect your operational reality

First of all, what are the problems of companies regarding their data?

The choice of a data catalog in your company

Contact us for more information on our data catalog for teams

1. One Data Catalog for all and all for one Data Catalog

2. From declaration to automation

3. Simple!

4. Progressively deploy a solution with the right support

5. From passive to active metadata

What is a data dictionary?

What is a business glossary?

The main differences between data dictionaries and business glossaries are:

What is the relation between a data dictionary and a business glossary?

Interested in automating a data dictionary and building a business glossary for your enterprise?