data democracy Archives

HCLSoftware Completes Acquisition of Metadata Management Software Provider Zeenea

by Anthony Corbeaux | Sep 12, 2024 | News & events

Acquisition will enable Actian, a division of HCLSoftware, to offer customers a complete data ecosystem.

SANTA CLARA, Calif., September 12, 2024 – HCLSoftware, the software business division of HCLTech, today announced that it completed the acquisition of Zeenea, an innovator in data catalog and governance solutions based in Paris, France. The acquisition of Zeenea enables Actian, a division of HCLSoftware, to offer a unified data intelligence and governance solution that empowers customers to seamlessly discover, govern, and maximize the value of their data assets. It also further extends Actian’s presence, workforce, and customer base in Europe.

“To become data-driven, organizations of all sizes need data governance to ensure the most effective and efficient use of quality data throughout its life cycle,” said Marc Potter, CEO at Actian. “With Zeenea as part of our portfolio, Actian offers customers a complete data ecosystem – making Actian a one-stop shop for all things data. Together, we will help our customers propel their GenAI and analytics initiatives forward by boosting confidence in data preparation and enhancing data readiness.”

According to ISG Research, 85% of enterprises believe that investment in generative AI technology in the next 24 months is critical. Data governance and data quality work in tandem to ensure the data feeding the GenAI solutions is accurate, complete, fit-for-purpose, and used according to governance policies.

Zeenea is recognized for its cloud-native Data Discovery Platform with universal connectivity that supports metadata management applications from search and exploration to data catalog, lineage, governance, compliance and enterprise data marketplace. Powered by an adaptive knowledge graph, Zeenea enables organizations to democratize data access and generate a 360-degree view of their assets, including the relationships between them.

About Actian

Actian  makes data easy. We deliver a complete data solution that simplifies how people connect, manage, govern and analyze data. We transform business by enabling customers to make confident, intelligent, data-driven decisions that accelerate their organization’s growth. Our data platform integrates seamlessly, performs reliably, and delivers industry-leading speeds at an affordable cost. Actian is a division of HCLSoftware.

About HCLSoftware

HCLSoftware is the software business division of HCLTech, serving more than 7,000 organizations in 130 countries in five key areas: Data and Analytics; Business and Industry Applications (including Commerce, MarTech Automation); Intelligent Operations; Total Experience; and Cybersecurity.

https://www.hcl-software.com/

For further details, please contact:

Danielle Lee, ActianDanielle.Lee@actian.com

Jeremy McNeive, HCLSoftware jeremy.mcneive@hcl.com

Harnessing the Power of AI in Data Cataloging

by Zeenea Software | Jul 8, 2024 | Data Catalog

In today’s era of expansive data volumes, AI stands at the forefront of revolutionizing how organizations manage and extract value from diverse data sources. Effective data management becomes paramount as businesses grapple with the challenge of navigating vast amounts of information. At the heart of these strategies lies data cataloging—an essential tool that has evolved significantly with the integration of AI, with promises of efficiency, accuracy, and actionable insights. Let’s see how in this article.

The benefits of AI in data cataloging

AI revolutionizes data cataloging by automating and enhancing traditionally manual processes, thereby accelerating efficiency and improving data accuracy across various functions:

Automated metadata generation

AI algorithms autonomously generate metadata by analyzing and interpreting data assets. This includes identifying data types, relationships, and usage patterns. Machine learning models infer implicit metadata, ensuring comprehensive catalog coverage. Automated metadata generation reduces the burden on data stewards and ensures consistency and completeness in catalog entries. This capability is precious in environments with rapidly expanding data volumes where manual metadata creation could be more practical.

Simplified data classification and tagging

AI facilitates precise data classification and tagging using natural language processing (NLP) techniques. By understanding contextual nuances and semantics, AI enhances categorization accuracy, which is particularly beneficial for unstructured data formats such as text and multimedia. Advanced AI models can learn from historical tagging decisions and user feedback to improve classification accuracy. This capability simplifies data discovery processes and enhances data governance by consistently and correctly categorizing data.

Enhanced Search capabilities

AI-powered data catalogs feature advanced search capabilities that enable swift and targeted data retrieval. AI recommends relevant data assets and related information by understanding user queries and intent. Through techniques such as relevance scoring and query understanding, AI ensures that users can quickly locate the most pertinent data for their needs, thereby accelerating insight generation and reducing time spent on data discovery tasks.

Robust Data lineage and governance

AI is crucial in tracking data lineage by tracing its origins, transformations, and usage history. This capability ensures robust data governance and compliance with regulatory standards. Real-time lineage updates provide a transparent view of data provenance, enabling organizations to maintain data integrity and traceability throughout its lifecycle. AI-driven lineage tracking is essential in environments where data flows through complex pipelines and undergoes multiple transformations, ensuring all data usage is documented and auditable.

Intelligent Recommendations

AI-driven recommendations empower users by suggesting optimal data sources for analyses and identifying potential data quality issues. These insights derive from historical data usage patterns. Machine learning algorithms analyze past user behaviors and data access patterns to recommend datasets that are likely to be relevant or valuable for specific analytical tasks. By proactively guiding users toward high-quality data and minimizing the risk of using outdated or inaccurate information, AI enhances the overall effectiveness of data-driven operations.

Anomaly Detection

AI-powered continuous monitoring detects anomalies indicative of data quality issues or security threats. Early anomaly detection facilitates timely corrective actions, safeguarding data integrity and reliability. AI-powered anomaly detection algorithms utilize statistical analysis and machine learning techniques to identify deviations from expected data patterns.

This capability is critical in detecting data breaches, erroneous data entries, or system failures that could compromise data quality or pose security risks. By alerting data stewards to potential issues in real-time, AI enables proactive management of data anomalies, thereby mitigating risks and ensuring data consistency and reliability.

The challenges and considerations of AI in data cataloging

Despite its advantages, AI-enhanced data cataloging presents challenges requiring careful consideration and mitigation strategies.

Data Privacy and Security

Protecting sensitive information requires robust security measures and compliance with data protection regulations such as GDPR. AI systems must ensure data anonymization, encryption, and access control to safeguard against unauthorized access or data breaches.

Scalability

Implementing AI at scale demands substantial computational resources and scalable infrastructure capable of handling large volumes of data. Organizations must invest in robust IT frameworks and cloud-based solutions to support AI-driven data cataloging initiatives effectively.

Data Integration

Harmonizing data from disparate sources into a cohesive catalog remains complex, necessitating robust integration frameworks and data governance practices. AI can facilitate data integration by automating data mapping and transformation processes. However, organizations must ensure compatibility and consistency across heterogeneous data sources.

In conclusion, AI’s integration into data cataloging represents a transformative leap in data management, significantly enhancing efficiency and accuracy. AI automates critical processes and provides intelligent insights to empower organizations to exploit their data assets fully in their data catalog. Furthermore, overcoming data privacy and security challenges is essential for successfully integrating AI. As AI technology advances, its role in data cataloging will increasingly drive innovation and strategic decision-making across industries.

What is a Data Democracy – From The Data Democracy Ebook Series by Ole Olesen-Bagneux

by Zeenea Software | Sep 1, 2023 | Data governance

Written by renowned O’Reilly author Ole Olesen-Bagneux, this ebook series exposes a completely new way of thinking about data in your company: the Data Democracy.

In this article, discover highlights from the first chapter of his ebook series “The Data Democracy” with a focus on what a data democracy is, why you need a data democracy, and the means of achieving it.

Why do you need a data democracy?

The purpose of a data democracy is to give every employee the possibility to thrive and progress in their career – by using company data to push forward on complex agendas and innovate.

A data democracy is also an invaluable advantage not only for the employees but for the company itself: Every company must encourage surprising, experimental usage of data to prosper and stay competitive. A data democracy enables companies to scale faster, and adapt in changing markets – all while its employees learn and grow!

We see data democracy as a logical target state of a term that has been floating around in the global data community these years: data democratization. Data democratization describes how modern, cloud-based tools with simple interfaces have ignited easy usage of data. More and more employees in more and more companies are doing more stuff with data – which is great!

However, quite surprisingly, just as data democratization as a process has been described and discussed, just little has its target state been defined.No one seems to know what a data democracy is. This needs to change. So what is a data democracy? Let’s define it!

What is a data democracy?

An enterprise data democracy can be defined as a capability in an enterprise that enables anyone to find and use anything, from anywhere, at anytime. Let’s break it down.

Anyone

Anyone should be able to search for data – and use whatever data they want. It is hard to find companies that oppose this. But – it is just as hard to find companies where this is possible. What does it take to search for your company data? If there is no dedicated platform to search for data, then, how can you actually expect anyone to be able to search for data – and in this way be able to participate in a data democracy?

In most companies, far from anyone can do this. Rather, it is but a few select data engineers and -scientists that work intensively with data, that can actually search for data. And even they suffer: They too dream of being able to search for data in a frictionless, smooth way. In a data democracy, anyone should be able to search for data. It’s employees in Human Resources, in your Legal department, in Research & Development, in Sales – and so on.

Remember that anyone means you, me, them – all of us! Anyone in the company!

Anything

When we talk about data democracy, we in fact talk about all data: anything. Far too many employees only have access to a very small part of the entire data landscape in their company. Employees are locked by their professional role: this defines their access to IT systems, and even what parts are inside those IT systems. Therefore, all employees in a company – anyone – need a place where they can search for all data in their company: Anything, meaning simply, all types of data from all over the company.

Anywhere

Not only must anyone be able to find anything, they also need to be able to do so from anywhere. Anything means all types of data – whilst anywhere is where the actual IT landscape comes into play. The same type of data can be placed in various different systems that deliver the same capability. In short: every company is different, both in terms of the data they have, but also in terms of the IT systems they store that data in. For anyone to be able to find anything, you need a dedicated solution. This is a data discovery platform. The benefit of this solution is that it only exposes metadata. Therefore, no confidential data is exposed or used – in this way, anyone can discover anything without risking compliance or regulatory issues.

Anytime

Finally, data needs to be discoverable and available anytime. On your data discovery platform, all data must be discoverable anytime in the sense that the metadata must be fresh and represent the sources adequately. Certain data sources change a lot, and they need to be continuously mirrored in the data discovery platform, to be relevant for discovery, whilst others are more static and only need to be updated once in a while.

Start your Data Democracy Journey – Download our Ebook Series

By signing up for our Data Democracy ebook series, discover a completely new way of thinking about data in your company. Learn about:

Why you need a data democracy, the causes of the absence of data democracies in companies, and what you can do about it,
The frightening alternatives to a data democracy (such as a data tyranny, monarchy, etc.)
The reality of the data government you find yourself in and how we can help you build a data democracy.

READ THE EBOOK SERIES

Data Literacy: The Must-Have Skill for Remote Workers

by Zeenea Software | May 4, 2023 | Data governance, Data Inspiration

The COVID-19 pandemic has forced organizations worldwide to adopt remote work as the new norm. In fact, according to McKinsey & Company, the pandemic accelerated remote work with up to 25 percent more workers than previously estimated needing to switch occupations. And in a world with increasing remote workers, the need for data-driven decision-making has become more crucial than ever before.

However, with remote work comes a new set of challenges for data-driven enterprises. To make informed decisions, remote workers need to understand, analyze, and interpret their data accurately. As a result, data literacy has become an essential skill for workers to succeed in a remote work environment.

In this article, we will explore the importance of data literacy in a world working remotely, its advantages and challenges, and some best practices to adopt for implementing data literacy for remote work.

The importance of data literacy

Let’s briefly define data literacy. Data literacy is the ability to understand, analyze, and communicate around data. Indeed, in today’s fast-paced and data-driven environment, data literacy enables individuals to better understand the data they work with, analyze it critically, and make informed decisions based on the insights gained from the data.

The importance of data literacy in today’s workforce therefore cannot be overstated. The amount of data being generated by organizations is growing exponentially, and the ability to access, analyze and interpret data is vital to making informed business decisions. With the right data literacy skills, employees can turn raw data into actionable insights that help them identify patterns and trends to achieve their strategic and business goals.

The challenges of becoming data literate when working remotely

With remote work, employees are not physically present in the same location as their colleagues or data sources. Therefore, remote workers need to be able to access, analyze, and interpret data independently, without relying on face-to-face interactions. Data literacy is crucial in ensuring that remote workers can effectively navigate data and use it as well as be able to communicate data effectively to their colleagues, which is essential for collaboration in a remote work environment. With the lack of face-to-face interactions, remote workers may not receive the necessary guidance or support to build their data literacy skills.

Another key challenge is the lack of access to their data sources. Remote workers need to be able to access data sources quickly and easily to be able to analyze their information. In addition, remote workers may also face challenges in terms of data security and protection. Therefore, efficient data management and analysis are critical in ensuring that remote workers can access and use data securely and effectively.

Finally, many organizations that aim to become data literate lack the appropriate data management tools. Without the appropriate solutions, it can be difficult to collect, organize, and analyze data in an effective manner. In addition, data users lack context on their data, leading to a siloed and incomplete understanding of their data. Having the right data management tools, such as data visualization software, data cataloging solutions, and data discovery platforms, can help data teams to better comprehend their data and gain deeper insights, leading to a more successful journey towards data literacy.

The advantages of data literacy for remote workers

When implemented effectively, data literacy has many benefits for remote workers.

First, data literacy enables remote workers to communicate and collaborate effectively with their colleagues. By understanding and analyzing data, remote workers can share their insights and findings with their colleagues, leading to better decision-making and outcomes. Additionally, data literacy enables remote workers to present data in a clear and concise manner, making it easier for others to understand and act upon the insights presented.

Second, data literacy can improve productivity and efficiency and can access, analyze, and interpret data quickly and accurately, enabling them to complete tasks more efficiently. By leveraging data insights, remote workers can identify patterns, trends, and anomalies in data, which can help them prioritize tasks, optimize processes, and achieve their goals more effectively.

Finally, data literacy can help reduce errors and risks in a remote work environment. By analyzing and interpreting data accurately, remote workers can identify potential errors or risks before they occur, allowing them to take proactive measures to mitigate them. Additionally, being data literate reduces the likelihood of making decisions based on assumptions or incomplete information. By leveraging data insights, remote workers can ensure that their decisions are informed, objective, and aligned with organizational goals.

Tips on creating a data literate environment for remote workers

Building data literacy skills in a remote work environment can be challenging, but there are several strategies that can be employed to develop these skills.

One of those solutions is to provide online training and resources for remote workers to build their data literacy skills. Online training modules, courses, and webinars can help remote workers develop their skills in data analysis, interpretation, and presentation. In addition, providing access to online resources such as data visualization tools, dashboards, and analytics platforms can enable remote workers to explore and analyze data independently.

Another strategy for building data literacy skills in a remote work environment is to incorporate data literacy into the remote work culture. Encouraging remote workers to share their data insights and findings with their colleagues can foster a culture of collaboration and knowledge-sharing, promoting the development of data literacy skills across the organization.

The future of data literacy in remote working

As data becomes more prevalent in remote work, the need for remote workers to develop and maintain their data literacy skills will become increasingly important. By investing in continuous learning and upskilling in data literacy, remote workers can effectively leverage data insights to make informed decisions, improve productivity, and reduce errors and risks.

At Zeenea, we are convinced that data literacy is an essential skill to master for any data-driven organization. This is why we developed a next-generation data discovery platform for all data and business initiatives from metadata management applications from search and exploration to data governance, compliance, and cloud transformation initiatives.

Are you ready to unlock the potential of data for your remote workers?

The traps to avoid for a successful data catalog project – Data Culture

by Zeenea Software | Sep 29, 2022 | Data Catalog

Metadata management is an important component in a data management project and it requires more than just the data catalog solution, however connected it may be.

A data catalog tool will of course reduce the workload but won’t in and of itself guarantee the success of the project.

In this series of articles, discover the pitfalls and preconceived ideas that should be avoided when rolling out an enterprise-wide data catalog project. The traps described in this are articulated around 4 central themes that are crucial to the success of the initiative:

Data culture within the organization
Internal project sponsorship
Project leadership
Technical integration of the Data Catalog

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Organizations with data as the sole product are very rare. While data is everywhere, it is often only a byproduct of the company’s activities. It is therefore not surprising to find that some collaborators are not as aware of its importance. Indeed, data culture isn’t innate and a lack of awareness of the importance of data can become a major obstacle to a successful data catalog deployment.

Let’s illustrate this with a few common preconceptions.

Not all collaborators are sensitive to what is at stake with metadata management

The first obstacle is probably the lack of a global understanding of the initiative. Emphasizing the importance of metadata management to colleagues who still misunderstand the crucial role the actual data can play in an organization is doomed to fail.

It’s quite likely that a larger program that includes an awareness initiative emphasizing the stakes around enterprise data management will have to be set up. The most important element to inculcate is probably the fact that data is a common good, meaning that the owners of a dataset have the duty to make it visible and understandable to all stakeholders and colleagues.

Indeed, one of the most common obstacles in a metadata management initiative is
the resistance to the effort needed to produce and maintain documentation. This is all the more of an issue when it is felt that the potential users targeted are limited to a small group of people who already fully understand the subject. When it is understood that the target group is in fact much larger (the entire organization and potentially all staff), it becomes obvious that this knowledge has to be recorded in a “scalable” manner.

A data catalog doesn’t do everything

A data culture-related issue can also affect those in charge of the project, although this is less common. An inaccurate understanding of the tools and their use can lead to mistakes and cause suboptimal, even detrimental, choices. The data catalog is a central software component for metadata management but it’s likely not the only tool used. It is therefore not advisable to try and do everything just with this tool. This may sound obvious but in practice, it can be difficult to identify the limits beyond which it is necessary to bring a more specialized solution into the mix.

The data catalog is the keystone to documentation and has to be the entry point for any collaborator with questions related to a concept linked to data. However, this doesn’t make it “the solution” in which everything has to be found. This nuance is important because referencing or synthesizing information doesn’t necessarily mean carrying this information wholesale.

Indeed, there are many subjects that come up during the preparation phases of a metadata management project: technical or functional modeling, data habilitation management, workflows for access requests, etc. All these topics are important, carry value, and are linked to data. However, they are not specifically destined to be managed by the solution that documents your assets.

It is therefore important to begin by identifying these requirements, defining a response strategy, and then integrating this tooling in an ecosystem larger than just the data catalog.

The 10 Traps to Avoid for a Successful Data Catalog Project

To learn more about the traps to avoid when starting a data cataloging initiative, download our free eBook!

READ THE EBOOK

10 Traps To Avoid For A Successful Data Catalog Project Mockup

Constitution of our Data Democracy

by Zeenea Software | Oct 24, 2021 | Data governance

Read the 10 rules you must follow for perfect data democratization in your organization.

Article 1 – Guidelines.

We are all Data Citizens of our organisation: From HR to marketing, R&D to IT Production, sales rep to commercial director, accountant to CFO, office management to COO, etc.

The purpose of this constitution is to set down the rights and duties of all stakeholders in any organisation that relies on data to function. As Data Citizens in a Data democracy, we are all committed to the spirit of the articles below.

Article 2 – No Data Democracy without Data access.

Data is one of the most important and commonplace assets an organisation holds.

As Data Citizens, we have the right to access all the necessary information relating to our company’s data that is relevant to our respective positions.

The Data Citizen is called upon to contribute towards enhancing its quality, its usability, its discovery and anything that helps our Data Democracy increase knowledge in general.

Article 3 – A Data Citizen is a Data Explorer.

A functioning Data Democracy should provide all the necessary means to help all Data citizens discover, understand and trust the Data. The freedom to become a fully fledged “Data Explorer” with ready access to all relevant and reliable data at any time is key to a fulfilled professional experience both for the citizen and the business.

In return, the Data Explorer commits to handling Data as a contributor rather than a mere consumer of the Data.

Article 4 – Removing Data silos.

The Data Citizen is expected to share all the Data produced in accordance with corporate and regulatory policies. He should never keep the data to himself and work actively on removing siloes.

Article 5 – Data Citizens are team players.

Each Data Citizen shall commit to assisting one another and helping anyone in her/his understanding of any particular dataset, its origin, its content, in accordance with their skills.

Article 6 – Breaking down barriers between Data Citizens.

Each Data Citizen is encouraged to make suggestions to upper echelons with a view towards continuous improvement of the Data.

Article 7 – Data Democracy and responsibility.

Each Data Citizen will be trained to respect company policies in terms of compliance, security, and ethics, and shall commit to following them thoroughly.

Article 8 – Inter-generational Data Democracy.

Each Data Citizen is responsible for the protection of its Data legacy. She/he is expected to build on the available Data and improve it for the next generation of Data Citizens.

Article 9 – Achieving both personal and professional goals through Data Democracy.

This constitution was written to help each and every Data Citizen achieve her/his personal and professional goals and contribute to the company’s success.

Article 10 – The golden path to the ideal Data Democracy.

As Data Citizens, we want Zeenea as our Data Catalog.

Download our constitution!

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Data strategy: how to break down data silos?

by Zeenea Software | Jun 8, 2021 | Data Inspiration

Whether it comes from Product life cycles, marketing, or customer relations, data is omnipresent in the daily life of a company. Customers, suppliers, employees, partners… they all collect, analyze and exploit data in their own way.

The risk: the appearance of silos! Let’s discover why your data is siloed and how to put an end to it.

A company is made up of different professions that coordinate their actions to impose themselves on their market and generate profit. Each of these professions fulfill specific missions and collect data. Marketing, sales, customer success teams, communication…all of these entities act on a daily basis and base their actions on their own data.

The problem is that, over the course of his or her career, a customer will generate a certain amount of information.

A simple lead, then becomes a prospect , who then becomes a customer…the same person may have different taxonomies based on which part of the business is analyzing this data.

This reality is what we call a data silo. In other words, data is poorly or never shared and therefore too often untapped.

In a study by IDC entitled “The Data-Forward Enterprise” published in December 2020, 46% of French companies forecast a 40% annual growth in the volume of data to be processed over the next two years.

Nearly 8 out of 10 companies consider data governance to be essential. However, only 11% of them believe they are getting the most out of their data. The most common reason for this is data silos.

What are the major consequences of data silos?

Among the frequent problems linked to data silos, we find first and foremost the problem of duplicated data. Since data is used blindly by the business, what could be more natural?

These duplicates have unfortunate consequences. They distort the knowledge you can have of your products or your customers. This biased, imperfect information often leads to imprecise or even erroneous decisions.

Duplicated data also take up unnecessary space on your servers. Storage space that represents an additional cost for your company! Beyond the impact of data silos on your company’s decisions, strategies, or finances, there is also the organizational deficit.

When your data is in silos, your teams can’t collaborate effectively because they don’t even know they’re mining the same soil!

At a time where collective intelligence is a cardinal value, this is undoubtedly the most harmful event caused by data silos.

Why does your company suffer from data silos?

There are many causes for siloed data. Most often, they are associated with the history of your information systems. Over the years, these systems were built as a patchwork for business applications that were not always designed with interoperability in mind.

Moreover, a company is like a living organism. It welcomes new employees when others leave. In everyday life, spreading data culture throughout the workforce is a challenge! Finally, there is the place of data in the key processes of organizations.

Today data is central. But when you go back 5 to 10 years ago, it was much less so. Now that you know that you are suffering from data silos, you need to take action.

How do you get rid of data silos?

To get started on the road to eradicating data silos, you need to proceed methodically.

Start by recognizing that the process will inevitably take some time. The prerequisite is a creating a detailed mapping of all your databases and information systems. These can be produced by different tools and solutions such as emails, CRMs, various spreadsheets, financial documents, customer invoices, etc.

It is also necessary to start by identifying all your data sources in order to centralize them in a unique repository. To do this, you can for example create gaps between the silos by using specific connectors, also called APIs. The second option is to implement a platform on your information system that will centralize all the data.

Working as a data aggregator, this platform will also consolidate data by tracking duplicates and keeping the most recent information. A Data Catalog Solution will prevent the reappearance of data silos once deployed.

But beware, data quality, optimized circulation between departments, and coordinated use of data to improve performance is also a human project!

Sharing best practices, training, raising awareness – in a word, creating a data culture within the company – will be the key to eradicating data silos once and for all.

What are the ingredients for becoming a good Chief Data Officer in 2021?

by Zeenea Software | Mar 18, 2021 | Data governance

In a world where data is a major strategic asset, the Chief Data Officer is undeniably a key role for enterprises today. In our last article on CDOs, we discussed what exactly a Chief Data Officer is, and some of his or her key missions in an organization. Now more than ever, as a key player in managing data processes and usages, the CDO must have both technical and human capacities. Let’s take a look at how to be a good Chief Data Officer in 2021!

Pedagogy, support, empathy, vision… Here are some of the many characteristics that can be difficult to combine and reconcile on a daily basis.

And yet, because the role of the Chief Data Officer is as strategic as it is operational, he or she must not only be able to rely on their technical competences, but must also back his or her actions with the support of general management, all while remaining in contact with the business teams.

In order to meet these challenges, the CDO must therefore demonstrate both know-how and interpersonal skills. On the one hand, they must be able to propose new solutions and tools that allow the company to correctly analyze and exploit data, and on the other hand, know how to put data at the center of the company, in order to build a data culture and create links between the business and IT.

An increasingly wide scope of action

In their study entitled What are the roles and challenges of today’s Chief Data Officer (CDO)? – Focus on a key function of data-driven transformation (French), PwC defines the challenges CDOs are currently facing:

“As data teams have been set up in large groups, the challenge now is shifting to get all the organization’s departments to work together. The acculturation of the company and the training of data teams are at the heart of the Chief Data Officer’s challenges. This reality is reinforced by another observation: “The CDO must adapt to the transition from legacy systems to new data storage and analysis technologies, as well as to interfaces that respond to new uses (Cloud, Data Marketplaces, Data virtualization, IoT, chatbot, etc.).

Finally, as the authors of the study’s summary pointed out, “with the growth in the number of use cases combining RPA and AI, the Chief Data Officer’s field of action is expanding”. Proof that the CDO’s missions are very critical for organizations.

Another study conducted by IDC on behalf of Informatica and published in August 2020, revealed that 59% of CDOs surveyed report directly to a key company official, including the CEO. And the Chief Data Officer is directly involved in business performance. In fact, the same study points out that 80% of the Chief Data Officer’s KPIs are related to business objectives (operational efficiency, customer satisfaction, data protection, innovation, revenue and productivity).

The CDO’s challenges on a daily basis

The essential role of the Chief Data Officer is to build a relevant, high-performing, and valuable data pipeline, while putting together a team capable of bringing this valuable asset to life and transforming it into raw material that can be used by all business lines.

This mission requires the Chief Data Officer to put together teams made up of competent and totally data-driven people. This is one of the major difficulties according to IDC. 71% of respondents have only four or fewer data managers, and 26% have none! The ability to recruit, surround oneself with and lead a data team is therefore a major challenge for the CDO.

But it is not the only one.

If we refer to the PwC study already mentioned above, it appears that for 70% of the Chief Data Officers questioned, data acculturation is implemented within their company. This acculturation is primarily achieved by setting up documentation on data that is shared and accessible to everyone, including non-IT profiles. This is another major challenge for CDOs, which is to act as a bridge between the IT players in the company and all of the business lines.

“We see that this is accentuated by the scaling up of data projects, moving from initiatives on a limited perimeter – more in the form of a “Proof of Concept” (PoC) – to global projects involving multiple stakeholders,” PwC stated. The CDO is responsible for developing data processes to improve data quality and is present on all fronts.

A true conductor who must know how to instill energy and dynamism to contribute to the economic recovery of companies in 2021!

The must-have roles for the perfect data & analytics team

by Zeenea Software | Jul 8, 2020 | Data Inspiration

As it’s been repeatedly said, digital business can not happen without data and analytics at its core. Technology can be a point of failure if not handled properly, but it is often not the most important roadblock to progress. In Gartner’s annual Chief Data Officer survey, the top roadblocks for success were human factors – culture, resources, data literacy and skills. A similar pattern emerges from another study, Gartner’s CEO and Senior Business Executive Survey, where “Talent Management” was listed as the “number one organizational competency to be developed or improved.”

In this article, we would like to focus on the key data and analytics roles & leaders that are essential for enterprises seeking a data-driven organization.

Support roles

Chief Data Officer

The Chief Data Officer, or CDO, is a senior executive responsible for enhancing the quality, reliability and access of data. They are also in charge of creating value from their data assets and from their data ecosystem in general. Through data exploitation and by enabling all forms of business outcomes through analytics, the CDO can produce more value with their enterprise data. There are many variations of the title such as CAO (Chief Analytics Officer), CDAO (Chief Data & Analytics Officer), CDIO (Chief Digital Information Officer), etc.

See more in our article “What is a Chief Data Officer?”

Data & Analytics Manager

As the title implies, the Data & Analytics manager is responsible for managing the data & analytics center and is responsible for its delivery throughout the entire organization. They are a key contributor to the strategy and vision for the data & analytics department, they build the roadmap and are responsible for budget and resource planning. Besides measuring the performance of their analytics team, they are also responsible for tracking the contribution of data analytics in regards to business objectives.

Data Architect

The Data Architect, also referred as the Information Architect, strengthens the impact and proves recommendations on business information. They make the information available and shared across the company by presenting how information assets drive business outcomes. They “own” the data models. They understand the impact various data analytics scenarios on the overall IT architecture (such as data science or machine learning) and work closely with the business department.

Analysts

There isn’t a single type of analysts, but rather a spectrum of analysts. Their roles depend on their use cases and vary by responsibilities and skill requirements. There are data analysts who have a foundational understanding of statistical analytics. They are, or work closely with domain experts to support business areas, processes, or functions.

Project Manager

The project manager is responsible for the successful implementation of all projects in the enterprise portfolio. They plan, execute and deliver projects in accordance with business priorities. Throughout the project’s lifecycle, the project manager tracks their project’s status and manages their teams to limit any risks. They are the primary point of contact for data and analytics initiatives.

Data Roles

Data Engineer

A Data Engineer involves collaboration across business units and IT units and is the practice of making the appropriate data accessible and available to various data consumers (data scientists, data analysts, etc.). They are primarily responsible for building, managing and operationalizing data pipelines in support of data and analytics use cases. Also, they are responsible for leading tedious tasks such as curating datasets created by non-technical users (through self-service data preparation tools for example).

Without data engineers, data & analytics initiatives are more costly, take longer to deploy, and are prone to data quality and availability problems.

Data Steward

Data stewards are the first point reference for data in the enterprise and serve as the entry point to access data. They must ensure the proper documentation of data and facilitate their availability to their users, such as data scientists or project managers for example. Their communication skills enable them to identify the data managers and knowers, as well as to collect associated information in order to centralize them and perpetuate this knowledge within the enterprise. In short, data stewards provide metadata; a structured set of information describing datasets. They transform these abstract data into concrete assets for the profession.

See here for more information on Data Stewards

Analytics roles

Data Scientists

A data scientist is responsible for modeling business processes and discovering insights using statistical algorithms and visualization techniques. They typically have an advanced degree in computer science, statistics or other related fields. Data Scientists contribute to building and developing the enterprise’s data infrastructure and supports the organization with insights and analysis for better decision making. They predict or classify information to develop better action models.

Citizen Data Scientist

Contrary to data scientists, a “Citizen Data Scientist” is not a job title. They are “power business users” who can perform both simple and sophisticated analytical tasks. They can execute a variety of data science tasks, supported by augmented analytics tools for data discovery, data preparation, and model deployment. Potential citizen data scientists will vary based on their skills and interest in data science and machine learning.

See here for more information on citizen data scientists

AI / ML Developer

Artificial intelligence / Machine learning developers are increasingly responsible for enriching applications through the use of machine learning or other AI technologies such as natural language processing, optimization or image recognition. They embed, integrate and deploy AI models that are developed by data scientists or other AI experts either offered by service providers or developed by themselves. Other key skills include identifying and connecting potential data assets, data quality, data preparation and how these are used for model training execution.

Conclusion

The growing importance and strategic significance of data and analytics is creating new challenges for organizations and their data and analytics leaders. Some traditional IT roles are being disrupted by “citizen” roles performed by nontechnical business users. Other new hybrid roles are emerging that cut across functions and departments, and blend IT and business skills.

By putting together these must-have roles, your enterprise is a step closer to becoming data-driven.

The Chief Data Officer’s evolution to a Data Democracy Sponsor

by Zeenea Software | Jul 22, 2019 | Data governance

Under the pressure of digital transformation, Chief Data Officers (CDO) have appeared within large companies. According to Gartner, 90% of large companies will have a CDO by the end of 2019.

The thousands of CDOs appointed in the course of the past few years were in charge of improving efficiency and capacity to create value for their organization’s information ecosystem. That is to say, they were invited to direct their organization in processing and exploiting information with the same discipline as the other, more traditional, assets.

Companies who valorize their information assets surpass their rivals in using them to reinvent, digitize, or eliminate existing processes or products.

The CDO’s missions can be summarized as exploiting and finding uses for corporate data as well as being in charge of developing the use of and trust of employees regarding internal enterprise data. As we have seen, these missions often collide with the powerful cultural restraints within organizations.

How have the Chief Data Officer’s missions evolved?

The CDO has many responsibilities. Gartner identified the main responsibilities of a CDO during their 2019 Data & Analytics event in London. These are, among others:

Defining a data and analytics strategy in their organization
Supervising operational initiatives in response to the established upstream strategy
Ensuring information made available on data is trustworthy and valuable
Constructing data governance
Creating business value in data analytics
Managing efforts regarding data science
Operating and maintaining efforts in infrastructure in response to data analysis needs
Etc.

We believe that this impressive list of responsibilities is complemented by another, which could serve as a common thread for all the others and facilitate them: promoting Data Democracy and supporting cultural changes.

At first, CDOs had to lead a mission to convince interest organizations to exploit data. The first few years of this mission were often supported by the construction of a data universe adapted to new uses, often in the form of a Data Lake or Data Mart. The investments agreed upon to construct these data universes were significant but often reserved to specialists. In brief, organizations had more so implemented Data Aristocracies rather than Data Democracies.

The CDO towards a new role

With the exponential development of data, the role of the CDO took a new scope. From now on CDOs must reconsider the organization in a cross-functional and globalizing way. They must become the new leaders in Data Democracy within companies and respond to the call of numerous data citizens who have understood that the way in which data is processed must change radically. The new CDOs must break the bonds of data silos.

In order to obtain the support for data initiatives from all employees, they must not only support them in understanding data (original context, production, etc.) but also help them to invest in the production strategy and the exploitation of data.

From now on, the involvement of stakeholders in the exploitation of data must extend to all levels of the enterprise. It is by facilitating understanding, exchanges, and access around data that organizations will become data-driven.

Download our white paper “How does Data Democracy strengthen Agile Data Governance?”

In order not to commit every employee to a level which is above them, and to respect their desires and limitations, a participatory approach will lead to the implementation of multi-disciplinary teams that will welcome the necessary skills and adequate positions for the deployment of agile data governance. Read more about the Chief Data Officer new role in governance in our white paper.

how does data democracy strengthen agile data governance white paper

Understanding the different Data Cultures

by Zeenea Software | Jul 16, 2019 | Data governance

Just like corporate or organizational culture, each enterprise that deals with data has its own data culture. We believe that what distinguishes Web Giants isn’t the structure of their governance, but the culture that irrigates and animates this organization.

At Zeenea, we believe in putting in place a Data Democracy. It refers to corporate culture, an open model where freedom rhymes with responsibility.

To better understand Data Democracy, it is necessary to compare it to other data cultures. Here are the main data cultures:

Data Anarchy

In this system, operational professions feel poorly served by their IT departments, and each one develops its own clandestine base (shadow IT) which serves their immediate interests while freeing them from all control regulations and conformity to standards. In 2019, this culture brings sizeable risks: data leaks, contravention of ethical regulations, service quality degradation, reinforcement of silos, etc.

Data Monarchy

This system translates to a very strong asymmetry in data access depending on the hierarchical position. Data, here, is very strictly controlled; its consolidation level is carefully aligned with the organizational structure, and its distribution is very selective.

This monarchical culture prevailed for a long time in Business Intelligence (BI) projects: data collected in data warehouses were carefully controlled, then consolidated in reports where access was reserved to a few select people who were close to decision-making bodies. This method promotes a “top-down” approach and willingly encourages a defensive strategy, where rules, restrictions, and regulations insulate data. Its main theoretical benefit is the almost infallible control over corporate data, but that translates into very limited access to data, only reserved to certain privileged groups.

Data Aristocracy

A Data Aristocracy is characterized by a more significant degree of freedom than in Data Monarchy, but which is solely reserved to a very select subset of the population, mainly expert profiles such as Data Engineers, Data Analysts, Data Scientists, etc. This aristocratic approach is often the one that brings the most successful data governance projects to the surface.

Such a culture can be favorable to more offensive strategies, as well as to heterogeneous one, combining top-down and bottom-up. However, it deprives the majority of employees access to data and thus, a certain number of possible innovations and valorizations.

Data Democracy

Data Democracy’s main objective is to make a company’s data widely accessible to the greatest number of people, if not to all. In practice, every employee is able to pull data values at any level. This freedom of access offers maximum opportunities to create value for the company; it provides each employee with the ability, at their level, to use all accessible and compatible resources within their needs in order to produce locally, and through a trickle effect, it will benefit the entire company.

This freedom only works if the regulations and the basic tools are implemented, and each employee is responsible for how they use their data. Therefore, the distribution of necessary and sufficient information is required to allow employees to make proper use of it while adhering to regulations.t

Download our white paper “How does Data Democracy strengthen Agile Data Governance?”

The democratic data culture presents an interesting challenge to balance: on one hand, you must ensure that the right to use data can truly be exercised, and on the other hand you must counterbalance this right with a certain number of duties. Find out how to construct a democratic data culture in our white paper, “How does Data Democracy strengthen Agile Data Governance?”.

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 does Data Democracy strengthen Agile Data Governance?

by Zeenea Software | Jun 10, 2019 | Data governance

In 2018, we published our first white paper “Why start an agile data governance?”. Our goal was to present a pragmatic approach on the attributes of such data governance, one that is capable of rising to the challenges of this new age of information:

We advocate for it to be bottom-up, non-invasive, automated and iterative. In a word, agile.

In this second edition, we decided to tackle the organization of this new agile data governance and its scaling process using the same mindset.

We believe that what distinguishes Web Giants in their approach to their data isn’t the structure of their governance but the culture that irrigates and animates their organization.

This culture has a name: Data Democracy.

Our white paper will address the following themes:

Assessing data governance

Our white paper assess the different governance bodies that we come across in traditional organizations today. The latter often takes on a defensive approach, usually inherited from Master Data Management or from larger initiatives for implementing information systems governance. Very centralized, sometimes bureaucratic, they focus on data control and conformity, often resulting in limiting data access among all company employees.

The concept of a data democracy

In order to understand what Data Democracy is, it is important to know that it is not a governance model. Data Democracy refers to a corporate culture, an open model where liberty goes hand in hand with responsibility.

Data Democracy’s main objective is to make a company’s data widely accessible to the greatest number of people, if not to all. In practice, every employee is able to pull data values at any level.

A democratic approach presents an interesting challenge to balance: on the one hand, you must ensure that the right to use data can truly be exercised, and on the other hand you must counterbalance this right with a certain number of duties.

Building a data democracy

The adoption of a data culture can only work if everyone benefits, hence the importance of communication previously mentioned when discussing rights and responsibilities. The balance between the two must be positive in the end, and governance must not introduce more restrictions than gains. Finally, the results must be made easier.

To enable everyone to find the necessary information. That is the main objective of a data catalog, which must, even more so than its basic function (referencing data and associated metadata), offer simplicity of use in order to navigate through an ocean of information.

The new roles of agile data governance

Under the pressure of digital transformation, new roles appear within large companies.

The Chief Data Officer: the data democracy sponsor

Among them, there is the Chief Data Officer, or CDO. They are in charge of improving efficiency and the capacity to create value for the information ecosystem of their organization.

With the exponential development of data, the role of the CDO took on a new scope.

From now on, CDOs must reconsider the organization in a cross-functional and globalizing way, and governance and corporate data management technology in enterprises.

They must become the new leaders in “Data Democracy” within companies and must respond to the call of numerous “data citizens” who have understood that the way in which data is processed must change radically. The new CDOs must break the bonds of data silos.

Are we all Data Stewards?

The concept of Data Stewardship stems from a much more traditional model. The organizations that already have Data Stewards tend to be quite large and established.

Everyone who uses sensitive data engages their responsibility regarding the way they use it. The regulations for the protection of sensitive data – regulatory or internal – must be applied in the same manner for all those who enter contact with it.

This dedication to involving everyone helps distribute responsibility for data, giving a broader sense of ownership, which encourages users to explore data themselves, and lastly decompartmentalizes data.

Download our white paper: “How does Data Democracy strengthen Agile Data Governance?

To know more about the organization of agile data governance, the definition of Data Democracy, and its new roles, download our second edition “How does Data Democracy strengthen agile data governance?”

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