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HCLSoftware Completes Acquisition of Metadata Management Software Provider Zeenea

HCLSoftware Completes Acquisition of Metadata Management Software Provider Zeenea

by | 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

Harnessing the Power of AI in Data Cataloging

by | 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.

How does a data catalog help companies implement successful Data Stewardship programs?

How does a data catalog help companies implement successful Data Stewardship programs?

by | Jul 6, 2022 | Data Catalog, Metadata Management

By implementing a data stewardship program in your organization, you ensure not only the quality of your data but also that it can be used easily and effectively by all your employees. As a key player in data governance and management, the Data Steward needs specific tools, the first of which is the data catalog. 

The role of data in companies is becoming increasingly strategic, and not just for large organizations! Indeed, to define business strategies, manage distribution, or organize production, the exploitation of data constitutes a major competitive advantage. To deliver its full potential, data must be reliable, of high quality, and perfectly organized. These characteristics are linked to a discipline: Data Stewardship. 

The Data Steward, also known as the Master of Data, acts as the guarantor of optimal data exploitation. How? By centralizing all data, regardless of its source, in an environment that is accessible to all business lines in a simple, intuitive, and operational manner. A Data Stewardship program is based on a rigorous methodology, a global vision of available data, and an ambition to rationalize data in order to develop a strong data culture. However, vision, understanding, and methodology do not exempt the Data Steward from relying on the right tools to accomplish their missions: a data catalog is one of the essential tools for a successful Data Stewardship project.  

 

A data catalog’s objectives

A data catalog exploits 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. 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:

  • Sustain a data culture,
  • Accelerate data discovery,
  • Build agile data governance,
  • Maximize the value of data, 
  • Produce better and faster
  • Ensure good control over data.

 

The benefits of a data catalog for Data Stewards

From importing new data sources to tracking information updates, the ability of a data catalog to track and monitor metadata in real-time automatically allows data stewards to gain efficiency. A data catalog provides 360° visibility into your data from its origin to all of its transformations over time. There are four key benefits to using a data catalog as part of a Data Stewardship program:

Benefit #1: Maintain up-to-date documentation

Your data is constantly active. It is collected, valued, exploited, enriched… To have a perfect understanding of your data assets, you need up-to-date documentation regarding its data sources and how they are used. A data catalog is designed to do just that.

Zeenea’s advantage: Our catalog automatically retrieves and collects metadata through our APIs and scanners to always ensure that your data is up-to-date. View your data’s origins and transformations over time with our smart lineage capabilities.

 

Benefit #2: Ensure data quality

The first vocation of a data catalog is to keep a clear view of your data via metadata. The definitions, structures, sources, uses, procedures to follow… by nature, metadata management by a data catalog contributes to guarantee the quality of your data.

Zeenea’s advantage: Our data catalog enables your Data Stewards to build flexible metamodel templates for predefined and custom item types. Simply drag & drop your properties, tags, and other fields into your documentation templates for all your catalog items.

 

Benefit #3: Comply with data regulations

Compliance with data regulations is a crucial issue in a Data Stewardship program. A data catalog, through its ability to organize data and centralize it in a clear, healthy, and readable environment, helps to comply with these regulatory requirements.

Zeenea’s advantage: Through machine learning capabilities, our Data Catalog speeds up time-consuming tasks by analyzing similarities between existing personal data. It provides smart recommendations by identifying and giving suggestions to tag personal data.

 

Benefit #4: Monitor data lifecycle

Between governance, quality, and security, your Data Stewardship project implies monitoring the lifecycle of your data in real-time. The data catalog responds to this challenge by offering you the possibility to monitor all activities affecting your data.

Zeenea’s advantage: our data catalog provides Data Stewards with a dashboard that tracks and monitors metadata activity. Check the completion levels of your documentation, the most frequently accessed and searched for catalog items, the connectivity status of your catalog, and get smart recommendations on the sensitivity level and additional properties to add to your fields.

Organization, knowledge, transparency, scalability… a data catalog is tailored to accompany your Data Stewardship project!

Start a Data Stewardship program with Zeenea

Zeenea Data Catalog provides a metadata management solution that enables Data Stewards to overcome the challenges associated with handling increasingly large volumes of data. Our solution helps organizations maximize the value of their data by reducing the time spent on complex and time-consuming documentation tasks, and by breaking data silos to increase enterprise data knowledge.

Contact us now for a free and personalized demonstration with one of our experts:

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What is the difference between a Data Owner and a Data Steward?

What is the difference between a Data Owner and a Data Steward?

by | Oct 12, 2020 | Data governance, Data Inspiration

What is the difference between a data steward and a data owner? This question comes up over and over again!

There are many different definitions associated with data management and data governance on the internet. Moreover, depending on the company, their definitions and responsibilities can vary significantly. To try and clarify the situation, we’ve written this article to shed light on these two profiles and establish a potential complementarity.

Above all, we firmly believe that there is no idyllic or standard framework. These definitions are specific to each company because of their organization, culture, and their “legacy”.

Data owners and data stewards: two roles with different maturities

The recent appointment of CDOs was largely driven by the digital transformations undertaken in recent years: mastering the data life cycle from its collection to its value creation. To try to achieve this, a simple – yet complex – objective has emerged: first and foremost, to know the company’s information assets, which are all too often siloed. 

Thus, the first step for many CDOs was to reference these assets. Their mission was to document them from a business perspective as well as the processes that have transformed them, and the technical resources to exploit them. 

This founding principle of data governance was also evoked by Christina Poirson, CDO of Société Générale during a roundtable discussion at Big Data Paris 2020. She explained the importance of knowing your data environment and the associated risks to ultimately create value. During her presentation, Christina Poirson developed the role of the Data Owner and the challenge of sharing data knowledge. Part of the business roles, they are responsible for defining their datasets as well as their uses and their quality level, without questioning the Data Owner:

“The data in our company belongs either to the customer or to the whole company, but not to a particular BU or department. We manage to create value from the moment the data is shared”.  

It is evident that the role of “Data Owner” has been present in organizations longer than the “Data Steward” has. They are stakeholders in the collection, accessibility and quality of datasets. We qualify a Data Owner as being the person in charge of the final data. For example, a marketing manager can undertake this role in the management of customer data. They will thus have the responsibility and duty to control its collection, protection and uses.

More recently, the democratization of data stewards has led to the creation of dedicated positions in organizations. Unlike a Data Owner and manager, the Data Steward is more widely involved in a challenge that has been regaining popularity for some time now: data governance.

In our articles, “Who are data stewards” and “The Data Steward’s multiple facets“, we go further into explaining about this profile, who are involved in the referencing and documenting phases of enterprise assets (we are talking about data of course!) to simplify their comprehension and use.

Data steward and data owners: two complementary roles?

In reality, companies do not always have the means to open new positions for Data Stewards. In an ideal organization, the complementarity of these profiles could tend towards :  

A data owner is responsible for the data within their perimeter in terms of its collection, protection and quality. The data steward would then be responsible for referencing and aggregating the information, definitions and any other business needs to simplify the discovery and understanding of these assets.

Let’s take the example of the level of quality of a dataset. If a data quality problem occurs, you would expect the Data Steward to point out the problems encountered by its customers to the Data Owner, who is then responsible for investigating and offering corrective measures.

To illustrate this complementarity, Chafika Chettaoui, CDO at Suez – also present at the Big Data Paris 2020 roundtable – confirms that they added another role in their organization: the Data Steward. According to her and Suez, the Data Steward is the person who makes sure that the data flows work. She explains:

“The Data Steward is the person who will lead the so-called Data Producers (the people who collect the data in the systems), make sure they are well trained and understand the quality and context of the data to create their reporting and analysis dashboards. In short, it’s a business profile, but with real data valence and an understanding of data and its value”. 

To conclude, there are two notions regarding the differentiation of the two roles: the Data Owner is “accountable for data” while the Data Steward is “responsible for” the day-to-day data activity.

Data Stewardship and Governance: The Data Steward’s Multiple Facets

Data Stewardship and Governance: The Data Steward’s Multiple Facets

by | Jul 11, 2019 | Data governance

Where Stewardship refers to the taking care of and the supervision of a specific property or organization, Data Stewardship refers to data supervision. Initially, the idea was that a domain expert would be in charge with qualifying and documenting data from their professional standpoint. In fact, Data Stewards are those who work closest to where the data is collected; they are often those who best understand the different aspects of data and the standards to which they must adhere to.

 

Data stewardship and governance: the responsibilities

In practice, Data Stewardship covers a wide range of responsibilities, depending on the maturity level of the organization. We can organize these responsibilities in four broad categories:

Operational supervision and quality

This refers to monitoring and supervising the complete life cycle of a dataset. 

More specifically, Data Stewards must define, and therefore implement, the necessary processes for the acquisition, storage, and distribution of datasets.

They must also ensure that the data produced fulfills the quality criteria that were defined (values, gaps, completeness, freshness, etc.) and that the procedures are put into place to evaluate and correct potential quality problems.

Documentation

A Data Steward is in charge of defining and documenting data and creating a glossary of industry-specific terms.

They must ensure that each element of a dataset possesses a clear definition and a specific use.

The documentation constitutes a collection of technical and functional metadata according to a meta model in common principle.

Conformity and risk management

Data protection and the management of regulatory risks or ethics is one of the most challenging aspects of the Data Steward’s role.

The regulatory environment around data is more restrictive and shifting. It’s up to them to ensure that the proliferation of data is framed by a collection of protocols ensuring conformity with the applicable standards – especially regarding privacy protection.

Security and access control

Finally, Data Stewards must define the rules governing data access.

These include the different levels of confidentiality and procedures, allowing the authorization of a person or group to access data.

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

Orchestrated by a Data Management division, implemented by different types of Data Stewards, data governance must be deployed in an organization. To ensure this deployment, several operational models are conceivable in theory – decentralized, federated, centralized, etc. We think what distinguishes organizations is not the structure of their governance but the underlying culture of this organization. This culture has a name: Data Democracy.

What is a Data Steward?

What is a Data Steward?

by | 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.