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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 Total accelerated their digital transformation

How Total accelerated their digital transformation

by | Jan 13, 2021 | Data Inspiration

Total, one of the 7 “SuperMajor” oil companies, has recently opened their Digital Factory earlier this year in Paris. The Digital Factory will bring together up to 300 different profiles such as developers, data scientists and other digital experts to accelerate the Group’s digital transformation.

More specifically, Total’s Digital Factory aims to develop the digital solutions Total needs to improve its availability and cost operations in order to offer new services to their customers. Their priorities are mainly centered around the management and control of energy consumption, the ability to extend their reach to new distributed energies, as well as provide more environmentally friendly solutions. Total’s ambition is to generate $1.5 billion in value per year for the company by 2025.

During France’s Best Developer 2019 contest, Patrick Pouyanné, Chairman and Chief Executive Officer of Total, stated:

 “I am convinced that digital technology is a critical driver for achieving our excellence objectives across all of Total’s business segments. Total’s Digital Factory will serve as an accelerator, allowing the Group to systematically deploy customized digital solutions. Artificial intelligence (AI), the Internet of Things (IoT) and 5G are revolutionizing our industrial practices, and we will have the know-how in Paris to integrate them in our businesses as early as possible. The Digital Factory will also attract the new talent essential to our company’s future.”

 

Who makes up the Digital Factory teams?

In an interview with Forbes this past October, Frédéric Gimenez, Chief Digital Officer and Head of the project described how the teams will be structured within Digital Factory. 

As mentioned above, the team will have around 300 different profiles, all working using agile methodologies: managerial lines will be flattened, teams will have great autonomy and development cycles will be short in order to “test & learn” quickly and efficiently. 

Gimenez explains that there will be multiple teams in his Digital Factory:

  • Data Studio, which will consist of data scientists. Total’s CDO (Chief Data Officer) will be the one in charge of this team and their main missions will be to acculturate the enterprise to data and manage the data competences of the Digital Factory.
  • A pool of developers and agile coaches. 
  • Design Studio, that will regroup UX and UI professionals. They will help come up with various creative ideas and will interfere not only at the analysis stage of Total’s business projects but also during the customer journey stages.
  • A “Tech Authority” team, in charge of the security and architecture of their data ecosystem, in order to effectively transform their legacy in a digital environment.
  • A platform team, in charge of various data storages such as their Cloud environment, their data lake, etc.
  • A Product & Value office in charge of managing the Digital Factory portfolio, assessing the value of projects with the business and analyzing all the use cases submitted to the Digital Factory.
  • A HR & a general secretariat 
  • Product Owners that come from all over the world. They are trained in agile methods on arrival and then immersed in their project for 4 to 6 months. They then accompany the transformation when they return to their jobs. 

These teams will soon be reunited in a 5,500m2 workspace in the heart of Paris in the 2nd arrondissement, an open-space favorising creativity and innovation. 

How governance works at Total’s Digital Factory

Gimenez explained that the business lines are responsible for their Digital Factory use cases. The Digital Factory analyzes the eligibility of their use cases through four criteria:

  • Value brought during the 1st iteration and during its scaling up 
  • Feasibility (technology / data)
  • Customer Appetence / Internal Impact
  • Scalability 

An internal committee at the Digital Factory then decides whether or not the use case is taken care of and the final decision is validated by Gimenez himself.  For good coordination with the business lines, the digital representatives in the branches are also located in the Digital Factory. They are responsible for acculturating the business lines and piloting the generation of ideas, but also for ensuring the consistency of their branch’s digital initiatives with the Group’s ambitions, Total calls them Digital Transformation Officers.

 

First success of Total’s Digital Factory

Digital Factory started this past March and deployed the first squads in April during the Corona virus lockdown in France. In the Forbes interview, Gimenez explained that 16 projects are in progress with a target of 25 squads in permanent operation.

The first two digital solutions will be delivered by the end of this year:

  • A tool for Total Direct Energie to assist customers in finding the best payment schedule using algorithms and data
  • A logistics optimization solution based on IoT trucks for the Marketing and Services branch, which will be deployed in 40 subsidiaries.

 In addition, Total managed to attract experts such as data scientists (despite a still very limited form of communication such as Welcome to the Jungle or Linkedin) and retain them by offering a diversity of projects.

“We are currently carrying out a first assessment of what has worked and what needs to be improved, we are in a permanent adaptation process.” stated Gimenez.

 

Digital Factory in the future?

Gimenez ended the Forbes interview by saying that the main reason for his project’s success is the general mobilization that everyone kept despite the sanitary context: “We received more use cases than we are able to deliver (50 projects per year to continuously feed our 25 squads)!”

Otherwise Total has two major KPI sets:

. measuring the proper functioning of the squads by examining the KPIs of their agile methodologies

. tracking the value generated

 

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” 

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IoT in manufacturing: why your enterprise needs a data catalog

IoT in manufacturing: why your enterprise needs a data catalog

by | 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” 

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How has data impacted the manufacturing industry?

How has data impacted the manufacturing industry?

by | Jan 11, 2021 | Data Inspiration

The place of data is – or should be – central to a manufacturing industry’s strategy. From production flows optimizations through predictive maintenance to customization, data exploitation is a major lever for transforming the industry. However, with great data comes great responsibilities! Here are some explanations.

The manufacturing industry is already on the way to becoming data-driven. In the 2020 edition of the Industry 4.0 Barometer, Wavestone reveals that 86% of respondents say they launched Industry 4.0 projects. From the deployment of IoT platforms, complete redesigns of historical IT architecture, movements towards the Cloud, data lake implementations… data is at the heart of manufacturing industry transformation challenges. 

“In 2020, we are starting to see more and more projects around data, algorithmics, artificial intelligence, machine learning, chatbots, etc.” Wavestone explains. 

All sectors are impacted by this transformation. According to Netscribes Market Research forecasts, the global automotive IoT market, for example, is expected to reach $106.32 billion by 2023. The driving force behind the adoption of data-driven strategies in the industry is the need for increased productivity at lower cost.

What are the data challenges in the manufacturing industry?

The use of data in the manufacturing industry is also a question of responding to a key issue: that of the mass-customization of production. A growing topic that particularly affects the automotive sector. Each consumer is unique and intends to have products that resemble them. However, in the past, manufacturing industries based their production methods on the volume of production and industry-specific standards! 

Mass-customization of production is, therefore, the lever of the data-driven revolution currently underway in the manufacturing industry. Nevertheless, other considerations come into play as well. A “smart” industrial tool makes it possible for these enterprises to reduce the costs and delays of production as well as respond to the general acceleration of the time-to-market. Data also contributes to meeting ecological challenges by reducing the production machines’ environmental footprint. 

Whether it is integrating IoT, Big Data, Business Intelligence, or Machine Learning, these technologies are all opportunities to reinvent a new data-based industry (embedded sensors, connected machines and products, Internet of Things, virtualization). 

But behind these perspectives, there are many challenges. The first of these is the extremely rigorous General Data Protection Regulation (GDPR) in application since May 2018 in Europe. The omnipresence of data in the industrial world has not escaped mafia organizations and cybercriminals who have been multiplying attacks on industry players’ IT infrastructures since 2017 with the infamous Wannacry ransomware. 

This attention is fueled by another difficulty in the industrial sector: older and legacy IT environments that are often described as technological hastles, multiplying potential vulnerabilities. The heterogeneity of data sources is another sensitive difficulty for the manufacturing industry. Marketing data, product data, logistics data, are often highly siloed and difficult to reconcile in real time.

The benefits of data for the manufacturing industry

According to the Wavestone Barometer statistics, 74% of the companies surveyed recorded tangible results within 2 years. Nearly 7 out of 10 companies (69%) report a reduction in costs, and 68% report an improvement in the quality of services, products or processes. 

On average, transformation programs regarding the creation or processing of data have led to the optimization of energy performance by 20 to 30% and a reduction in downtime from better monitoring of equipment that can reach up to 40% in some sectors. 

Increased traceability of operations and tools, real-time supervision of the operating conditions of production tools, all of which contribute to preventing errors, optimizing product tracking, but also to detecting new innovation levers related to the analysis of weak signals thanks to AI solutions for example. 

At the heart of the manufacturing industry’s transformation is the need to rely on data integration and management solutions that are powerful, stable and ergonomic, to accelerate the adoption of a strong data culture.

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” 

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