We are working on configurations. out of the box for all extensions of the standard DCAT-AP —such as Health, Geospatial, Statistics, or Energy—with the aim of enabling any organisation to activate a complete solution without the need for programming. Thanks to the collaboration with DQTeam, The first of these configurations is now available: Health DCAT-AP, designed to manage open data catalogues and health data spaces in a simple, interoperable manner that complies with the European profile.

Strategic alliance with our partners. Objective: from standard to action.

Thanks to the joint work with DQTeam, We have defined and parameterised a solution in Anjana Data that allows our clients to:

• To start up a 100% governance aligned with standards DCAT-AP and its healthcare extension Health DCAT-AP
  
• Configure the official roles defined by Health DCAT-AP (publisher, creator, rights holder, contact point, etc.)
  
• Activate complete metadata templates for datasets, distributions, and catalogues
  

All this under a technical and functional nomenclature that is fully aligned with the standard, ensuring full interoperability between European institutions, regions and platforms.

Ready to deploy: no code, no friction

The solution features a out-of-the-box configuration kit which allows:

• Implementations in record time
• No programming required
• Customisable templates to adapt metadata to the specific needs of each organisation

Anjana Data allows you to start with a basic configuration and evolve according to your data governance needs.

True interoperability: open APIs + metadata federation

Anjana Data has been designed with an architecture 100% interoperable, which allows:

• Share and federate metadata between different instances or versions of the Health DCAT-AP
  
• Bringing the logic of transformation where it needs to be: plugins and processes that consume APIs
  
• Seamlessly integrate with external portals, public catalogues, and sector-specific data spaces.
  

All thanks to your REST API documented with Swagger and to their plugin development kits, designed for heterogeneous environments.

An interface to govern your open data portals

Beyond the technical back-end, Anjana Data offers a simple, intuitive interface geared towards business users, where possible:

• Inventory datasets, distributions, and catalogues
  
• Manage open data portals, sectoral data spaces, or federated networks.
  
• Locate and apply filters to find and access actual distributions

Would you like to see it in action?

Here you go. practical demonstration how the Health DCAT-AP-based solution is configured and used within Anjana Data, with a comprehensive experience of searching, filtering, exploring metadata, and downloading data from an interoperable dataset:

Watch video on YouTube – Health DCAT‑AP demo with DQTeam and Anjana Data

Ready to govern your data with European standards?

If your organisation wishes to align your data strategy with DCAT-AP, DAMA, UNE or the Data Act, Now you can do it immediately and without development, with a modular, extensible and 100% interoperable approach.

Get in touch with us to learn how to activate this setting in your environment or try a customised demo.

The Knowledge Marketplace at the IFT is now a tangible and transformative reality. In a context where data is growing uncontrollably and strategic decisions depend on reliable information, the Federal Telecommunications Institute of Mexico has managed to structure its internal knowledge, making it accessible, governed and value-oriented.

Thanks to Anjana Data, the IFT has gone from having no governed assets to creating, in just 10 weeks, a Knowledge Marketplace Complete. An environment where data is converted into actionable knowledge, available to all profiles and aligned with the organisation's strategic vision.

This milestone not only marks the beginning of a new culture data-driven, but rather demonstrates how a public institution can generate real and early impact through a modern Data Governance ecosystem focused on democratisation of business knowledge.

From zero to Knowledge Marketplace: IFT takes an exponential leap forward with Anjana Data

The IFT did not start from a mature data governance base. It started from scratch: without a centralised catalogue, without defined management flows, without a semantic structure or standardised access mechanisms. The usual approach would be to start slowly, with a basic technical catalogue or an initial Data Marketplace. But the IFT opted for take an ambitious strategic leap: build a genuine Knowledge Marketplace.

Thanks to Anjana Data, That leap has been possible and successful:

• A smart and governed catalogue with over 130 data and information assets in just 10 weeks.
• A living business glossary, which links organisational concepts with technical assets to facilitate cross-functional understanding.
• They were enabled. governance workflows, ensuring compliance, traceability, versioning, and auditing.
• A eCommerce experience, where any user can find, understand, and request the knowledge they need.

The result: an environment where data becomes useful knowledge, with context, governance and purpose, ready to be driven by all areas of the organisation.

Would you like to know more about the Knowledge Marketplace concept? Here we explain it in detail.

Flawless execution, measurable results

This ambitious project was rolled out quickly and efficiently thanks to three key factors:

• Flexible and scalable technology (Anjana Data) which was adapted to the organisational and technical requirements of the IFT.
• An expert implementation team (Management Solutions), with in-depth knowledge of the platform and best practices in data governance.
• Agile and empowered project management on behalf of the IFT, with a key Project Manager to remove obstacles and speed up decisions.

The achievements speak for themselves:

• +130 governed assets
• Cases of strategic value activated at the close of the project
• Governance implemented at all levels: technical, semantic, operational, and organisational
• Active participation from all key areas: security, networks, business, and IT

The impact: much more than well-managed data

The IFT has not only structured its information assets: it has also implemented a new culture based on data and shared knowledge, that:

• Promotes data sovereignty without restricting access.
• Reduces the barrier to entry to information.
• Democratise the use of knowledge throughout the organisation.
• Ensures regulatory compliance and traceability.

This project is real proof that Data governance does not have to be slow or complex.. With the right vision and the right tools, you can go from zero to strategic in a matter of weeks.

Would you like to know all the details?

Download the full success story here and discover how IFT has managed to transform its data ecosystem into a Knowledge Marketplace with Anjana Data.

Do you want to make this leap in your organisation?
Contact us at info@anjanadata.com, We will be delighted to help you design your path towards modern, agile data governance that generates value.

This article has not used ChatGPT or any other Generative Artificial Intelligence to generate the content but has been produced entirely by a flesh and blood human.

Let us be clear, most of the strategies of Data & AI Governance The new policies promoted in recent years have failed, are failing or will fail.. And it is not only us who say so, but practically all the experts and the most reputable consultancy firms such as Gartner say so in a multitude of their reports.

In 2024, with the explosion and commoditisation of Generative Artificial Intelligence, the need for effective and efficient Data Governance is once again evident and the vast majority of organisations are beginning (or returning) to focus on launching initiatives to design, implement and operationalise data governance strategies. Data Governance, This time accompanied by initiatives from AI Governance.

This once again opens up a huge opportunity for organisations to transform themselves to finally become Data-driven But there is also the possibility that many of them will fail just as others have in the past, or that even those that have already failed will not have learned from their mistakes and will again make a major blunder.

In this sense, what an organisation may need will vary greatly from one organisation to another and that is why the people in charge of these initiatives and who do not have the necessary knowledge or experience to face the challenge with guarantees, work hard to read articles on the internet, train themselves in different subjects, ask experts, talk to consultants and/or manufacturers, ...

Different approaches to the challenge

In one way or another, these organisations usually end up contracting a consultancy project to a company they trust or they directly buy a technological tool, thinking that this will solve the problem.

From our knowledge of the market, what we see is that the vast majority of proposals for such organisations offer one of the following approaches:

1. Basic «Buy» technology approach: It usually consists of the deployment of a complete ecosystem of one of the major Clouds together with a discourse that claims to cover everything necessary with guarantees at an affordable cost (something that common sense would suggest is more than questionable). O platforms all-in-one which rarely give good results and are usually tremendously expensive and complex. In our humble opinion, we consider this strategy to be wrong because it only seeks to purchase technology and also requires high investments (although at first it may seem otherwise, especially given the aggressive discounts that some manufacturers offer for the first year).
2. Advanced «Buy» technological approach: It includes the implementation of benchmarks comprehensive studies in which the following are analysed different market tools from different perspectives, which then lead to the elaboration of a shortlist of finalists with which they intend to carry out one or more PoCs (often without really knowing what they want to test or what the analysed tools are going to be used for). Apart from the fact that we have seen few exercises of this type that are really well focused with a purpose other than «buying technology to solve a problem". automagically In our humble opinion, it is an exercise that does not add any value to the vast majority of organisations because they are not mature enough to be worth the time and effort required.
3. Build« technological approach: It consists of committing to the realisation of developments in software made-to-measure The problem with this approach is that it only serves to justify expenditure and to deliver something that will partially meet the identified needs. The problem with this approach is that it only serves to fulfil short-term requirements and has a very limited ROI. In addition, this approach does not accompany the organisation as it grows in maturity level, so at some point they will be forced to change their strategy as they will not be able to support the investment necessary to maintain and evolve a custom developed software that adapts to their changing needs and a maturity level that grows over time.
4. Traditional consultancy approach: It is the most typical and widespread and basically involves the realisation of a project of «PPT strategic consulting» which includes excessive analysis work for the subsequent preparation of a large number of «paper» deliverables that provide little or no value to the vast majority of organisations that want to have something tangible in a short time (less than 6 months) with which they can get started and from there grow and scale.

Our vision is that the formula has to be different from previous approaches in the vast majority of cases and that is why we are committed to innovative, differentiated and disruptive approaches and we ally ourselves with those who share our values and our philosophy and who also like to be very practical and pragmatic to be able to bring value to their clients without grandiloquent speeches and with much more reality than PPT, but being clear that technology is not an end but a means to face a business challenge.

The reasons to go for a different approach driven by #genAnjana

If you agree with us so far, then we speak the same language, you will understand perfectly what follows and you will ask us how we can help you.

So, here are the 7 reasons why you should consider Anjana Data to successfully implement and operationalise your Data & AI Governance:

1️⃣ We are recognised experts in Data Management, We have many years of REAL experience among our professionals and we don't sell Smoke and Black Magic. Any tools and/or words you've seen out there that contain Data o AI I'm sure we've read and researched about it and even rolled up our sleeves and got down in the mud in real situations, so we can help you demystify false beliefs, unmask the sell-outs, separate the wheat from the chaff and turn your vision into something down-to-earth that has a real impact on your life. tangible business impact.

2️⃣ We are a Product Company and Software Manufacturer, We don't offer professional services or consultancy beyond what is related to our own technology that we design, develop, maintain, evolve and commercialise ourselves, and we don't get involved where we don't contribute something really differential, so we know well what we do. We sell technology but with a purpose, we hate the Vendor Lock-In, We include a lot of documentation and training, we have a Value Proposition business-oriented, disruptive and differential and a Clear pricing model without small print. And because we are a very niche company, in order to reach where we can't reach alone, we have built a Ecosystem and a Community working hand in hand with our partners, we are unique in partners to offer the best possible service to our customers.

3️⃣ Our value proposition, our team, our technology, our positioning, and our business model are endorsed by independent experts such as Gartner, the leading Cloud Service Providers (AWS, Google and Microsoft), several of the most cutting-edge and market-leading Software Manufacturers, top-level Consultancies and Integrators and different Associations, Organisations and Institutions in the world of Data and AI. In addition, we are Innovative SME and company GovTech and we have important certifications such as the National Security Scheme.

4️⃣ We know the market inside out and are aligned with the latest trends. (#DataSpace, #DataLakeHouse, #DataFabric, #DataMesh, #DataMarketplace, #AdaptiveDataGovernance, #DataSharingAgreements, #DataContracts, #DataOps, #AIGovernance, #DataEthics, #DataMonetization, ...) and the main frameworks (#DAMA, #UNE, #Gaia-X, #IDSA, ...), standards and regulations (#GDPR, #DataProtectionLaws, #DataAct, #DataGovernanceAct, #AIAct, ...) at a global level to which we also add our philosophy and vision based on our experience. We are not trying to reinvent the wheel, but rather we are trying to apply common sense and we choose to propose solutions that we have seen that work for specific problems, trying to avoid things that do not work, because we have experienced it in our own flesh.

5️⃣ We have been pioneers in the market for a long time and are always at the forefront. in our field of expertise but we are very practical and pragmatic, This gives us a long-term vision while focusing on the long-term goals of the Quick Wins that everyone wants in the short term. We started 2019 with a jaw-droppingly innovative and disruptive discourse that even the biggest players have now embraced almost word for word but that few have yet managed to truly materialise.

6️⃣ We are empathetic, honest, sincere, approachable, agile and flexible. but also tremendously professional and disciplined. Accompanying our clients in achieving their goals is what drives us and we always give a little bit more when it is needed but to be effective and efficient we need to maintain an methodology that we have proven to work, Our values define what we do and what we do not do, based on relationships of trust and avoiding the more traditional, rigid and heavy bureaucracy. Our values define what we do and the #genAnjana is something that if you catch it, you can go a long way.

7️⃣ Our clients and partners, All of them are leading organisations in the field of Data Management, both in the public and private sectors, and they speak for us. They are the most important and the real protagonists of all this, so we always try to get them to talk to each other, to share their concerns, their ideas, their experiences, their mistakes, their learning and their good practices, and thanks to this we can also grow together to improve our formula. We count on numerous real-life success stories that demonstrate that what we preach can be achieved and that the Proactive and Preventive Data Governance is not a utopia but something possible and achievable for any organisation regardless of its level of maturity.

Speaking of real use cases, here are a few examples

If you've made it this far and like what you're reading, it means that you share our vision, and that of our clients and our customers. partners. HOORAY!

At this point you are ready for us to tell you about some of the different use cases that you can implement with Anjana Data Platform.

As you may already know or have guessed from reading this article, organisations around the world, of different industries, sizes and maturity levels, rely on Anjana Data to implement a wide variety of Data Governance & AI use cases aligned with their data strategies.

Following our philosophy of adding value to the business, we identify among our customers different use cases which have been successfully solved thanks to the incorporation of the Anjana Data Platform in its stack technology, considering technological architectures and Data & AI ecosystems of very different natures.

In this sense, it is also important to identify the global requirements of the organisation in order to be able to provide the most appropriate solution approach to meet its business objectives, which may consider multiple technological architectures incorporating different pieces of different vendors which are natively integrated in order to build a fully governed Data & AI ecosystem.

The following is a comprehensive list of different initiatives that have been successfully implemented by our clients:

If you identify one or more of these use cases aligned with your strategy, do not hesitate to write to us, we will be happy to help you.

Knowing the lineage of data is very important from a data governance perspective in order to:

• Knowing how information flows throughout the organisation
• Understanding the data value chain and its processes
• Understanding the lifecycle of data assets and how they are being generated
• Visualise dependencies between data assets and processes to manage the potential impacts generated by changes and modifications.
• Facilitate the search for process errors, quality issues, service degradation, etc.

The data lineage It comes in many forms, and perhaps one of the best known is the technical lineage. That is, knowing, from a technical point of view, how data moves from one place to another through the processes that are executed on it, either automatically or manually.

However, it should be noted that, even though this is technical lineage, this information must be useful for data governance. Thus, for lineage information to be valuable, it must be interpretable, and in the vast majority of cases, a string of activity logs spewed out by a machine is of little use. That is why it is most common for technical lineage to have to be captured, interpreted, and translated in order to be of any use.

Obtaining the lineage

In this context, in order to obtain the technical lineage of the processes that move data from one site to another, we can rely on several techniques:

• Extract lineage through the identification or inference of relationships between objects, which are declared in the form of metadata: This is typically the case with ETLs that operate with parameters and define all the processes to be executed by metadata.
• Extracting lineage through source code parsing: This is no easy task, and its complexity depends greatly on both the programming language (SQL is not the same as Java) and the programmer who wrote the code (depending on the functions, methods, or variables used). Given that we are entering murky waters here and could encounter anything, the only certainty is that the guarantee that can be offered in these situations is usually very low and, in the vast majority of cases, a significant gap must be assumed.
• Extracting lineage from the recovery and interpretation of audit logs: This is what we at Anjana Data call dynamic lineage, and in many cases it is the only way to obtain the most complete trace possible, even though you will only be able to capture what is executed. To implement this, you need to carry out native integration with data platforms and technologies to understand how logs work, know where and how to retrieve them, and finally be able to interpret the captured information, which usually has to be translated to be valuable.

Furthermore, given the variability of languages, platforms, technologies, etc., the spectrum we encounter becomes too broad to cover completely. That is why, when we talk about lineage, we usually have to seek compromises and apply Pareto's Law, especially in certain specific scenarios.

Spark and the data lineage

As we have already seen, not all data processing technologies make it easy for us to obtain the internal lineage of their processes, and among all of them, Spark is one of the most complex.

Spark is an open-source distributed processing technology that offers greater use of the possibilities of distributed data clusters. As it is an open-source project, different distributions are available from various technology providers, such as:, Cloudera, AWS EMR, GCP DataProc, or Databricks, the most famous of these, which is also offered natively by Microsoft in your Azure cloud.

One of Spark's most distinctive features is that it does not have its own storage, but rather uses the memory of the machines that make up the cluster where it runs and is capable of retrieving data from different types of storage, such as HDFS., S3, Cloud Storage, Blob Storage, etc., or streaming systems such as Kafka.

In this regard, Spark distributes tasks across the different nodes of the cluster during execution, using the memory and processors of the different machines. That is why, by its very definition, it seems difficult to obtain a complete trace of the processes executed that move or transform data. Furthermore, this is not something that is completely and natively resolved in any of the current implementations of Spark, as they are all designed and optimised for data processing and not for data governance.

When we talk about data governance and Spark appears in the picture as the technology involved, the vast majority of professionals throw up their hands in horror or assume there is a significant gap. Certainly, activating low-level log capture can provide us with a lot of information about processes, but it also penalises performance, so a compromise solution must be found.

Obtaining Spark lineage with Anjana Data

As we have seen, obtaining the trace of Spark processes is not an easy task, mainly for the following reasons:

• Since it does not have its own storage, the metadata that can be obtained from Spark processes at rest is null. When not running, the most we can obtain is the metadata of the datasets that may participate in those processes as inputs or outputs, but we cannot know anything about what happens in between or how the output data is generated from the input data.
• The way Spark encodes the processing of datasets, whether in an RDD or a Spark Dataset, can be written in several languages (Scala, Java, Python) and operations can be masked according to the encoding. Therefore, parsing source code to obtain traces is not a viable option when it comes to Spark.
• The information that can be obtained from the execution logs is never complete or interpretable and depends largely on both the Spark distribution and the audit configuration. In many cases, the most that can be obtained is the input-output ratio at a rough level, or, on other occasions, the amount of logs to be interpreted will be unmanageable.

However, thanks to Anjana Data's approach and implementation, you can get a very reliable picture at a granular level (field level and applied functions) that no other solution on the market is able to offer. How? We can't tell you everything, but we can give you a few brief glimpses.

Essentially, what Anjana Data does is apply a combination of the three techniques mentioned above, intercepting each of the processes just before they are executed.

To execute processes, Spark creates an execution plan (DAG) before dividing the process into tasks that are launched in parallel. This is the only moment when all the information is available in a single point, just before it is distributed to all the elements responsible for its execution. By including a specific agent that is always invoked in each and every default execution, all this information can be captured and extracted with the required level of detail. Furthermore, all this can be done centrally and non-invasively, without the need for programmers to include anything in their processes.

Finally, all this information is processed and interpreted by one of the components of Anjana Data's architecture and then served to the solution's CORE, where it is cross-referenced with governed information to generate a lineage of valuable data that is made available to the end user.

Anjana Data can do this and much more... Want to find out more?

Request a demonstration and we'll tell you all about it!

Anjana Data has positioned itself among customers, consultants and experts as one of the best alternatives to the data governance solutions currently leading the market due to its innovative and disruptive approach. Anjana Data provides organisations with a number of value-added differentiating features while empowering them to implement effective and efficient data governance.

Anjana Data stands out in three ways fundamental pillars, making it the most innovative and disruptive solution for data governance on the market. With a approach collaborative, proactive and preventive government, focused on metadata and agnostic to data processing technologies, a architecture state-of-the-art, scalable and interoperable platform and a revolutionary licensing model With its pay-per-use model, Anjana Data is poised to change the way organisations understand, implement and operationalise their data governance programmes. 

As a product-oriented company, 100% also has a very aggressive roadmap to continuously evolve and expand the capabilities of the solution in each version, as well as to develop native integrations with a multitude of data storage, processing and exploitation technologies in order to implement proactive and preventive data governance as a facilitator of DataOps. This strategy also includes building a broad ecosystem of partners to help customers on their data-driven journey.

In addition to the aforementioned features, to address the current challenges involved in implementing a data strategy within the organisation, Anjana Data relies on value-added features that will provide the necessary tools to empower stakeholders of data within the organisation, enabling them to implement effective and efficient data governance.

In this way, the solution focuses on the HOW rather than the WHAT.

“We believe that a renewed and purpose-driven data governance is possible if we focus not only on WHAT needs to be covered in terms of functionalities but also in HOW These capabilities will be integrated into the organisation's business processes and technological architecture. This change in mindset gives organisations the ability to operationalise data governance, unlocking the full potential and value of their data. – Anjana Data Team.

Today, organisations face many diverse and difficult challenges when it comes to implementing a data-driven strategy. In this regard, there is a new paradigm in which DATA becomes a strategic asset that must be governed in a different way than we were accustomed to.

And it is this difference that allows Anjana Data to be a distinctive data governance solution, capable of implementing governance incrementally and iteratively to enhance synergies between areas within the organisation and maximise the productivity of people who work with data, with a focus on technical process automation, cost savings and information self-service.

So... How do you know? When Anjana Data can assist you with your strategy Changing the government's view of data in your organisation? 

Below are a few examples of cases to look out for:

• The Data Office has failed in its attempt to implement the data governance model within the organisation.
• If data governance is understood as a bureaucracy between stakeholders of data, which requires the automation of governance processes.
• The organisation is seeking a distinctive solution based on interoperability, scalability and non-dependence on suppliers.
• The organisation is concerned about the initial investment, the short-term return on investment, the time to value and the time to market.
• Concepts such as Big Data, Artificial Intelligence, advanced analytics and technologies cloud-native They are seen as «black boxes» by data office teams, business users, and legal and compliance departments.
• The organisation is already working on or is prepared to evolve towards a complex hybrid and multi-cloud architecture.
• There are walls between departments, areas, and data domains.
• The organisation is attempting to implement DataOps real on innovative data platforms with democratic data self-service.
• The Data Office wants to move from “passive governance» to “proactive and preventive governance” of data.
• The Data Office is seeking to centralise access to data and manage its use for different data environments.
• There are many data silos and diverse technologies used within the organisation.
• The aim is to build a Marketplace technology-agnostic data using smart data contracts
• There are barriers between business users and IT teams.

Finally, in addition to all these aspects, it is very important to determine whether or not there is a data culture within the organisation and whether it is necessary to establish collaboration around data. The correct implementation of a data governance framework using Anjana Data will enable the organisation to achieve multiple benefits.

Anjana Data announces a new version of its Data Governance solution with new functionalities, available from 15 July.

The new version of Anjana Data incorporates extended features for the Business Glossary such as the ability to create new entity types with their own attributes based on dynamic forms. Many of the enhancements are aimed at UX & UI, audit functions as well as back-end and architecture improvements; the incorporation of Artificial Intelligence for user assistance, new workflow configuration capabilities. The new version also features new native integrations.

“In this new version of Anjana Data we have focused on building on what the solution already offered, especially improving the user interface design and providing a better user experience. In parallel, we have invested a lot of time and effort in extending the native integration capabilities to offer proactive and preventative governance over data platforms such as Azure, AWS, Denodo, Snowflake, as well as Cloudera and Hadoop. Finally, we can already say that we have the first user assistance use cases based on Artificial Intelligence & Machine Learning algorithms. In any case, version 4.0, which will be released at the end of September this year, is going to be a new turning point, we are at an incredible pace for a company of our characteristics”.” says Mario de Francisco, CEO of Anjana Data.

Extended features for the Business Glossary

Anjana Data aims to become one of the benchmark solutions in the world of Data Governance with a approach innovative and disruptive, bridging the worlds of business and technology and offering adaptability and flexibility capabilities hitherto not offered by any other solution, which are more than necessary in the new era of Big Data & Multi-Cloud.

That is why in this new version, Anjana Data incorporates improvements in the management of permissions and privileges in the Enterprise Glossary, based on the defined governance model. In addition, it offers the possibility of creating new types of entities with their own attributes based on dynamic forms. In this way, the organisation can create as many entity types as it wishes (i.e. metrics, reports, business rules, data quality rules, KPIs, ...).

Regarding the relationship between objects, Anjana Data includes extended attributes and can also create different types of relationships to classify them from a semantic point of view. Each object can be related to any other object not only within the Business Glossary but also to any object within the Data Catalogue. When creating the objects and defining the associated template, attributes can be set as mandatory or optional. 

UX & UI improvements

Other enhancements in the new version relate to navigation between objects throughout the user interface. In Anjana Data, an object is any element within the metamodel that contains metadata attributes.

To optimise the user experience in the Business Glossary, Anjana Data incorporates a new wizard for the creation of objects. This is especially useful as the wizard will guide the user to facilitate the creation of new objects regardless of type (any type of entity and any type of relationship) and the user will be able to choose between manual creation or import from an Excel spreadsheet based on the names of the headers without requiring a fixed structure.

Anjana Data has also introduced an improved view of each object showing in its main view the most important and recent information related to it and a new section within the object view showing all users of the object. 

AI features for user assistance

Anjana Data is advancing its AI-powered features and a new module has been integrated into the solution architecture for user support based on metadata information, data profiling techniques and user behaviour.

In this new version, the corresponding algorithms run in the background and will show recommendations to users in a non-invasive way. Algorithms for specific use cases will be released within future updates, with the first ones included in this release:

• Recommendation of business terms that may be of some interest to the user.
• Recommendation for the generation of new relations between objects within the Business Glossary.

Enhanced auditing functions and new workflow configuration capabilities

While Anjana Data has put the main focus on user experience improvements, another important part of this new version is the auditing functions. Thanks to the Minerva (based on SolR) it is now possible to see more detailed information in the internal audit logs, as well as having the possibility to select which actions should be audited and which should not. 

Thus, each time a value of any attribute changes, a snapshot of the object will be saved to allow the regeneration of an object with the corresponding values at a specific point in its history.

The new version also introduces new configuration capabilities in the validation flows through the module Hermes (based on Activiti BPM), allowing the possibility to define the triggering of parallel validation steps within a workflow configuration and the dynamic generation of validation steps depending on the type of object, the action performed and the user who has performed this action.

New native integrations

In terms of native integrations, thanks to the new plugins added to the modules Tot y Heimdal, In addition, different types of integrations have been developed between Anjana Data and the technologies used within various data platforms.

Metadata collection and import, sample data query, active governance and dynamic data lineage capture (if applicable) are now possible on the following technologies: 

• RDBMS supporting Generic JDBC 
• SAML 2.0 & OAuth based identity access management systems
• Cloud-native technologies from Microsoft Azure and AWS
• Denodo (Beta version)
• QlikSense (Beta version)
• Snowflake (Beta version)
• Confluent & Apache Kafka (Beta version)

From 15/07, Anjana Data v3.2 will be made available to users interested in discovering how Anjana Data can add value to the data strategy, changing the vision of Data Governance in an organisation. The company is also already working on a new version v4.0 (planned for the end of September 2020) which will again be a turning point in the evolution of the solution.