From Data Portals and Internal Catalogues to Intelligent Data Sharing

The current revolution around the data sharing, the data contracts, interoperability and Data Marketplaces has its roots in two key currents:

• Public Data Portals, born of the movement Open Data, oriented to the external sharing of datasets by governments and institutions. Tools such as CKAN, Socrata o OpenDataSoft and standards such as DCAT-AP were instrumental in establishing solid foundations for semantic and structural interoperability.
• Enterprise Data Catalogues, IT-driven, IT-driven and Data Governance, with a focus on internal data governance, discovery, traceability and quality.

From this merger, and driven by the consolidation of the e-commerce the Data Marketplacea hybrid environment for publishing and consuming data assets, both internally and externally, under a framework of control, security and collaboration.

Anjana Data: from Data Marketplace Visionaries to Knowledge Marketplace Builders

It has been almost 6 years, when he was born Anjana Data We were already talking about Data Portal, Shopping cart y Data Sharing Agreements (Link to the article), whose conjunction later became the now so famous concept of the Data Marketplace which we have started to implement in leading organisations such as the Banco Santander:

• Santander Group & Anjana Data: The journey towards data driven self-service
• Anjana Data and Santander Group offer the keys to achieving success in the Single Data Marketplace at the 2021 Data Management Summit

But in recent years (and almost without realising it) we have gone a step further by enabling organisations to build what we have called Knowledge Marketplace, where data becomes reusable, sharable, governed and actionable information and intelligence products.

What is a Knowledge Marketplace?

One Knowledge Marketplace is an evolution of the Data Marketplace, which makes it possible to manage not only datasets, but also:

• Information Products: reports, KPIs, dashboards, advanced analytics.
• AI products: models, algorithms, pipelines, reusable APIs.
• Enriched knowledge assetswith metadata, contracts, policies and context of use.

These products are designed and managed under a product thinking, where each asset has a purpose, a target audience, a responsible party and a defined life cycle.

Flexibility, scalability, interoperability and usability: key requirements

One of the great challenges today is that organisations are operating in environments very diverse, heterogeneous and changing, The modern platform must be able to meet the needs of its users, both in terms of technology, culture and organisational maturity. A modern platform must therefore comply with four fundamental requirements:

1. Flexibility and no-code customisation

Anjana Data Platform is highly configurable and customisable, without the need for custom development. Thanks to its advanced parameterisation and no-code approach, The system adapts to the operating models, organisational structures and data domains of each organisation, without imposing closed or rigid models.

2. Technical and functional scalability

The platform is built on an architecture modular and scalable, The new system, which allows you to grow from a minimal deployment to a global, federated and distributed governance ecosystem, supporting multiple domains, organisations and territories.

3. Full interoperability and no black boxes

Anjana Data is completely apified and contains no «black boxes». All components are accessible via REST APIs and it has native bi-directional connectors to:

• Identity Management Systems (LDAP, SAML, OpenID, OAuth2...)
• Data management technologies (ETLs, Data Lakes, BI, catalogues, MDMs...)
• Main Cloud providers (Azure, AWS, Google Cloud...)

This guarantees its seamless integration into the existing technology ecosystem, without lock-in and dependencies.

4. User experience for non-technical profiles

In an environment where information and knowledge must be democratised, it is essential that the platform is not only designed for expert users or technical profiles.
Anjana Data Platform offers a intuitive interface, with a reduced learning curve, designed for:

• Facilitating self-service data, information and AI.
• Enable business users to discover, request and consume knowledge products without friction.
• Encourage mass adoption of data governance throughout the organisation.

This user-centred approach allows the value of knowledge is not confined to specialised areas, but flow with context and utility to all business units.

Metadata Lake & Active Metadata: the intelligent heart of the Knowledge Marketplace

In a modern data ecosystem, the real value is not just in the data, but in the context of the data.The following information is available: origin, use, quality, responsible parties, relations, legal conditions and policies. In other words, in the metadata.

But metadata can no longer be static or disconnected. To scale a Knowledge Marketplace, we need a Metadata Lake with the capacity to Active Metadata.

What is a Metadata Lake?

It is a centralised and governed repository that integrates all technical, business, operational, legal and organisational metadata in a structured, dynamic and enriched way, and makes it available to all systems and users.

What is Active Metadata?

The Active Metadata converts this metadata into live and operational elements, which:

• They trigger automatic flows and governance policies.
• They feed recommendation engines and contextual rules.
• They allow synchronisation with other tools in real time.
• They enable business decisions based on contextualised knowledge.

How does it materialise in the Anjana Data Platform?

Anjana Data Platform allows you to leverage all of its governance capabilities by building a Metadata Lake with the capacity to Active Metadata, al:

• Offer a customisable and extensible semantic metamodel, based on typed entities and relationships.
• Organise information assets by domains.
• Support a multitude of metadata attributes of different types, from the simplest to the most complex.
• Enable full lifecycle management of information assets and their different versions.
• Integrate a configurable event and policy system.
• Expose everything through open APIs and bi-directional connectors.
• Convert the metadata in the operational axis of governance and knowledge sharing.

Data Sharing Agreements and Data Contracts: clear rules of the game

In order for a Knowledge Marketplace scalable, secure and reliable operation, there is a need to establish explicit and automated sharing and governance frameworks. In this sense, Anjana Data Platform incorporates natively:

• Data Sharing Agreementsagreements between domains or external parties on terms of use, frequency, security, privacy, licensing, etc.
• Data Contractstechnical and business compromises between producers and consumers on the content, structure, quality, availability and maintenance of products.

These elements are fundamental to building trust and enabling sustainable knowledge-sharing models, interoperable and traceable, as promoted by initiatives such as IDSA, Gaia-X and the Data Governance Act.

If you want to learn more about these concepts, here are some additional resources:

• Data Sharing Agreements and governed data self-service
• How to successfully build an internal Data Marketplace for your organisation
• Factsheet: Data Sharing Agreements

Proactive and preventive government vs. passive and reactive government

In many traditional approaches to Data Governance, In the case of the EU, policies are implemented late, when the problems have already occurred. Anjana Data is committed to a proactive and preventive model, in which:

• The government is integrates from the design of the knowledge product.
• It is validated ex ante compliance with policies, rather than just ex-post auditing.
• It connects directly to the demand management and daily operations, The new system, allowing the processes of access, use and re-use to be governed from the outset.

This approach facilitates the operationalisation of government, making it visible, measurable, and actionable, which is key to scaling a Knowledge Marketplace real.

If you would like to know a little more about the differences between these approaches, we suggest you take a look at this paper of our CEO Mario de Francisco.

Perfect fit with modern architectures

Anjana Data is aligned with the main emerging architectures:

• Data FabricAccess orchestration and governance over distributed data.
• Data Mesh: enabling responsible domains with federated governance.
• Data Spacessemantic, organisational, technical and legal interoperability between organisations.

All this, with metadata compatible with DCAT-AP, facilitating the exchange of catalogues and resources with European public and private platforms.

In this context, we have joint value propositions with the following vendors, offering native integrations with several of the technology pieces that they incorporate in their suites or platforms:

• Amazon Web Services
• Microsoft Azure
• Google Cloud Platform
• Denodo
• Cloudera
• Oracle
• SQL Server
• Snowflake
• Databricks
• Teradata

For each of these joint value propositions, there are also different approaches, depending on the capabilities to be covered or the final architecture to be built. Moreover, these value propositions are combinable, so that the resulting final architecture for a customer can be the combination of different joint value propositions, covering different capabilities depending on the technologies to be considered.

To take an example taken to the extreme, we could be considering a hybrid multi-cloud architecture with native AWS, Azure and GCP pieces, traditional DWH with Oracle, SQL Server or Teradata, an on-premise Datalake with Cloudera, a Big Data Platform in the Cloud with Snowflake or Databricks, a virtualisation layer with Denodo and all governed by Anjana Data Platform with the corresponding native integrations to cover the desired capabilities.

Thanks to this approach, the Knowledge Marketplace will always remain agnostic to the underlying technology architecture but we will be able to take advantage of all the native capabilities that support governance and are offered out of the box by the different pieces of that architecture (such as catalogues, metadata management, security, auditing, ...) and then integrate them into the higher level governance layer that will always remain in the Anjana Data Platform.

An example of such complex architectures can be found at this resource which we publish together with our partner Denodo.

Compliance and regulatory alignment

The platform is designed in compliance with the main regulatory frameworks and international standards:

• Data Governance Act & Data Act
• DAMA-DMBOK2 (Special appeal)
• UNE 0077, 0078, 0079
• Gaia-X & IDSA
• DCAT-AP (interoperable semantics)
• AI Act (Special appeal)
• GDPR and Data Protection Laws (Special appeal)

This makes it possible to operate in both closed environments and open ecosystems with guarantees of sovereignty, ethics and legality.

Analyst-endorsed vision adopted by industry leaders

The evolution towards a Knowledge Marketplace is not just an emerging trend, but a reality backed by analysts and embraced by leading suppliers and organisations:

Analyst perspectives

• Gartner, in its report Hype Cycle for Data Management 2024, The report highlights the growing importance of the AI Data Marketplaces as key catalysts for data-driven innovation and advanced AI governance. .
• Forrester, in its report Data Governance Market Trends 2024, The report stresses that data governance is essential to power AI-driven applications, addressing challenges such as data silos and low data literacy. .
• BARC, in its Data, BI & Analytics Trend Monitor 2024, The report identifies data governance and data literacy as key trends for data-driven organisations. .
• IDC, in his study Worldwide Data Modernization Services 2024, The report evaluates providers in the data modernisation services market, highlighting the need for platforms that facilitate digital transformation and modernisation of data management. .

Leading vendor adoption and alignment with modern architectures

The vision of Knowledge Marketplace as the backbone of data governance and knowledge management is perfectly aligned with the technology strategies promoted by leading cloud service and data platform providers.

Anjana Data Platform fits naturally into the environments of AWS, Microsoft Azure and Google Cloud Platform (GCP), thanks to its modular, fully unified, agnostic and scalable architecture. This integration is materialised through:

• Bi-directional native connectors that facilitate interaction with the identity, storage, processing and catalogue services offered by these hyperscalers.
• Full interoperability with multi-cloud and hybrid ecosystems, favouring environments where different technologies and distributed data architectures coexist.
• Government decoupled from infrastructure, This allows organisations to maintain control and traceability of their knowledge assets no matter where they reside or by what technology they are managed.

Our proposal also complements and powers platforms such as Snowflake, Databricks, Cloudera, Denodo and other widely adopted data technologies., The new system provides a unifying layer of governance, active metadata, contract management and business-driven user experience, enabling a true cross-platform view.

This approach places Anjana Data Platform as a solution agnostic, integrative and ready to operate in domain-oriented architectures, where data, AI products and information flow in a governed manner between services, clouds, technologies and users, meeting the highest standards of interoperability, scalability and digital sovereignty.

Pioneering customers adopting our vision

Our vision of a Knowledge Marketplace has been adopted by leading customers in a variety of industries who have transformed their data strategies to drive innovation and operational efficiency. These customers have successfully implemented Anjana Data Platform, highlighting:

• The ability to integrate data governance into operational flows, promoting a proactive and preventive approach.
• Ease of use for non-technical profiles, thanks to an intuitive interface and a reduced learning curve.
• Full interoperability with existing systems, facilitating frictionless integration into diverse technology ecosystems.

These success stories reflect how Anjana Data Platform not only anticipated the evolution towards a Knowledge Marketplace, It has materialised it, providing organisations with a living, governed and interoperable platform to transform their data strategy.

See it in action

Demo: Knowledge Marketplace with Anjana Data Platform 25.1

Conclusion

The future of data is not about collecting assets, but about transform them into operational and shared knowledge.
This requires a living, governed, interoperable platform designed to scale.

Anjana Data not only anticipated this vision: has made it a reality.
With modular architecture, embedded governance, active metadata, contracts, automation, product focus, and user experience, it enables organisations to build your own Knowledge Marketplace new generation.

Ready to transform your data strategy?

Data governance and AI are all the rage and that is why, after publishing several Market Guides For this area in recent years, Gartner has just published the first Magic Quadrant y Critical Capabilities for the field of Data & Analytics Governance Platforms.

In these reports, Gartner has selected the 16 vendors most relevant worldwide for this segment and, as could not be otherwise, Anjana Data has been included in the list..

Since our inception in 2019, we have enjoyed many successes, but appearing in a Magic Quadrant (none other than the first one published for such an important segment as Data and Analytics Governance) is not just another success. It represents true recognition of our innovative and disruptive vision, to our differential value proposition, to our work, effort and dedication over the last five years, and everything we once decided to call #GenAnjana.

This recognition places us at the very top of the global data management technology provider industry. Just as it sounds.

It is not very common for this type of world-class reports carried out by analysts for whom the American market is the 90% of the world, a Spanish company slips in, with just over 5 years of existence, whose focus is on the Iberian and Latin American markets where we provide tremendous value, which has no backing whatsoever. Venture Capital nor any Private equity nor any large corporation that finances it, etc. That is why, for us, in addition to rubbing shoulders with the major players at a global level with those we are already competing head-to-head with in Iberia and Latin America, to be one of the small players in a Magic Quadrant It is something tremendously special and something we are extremely proud of.

This recognition is not only a source of pride, but also a testament to the impact of our vision and technology on the market. In this article, we want to explain what these reports mean, why they are important, and how they position Anjana Data.

What are these reports and why are they so important?

The Magic Quadrant it is one of the most recognised reports in the technology industry. Its objective is to provide a clear assessment of the main suppliers in a specific market, using two axes: Capability to Execute (execution capacity) and Completeness of Vision (vision integrity). This results in four categories of providers:

• Leaders: Excellence in both execution and vision.
• Visionaries: Innovators with great vision, but still in the process of consolidating their execution.
• Challengers: Solid in execution, but with a less developed vision.
• Niche Players: Suppliers that excel in specific niches or particular regions.

For its part, the report Critical Capabilities complements Magic Quadrant providing a more technical and detailed assessment of key functionalities from each supplier, evaluating them according to specific use cases such as enterprise control, domain analytics y experimentation.

These reports not only provide a clear view of the market, but also help companies identify the most appropriate solutions for their needs.

Here are the links to the reports, but Please note that you must be a Gartner client to access the content.. Unfortunately, we cannot share it because we do not have authorisation to do so.

• Magic Quadrant
• Critical Capabilities

The Data & Analytics Governance Platforms market according to Gartner

Gartner defines this segment as platforms that offer an integrated set of technological and business capabilities to develop, implement, and monitor governance policies in business systems. These platforms are essential for ensuring that data is reliable, secure and aligned with the company's strategic objectives.

What sets these platforms apart from traditional data management is their focus on policy creation and compliance, rather than policy implementationSpecifically, Gartner distinguishes this field from other areas of data management, such as Data Quality o Master Data Management (MDM), due to its focus on the organisation, control and accountability of data within a business ecosystem.

Data governance not only ensures regulatory compliance, but also builds trust in data and empowers analytics-driven decision-making.. Thus, unlike other markets within data management, data governance focuses on the design, definition and implementation of policies and procedures, as well as ensuring regulatory compliance, delegating more operational and technical aspects to complementary solutions.

The role of data governance is more relevant than ever in a world where organisations handle massive volumes of information and must comply with increasingly stringent regulations, such as GDPR, CCPA, AI Act or the emerging Privacy and Data Protection Laws in Latin America. This context has prompted companies to seek solutions that guarantee robust and scalable data governance.

Our positioning as a niche player

In this first Magic Quadrant from Data and Analytics Governance Platforms, Anjana Data has been included as Niche Player. What does this mean? Essentially, it means that we are a specialised player, with a clear and distinct approach, which excels in specific areas of the market.

In this context, our positioning as Niche Player has a logical explanation:

1. Geographical focus: We are deeply rooted in the markets of Iberia and Latin America, which has limited our global expansion for now, but has also allowed us to be leaders in our region. In this regard, our focus will remain the same in 2025, but we have also already launched some initiatives to gradually enter other European countries.
2. Specialisation in Government: Our platform prioritises governance capabilities, integrating with third-party tools and solutions open-source for functions more focused on Data Management, such as Data Quality, MDM o Virtual Data.
3. Growth phase: We are a scale-up in full expansion, and we are still in the process of achieving our maximum potential.

Our unique selling point

In the markets of Iberia and Latin America, the presence of the vast majority of vendors that appear on the quadrant is considerably reduced. In fact, we almost always find ourselves competing head-to-head with the vendors identified in this quadrant as Leaders in most cases, standing out from them by offering a most innovative and disruptive vision, a more agile, approachable approach and some more flexible capabilities both for different degrees of maturity as to cover the changing needs of organisations.

We recently wrote a article Speaking in detail about this, but summarising it in three points, our clients choose us because we provide:

• Flexibility and customisation: Our platform is designed to start with the most basic use cases but also to quickly adapt to highly complex environments by offering advanced capabilities.
• Localised and specialised support: We understand the needs of the market and our customers more deeply than global giants, and we are able to offer them much more effective and efficient solutions in record time.
• Collaboration and shared vision: Together with our partners, We partner with our clients to support them and develop initiatives that truly add value.

Looking back to look forward again

This recognition coincides with a very successful year-end and marks the beginning of an exciting new phase for Anjana Data. It motivates us to continue expanding our capabilities and increasing our impact in the global market, while continuing to lead in our region.

But this recognition would not be possible without all those who accompany us on this journey. To all of them, THANK YOU!

Thank you to everyone who shares the #GenAnjana.

Thank you to all the Anjaneros and also to those who at some point have been or felt like part of this family, for contributing so much.

We would like to thank our current and future customers and partners for placing their trust in us, but also our past customers and partners for helping us learn along the way.

Thanks to those who put up with us on a daily basis, both professionally and personally, which is no easy task.

Thanks to our investors who have staked their personal wealth accumulated after years of hard work, dedication and effort.

We would like to thank the institutions and organisations, both public and private, and all the professionals and experts who have supported us and continue to support us on this journey.

Thanks to our competitors who make us better every day.

Thanks to the Gartner analysts with whom we have had the pleasure of speaking throughout this time, especially those who have prepared these reports: Guido de Simoni, Anurag Raj, Melody Chien, Stephen Kennedy. And also to those with whom we have shared numerous discussions, such as Saul Judah, Andrew White, Amy Bickel, Lydia Ferguson, Robert Thanaraj, Georgia O'Callaghan, Jason Medd, Christopher Long, Mark Beyer, among others.

And, of course, thank you to everyone in the ecosystem and the Data Management community, from whom we learn a little more every day.

If you would like to learn more about how we can help you transform your Data Governance & AI strategy, please do not hesitate to contact us. This is just the beginning of a great journey!

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.

We are pleased to announce that, thanks to our close strategic partnership with Amazon Web Services (AWS), We have launched a Free Trial version of our platform in SaaS mode, now available to the public at the AWS Marketplace. This initiative not only represents an important milestone in our journey, but also reinforces our commitment to breaking down the barriers to entry that currently exist for any organisation to implement a advanced Data Governance & AI platform.

What does the Free Trial version include?

The version Free Trial from Anjana Data Platform in SaaS mode allows organisations to experience first-hand all the key functionalities of our platform in an environment sandbox independent and independent. Thus, for a limited period of time, AWS customers will be able to access, free of charge, the full capabilities of Data Governance & AI that our platform offers. This trial version is an exceptional opportunity for any organisation to evaluate how Anjana Data can transform its approach to operationalising its Data Governance & AI Model.

Most notably, thanks to our partnership with AWS, all costs associated with SaaS during this trial period will be covered by AWS. This means that organisations can explore and test the platform without compromising their budget, allowing them to make informed decisions about future technology investments.

Benefits for AWS customers

Anjana Data Platform integration into the AWS Marketplace provides AWS customers with a significant advantage: the ability to rapidly deploy the most innovative and differentiated Data Governance & AI Platform on the market directly from the AWS infrastructure they already use. This not only simplifies deployment, but also ensures that data remains within the secure and trusted AWS environment, complying with the highest security and privacy standards.

The Free Trial version allows organisations to conduct a thorough evaluation of the platform, understanding how Anjana Data Platform aligns with their specific needs and strategic objectives. In addition, the SaaS mode facilitates agile and flexible deployment and greatly simplifies all maintenance and administration tasks, thus reducing the operational burden and allowing organisations to focus on what really matters: extract value from their data.

Professional Services on AWS Marketplace

In addition to the SaaS offering of the Anjana Data Platform, we have also made available on the AWS Marketplace our Professional Services offer in a public way. Through this option, both our customers and our partners can hire bags of hours of expert support from our Customer Success and Global Support teams both for Proof of Concepts (PoCs) and for the implementation of our platform. This offering is ideal for those looking to ensure a smooth and effective transition to a Data & AI driven ecosystem optimised with Anjana Data.

A strategic relationship that enhances value

Our collaboration with AWS is not simply a technology integration; is a strategic alliance that continues to evolve to maximise the value we deliver to our customers. By combining Anjana Data's Data Management & AI expertise with the infrastructure and global reach of AWS, we deliver a unique and powerful value proposition. This synergy allows us to not only offer advanced solutions, but also to tailor them to the specific needs of each organisation, ensuring successful implementation and a clear and measurable ROI.

At Anjana Data, we are committed to our customers' success, and we strongly believe that these new offerings in the AWS Marketplace are an important step in that journey. We invite all organisations interested in improving their Data & AI Governance to take advantage of this opportunity and discover how Anjana Data Platform can transform their Data & AI Strategy.

Start your Free Trial today in the AWS Marketplace and join the new era of Data Governance & AI with Anjana Data and AWS.

In this era of digital transformation, where data is one of the most important strategic assets for organisations and artificial intelligence is changing the rules of the game, it is essential that organisations have access to useful information for decision-making in an easy, fast, flexible and secure way, while also complying with increasingly strict regulatory, normative and ethical requirements. Through this agreement, Sopra Steria will include Anjana Data Platform in its product and service offering so that its customers can rely on cutting-edge, innovative and disruptive technology to achieve their objectives in the area of “Data Governance and Artificial Intelligence”.

This partnership represents a strategic synergy. On the one hand, Anjana Data provides a robust platform based on advanced technology for the implementation and operationalisation of data governance and AI with a proactive and preventive approach, while Sopra Steria contributes by facilitating its implementation and ensuring the highest level of support for its customers through a specific range of services related to the use of this technology, thanks to its in-depth knowledge of digital transformation and expertise in data management. This partnership will pave the way for customers to achieve efficient and effective data management, enabling them to make better decisions and gain a comprehensive understanding of their data lifecycle.

Links to published press releases:

Sopra Steria and Anjana Data join forces to offer a comprehensive solution that helps organisations with data and AI governance and control (laecuaciondigital.com)

Sopra Steria and Anjana Data join forces to offer a comprehensive solution (dirigentesdigital.com)

Sopra Steria and Anjana Data join forces in pursuit of data governance and control and AI (altodirectivo.com)

When we create Anjana Data As an independent company in mid-2019, we could not have imagined that in just two years we would be recognised by Gartner as one of the most relevant solutions on the market in the field of Global Data Governance, one of the most demanding markets, which has been undergoing radical change in recent years, and which almost all of the vendors leaders in data solutions, in which we compete daily with multinational companies with considerable clout and very powerful solutions, and in which new ones appear every week. start-ups with innovative solutions or new initiatives are launched open-source seemingly promising.

Furthermore, it is a market where there is still a long way to go and much evangelisation to be done. It has very high barriers to entry and very long sales cycles, so at first glance everything would suggest that entering the Champions League and rub shoulders with the vendors most representative seems complicated. But sometimes these things happen, and that's when you look back and realise everything you've achieved in such a short time. That's when you really think that be faithful to a philosophy, put it into practice and convey it with passion It can be more powerful than the solutions that have been leading the market for years, than the marketing messages launched by the world's most powerful technology companies, or than their sales teams with their commercial strategies.

And that's also when, after puffing out your chest a little for the recognition of having done things well, you feel like thanking a lot of people. That's why this article has three main objectives:

1. To echo one of the most important milestones to date for Anjana Data (It's not every day that you are recognised as one of the world's leading vendors in your market).
2. Do a little market analysis in which we place ourselves (the typical one of where we come from, where we are and where we are going).
3. And, of course, thank We are enormously grateful to everyone who has placed their trust in Anjana Data, and of course to those who have not. Both groups have given us the strength to get this far and continue to give us the strength to move forward on this journey, which has only just begun.

Starting with point one, we are very excited to share with everyone that Anjana Data has been included by Gartner in the Market Guide for Data and Analytics Governance Platforms as one of the most prominent solutions on the market for data governance. Furthermore, it is particularly noteworthy that it is the only Spanish solution of all those included, and also the only one that generates genuine content in Spanish as its main language (although we also translate it into English, as it is commonly accepted as the universal language in technology and business).

The market for technological solutions that support data governance is evolving at a rapid pace, which is why Gartner has begun to replace some of the Magic Quadrants most recognised in this segment for other types of reports such as Market Guide and the Vendor Identification Tools. In this context, in December 2021, Gartner published its predictions for what it has termed Data and Analytics Governance Platforms and in both cases, Anjana Data appears as one of the leading solutions on the market.

Specifically, the fact of being included in the Market Guide places Anjana Data in a privileged position, as only the following are selected in this report: vendors most representative of the market that meet a series of capabilities and requirements, which have been evaluated by some of the best independent analysts following a methodology of recognised prestige.

And what is meant by Data and Analytics Governance PlatformsWell, Gartner states the following: “A data and analytics governance platform is a set of integrated business capabilities that help business leaders and users to evaluate and implement a diverse set of governance policies and monitor and enforce those policies across their organisations’ business systems. These platforms are unique from data management and discrete governance tools in that data management and such tools focus on policy execution, whereas these platforms are used primarily by business roles, not only or even specifically IT roles.”

Thus, Gartner establishes the following capabilities within the scope of this type of platform:

• Access control
• Enable metadata
• Analytics
• Business glossary
• Connectivity/integration
• Data catalogue
• Data classification
• Data dictionary
• Data lineage
• Impact analysis
• Representation of information policy (high level)
• Matching, linking and merging
• Orchestration/automation
• Profiling
• Rules management (low-level)
• Tag management
• User interface (as support for all governance-related roles)
• Gestión del flujo de trabajo
• Task management
• Model management
• Security (on the platform itself)
• Organisation and role models

With regard to the market, as indicated Gartner: “The data and analytics governance platforms market is in its infancy. Overall, data and analytics governance has attracted technology investments that provide organisations with capabilities from a range of technologies, both broad and deep.”

The importance of this market in the current ecosystem is therefore clear, as is the long road that still lies ahead. Similarly, Gartner states that this occurs due to the difference in required capacities between Data Management y Data Governance: “There is a need for convergence of capabilities with the recognition that the work of data and analytics governance is different from the work of data management. Although the capabilities that serve both are similar, the context in which those same capabilities are used differs between governance and management.”.

In this context, we can analyse the main drivers which have driven the evolution of the market and also those that will shape its future, which goes far beyond the functionalities and features offered by this type of solution.

If we focus on the functional vision of these solutions aligned with the business requirements of organisations:

• The data becomes one of the most important strategic assets for organisations and they all strive to become Data-driven. This means that data as an asset is no longer something that falls under the umbrella of IT and is now becoming much more important for business areas, which demand technological solutions with a business vision that facilitate the management and governance of the data they work with on a daily basis. This is why intuitive solutions with a more manageable learning curve are also beginning to be required.
• The consolidation of the data economy and the need to share reliable, high-quality data Both internally and externally, organisations are committed to creating data spaces, which must be based on fully governed ecosystems that provide reliability and transparency to the processes of publishing, sharing and consuming information. This requires technological solutions that support such creation and facilitate its operation and maintenance.
• The emergence of concepts more closely related to business language, such as “Data Culture”, “Data Literacy”, “Breaking down information silos”, “Democratisation of data”, “Information Self-Service,” “Data Monetisation or Infonomics,” and an increase in the capabilities of non-technical profiles when working with data mean that data management must be understood from a non-technical point of view and with a clear objective of generating value for the business.
• The need to have agile and flexible procedures Supported by clear and concise policies that are understood and adopted by all stakeholders in the organisation, it is necessary to have technological tools that assist with their implementation and automation, involving the various identified actors.
• The emergence of new regulations and rules Both sectoral and at different state levels, this leads organisations to decide to invest more in technological tools that guarantee data governance and the auditing of their processes.
• Organisations are beginning to perceive the value of data governance In terms of increasing efficiency, reducing costs and better managing the risks inherent in data use, investment in this type of solution is beginning to grow, but above all with a focus on achieving the automation of common technical processes. In this context, tools that are disconnected from the rest of the data ecosystem and do not rely on collaboration between different profiles to achieve these objectives are no longer an option.
• It has been demonstrated that a approach Big Bang is not suitable for this type of transformational initiative, so solutions that are very complex to implement are also ruled out, and much more iterative, incremental and scalable models are favoured. Additionally, given the need to adapt and customise solutions to the operational reality of each organisation, tools with models are no longer considered relevant. out-of-the-box not very flexible or expandable.
• The explosion in the use of Artificial Intelligence y Machine Learning The low profitability obtained from this type of initiative, despite the large investments made, is forcing organisations to rethink their management and governance models for the raw material that feeds algorithms: data. This means that, once again, the adaptability and customisation capabilities of a data governance solution are key to covering these scenarios.

On the other hand, if we think from a more technological view and fit with the technical architectures of organisations:

• The maturity achieved by key technologies such as IoT, Big Data y Cloud enables organisations to have access to a multitude of data and capabilities to obtain value from their exploitation at a much lower cost than in the past. This means that huge amounts of data (structured, semi-structured and unstructured), in a multitude of different formats (tables, views, files, documents, images, audio, video, events, etc.), and with different generation frequencies (streaming, real-time, near real-time, batch, etc.) now have to be managed and governed, with all that this entails from a technical point of view.
• There is a flood of new technologies specialised in solving specific problems in the different phases of the data life cycle, which coincides with a low acceptance of technological standards. This makes the variability of formats and types of data and processes to be managed and governed literally unmanageable. This situation is exacerbated when many systems are still in place. legacy or custom developments made with obsolete technologies and black boxes, whose inner workings are not easily accessible or interpretable. Technology that offers added-value features is therefore required to manage this type of integration, and native integrations between different solutions are even becoming more than commonplace. vendors as well as company acquisitions and integrations.
• The vast majority of organisations that want to become data-driven or those born with this vision, are beginning to position themselves clearly in the face of the consolidation of hybrid architectures, multi-cloud, scalable, without black boxes, interoperable and based on integrated solutions composed of different parts. The search is no longer for a large all-in-one platform with deployment. on-premises, vendor lock-in is avoided, the open-source losing momentum (examples such as Hadoop-Cloudera, Kafka-Confluent, Spark-Databricks, etc.), open solutions are sought with internal data repositories available for exploitation, which can be easily integrated into any technological architecture (API-first) and, above all, solutions are prioritised. Cloud-first.
• Cloud providers are becoming so influential in today's ecosystem that the use of their native and managed services by any technology is becoming almost essential for architecture and infrastructure teams in order to facilitate the deployment, operation and maintenance of technical platforms. It is also becoming very important to have different deployment and service model alternatives, seeking automation in CI/CD circuits and placing great emphasis on the presence of solutions in different Marketplaces from cloud providers such as applications Cloud-native.
• New concepts in data architecture and technical architecture such as Data Lakehouse, Data Fabric, Data Mesh, Data Marketplace y DataOps are beginning to gain momentum in the market, promoted both by leading analysts and gurus and by the vendors themselves. Organisations attempting to adopt these types of models require technological tools capable of operationalising them and bringing them from paper to everyday reality, which requires a degree of flexibility and adaptability that was not necessary until now.

And finally, if we take into account variables more closely linked to economic aspects:

• It is no longer common to invest in tools that require a high initial outlay, as organisations' budgets are very tight and there is a growing internal demand for proof of Positive ROI in the short/medium term that manages to convince senior management that investing in this type of technology is worthwhile in the long term.
• Following the model offered by cloud providers, there is a shift away from the customary practice of acquiring perpetual licences for a specific version of software towards the search for models of much more flexible pricing and pay-per-use oriented, without excessive long-term commitments and including a range of value-added services (constant updates, support included, access to development resources, training and communities of interest, etc.).
• Organisations seek models that do not compromise their scalability So, while they demand licensing models linked to pay-per-use, they also request special conditions for high volumes of use (users, concurrency, use cases, storage, processing, etc.) and heavily penalise hidden or indirect costs based on variables over which they have no control (connectors, sources, professional services, custom developments, expert support, etc.). All of this has a direct impact on key aspects such as time-to-value, the time to market and the Total Cost of Ownership.
• Invoice through the cloud providers (through the Marketplaces) is becoming increasingly common and is viewed positively by organisations because it allows them to reduce the number of suppliers, centralise IT costs, improve management, and obtain special conditions and discounts because Cloud providers themselves are pushing so hard to vendors how to adapt to this new scenario.

 In short, Gartner establishes the following with regard to the needs that solutions focused on this market must meet: “The needs associated with data and analytics governance have never been centralised and consolidated, yet time and again, siloed solutions were the only tools employed. If the level of data and analytics governance support does not reflect the realities of digital business, critical business operations will function suboptimally or fail, causing significant and lasting damage to the organisation. This is evidenced by a recent data and analytics governance survey, which shows organisations falling well short of reaching their governance objectives. Even when they do not fail outright, business operations will limp along meekly and gradually decline in performance, leading to ever-greater malaise. If, however, the level of data and analytics governance is overbearing, complex or overengineered, or continues to be fragmented, the time to value of the initiative will be impacted, and less business value will be delivered at a higher cost.”.

With all this in mind, the logical question that arises is: How does Anjana Data position itself in this market, and what is our value proposition? Well, if you continue reading this article and still don't know what Anjana Data offers compared to other solutions, in addition to covering the features already identified, I will summarise them for you here:

• Provides a collaborative approach With business acumen but with a global reach at all levels of any type of organisation, serving as a meeting point for both business and technical profiles thanks to the personalisation and customisation capabilities of the operating model.
• It enables the creation and maintenance of a common language for the entire data ecosystem, fully customisable and adapted to the organisation without the need to be subject to technological developments thanks to the implementation of a metamodel. “TECHNOLOGY-AGNOSTIC METADATA-CENTRIC”.
• The possibility of creating a single point of contact for different stakeholders data, integrated with demand management, covering your different needs in a personalised way thanks to a carefully designed UX&UI that is particularly intuitive and has an acceptable learning curve.
• Native bidirectional integration with a multitude of technologiesof different types, with varied characteristics and serving multiple purposes related to data management.
• Capabilities for the implementation of the “PROACTIVE AND PREVENTIVE GOVERNMENT” so that an organisation can build ecosystems Data Marketplace, Data Fabric y DataOps based on the automation of common technical processes and on the principles Governance-first y Governance by design fully integrated with your data platforms.
• One functional and technical architecture state-of-the-art, based on the basic principles of modularisation, scalability, interoperability, flexibility and adaptability, and supporting complex hybrid, multi-environment and multi-cloud.
• No black boxes and multiple alternatives for expanding the solution's capabilities and developing new connectors, as well as the possibility of launching customised actions or ad-hoc developments through the use of interceptors or the configuration of tasks in the steps of the natively integrated BPM workflows.
• Take advantage of, take advantage of and complement all the capabilities of native cloud technologies both those with a greater focus on governance (identity management, data access permission management, data catalogues, data structure management, audit log monitoring, etc.) and those geared towards data processing (ingestion, storage, processing, and exploitation).
• Native use of services managed by Cloud providers themselves to facilitate everything related to the management, operation, and maintenance of infrastructure (provision of machines, deployment of services, installation, configuration of connections, application of security policies, monitoring, backups, high availability, etc.).
• Availability of the solution in the marketplaces of the main clouds in different modalities adapted to customer needs (from transactional native applications with deployments in IaaS/PaaS to SaaS and BYOL modalities).
• Adapted pricing model to the changing needs of organisations in order to reduce initial investment as well as time to market, time-to-value y TCO while maximising the ROI linked to the implementation and use of the solution.

Finally, regarding the future of the market, Gartner establishes the following: “The points above refer to and focus on the capabilities organisations need to meet their data and analytics governance needs. This does not dictate how vendors will behave. Some will partner and integrate solutions to form interoperable platforms. Some will acquire others to attempt the same. Some will remain focused on niche or stand-alone segment needs. The next few years will be marked by ongoing and increased acquisitions and developments, even as other markets such as data management, analytics, BI and data science develop capabilities in this lucrative and growing market.”

We are therefore facing a tremendous opportunity but also a highly unpredictable market. However, what is clear is that the adoption of solutions in this segment will experience exponential growth in the coming years and will surely tend to stabilise and achieve the necessary maturity for organisations to obtain the value they need through the management and governance of their data.

At Anjana Data, our goal is to minimise time-to-market and time-to-value in the proof of concept or implementation of our data governance solution and to be able to demonstrate a real return on investment within a very short time frame.

 We have experienced this first-hand many times throughout our professional careers: acquiring technology with extremely high initial investments, implementation plans with enormous projects, hordes of consultants to configure and test that technology... and then, when it comes time to deliver the Go-Live For the transition to production, after many twists and turns, replanning and budget adjustments, the results are not as expected, no one takes responsibility, and then the entire strategy is rethought.

 That is why, at Anjana Data, we do not believe in that model of mammoth implementations that drag on and seek a Big Bang very difficult to manage and, on the contrary, we advocate pilots and implementation plans based on the same philosophy as agile methodologies, seeking to demonstrate real value in a very short time.

 Furthermore, in order to demonstrate this real value, the implementation of Anjana Data is not limited to the technical tasks necessary to implement the solution; rather, we seek to cover the end-to-end from analysing the need to implementing a first use case in production that allows us to obtain the first measurable results.

So, how do we conduct a proof of concept at Anjana Data? 

First of all, we try to understand as best as possible the client's needs and their main pain points and we began working with him on a proposal for using Anjana Data tailored to his requirements, offering different configuration and usage alternatives for the solution within the capabilities of Anjana Data.

 Next, with the aim of drawing up a detailed work plan with defined activities, milestones and deadlines, we sought to narrow down and define the implementation scenario as precisely as possible, also identifying a series of success metrics that could demonstrate in quantifiable terms what had been achieved and the value obtained.

 Therefore, to achieve this objective, we follow a series of steps that can be grouped into the following blocks: 

1.- We choose a limited use case.

A use case or use case diagram is commonly understood as a description of the activities that someone or something must perform in order to carry out a process.

 In this context, within this first point, we must select a use case where Anjana Data meets the identified needs and its incorporation into the stack technology represents a differential added value. A detailed description of the use case is very important because it will allow us to define the scope of the initial implementation and measure what has been achieved in order to sell it internally.

 Here are some examples of use cases:

• A specific domain of information, for example customer contact details or financial data related to contracts.
• A specific data initiative or project, for example, the creation of a sandbox for advanced analytics or the creation of a product data MDM.
• A regulatory case, for example related to GDPR or RDA.
• A process end-to-end information exploitation, such as the generation of a recurring management report or the generation of the income statement.
• A specific technological environment such as a data lake, a cloud environment, or an analytics area.

No use case is better than another; the selection of the use case will depend entirely on the needs of the organisation, its importance to senior management, the complexity of its implementation, and the ease of achieving the involvement of the stakeholders, among other variables.

2.- We define the technological scenario involved.

Based on the chosen use case, it is important to define the technological scenario involved in this use case, since the more clearly defined it is and the fewer technologies involved, the less complexity we will incorporate into the initial implementation plan.

One of Anjana Data's distinguishing features is its extensive native integration with other technologies to incorporate the data governance solution as the central axis of the data ecosystem. This makes this point particularly delicate, and it is very important not to fall into the trap of trying to cover too many technologies in the initial phase, because there are many aspects of technological integration that can complicate and delay the pilot or implementation plan.

At this point, it is important to identify the following technologies:

• Data storage repositories.
• Data processing systems.
• ETLs and data services.
• Operational and BI tools.
• Identity management systems.
• Permit management systems.

3.- We identify the stakeholders key.

At this point, the people involved must feel empowered to make decisions and see themselves as part of the change process that is about to take place in the organisation, perceiving its value and, in turn, assuming a series of responsibilities regarding data that they may not have had until now.

It is very important to make the stakeholders  that data governance is not about “putting obstacles in the way” or “playing the bad cop”, but rather about equipping the organisation with the necessary capabilities to generate value for the business through better use and processing of data. To do this, it is essential to also provide them with the necessary tools and resources, because without them, their workload will increase and they will not see a quick return.

 That is why during the pilot or implementation plan for Anjana Data, it will be necessary to work on training and change management with the different stakeholders identified. 

4.- We define the metrics for success.

Success metrics will help us to demonstrate in a quantifiable way what has been achieved and the value obtained. These metrics will also depend on the selected use case, but as a general rule we can classify them into the following groups:

• Reuse of data.
• Satisfaction of the stakeholders.
• Cost savings.
• Improved efficiency and productivity.
• Reduction of operational risk.
• Regulatory compliance (if applicable).
• Data monetisation (if applicable).

Anjana Data's pilot or implementation plan

With all this in mind, we outline a project plan with a specific duration (approximately three months), which is agreed upon with the client before commencement. This plan typically includes the following activities:

• Assessment of infrastructure and architecture and technical design proposal.
• Deployment of all services according to the chosen deployment model and configuration of connections with the customer's systems.
• Implementation of the selected use case and support in defining the Anjana Data configuration to meet customer needs (governance model and metamodel).
• Anjana Data configuration as defined.
• Training sessions and change management.
• Support during initial data uploads, functional testing, and use case execution.
• Review of success metrics and drawing of conclusions.

And what do we achieve with this way of working?

Thanks to this approach, we have not only managed to minimise the time to market and the time-to-value in the implementation of our data governance solution, but it also allows us to demonstrate a real return on investment within a very short period of time.

On the other hand, we managed to empower both the organisation and stakeholders so that they can evolve in their implementation of data governance according to their needs and requirements and thus also extend the coverage of Anjana Data to more use cases independently, resulting in a maximum reduction of the dreaded vendor lock-in, from which we try to escape as much as possible.

Furthermore, we always certify Anjana Data implementations, and until certification is complete, the solution's user licences do not become valid. This demonstrates our commitment to our customers and our confidence that Anjana Data meets expectations and fulfils identified needs.

Although the Data Governance It mainly deals with cultural and organisational aspects and cannot be resolved solely through technology, although technological solutions play a fundamental role and are more than necessary to achieve the implementation of an effective and efficient governance model. That is why there are many solutions on the market that serve as accelerators for achieving good data governance, as well as helping organisations to build and maintain the data culture necessary at all levels to achieve the goal of becoming data-driven.

From the version 2 of the DAMA-DMBOK we can draw some very interesting conclusions:

• Organisations that establish a formal Data Governance programme are much better equipped to increase the value they derive from their data assets.
• The Data Governance function guides all other Data Management functions.
• The purpose of the Data Governance is to ensure that data is managed appropriately, in accordance with a series of policies and best practices.
• Data governance focuses on how decisions are made about data and how processes and people are expected to behave in relation to data.
• Data governance is not an end in itself; it needs to be directly aligned with the organisation's strategy.
• Data governance is not a one-off exercise; it requires an ongoing programme focused on ensuring that the organisation derives value from its data and reduces data-related risks.
• Data governance is different from IT governance.
• The objective of the Data Government is to enable the organisation to manage data as an asset.
• A Data Governance programme must be sustainable, embedded and measurable.
• Data governance cannot be implemented overnight and requires planning.

In short, the fact that the Data Governance The fact that it is so closely linked to these cultural and organisational aspects makes evaluating the technological solutions that can support us in this area difficult and complex. This forces us to broaden our horizons beyond an evaluation based on the coverage of available functionalities or modules and also consider a series of value-added features that we must incorporate into the assessment.

Features and modules

On the one hand, if we consider the functionalities and modules “specific” to Data Governance, we can mention:

• Glossary of business terms
• Metadata management with Dictionary and Catalogue
• Data traceability and lineage
• Architecture, design, and data modelling
• Workflow and business process management
• Master and reference data management
• Data quality
• Data incident management
• Data security (policies, access and use, user roles and profiling, data obfuscation)
• Dashboard
• DataLabs and Sandboxes Management
• Content Management and Publishing Portal
• Data services management
• Audit support

However, evaluating a solution solely on the basis of the completeness of these functionalities will mean that we only see part of the picture and may make a decision that we regret later on, especially when it is utopian to think that a single technological solution can accommodate all these functionalities in a self-contained manner. That is why, to ensure that this does not happen, we must weigh up the analysis of feature coverage alongside another type of analysis based on a series of value-added characteristics.

Value-added features

These features will enable us to grow in the role of Data Governance in a timely manner according to the specific needs of the organisation:

• Automation: processes should be as automated as possible to free users from the burden of using tools.
• UX & UI: The user interface, as well as its navigation and usability, must be as intuitive and user-friendly as possible for all types of audiences, so that any user feels comfortable using it.
• Interoperability: it must be able to share and exchange data with other systems; it must not be a “black box” or a closed component, allowing interconnection with different types of systems through connectors and enabling the use of standards.
• Customisation: as configurable as possible in order to support the strategy and governance model defined by the organisation.
• Modularisation: the different functionalities should be understood as independent parts, so that the use of one does not limit the use of others, allowing the necessary modules to be used without compromising the overall experience.
• Multi-environment: the ability to centrally manage multiple platforms supported by different technologies from a single instance.
• Scalability: adaptable as data volume and processing and response requirements increase, maintaining stable performance over time.
• Adaptability: it must be able to adapt to the needs and realities of the organisation over time.

Additionally, from a longer-term perspective, the characteristics that require special attention and care are:

• Vendor lock-in: as far as possible, efforts should be made to ensure that the solutions selected do not “tie” the organisation to a single vendor acquiring large dependencies and thus avoiding the migration from one solution to another having major consequences.
• Learning curve: as powerful solutions, the learning curve should not be a problem for users, who should not have to invest a large number of hours in learning how to use the solution or require very specific and costly training and certification.
• User limit: If we want to extend data governance to the entire organisation, we must consider solutions that do not license by user, as this can result in limited use of the solution due to skyrocketing costs in relation to the increase in users and not based on actual use.
• Licence cost: the cost must be flexible and scalable, tending towards pay-per-use, allowing total control over ROI without requiring a high initial investment to maximise the time to market and the time-to-value.

What can we find on the market?

Looking at the market, given that we are talking about technology, the manufacturers of data storage and processing solutions themselves often offer modules geared towards data governance within their own platforms, but generally with a biased and poorly interoperable vision, representing an integration problem between technologies and resulting in a new challenge of application and technology governance.

On the other hand, given the existing market need, in recent years new providers have emerged that specialise in developing specific, independent solutions with an agnostic approach to data storage and processing technologies, providing this practice with a new set of tools to facilitate its implementation. This group includes, for example, Anjana Data.

Despite this, due to the complexity and breadth of the practice, solutions tend to focus on offering a series of specific functionalities and capabilities, and it seems very difficult, if not impossible, to find a single solution that covers everything. Therefore, it is advisable to look for the different pieces that help us build the puzzle of solutions that support Data Governance based on the needs of the organisation, starting with the most critical aspects.

In addition, the market for specific solutions for “Data Governance” It has not been around for very long and is not widely used, except in the US, where it does represent a high volume of business. In fact, neither Gartner nor Forrester have yet created a quadrant or curve for this area, with solutions falling under “Metadata Management”, “Master Data Management” and “Data Quality”.

Finally, within the spectrum of Data Governance technology solution providers, we can group vendors into different categories... but that is a topic for another article entirely.