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!

Anjana Data has obtained the Fundamental Technical Review (FTR) from AWS, which certifies that the platform meets all the requirements of adaptability and flexibility that enable effective collaboration with the AWS Cloud. This not only establishes a seal of assurance for any Anjana Data and AWS customer but also positions Anjana Data as one of the AWS recommended Data Governance & Analytics Platforms within the category that AWS recognised as Independent Software Vendors (ISVs) and qualifies it to be part of a number of specific AWS partner programmes for the acceleration of their joint business.

Thus, Anjana Data and AWS take another step forward in their partnership agreement after three years of working together. The reason for the success of this collaboration and the commitment of both companies to take it to the next level is that Anjana Data and AWS share the goal of helping organisations with their data strategy with a common vision: «Organisations that do not use innovative technologies as enablers of a modern and flexible approach to the implementation and operationalisation of their data governance and analytics frameworks will be at risk in the short term.».

This new agreement comprises the following lines of work, in addition to those already being worked on jointly by the two companies:

• Development of a value proposition to offer our customers our shared vision through the implementation of a comprehensive, innovative and distinctive solution, fully integrated natively on AWS.

• Design, development and availability of new mechanisms of integration between different parts of AWS and Anjana Data, such as the evolution of existing integrations with IAM, Glue Catalogue and Lake Formation for the development of new integration components with DataZone and Cloud Trail.
• Availability of different Anjana Data offerings in the AWS Marketplace, especially boosting the SaaS offering, so that AWS customers can have a much richer Anjana Data deployment experience and can also benefit from the advantages offered by AWS to its customers who are consumers of Marketplace applications.
• Design and development of a specific business and commercial plan thanks to which our clients will be able to benefit not only from the power of the solutions of both companies but also from credits, discounts and joint adoption and support plans.
• Launch of joint venture marketing and communications activities to position our joint value proposition in the market, also giving visibility to the success stories already achieved in our joint customers.

In terms of the joint value proposition, a Data Governance & Analytics Platform such as Anjana Data has all the elements of governance and orchestration of the flows of a governance operating model, which is where the roles exercise responsibility for the data. The AWS cloud lacks these functions, but it provides great value in knowing what happens to the data (how it is used, who uses it, where it moves, what that data is and its classification) and all that happens to the data gives us a real and dynamic view of its lifecycle within the cloud environment. All this information is of great value to incorporate into Anjana Data, unifying both visions and capabilities. In this context, the highlights of the Anjana Data and AWS value proposition are:

• Anjana Data complements and extends the capabilities of the cloud-native Data Catalogue (AWS Glue Catalog) to offer advanced features for the implementation of proactive and preventive, as well as effective and efficient Data Governance (automation of common technical processes). Therefore, we can say that Anjana Data establishes a business-oriented and technology-agnostic governance layer that extends to the entire organisation (technical and non-technical users) the possibility of using all these capabilities.
• Anjana Data's Data Sharing Agreements allow users to request access to the data they are interested in within a complete, governed Data Marketplace ecosystem. Through the use of plug-ins, we can leverage the power of Anjana Data and its integration with IAM and Lake Formation to allow such requests to be made on the AWS platform in an automated and controlled manner.
• Anjana Data can provide through plugins that interact with the APIs of the different parts of AWS, both labels and specific columns (Personal Information, Sensitive, etc.) interacting with Glue Catalog and Lake Formation from the Anjana Data Glossary and Data Catalog.
• Similarly, in the other direction, the Anjana Data APIs can be invoked from the various pieces of AWS to provide the governance layer with all the low-level data management information that allows non-technical users to have visibility into what is happening with data assets on the AWS platform.

Anjana Data launches two new levels of its Official and Public Certification, with the aim of continuing to offer training on the most innovative and disruptive solution for Data Governance. The new training modules offered by Anajana Data are as follows:

Advanced (functional path): Aimed at more advanced profiles within data management such as Data Office teams, architects, functional and technical stewards and data governance consultants who have to functionally define the configuration of the solution (governance model, metamodel and metadata attribute templates).

Configuration Administrator (technical path): Aimed at hybrid profiles (functional/technical) with knowledge of SQL, BPM management, identity management (IAM, AD, LDAP…) and YAML files. This certification enables the person who takes it to configure the solution according to the defined functional requirements. Although it is not necessary, it is recommended to have completed the Advanced level to be able to functionally understand all the configuration that is carried out in Anjana Data.

The functioning of the Anjana Data Official Certification is divided into two clearly differentiated areas: functional and technical. The first (functional) refers to Advanced and Expert levels of certification, which are aimed at the practical use of the solution and knowledge of all configuration and adaptability capabilities. The second (technical) level encompasses two clearly differentiated paths between Administrator, which is divided into Configuration Administrator and Platform Administrator, and Developer.

In order to gain access to any of these levels, the student must have previously completed the User level., and, preferably, the Advanced level of Anjana Data’s Official Certification. Additionally, the levels of the certification are progressive according to the area of work that the student wishes to study and can also combine levels from both areas in order to have a more complete vision of the solution.

All certificates are valid for a certain period of time, have certain examination rules and a different pass percentage. Therefore, at Anjana Data, we recommend reading the general information about each of the levels available on our website and in Hadoop Training.

Anjana Data is the innovative and disruptive enterprise solution for Data Governance; designed to help organisations in the implementation of their data strategy. The training offered by the Anjana Data certification aims to enable the student to help the entity in its data-driven journey with a more general vision of what it means to implement a Data Governance programme in an organisation thanks to Anjana Data.

Anjana Data launches two of the new levels of its Official and Public Certification with the aim of continuing to offer learning at its different levels on the most innovative and disruptive solution for Data Governance. The purpose of this study is to guarantee that all those students who have passed it are qualified and have the knowledge and skills necessary to perform the tasks related to Anjana Data, from its daily use in an organisation to the implementation and complete configuration of the solution.

The new levels are Advanced and Configuration Administrator. Both will be available from Friday, 16 July 2021, at 12:00 hours through the Hadoop Training platform, and registrations must be made only through the form that is available in the section of ‘CERTIFICATION‘ from the website of Anjana Data.

Anjana Data Certification has become another lever of the company to transfer to the organisations and their stakeholders the importance of data in the current era to have useful and quality information that allows it to be used in a simple way, fast, flexible and safe allowing you to make better decisions. In addition, offering all the training through pre-recorded online sessions, led by professionals experts in Data Governance.

The operation of the Anjana Data Official Certification is divided into two clearly differentiated areas: functional and technical. The first (functional) refers to the Advanced and Expert levels of certification, which are aimed at the knowledge and practical use of the solution. While, the second (technical), encompasses two clearly differentiated paths between Administrator, which is divided into Configuration Administrator and Platform Administrator, and Developer.

In order to gain access to any of these levels, The student must first have taken the User Level of the Official Anjana Data Certification.. In addition, the levels of certification are progressive according to the area of work You want to study and you can also combine levels of both areas to have a more complete view of the solution.

Anjana Data is the innovative and disruptive solution for Data Governance, designed to help organisations implement their data strategy. The training offered by the Anjana Data certification aims to enable students to assist the entity in its data-driven path with a more general vision of what it means to implement a Data Governance programme in an organisation thanks to Anjana Data.

In this context, we come from a world where data was organised in silos guarded by pure IT teams (Systems, DBAs, etc.) and these were served in the form of reports, metrics or projects fully cooked and with long development times; this is something that, to this day, has become totally obsolete and is already breaking up in many organisations.

For years, we have been experiencing a technological torrent that brings new capabilities in data processing (Big Data, Cloud, IoT, Advanced Analytics, AI & ML, etc.). We have a greater data culture among people and organisations, there is more training in data, data literacy and data storytelling, and new skills are being acquired to consume data and generate insights in business areas thanks to more and better data analysts and scientists, etc. so it is clear that data is no longer something that can be guarded by pure IT teams and served in long development times, but something that must be put at the service of the business (people or algorithms) for rapid and efficient decision-making that generates a competitive advantage.

What are the benefits of sharing data within an organisation or between organisations?

• Maximise synergies between different actors to improve their daily operations. We can see this in the environment of a single organisation where different departments have to share information in order to be able, in their day-to-day work, to carry out their tasks, activities or make certain decisions. Another very clear case is for research and innovation, hence the fact that there are many Open Data projects worldwide focused on open data sharing (the latest and largest one in the European Union

• Furthermore, Compartir esos datos mejora la calidad de los datos clave para tomar mejores decisiones más rápidamente.. If data is made available to other consumers, producers will place greater emphasis on sharing quality data that makes it easier to make decisions more quickly.

• Reuse developments and work already done by increasing the efficiency of data projects. In this way, we will ensure that each data initiative that requires the development of processes or previous analytical work does not have to start from scratch and can reuse the work already done in common frameworks or collaboration models.

• Reducing operating costs and improving the productivity of professionals. Data sharing also leads to savings in operating costs because it prevents duplication of data for certain activities or overlapping manual data management tasks (search, understanding, request, cleaning, etc.), and this results in an improvement in the productivity of the organisation's professionals.

• In turn, it also aims to improve the time-to-market and time-to-value of new products and services of any kind. Today, a large part of the products or services that are launched into the market, both from the public administration and from private organisations, depend to a great extent on analyses made from historical data or from future predictions. We all know the very successful case of the Netflix recommendations algorithm (https://research.netflix.com/research-area/recommendations)

• In turn, it also aims to improve the time-to-market and time-to-value of new products and services of any kind. Today, a large part of the products or services that are launched into the market, both from the public administration and from private organisations, depend to a great extent on analyses made from historical data or from future predictions. We all know the very successful case of the Netflix Recommendation algorithm. 

• Provide transparency and confidence both in data and in the processes of capture, storage, transformation and consumption of data and information. If we share, it is clear that we have nothing to hide and this is something key especially for the Public Administration of the 21st century. The case of Estonia is one of the most talked about in this example: https://www.tallinn.ee/eng/Uudis-Regions-and-Cities-contribute-the-most-of-open-data-in-Estonia-Tallinn-is-the-top-publisher-overall

Once we are clear about the benefits of sharing data, this leads to a dilemma between two parties: on the one hand, we need to enhance the use and reuse of data and for that we need to democratise its access; and on the other hand, we have to have control over the use that is made of that data to ensure regulatory compliance and the safety of those data. In the end, there is always that turning point that makes us reflect on the different paths involved in sharing data.

«Aurea mediocritas: At the midpoint is virtue» – Aristotle

That is the middle ground that is achieved with the entry into play of Data Sharing Agreements (DSAs) to share data in an environment where we feel comfortable and there is a government. In this sense, we always have to seek the balance between giving value to the business and complying with the regulation; follow what the business needs, but at the same time not having great technological costs; enhance self-service, but having control of the information that is being made available to the rest; and then also pursuing innovative and disruptive solutions without forgetting that the traditional cannot disappear overnight.

The data is a strategic asset, but has different perspectives.

Whenever we discuss data sharing, what we mean is making information available by producers for use by consumers, and in that process there are roles that have to ensure that everything works correctly. Ultimately, each person or type of role will have their own profile, their own visions and needs; and thanks to DSAs we will be able to cover all of them.

If we look at a data consumer's point of view, they need to have access to knowledge of the data because of its context and meaning; a guarantee of the quality of the data used for decision-making; compliance with SLAs by producers; and regulatory compliance from the point of view of use.

From the point of view of a producer, this seeks to control the production processes and the availability of their data, prepare and certify the data that will be shared, know which processes and which people are using the data and for what, and comply with data regulation and regulations.

From a cross-cutting management and monitoring perspective, the objectives will be: maximising the ROI of data initiatives, increasing efficiency in process productivity, automating and reducing operational costs and risks, achieving a homogeneous view of information consumption, and ensuring regulatory compliance.

But what exactly is a Data Sharing Agreement?

A DSA is essentially a mechanism for sharing data between producers and consumers in an agile and personalised manner, facilitating regulatory and regulatory compliance in the use of data. In addition, thanks to DSAs, we can standardise the way in which data and information access are requested, granted, and managed, gaining in efficiency and productivity.

On the other hand, DSAs are represented as logical assets that group different technical data assets or Datasets (tables, files, views, documents, events, etc.) so they offer stakeholders a new experience of sharing information by facilitating all the technical complexity behind data sharing by containing all the metadata information of the technical assets they encompass. Thus, DSAs bring the data closer to the business consumer, abstracting them from all the underlying technical concepts.

Finally, DSAs also allow for the definition, implementation and operation of Data Contracts in order to provide the necessary flexibility to implement different data access policies for each specific case of information sharing, since sharing public data from three years ago is not the same as sharing citizens' addresses or confidential information about an organisation's strategy. There are times when a Data Contract can serve multiple purposes and multiple consumers, but there are other times when specific Data Contracts are needed for very specific cases.

But what exactly is a Data Sharing Agreement?

Incorporating innovative elements in data governance such as DSAs is not an easy task, but if you are genuinely interested and want to do it, the best thing is to have the right people, the right processes and the necessary technology. If you want to know more about how to operate DSAs, different use cases and success stories, take a look at this. document, this webinar or contact us.

The new version of Anjana Data includes an evolution in the tools available to organisations to extract the greatest possible value from the data thanks to the operationalisation of effective and efficient data governance. The improvements in this version revolve around the evolution of the CORE Metamodel and the improvement of the user experience. In addition, it includes new components and specific views to simplify the search for objects for users and the consumption of information through the graphical interface of the application.

Anjana Data v4.1 implements an evolution of the CORE Metamodel on which the Business Glossary and Data Catalogue are based, allowing users to add new types of attributes to dynamic forms and configure new validation rules within metadata templates for any object.

Regarding the UX & UI (user experience), the application continues with its evolution towards an increasingly intuitivo e intuitivo design in order to facilitate the day-to-day of users in relation to Data Governance. Thus, in this version the focus has been on improving the user experience when editing objects, reducing the number of clicks needed to make changes, as well as the consumption of more detailed information without the need to move between different screens of the application.

Another new feature incorporated into Anjana Data v4.1 is a new component for the ‘Object Search Bar’, which is located at the top of the user interface in all views of the application and aims to provide more relevant information about the desired objects. In addition, the use of this component is extended to other parts of the application where it is necessary to search for metamodel objects (e.g. to incorporate Datasets into DSAs).

On the other hand, in relation to the graphical representation of the lineage of the data, In this version 4.1, a series of additional capabilities have been included in the advanced 3D graphics library, thereby improving the user experience when working with this type of graph-oriented visualisation.

Regarding audit actions, a complete review of all automatically generated audit messages related to the auditable actions has been performed, resolving some problems detected in relation to the information displayed on the screen. In addition, the Administration and Configuration Portal has improved the capabilities of the ‘Workflows Design Tool’, giving users the ability to include more actions in an easier and faster way following the BPMN2.0 standard.

Finally, some technical improvements have been developed in several of the back-end modules of the architecture, which results in better performance of the entire solution while making some of these modules easier to implement and configure.

Release date: 26 February 2021

*All information about Anjana Data v4.1 is available on the organisation's website by downloading Release Notes – Anjana Data 4.1.

The DataOps manifesto brings together a series of practices that were published in 2017 to try to solve problems related to the inefficiency of data generation and processing processes, as well as the quality of data in relation to inconsistency errors and inconsistencies between data. Despite what one might think at first, DataOps is not just DevOps for data, although the idea is to apply this concept, which is very widespread and implemented in the field of software development and operation, to the field of data.What the DataOps manifesto says, complemented by the initiative that came out in 2018 called ‘The DataOps Philosophy’, is that DataOps is a combination of agile methodologies, DevOps concepts and everything known as Lean Manufacturing.. In this way, in addition to the concepts of DevOps, it also incorporates management concepts more related to agile methodologies and other concepts closer to the world of industry and manufacturing and production processes.

Thus, the purpose of DataOps is to manage in an agile way the entire DevOps part (idea, development, production of the software) together with the value chain and the life cycle of the data. In this context, DataOps is a series of techniques, methodologies, tools and processes that together help the organisation, or a specific project, to extract greater value from the data thanks to the automation of the processes that occur in the data lifecycle.. All of this with the aim of achieving greater profitability from data analytics projects and initiatives.

From a functional architecture point of view, a DataOps The platform aims to:

• Integrate data from different sources, automatising their ingestion, loading and availability.
• Control the storage of data with its different versions over time, historicising the information and data transformation processes.
• To have a centralised management of metadata that serves not only to know the available information but also to activate and configure the platform’s processes.
• To have under control all the management related to the request, authorisation and access permissions to data for consumption and exploitation.
• And, finally, to apply analytical, reporting and dashboarding mechanisms and techniques to monitor and track what is happening throughout the platform.

All this, operated by a DataOps team to facilitate the sharing of data and the development of projects and initiatives on the platform for producers and consumers of information.Furthermore, on the one hand, it is necessary to seek alignment with the automatisation of software deployments to achieve continuous integration, while on the other hand, in relation to data flow, Data pipelines must be orchestrated, tested, automated, and monitored. within the platform itself: as always, moving data from areas where the information is raw, until it is treated, refined and enriched in subsequent layers to finally be able to perform analytics on the information in the layers of exploitation.

In this context, Data Governance is often positioned at the end of the value chain and the data lifecycle, which is entirely incorrect, and we will explain why.

Throughout the entire data value chain, a multitude of roles coexist that will have to collaborate with each other, from developers to business users, including architects, operations teams, systems technicians, etc. Therefore, it is necessary to implement management methodologies (in this case agile) as a basic principle and with continuous change management, fundamental parts of Data Governance. Only in this way can we finally achieve the automation of technical processes that provide us with greater efficiency and security, which is one of the main objectives pursued by DataOps.

In addition, to achieve DataOps objectives, it is necessary to have complete control over the data lifecycle, to separate the information into logical layers and to know exactly how it flows through the data ecosystem as a whole (lineage and traceability), as well as to consider how the data lifecycle fits in with the software lifecycle. Finally, the importance of achieving full integration with the technical architecture should not be forgotten either, as without integrating the different technical parts of the architecture, it will not be possible to automate the corresponding data processes. And, after all, these are all parts where Data Governance you have a lot to contribute.

Finally, to achieve complete integration and automation of processes, tres niveles esenciales de gestión must be incorporated:

• Gestión de la demanda en proyectos de datos: changes to existing information, new use cases and exploitation of information, capture and ingestion of new data, …
• Gestión de metadatos y versiones de estructuras de datos, since metadata will be the central piece that will enable the automation of many functions around this DataOps concept. 
• Centralised management of data access permissions, so that in the end it is possible to know who consumes what information and for what purpose.

Therefore, after having unpacked all that is necessary to achieve the implementation of a DataOps model, we can understand it as the natural evolution of Data Governance. Data Governance by design (governance-first or governance-by-design), proactive and preventive, which is located at the beginning of the data value chain and which accompanies the different stakeholders throughout the data life cycle, being the central axis of the processes, and providing at all times a global vision that will allow us to achieve the desired effectiveness and efficiency.

Despite what one might think at first, the proactive and preventative Data Governance situated at the beginning of the data value chain attempts to reduce bureaucracy as much as possible with clear and concise policies and procedures, providing flexibility and agility to the processes (especially management) and thus maximising synergies between the different projects and use cases, promoting reuse of both information and existing processes in the platform. In short, thanks to this vision of Data Governance, Estamos habilitando un único punto de entrada al ecosistema de datos. for all data stakeholders by applying a collaborative approach to:

• Integration with demand management.
• Integration between technologies and parts of the ecosystem.
• Automation of common technical processes.
• Incremental and iterative approach by use cases.
• Democratisation and data-driven self-service.
• Monitoring for continuous improvement.

All this is only possible by construir un solo, centralised metadata repository as the centrepiece of the data ecosystem and abstracting data governance and management from the underlying technologies and platforms, as Data Governance has a very different focus than data capture, storage, treatment, processing and exploitation technologies, which are much more concerned with process performance than with proper data management.

Finally, complementing this repositorysitory with a common and unique language based on the knowledge of the business and the organisation (known as Business Glossary) will in turn allow us to build a semantic map of information assets (incorporating the corresponding taxonomies) that will enable us to bring the data ever closer to the business users and continue to build bridges with the technical teams.

If you want to know some praticable instances of DataOps you can see this presentation and if you want to know how you can implement a DataOps model thanks to Anjana Data, write to us and we will be happy to assist you 🙂

Anjana Data has become one of the best alternatives to the data governance solutions that currently lead the market because of its innovative and disruptive approach. Anjana Data provides organisations with a number of differential value-added features while empowering them to implement effective and efficient data governance.

Anjana Data is distinguished by three fundamental pillars, which make it the most innovative and disruptive solution for data governance in the market. With proactive and preventative collaborative governance approach, metadata-centric and data processing technology agnostic, best-in-class architecture that is scalable and interoperable, and a revolutionary pricing model, Anjana Data is ready to change the way organisations understand, implement, and operate their data governance programmes.

As a 100% product-oriented company, it 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 DataOps enabler. This strategy also includes building a broad ecosystem of partners to assist customers in their data-driven journey.

In addition to the particularities mentioned above, to address the current challenges of implementing a data strategy in the organisation, Anjana Data based on added-value characteristics which will provide the required tools and mechanisms in order to empower data. stakeholders in 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 fit-for-purpose data governance is possible by not only focusing on WHAT needs to be covered in terms of functionalities but also on HOW Those features will be integrated into your business processes and your data IT architecture. This change in mindset will give you the opportunity to operationalise data governance, identifying the value you are pursuing.” – Anjana Data Team.

Nowadays, organisations need to face many diverse and hard-to-beat challenges when trying to implement a data-driven strategy. There is a new paradigm out there where DATA turns into a strategic asset that needs to be governed in a different way than before.

And it is that difference that allows Anjana Data to be a differential data governance solution, able to implement governance in an incremental and iterative way in order to improve synergies between areas and maximise the productivity of people working with data, focusing on the automation of technical processes to achieve cost savings and support data self-service.

So… How do you know? When Anjana Data can assist you with your strategy by changing the vision of data governance in your organisation?

Here are a few cases to watch out for:

• The Data Office has failed when attempting to operationalise the data governance model within the organisation.
• Data governance is understood as bureaucracy among data stakeholders, so it requires automation of governance processes.
• The organisation is seeking a distinctive solution based on interoperability, scalability, and no vendor lock-in.
• The organisation is concerned about initial investment, short-term ROI, time-to-value and time-to-market.
• Big Data, Analytics and Cloud-native technologies appear to be “black boxes” for the Data Office, Business and Legal teams.
• The organisation is already working on or willing to move into a complex hybrid and multi-cloud architecture.
• Walls exist between departments, areas and data domains.
• The organisation is willing to implement real DataOps over innovative data platforms with democratic self-service.
• The Data Office wants to evolve from “passive data governance” to “proactive and preventive data governance”.
• The Data Office is endeavouring to centralise data access and data usage management for different data environments.
• There are numerous data silos and diverse technologies being utilised within the organisation.
• Building a technology-agnostic data marketplace using intelligent data contracts.
• There are barriers between business and IT teams.

Finally, in addition to all these aspects, it is very important to detect whether or not there is a A data culture within the company and collaboration around data needs to be established.. The correct implementation of a data governance framework used by Anjana Data will lead organisations to achieve multiple benefits (link to product page).

Download the release notes – Anjana Data V3.2