El Gobierno del Dato & la IA están de moda y por eso, después de haber publicado varios Market Guides para este ámbito en los últimos años, Gartner acaba de publicar los primeros Magic Quadrant y Critical Capabilities para el ámbito de Data & Analytics Governance Plaftorms.

En estos informes, Gartner ha seleccionado a los 16 vendors más relevantes a nivel mundial para este segmento y, como no podía ser de otra forma, Anjana Data ha sido incluida en la lista.

Desde nuestro nacimiento en 2019 hemos ido cosechando muchos éxitos pero aparecer en un Magic Quadrant (nada menos que en el primero que se publica para un segmento tan relevante como lo es el de Data & Analytics Governance) no es simplemente otro éxito más. Esto supone un verdadero reconocimiento a nuestra visión innovadora y disruptiva, a nuestra propuesta de valor diferencial, a nuestro trabajo, esfuerzo y dedicación en estos últimos 5 años, y a todo aquello que un día decidimos llamar #GenAnjana.

Este reconocimiento nos sitúa en lo más alto de la cúspide de las empresas proveedoras de tecnología para gestión de datos a nivel mundial. Tal y como suena.

Y es que no es muy habitual que en este tipo de informes de clase mundial realizado por analistas para los que el mercado americano es el 90% del mundo, se cuele una empresa española, con poco más de 5 años de vida, cuyo foco son los mercados de Iberia y Latinoamérica donde aportamos un valor tremendo, que no tiene detrás ningún Venture Capital ni ningún Private Equity ni ninguna gran Corporación que la financie, etc. Es por eso que para nosotros, además de por aquello de codearnos con los big players a nivel global con los que ya venimos compitiendo cara a cara en Iberia y Latinoamérica, ser una de las bolitas de un Magic Quadrant es algo tremendamente especial y de lo que nos sentimos enormemente orgullosos.

Este reconocimiento no solo es un motivo de orgullo, sino también un testimonio del impacto de nuestra visión y tecnología en el mercado. En este artículo queremos explicar qué significan estos informes, por qué son importantes y cómo posicionan a Anjana Data.

¿Qué son estos informes y por qué son tan importantes?

El Magic Quadrant es uno de los informes más reconocidos en la industria de la tecnología. Su objetivo es ofrecer una evaluación clara de los principales proveedores en un mercado específico, utilizando dos ejes: Ability to Execute (capacidad de ejecución) y Completeness of Vision (integridad de visión). Esto da lugar a cuatro categorías de proveedores:

• Leaders: Excelencia tanto en ejecución como en visión.
• Visionaries: Innovadores con gran visión, pero aún en proceso de consolidar su ejecución.
• Challengers: Sólidos en ejecución, pero con una visión menos desarrollada.
• Niche Players: Proveedores que destacan en nichos específicos o en regiones concretas.

Por su parte, el informe Critical Capabilities complementa al Magic Quadrant proporcionando una evaluación más técnica y detallada de las funcionalidades clave de cada proveedor, evaluándolas según casos de uso específicos como enterprise control, domain analytics y experimentation.

Estos informes no solo proporcionan una visión clara del mercado, sino que también ayudan a las empresas a identificar las soluciones más adecuadas para sus necesidades.

Aquí te dejamos los enlaces a los informes pero recuerda que tienes que ser cliente de Gartner para poder acceder al contenido. Lamentablemente, nosotros no podemos compartirlo porque no tenemos la autorización para ello.

• Magic Quadrant
• Critical Capabilities

El mercado de Data & Analytics Governance Platforms según Gartner

Gartner define este segmento como plataformas que ofrecen un conjunto integrado de capacidades tecnológicas y empresariales para desarrollar, implementar y monitorizar políticas de gobernanza en sistemas empresariales. Estas plataformas son fundamentales para garantizar que los datos sean fiables, seguros y estén alineados con los objetivos estratégicos de la empresa.

Lo que diferencia a estas plataformas de la gestión de datos tradicional es su enfoque en la creación y cumplimiento de políticas, en lugar de la ejecución de las mismas​. En concreto, Gartner diferencia este ámbito de otras áreas de la Gestión de Datos, como Data Quality o Master Data Management (MDM), por su enfoque en la organización, control y responsabilidad sobre los datos dentro de un ecosistema empresarial.

La gobernanza de datos no solo asegura el cumplimiento normativo, sino que también crea confianza en los datos y potencia la toma de decisiones basada en analítica. Así, a diferencia de otros mercados dentro de la gestión de datos, el gobierno de datos se centra en el diseño, la definición y la aplicación de políticas y procedimientos así como en garantizar el cumplimiento regulatorio, delegando aspectos más operativos y técnicos en soluciones complementarias.

El papel del gobierno de datos es más relevante que nunca en un mundo donde las organizaciones manejan volúmenes masivos de información y deben cumplir con normativas cada vez más estrictas, como GDPR, CCPA, AI Act o las Leyes de Privacidad y Protección de Datos emergentes en Latinoamérica. Este contexto ha impulsado a las empresas a buscar soluciones que garanticen un gobierno de datos robusto y escalable.

Nuestro posicionamiento como Niche Player

En este primer Magic Quadrant de Data & Analytics Governance Platforms, Anjana Data ha sido incluida como Niche Player. ¿Qué significa esto? Básicamente, que somos un jugador especializado, con un enfoque claro y diferenciado, que destaca en áreas específicas del mercado.

En este contexto, nuestro posicionamiento como Niche Player tiene una explicación lógica:

1. Foco geográfico: Estamos profundamente enraizados en los mercados de Iberia y Latinoamérica, lo que ha limitado nuestra expansión global por ahora, pero también nos ha permitido ser líderes en nuestra región. En este sentido, nuestro foco va a seguir siendo el mismo en 2025 pero también hemos lanzado ya algunas iniciativas para entrar poco a poco en otros países de Europa.
2. Especialización en Gobierno: Nuestra plataforma prioriza las capacidades de gobernanza, integrándose con herramientas de terceros y soluciones open-source para funciones más centradas en Gestión de Datos, como Data Quality, MDM o Data Virtualization.
3. Fase de crecimiento: Somos una scaleup en plena expansión, y aún estamos en proceso de alcanzar nuestro máximo potencial.

Nuestro valor diferencial

En los mercados de Iberia y Latinoamérica, la presencia de la gran mayoría de vendors que aparecen en el cuadrante se reduce considerablemente. De hecho, casi siempre nos encontramos compitiendo cara a cara con los vendors identificados en este cuadrante como Leaders en la mayoría de las oportunidades, destacando frente a ellos por ofrecer una visión más innovadora y disruptiva, un enfoque más ágil, cercano y unas capacidades más flexibles tanto para distintos grados de madurez como para cubrir las necesidades cambiantes de las organizaciones.

Hace poco escribimos un artículo hablando en detalle sobre esto pero, resumiéndolo en 3 puntos, nuestros clientes nos eligen porque aportamos:

• Flexibilidad y personalización: Nuestra plataforma está diseñada para empezar por los casos de uso más básicos pero también para adaptarse rápidamente a entornos altamente complejos ofreciendo capacidades avanzadas.
• Soporte local y especializado: Entendemos las necesidades del mercado y de nuestros clientes de manera más profunda que los gigantes globales y somos capaces de ofrecerles soluciones mucho más efectivas y eficientes en tiempo récord.
• Colaboración y visión conjunta: Junto con nuestros partners, nos asociamos con nuestros clientes para apoyarles y desarrollar con ellos iniciativas que realmente les aporten un valor diferencial.

Echar la vista atrás para volver a mirar al futuro

Este reconocimiento coincide con un cierre de año con muy buenos resultados y marca el comienzo de una etapa emocionante para Anjana Data. Nos motiva a seguir expandiendo nuestras capacidades y aumentando nuestro impacto en el mercado global, mientras continuamos liderando en nuestra región.

Pero este reconocimiento no sería posible sin tod@s aquell@s que nos acompañan en este viaje. A tod@s ell@s, ¡GRACIAS!

Gracias a tod@s aquell@s que comparten el #GenAnjana.

Gracias a tod@s l@s anjaner@s y también a los que en algún momento han sido o se han sentido uno más de esta familia, por haber aportado tanto.

Gracias a nuestros clientes y partners, a los actuales y los futuros por confiar en nosotros pero también a los pasados por habernos servido de aprendizaje en el camino.

Gracias a l@s que nos aguantan de forma continua, tanto en lo profesional como en lo personal, que no es fácil.

Gracias a nuestros inversores que han apostado su patrimonio personal recogido después de años de mucho trabajo, dedicación y esfuerzo.

Gracias a las instituciones y organismos, tanto públicas como privadas, y a tod@s aquell@s profesionales y expert@s que nos han apoyado y nos siguen apoyando en este camino.

Gracias a nuestros competidores que nos hacen ser cada día mejores.

Gracias a los analistas de Gartner con los que hemos tenido el placer de hablar en todo este tiempo, en especial a aquellos que han elaborado estos informes: Guido de Simoni, Anurag Raj, Melody Chien, Stephen Kennedy. Y también a aquellos con los que hemos compartido numerosos debates como Saul Judah, Andrew White, Amy Bickel, Lydia Ferguson, Robert Thanaraj, Georgia O’Callaghan, Jason Medd, Christopher Long, Mark Beyer, entre otros.

Y por supuesto gracias a todas aquellas personas del ecosistema y de la comunidad del Data Management de las que siempre aprendemos cada día un poquito más.

Si deseas saber más sobre cómo podemos ayudarte a transformar tu estrategia de Gobierno de Datos & IA, no dudes en contactarnos. ¡Esto es solo el principio de un gran viaje!

Anjana Data has obtained the Foundational Technical Review (FTR) from AWS, which certifies that the platform meets all the requirements of adaptability and flexibility that make it possible to work well 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 3 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 organizations in their Data Strategy with a common vision: “Organizations that do not use innovative technologies as enablers of a modern and flexible approach to the implementation and operationalization of their Data Governance & 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 complete, innovative and differential solution, fully integrated natively on AWS.

• Design, development and availability of new integration mechanisms between different parts of AWS and Anjana Data, such as the evolution of existing integrations with IAM, Glue Catalog and Lake Formation or the development of new integration parts 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 marketing and communications actions 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 Catalog (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 organization (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 the following:

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 the certification, which are aimed at the practical use of the solution and the knowledge of all the 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 exam rules and a different pass percentage. Therefore, from Anjana Data we recommend reading the general information of each of the levels that is available on our website and in Formación Hadoop.

Anjana Data is the innovative and disruptive enterprise solution for Data Governance; designed to help organizations 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 organization 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 organization to the implementation and complete configuration of the solution.

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

Anjana Data Certification has become another lever of the company to transfer to the organizations 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 business, innovative and disruptive solution for the Data Government, designed to help organizations implement their data strategy. The training offered by the Anjana Data certification aims to make the student be able to help the entity in its data-driven path with a more general vision of what it means to implement a Data Governance program in an organization thanks to Anjana Data.

In this context, we come from a world where data was organized in silos guarded by pure IT teams (Systems, DBAs, …) 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 organizations.

We have been living for years a technological torrent that brings new capabilities in data processing (Big Data, Cloud, IoT, Advanced Analytics, AI & ML, …), we have a greater data culture of people and organizations, there is more training in data, data literacy and data storytelling, new skills are acquired to consume data and generate insights in business areas thanks to more and better data analysts and scientists, … so it is clear that the 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 in an organization or among organizations?

• Maximize synergies between different actors to improve their daily operations. We can see this in the environment of a single organization where different departments have to share information in order to be able, in their day to day, to carry out their tasks, activities or to 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 last one and the largest one in the European Union

• In addition, sharing that data improves the quality of key data for better decision-making faster. If data is made available to other consumers, producers will put more emphasis on sharing quality data that makes it easier to make decisions faster.

• 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.

• Saving on 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, …), and this results in an improvement in the productivity of the organization’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 organizations, 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 organizations, 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 democratize 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” – Aristóteles

That is the middle point that is achieved with the entry into play of the 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 views

Every time we talk about sharing data, what we mean is to make information available by producers for use by consumers, and in that process there are roles that have to ensure that everything works correctly. In the end, each person or type of role will have their profile, their 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 point of view, 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 really is a Data Sharing Agreement?

A DSA is basically a mechanism for sharing data between producers and consumers in an agile and personalized way, facilitating regulatory and regulatory compliance in the use of data. In addition, thanks to DSAs we can standardize 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, …) so they offer stakeholders a new experience of sharing information by facilitating all the technical complexity below that data sharing’ by containing all the metadata information of the technical assets they encompass. Thus, the DSAs bring the data closer to the business consumer, abstracting him from all the underlying technical concepts.

Finally, DSAs also allow for the definition, implementation and operation of the Data Contracts in order to provide the necessary flexibility to land the different data access policies to be implemented for each of the particular cases of information sharing, since sharing public data from 3 years ago is not the same as sharing the address where citizens live or sharing confidential information about an organization’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 really is a Data Sharing Agreement?

Incorporating innovative elements in data governance such as DSAs is not an easy task, but if you’re really 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 organizations to extract the greatest possible value from the data thanks to the operationalization of an effective and efficient data government. The improvements of 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 to 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 intuitive and friendly design in order to facilitate the day-to-day of users in relation to the Data Government. 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 that incorporates 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 wanted objects. In addition, the use of this component is extended to other parts of the application where it is required to search for metamodel objects (e.g. to incorporate Datasets to DSAs).

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

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 a better performance of the entire solution while giving a greater ease of implementation and configuration of some of these modules.

Release date: February 26, 2021

*Toda la información de Anjana Data v4.1 está disponible en la página web de la organización, descargando Relase 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 organization, 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 platform aims to:

• Integrate data from different sources, automating their ingestion, loading and availability.
• Control the storage of data with its different versions over time, historifying 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.Additionally, on the one hand, it is necessary to seek the fit with the automation 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 totally wrong 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 has a lot to contribute.

Finally, to achieve a complete integration and automation of processes, three essential management layers must be incorporated:

• Demand management in data projects: changes to existing information, new use cases and exploitation of information, capture and ingestion of new data, …
• Management of metadata and versions of data structures, since metadata will be the central piece that will allow the automation of many functions around this DataOps concept. 
• Management of data access permissions in a centralised way, 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. A 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 preventive Data Governance located at the beginning of the data value chain tries 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 the synergies between the different projects and use cases, promoting the reuse of both information and existing processes in the platform. In short, thanks to this vision of Data Governance, we are enabling a single entry point to the data ecosystem for all data stakeholders by applying a collaborative approach to:

• Integration with demand management.
• Integration between technologies and pieces 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 building a single, 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 repository with a common and unique language based on the knowledge of the business and the organization (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 practical cases 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 help 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 organizations with a number of differential value-added features while empowering them to implement effective and efficient data governance.

Anjana Data is differentiated by three fundamental pillars, which make it the most innovative and disruptive solution for data governance in the market. With a 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 organizations understand, implement, and operate their data governance programs.

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 organization, Anjana Data based on added-value characteristics which will provide the required tools and mechanisms in order to empower data stakeholders in the Organization, allowing them to implement a effective and efficient data governance.

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 are going to be integrated within your business processes and your Data IT architecture. This change of the mindset will give you the chance to operationalize data governance spotting the value that you are pursuing.” – Equipo Anjana Data.

Nowadays, organizations 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 DATAturn into a strategic asset which 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 a governance in an incremental and iterative way in order to improve synergies between areas and maximize 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 help you in your strategy by changing the vision of data governance in your Organization?

Here are a few cases to look out for:

• The Data Office have failed when trying to operativize the data governance model within the organization.
• Data governance is understood as bureaucracy among data stakeholders, so it requires governance processes automation.
• The organization is looking for a differential solution based on interoperability, scalability and no vendor lock-in.
• The organization is worried about initial investment, short-term ROI, time-to-value and time-to-market.
• Big Data, Analytics and Cloud-native technologies look like “black boxes” for the Data Office, Business and Legal teams.
• The organization 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 organization 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 trying to centralize data access and data use management for different data environments.
• There are plenty of data silos and diverse technologies being used in the organization.
• Building a technology-agnostic data marketplace using intelligent data contracts.
• Walls exist between Business and IT teams.

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

Download the release notes – Anjana Data V3.2