Anjana Data named in Gartner's Market Guide as one of the best solutions for data governance

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.