Anjana Data named in Gartner Market Guide as one of the best solutions for Data Governance

When we created 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 in the global Data Governance market, one of the most demanding markets, which has been changing radically in recent years, a market in which almost all the leading vendors in data solutions are signing up, in which we compete on a daily basis with multinational companies with a lot of muscle and very powerful solutions, and in which every week new start-ups appear with innovative solutions or new open-source initiatives are launched, apparently promising.

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

And that’s also when, after you’ve puffed out your chest a little at the recognition of having done well, you feel like thanking a lot of people. That is why this article has three main objectives:

1. To echo one of the most important milestones to date for Anjana Data (it is not every day that you are recognised as one of the most representative vendors worldwide in your market).
2. Carry out a small analysis of the market in which we are framed (the typical analysis of where we come from, where we are and where we are going).
3. And, of course, a big thank you to all those who have placed their trust in Anjana Data and, of course, to those who have not. Both have given us the strength to get this far and continue to give us the strength to continue 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 & Analytics Governance Platforms as one of the most outstanding solutions in the market for Data Governance. Moreover, it is especially relevant to point out that it is the only Spanish solution of all those included and also the only one that generates genuine content in Spanish as the 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 technology solutions that support data governance is evolving at a very fast pace and that is why Gartner has begun to replace some of the most recognised Magic Quadrants in this segment with other types of reports such as Market Guide and Vendor Identification Tools. In this context, in December 2021 Gartner has published those related to what they have called Data & Analytics Governance Platforms and in both of them Anjana Data appears as one of the main solutions in the market.

Specifically, being included in the Market Guide puts Anjana Data in a privileged position, as this report only selects the most representative vendors in 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 & Analytics Governance Platforms? Well, 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 organizations’ 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 management
• Active metadata
• Analytics
• Business glossary
• Connectivity/integration
• Data catalog
• Data classification
• Data dictionary
• Data lineage
• Impact analysis
• Information policy representation (high level)
• Matching, linking and merging
• Orchestration/automation
• Profiling
• Rules management (low-level)
• Tag management
• User interface (as support for all governance related roles)
• Workflow management
• Task management
• Model management
• Security (on the platform itself)
• Organization and role models

Regarding the market, as Gartner points out: “The data and analytics governance platforms market is embryonic. Overall, data and analytics governance has attracted technology investments that give organizations 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 ahead. Gartner also states that this is due to the difference in capabilities required between Data Management and Data Governance: “There is a need for convergence of capabilities with the recognition that the work of data and analytics governance is different than 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 that have led to the evolution of the market and also those that will mark the future of the market, which goes far beyond the functionalities and features offered by this type of solutions.

If we focus on the functional vision of these solutions aligned to the business requirements of the organizations:

• Data is becoming one of the most important strategic assets for organizations and they are all striving to become Data-Driven. This means that data as an asset ceases to be something under the umbrella of IT and becomes of much greater interest to business areas, who demand technological solutions with a business vision, which facilitate the management and governance of the data they work with on a daily basis. This is why they are also beginning to require intuitive solutions with a more manageable learning curve.
• The consolidation of the data economy and the need for organizations to share reliable and quality data both internally and externally has led to the creation of data spaces, which must be based on fully governed ecosystems that provide reliability and transparency to the processes of publication, sharing and consumption of information. This requires technological solutions that support such creation and facilitate its operation and maintenance.
• The emergence of concepts that are closer to the language of business such as “Data Culture”, “Data Literacy”, “Breaking information silos”, “Democratisation of data”, “Self-service of information”, “Monetisation of data or Infonomics” and an increase in the capabilities of non-technical profiles when working with data mean that data management has to be understood from a non-technical point of view and with a clear objective of generating value for the business.
• The need for agile and flexible procedures supported by clear and concise policies that are understood and adopted by all those involved in the organization makes it necessary to have technological tools that help with the operationalisation and automation of these procedures, involving the different actors identified.
• The emergence of new regulations and standards both at sectoral and governmental levels makes organizations decide to invest more in technological tools to ensure data governance and auditing of their processes.
• Organizations are beginning to perceive the value of data governance in terms of increased efficiency, cost reduction and better management of the risks inherent in the use of data, with the result that investment in this type of solutions 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 that do not rely on the collaboration of different profiles to achieve these objectives are no longer an option.
• It has been demonstrated that a Big-Bang approach is not suitable for this type of transformational initiatives, so solutions that are very complex to implement are also discarded in favour of models that are much more iterative, incremental and scalable. In addition, given the need to adapt and customise solutions to the operational reality of each organization, tools with out-of-the-box models that are not very flexible and extensible are no longer considered relevant.
• The explosion in the use of Artificial Intelligence and Machine Learning and the low profitability obtained from this type of initiatives despite the large investments made forces organizations to rethink their management and governance models for the raw material that feeds the algorithms, the data. This means that, once again, the ability to adapt and customise a data governance solution is key to being able to cover these scenarios.

On the other hand, if we think from a more technological point of view and from the point of view of fitting in with the technical architectures of organizations:

• The maturity reached by key technologies such as IoT, Big Data and Cloud makes it possible for organizations to have at their disposal 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, videos, 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 lifecycle, coinciding with a low acceptance of technological standards, which makes the variability of formats and types of data and processes to be managed and governed literally unmanageable. This situation is exacerbated by the fact that many legacy systems or customised developments are still being maintained using obsolete technologies and black boxes, the guts of which are not easily accessible or interpretable. Technology that offers value-added features is therefore required to manage these types of integrations or even native integrations between solutions from different vendors as well as company acquisitions and integrations are becoming more common.
• The vast majority of organizations that want to become data-driven or that are born with this vision are beginning to position themselves clearly towards the consolidation of hybrid, multi-cloud, scalable architectures, without black boxes, interoperable and based on integrated solutions made up of different pieces. The search is no longer for a large all-in-one platform with on-premise deployment, vendor lock-in is being avoided, open-source is losing ground (examples such as Hadoop-Cloudera, Kafka-Confluent, Spark-Databricks, etc.), open solutions are being sought with internal data repositories available for exploitation, which are easily integrated into any technological architecture (API-first) and, above all, Cloud-first solutions are being prioritised.
• Cloud providers are gaining so much weight in the current ecosystem that the use of their native services managed by any technology is becoming almost essential for architecture and infrastructure teams in order to facilitate deployments, operation and maintenance of technical platforms. It is also becoming very important to have different deployment alternatives and service models, seeking automation in CI/CD circuits and giving a lot of weight to the presence of solutions in the different marketplaces of Cloud providers as Cloud-native applications.
• New concepts of data architecture and technical architecture such as Data Lakehouse, Data Fabric, Data Mesh, Data Marketplace and DataOps are beginning to gain momentum in the market, promoted both by the main analysts and gurus and by the vendors themselves. Organizations trying to adopt this type of models require technological tools capable of operationalising them and taking them from paper to day-to-day reality, which requires flexibility and adaptability capabilities that were not necessary until now.

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

• It is no longer common to bet on tools that imply a high initial investment, since the budgets of the organizations are very tight and it is starting to be required internally to demonstrate a positive ROI in the short/medium term to convince senior management that the investment in this type of technology is worthwhile in the long term.
• Following the model offered by Cloud providers, there is a move away from the custom of acquiring perpetual licences for a specific software version towards the search for much more flexible pricing models oriented towards pay-per-use, without excessive permanence commitments and which include a series of value-added services (constant updates, support included, access to development resources, training and communities of interest, etc.).
• Organizations seek models that do not compromise their scalability in such a way that, 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, fonts, professional services, customised developments, expert support, etc.). All this has a direct impact on key aspects such as time-to-value, time-to-market and Total Cost Of Ownership.
• Invoicing through Cloud providers (via Marketplaces) is becoming more and more common and organizations value it positively because they are able to reduce suppliers, centralise IT costs, improve their management and also obtain special conditions and discounts because Cloud providers themselves are pushing both vendors and customers to adapt to this new scenario.

 In short, Gartner states the following regarding the needs that solutions focused on this market have to 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 organization. This is evidenced by a recent data and analytics governance survey, which shows organizations falling well short of reaching their governance objectives. Even when they don’t 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.

And with all this, the logical question that arises is: How does Anjana Data position itself in this market and what is our value proposition? Well, if you are still reading this article and you still don’t know what Anjana Data offers compared to other solutions, apart from covering the functionalities already identified, I will summarise them for you in these lines:

• It provides a collaborative approach with a business vision but with a global reach at all levels of any type of organization, serving as a meeting point for both business and technical profiles thanks to the personalisation and customisation capabilities of the operating model.
• It allows the creation and maintenance of a common language for the entire data ecosystem, fully customisable and adapted to the organization without the need to be subject to technological evolutions thanks to the implementation of a metamodel “TECHNOLOGY AGNOSTIC METADATA CENTRIC”.
• The possibility of creating a one-stop shop for different data stakeholders, integrated with demand management, covering their different needs in a personalised way thanks to a carefully designed UX&UI that is particularly intuitive and with a manageable learning curve.
• Native bi-directional integration with a multitude of technologies of different nature, with varied characteristics and serving multiple purposes related to data management.
• Capabilities for the implementation of “PROACTIVE AND PREVENTIVE GOVERNANCE” so that an organization can build Data Marketplace, Data Fabric and DataOps ecosystems based on the automation of common technical processes and on the Governance-first and Governance-by-design principles fully integrated with its data platforms.
• A state-of-the-art functional and technical architecture, based on the basic principles of modularisation, scalability, interoperability, flexibility and adaptability and supporting complex hybrid, multi-environment and multi-cloud scenarios.
• Without black boxes and with multiple alternatives for the extension of the solution’s own capabilities as well as for the development of new connectors, and 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.
• Leverage, take advantage of and complement all the capabilities of native Cloud technologies, both those with a greater focus on governance (identity management, data access permissions management, data catalogues, data structure management, audit log monitoring, etc.) and those oriented towards data processing (ingestion, storage, processing and exploitation).
• Native use of the services managed by the Cloud providers themselves to facilitate everything related to the management, operation and maintenance of the infrastructure (provision of machines, deployment of services, installation, configuration of connections, application of security policies, monitoring, back-ups, high availability, etc.).
• Availability of the solution in the Marketplaces of the main Clouds in different modalities adapted to the needs of the clients (from transactional native application with IaaS/PaaS deployments to SaaS and BYOL modalities).
• Pricing model adapted to the changing needs of organizations to reduce initial investment as well as time-to-market, time-to-value and TCO while maximising the ROI linked to the implementation and use of the solution.

Finally, regarding the future of the market, Gartner states the following: “The points above refer to and focus on the capabilities organizations need to meet their D&A 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 with 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 faced with a tremendous opportunity but also with the unpredictability of the market, although 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 organizations to obtain the value they need through the management and governance of their data.