Which added value features should I consider for technology solutions that support my Data Governance programme?

While Data Governance is fundamentally about cultural and organisational aspects and cannot be solved only by technology, technological solutions play a fundamental role and are more than necessary to achieve the implementation of an effective and efficient governance model. That is why there are many solutions on the market that work as accelerators to achieve the desired level of data governance, as well as to help organisations build and maintain that data culture necessary at all levels to reach the goal of becoming data-driven.

From version 2 of the DAMA-DMBOK we can extract some very interesting ideas:

• Organisations that establish a formal data governance programme are better able to increase the value they get from their data assets.
• The Data Governance function guides all other data management functions.
• The purpose of Data Governance is to ensure that data is managed properly, in accordance with policies and best practices.
• Data governance focuses on how decisions are made about data and how processes and people are expected to behave in relation to data.
• Data governance is not an end in itself; it needs to be aligned directly with organisational strategy.
• Data governance is not a one-off task. Governing data requires an ongoing programme focused on ensuring that an organisation derives value from its data and reduces risks related to data.
• Data governance is separate from IT governance.
• The goal of Data Governance is to enable the organisation to manage data as an asset.
• A Data Governance programme must be sustainable, embedded and measured.
• Data governance cannot be implemented overnight. It requires planning.

In short, the fact that Data Governance is so linked to these cultural and organisational aspects makes it difficult and complex to evaluate the technological solutions that can support us in this area. This forces us to extend our views beyond an evaluation based on the coverage of functionalities or modules available and also consider a catalogue of added value features that we should include in the assessment.

Functionalities and modules

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

• Business Glossary
• Metadata management with Data Dictionary and Catalogue
• Traceability and data lineage
• Architecture, design and data modelling
• Workflows de validación y gestión de procesos empresariales
• Master and Reference Data Management
• Data Quality
• Management of data issues
• Data security (Policies, access and use, user roles and profiling, data obfuscation)
• Dashboard with Key Performance Indicators
• Management of DataLabs and Sandboxes
• Content Management and Communication Portal
• Data Services Management
• Support for Auditing

However, evaluating a solution only by the degree of completeness of these functionalities will mean that we only see part of the picture and that we may make a decision that we will regret after some time, especially when it is unrealistic to think that a single technological solution can accommodate all these functionalities in a self-contained way. That is why, to ensure that this does not happen, we must weigh up the functionality coverage analysis together with another type of analysis based on a set of added value features.

Valorizantes

These characteristics will enable us to develop the Data Governance function in a timely manner and in accordance with the specific needs of the organisation:

• AutomationThe processes must be as automatic as possible to reduce users' manual workload and tasks.
• UX & UIThe user interface, as well as its navigation and usability, should be as intuitive and user-friendly as possible for all types of audiences so that any user feels comfortable using it.
• Interoperability: it must be able to share and exchange data with other systems, it must not be a «black box», nor a hermetic component, allowing interconnection with different types of systems through connectors and allowing the use of standards.
• Customisation: as configurable as possible to support the strategy and the governance model defined by the organisation.
• Modularisation: the different functionalities should be understood as independent pieces, so that the use of one of them does not limit the use of others, allowing the use of the necessary modules without damaging the overall experience.
• Multi-environment: capacity to govern multiple platforms supported by different technologies in a centralised manner from the same instance.
• Scalability: adaptable as data volume increases along with processing and response needs, maintaining stable performance over time.
• Adaptability: it must be able to adjust to the needs and the reality of the organisation over time.

Additionally, from a more long-term point of view, the characteristics which we should pay special attention to and concentrate on are:

• Vendor lock-in: as far as possible, we must try to ensure that the solutions selected do not «tie» the organisation to a single vendor by acquiring large dependencies and thus avoid migration from one solution to another with great consequences.
• Learning curve: being powerful solutions, the learning curve should not be a problem for users, who should not have to invest a great deal of time in learning how to use the solution or require very specific and expensive training and certification.
• User limit: if we want to extend data governance to the entire organisation, we must consider solutions that are not licensed per user, as this can result in limited use of the solution, since costs increase in relation to the increase in users and not based on actual use.
• Licence feesThe cost must be flexible and scalable, tending towards a pay-per-use model, allowing total control over the ROI without requiring a high initial investment, in order to maximise time-to-market and time-to-value.

What can we find in the market?

Looking at the market, given that we are talking about technology, storage and data processing solutions vendors usually offer modules oriented to data governance within their own platforms, but generally with a biased vision and low interoperability, becoming a problem of integration between technologies and resulting in a new challenge for the governance of applications and technology.

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

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

In addition, the market for specific «Data Governance» Solutions have not been around for long and are not very widespread, except in the USA where they do represent a high volume of business. In fact, for both Gartner and Forrester there is not yet a quadrant or a wave respectively in this area, placing the solutions between «Metadata Management», «Master Data Management» and «Data Quality».

Finally, within the spectrum of Data Governance solutions vendors, we can group them into different groups... but this is something that warrants another complete article 😊