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

Anjana Data announces a new version of its solution for Data Governance with new features, available from 15th of July.

The new version of Anjana Data incorporates extended features for the Business Glossary such as the possibility to create new types of entities with their own attributes based on dynamic forms. Many of the enhancements are related to UX & UI improvements, auditing functions, back-end & architecture improvements; the addition of AI-powered features for user assistance and new capabilities for workflows configuration. In addition, this new version also includes new native integrations with data platforms.

“In this new version of Anjana Data, we have focused on consolidating the features already offered by the solution, improving above all the design of the user interface and offering a better user experience. In parallel, we have invested a lot of time and effort in extending native integration capabilities to provide proactive and preventive governance over data platforms such as Azure, AWS, Denodo, Snowflake, as well as Cloudera and Hadoop. Finally, we can already say that we have included the first use cases of user assistance based on Artificial Intelligence & Machine Learning algorithms. However, version 4.0, which will be released at the end of September this year, is going to be a new turning point, we are taking an incredible rate for a company of our characteristics.”, says Mario de Francisco, CEO at Anjana Data.

Extended features for Business Glossary

Anjana Data aims to become one of the reference solutions in the world of Data Governance with an innovative and disruptive approach, building bridges between the worlds of business and technology and offering adaptation capabilities and flexibility not offered by any other solution until now, which are more than necessary in the new era of Big Data & Multi-Cloud.

That’s why in this new version, Anjana Data incorporates enhanced permissions and privileges management within the Business Glossary based on the defined governance model. In addition, it offers the possibility to create new types of entities with their own attributes based on dynamic forms. In this way, the organization can create as much types of entities as wanted (i.e. metrics, reports, business rules, data quality rules, KPIs, …).

Regarding relations between entities, Anjana Data includes extended attributes and it is also possible to create different types of relations to classify them from a semantic point of view. Every object may be related to any other object not only within the Business Glossary but also to any object within the Data Catalog. By creating the objects and defining the associated template, attributes may be set as mandatory or optional.

UX & UI improvements

Other improvements in the new version are related to navigation between objects in the whole UI. In Anjana Data, an object is any element within the metamodel containing metadata attributes.

To optimize the user experience in the Business Glossary, Anjana Data has a new assistant wizard for the creation of objects. This is especially useful as the wizard will guide the user in order to make it easier for her/him to create new objects no matter the type (any type of entity and any type of relation) and the user may choose between manual creation or importation from an Excel spreadsheet based on the names of the headers not requiring a fixed structure.

Anjana Data has also introduced an enhanced view of each object where the most important and recent information related to the object is shown in its main view and a new section included within the view of the objects where all users to the object are displayed.

AI-powered features for user assistance

Anjana Data continues to advance in its AI-powered features, so a new module has been integrated within the architecture of the solution in order to leverage AI-powered features for user assistance based on metadata information, data profiling techniques and users behavior.

In this new version, the corresponding algorithms run on the background and will rise recommendations to the users in a non-invasive way. Algorithms for specific use cases will be released within the future upgrades, being the first ones included in this release:

• Recommendation of business terms which may be of some interest for the user.
• Recommendation of the generation of new relations between objects within the Business Glossary.

Enhanced audit features and new capabilities for workflows configuration

Although the new version of Anjana Data is focused on user experience improvements, another important part of this new release is the audit features. Thanks to the Minerva module (based on SolR) it is now possible to view more detailed information in the internal audit logs, as well as have the ability to select which actions need to be audited and which not.

Thus, every time that a value of any attribute changed, a snapshot of the object will be saved in order to allow the regeneration of an object with the corresponding values in a specific time during its history.

The new version also presents new configuration capabilities of the validation workflows engine using the Hermes module (based on Activiti BPM), which allows the definition of parallel validation steps within a workflow configuration and the dynamic generation of validation steps depending on the type of the object, the action performed and the user that has performed this action.

New native integrations

In terms of native integrations, thanks to the new plugins added to Tot and Heimdal, new native integrations have been developed to perform different kinds of interactions between Anjana Data and the technologies used within several data platforms.

Metadata harvesting and importation, sample data querying, active governance, and dynamic data lineage capture (when applies) are now possible over the following technologies: 

• RDBMS supporting Generic JDBC 
• SAML 2.0 & OAuth based Identity Access Management Systems
• Microsoft Azure and AWS cloud-native technologies
• Denodo (Beta version)
• QlikSense (Beta version)
• Snowflake (Beta version)
• Confluent & Apache Kafka (Beta version)

* Details can be found in the public technical documentation in the Release Notes and in the technical documentation for connectors (available only for customers).

From 15th of July, Anjana Data v3.2 will be available to users interested in discovering how Anjana Data can add value to the data strategy, changing the vision of Data Governance in an Organization. The company is also already working on a new version v4.0 (planned for the end of September this year) which will again mark a milestone in the evolution of the solution.

Anjana Data releases a new version, 3.0, which includes a new generation Business Glossary, flexible and customizable, fully integrated over Data Catalog and an evolved CORE metamodel. It also incorporates new workflow management mechanisms, dashboarding and reporting tools, AI & ML-based assistance pills and technical improvements within its whole microservices architecture.

Anjana Data is a unique and differential Data Governance solution, based on cutting-edge and consolidated open-source technologies, designed to help organizations in their data strategy roadmap implementation in the Big Data, Multi-Cloud and Data-Driven era.

Its release 3.0, which will be available from the second week of April, brings an update of improvements and additional features such as the integration of a fully flexible and customizable next generation Business Glossary integrated over Anjana Data Catalog to help build a common language and a single semantic layer across the whole organization.

The solution also integrates a new built-in BPM for validation workflows based on Activiti, giving the possibility of improving management, performance and capabilities in the automation of procedures and data governance processes.

In addition, this version 3.0 of Anjana Data includes the integration of self-service dashboarding and reporting tools that ease data governance implementation control and follow-up by the integration of open-source solutions Grafana and Hue in the technology stack.

With this launch, Anjana Data intends to continue with the unstoppable growth of the first quarter of the year, “We are very happy not only for the welcome that our vision is having in the market but also for the performance of the team which is making it possible for that vision to be materialized in the solution release after release. The feedback we are receiving from our clients is very positive and it inspires us to continue in this line”, said Mario de Francisco, CEO of Anjana Data.

Ana Melcón, Head of Product & Development, also quotes: “In Anjana Data, we are a multidisciplinary team working side by side with our customers and we are constantly setting ourselves new challenges and goals. That fact forces us to move on and keep adding new features to foster the solution coverage in order to maintain the level of innovation that the market needs”.

Next-Generation Business Glossary

Anjana Data’s Business Glossary is based on three main objects: containers, terms and relations; each one with their own flexible and customizable attributes based on dynamic generation of templates and forms. The flexibility of this design gives organizations the ability to model their semantic reality, easily managing ontologies, taxonomies and exceptions, providing a pure business vision.

This Business Glossary will be fully in-sync automatically and assisted with CORE metamodel to offer users the possibility to seamlessly perform a complete trip over data both top-down and bottom-up. With this feature in place, it will be possible to easily move from the business vision to the technical point of view and vice versa from any place in the solution by combining both worlds.

Other new features included in Anjana Data’s Business Glossary are:

• Advanced search engine and terms dictionary to easily find what you are looking for.
• Flexible roles-based governance model for terms and relations management and maintenance along with customizable validation workflows.
• Pre-defined controls to guarantee uniqueness, consistency and integrity of business terms.
• Data assets crowdsourcing in order to create actionable knowledge based on governed data.
• Alerts & notifications module is integrated in order to enhance stakeholders collaboration and interaction under an advanced UI & UX.
• Incorporation of user assistance capabilities and support for manual tasks based on advanced analytic algorithms, Artificial Intelligence and Machine Learning for a better user experience.

New built-in BPM for validation workflows 

Processes management becomes faster and more intuitive with Anjana Data and the integration of Activiti, an open-source workflow engine written in Java that can execute business processes described in BPMN 2.0. This integration improves the management, performance, and capabilities of approval workflows.

This version also includes basic features that cover the same capabilities included in Anjana Data v2.0 but extended to the new Business Glossary module. The incorporation of Activiti into the technology stack gives Anjana Data the possibility to easily offer extended features about validation workflows in the near future along with a new experience for workflows design, customization and management.

Additional new features in release 3.0 of Anjana Data include:

Dashboarding and reporting

An important added value in this release 3.0 of Anjana Data is the integration of the self-service dashboarding and reporting tools, Grafana and Hue, which facilitate the control and monitoring of the implementation of the Data Governance model in the Organization, from Anjana Data.

Grafana allows the user to query, visualize, alert and expose graphically the activity within Anjana Data in a simple and attractive way even in real time, being specially indicated to represent information in timelines. On the other hand, Hue, brings the best metadata (hosted in Anjana Data) querying experience in a quick and easy way with the most intelligent autocomplete capabilities, query sharing, charts, dashboards and downloading tools. All this without programming because the analysis is done dynamically by clicks & drag & drops.

These tools included in this new reporting and dashboarding module give solution users the possibility to easily create and share their own dashboards and reports from stored data in Anjana Data’s internal repositories.

Data Catalog and Data Lineage

Besides the above improvements, the new release of Anjana Data extends its CORE metamodel with additional metadata to provide greater context and insight into the data while making their governance easier. As an important improvement, taxonomies and custom metadata now may be added at field level.

In relation to data lineage features, the possibility of adding detailed functions at field-level when declaring or importing processes and instances into the metamodel is included. In this way, the option to drill-down from the object level to the field level is allowed to have a granular view of the data movement. In addition, in the extended data lineage visualization, the information regarding the users adhered to the DSAs has been widen too (Name, Role, Business Unit, …).

Back-end improvements

Finally, various improvements at the technical level have also been included to further ensure the scalability, interoperability and easy-of-use of the solution:

• Public API 3.0 with extended features and improved capabilities and administrative API 1.5 totally independent from the Public API for administration purposes.
• New Oauth based authentication that enables seamless integration with a multitude of identity management systems.
• Code refactoring for performance enhancement and software quality improvement:

• Roles and privileges management
• Dynamic indexing of objects within Data Portal
• Management of the execution steps within approval workflows

To request a demo of the Anjana Data solution click here.

Additional resources: 

• For more information about Anjana Data, download our brochure.
• Check out what’s new on Linkedin and Twitter.

About Anjana Data: 

Anjana Data was founded in Madrid in 2019, as a product-oriented company to develop, support and market its own innovative and disruptive data governance solution. Anjana Data is not just a Data Governance Tool, it is the implementation of a philosophy as a result of an extensive experience in data related projects.

While Data Governance is fundamentally about cultural and organizational 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 organizations 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:

• Organizations that establish a formal data governance program 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, according to 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 aligned directly with organizational strategy
• Data governance is not a one-time thing. Governing data requires an ongoing program focused on ensuring that an organization gets 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 organization 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 organizational 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 catalog of added value features that we should include into the assessment.

Functionalities and modules

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

• Business Glossary
• Metadata management with Data Dictionary and Catalog
• Traceability and data lineage
• Architecture, design and data modeling
• Validation workflows and business process management
• Master and Reference Data Management
• Data Quality
• Data issues management
• Data security (Policies, access and use, user roles and profiling, data obfuscation)
• Dashboard with KPIs
• 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 can make a decision that we will regret after some time, especially when it is utopian 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.

Added value features

These characteristics will allow us to grow in the function of Data Governance in time and form according to the specific needs of the organization:

• Automation: the processes must be as automatic as possible to reduce users manual workload and tasks.
• UX & UI: the user interface, as well as its navigation and usability should be as intuitive and 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.
• Customization: as configurable as possible to support the strategy and the governance model defined by the organization.
• Modularization: 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 total experience.
• Multi-environment: capacity to govern multiple platforms supported by different technologies in a centralized way from the same instance.
• Scalability: adaptable as data volume increase along with the needs of processing and response, keeping performance stable over time.
• Adaptability: it must be able to adjust to the needs and the reality of the organization over time.

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

• Vendor lock-in: as far as possible, we must try to ensure that the solutions selected do not “tie” the organization 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 the users, who should not invest a great number of hours in learning the use of the solution or need very specific and expensive trainings and certifications.
• User limit: in case we want to extend data governance to the whole organization we must consider solutions that do not license by users since this can result in a limited use of the solution as the costs increase in relation to the increase of users and not based on the real use.
• Cost of licenses: the cost must be flexible and scalable, tending to be under a pay-per-use model, allowing total control over the ROI without consider a high initial investment to maximize 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.

In spite of this, due to the complexity and extent of the practice, the solutions are usually focused on offering a set of functionalities and specific capacities 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 organization, starting with the most critical aspects.

In addition, the market for specific “Data Governance” solutions has not existed for long and is 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 gives for another complete article 😊

Mario de Francisco, CEO of Anjana Data, with Ana Melcón, Head of Development and Product, Óscar Santiago CTO, have made a new appointment with the podcast “Territorio Big Data” from Big Data Magazine.

During the interview we could know how Anjana works, its news and the problems they face when solving problems in the Data Governance of their partners. Some current issues were also present throughout the talk.

You can listen to the audio of the interview in Territorio Big Data. It is also available in iVoox, iTunes, Spotify and Youtube.

Mario de Francisco talked to ELDERECHO.COM about GDPR, technological reality and how optimal management of data-based information allows companies to be more innovative, progressive and competitive.

1.- Mario, why does optimal management of information based on data allow companies to be more innovative, progressive and competitive?

Companies are a reality invented by human beings and, as such, they are managed by people, who have a differential element that is the capacity to make decisions and, applied to the business world, these decisions have consequences on the life and development of companies.

If we apply the scientific method to business reality, empowering decision making with data-based information will increase the probability of success of those decisions and leave less room for chance or uncertainty. Also, for these decisions to be good, there must be a good amount of quality data, effective and efficient processing and exploitation processes, technologies and tools according to the needs and, last but not least, people with the right skills.

2.- How would you quickly and simply describe what intelligent databases consist of and how they relate to Smart Data Governance?

Honestly, I am not a big fan of this kind of concepts because in my opinion they only make it difficult to understand them. In this case, the terms “intelligent” and “Smart” refer to the application of advanced analytical algorithms and Artificial Intelligence, so basically we could be talking about applications of these algorithms to improve the use of databases as well as for the purpose of implementing a more effective and efficient data governance. Recently I wrote an article about this kind of applications trying to explain why they can be something that companies should consider to implement and also trying to land some concrete cases. At Anjana Data we are already starting to work on concrete use cases because the differential value they can offer is immense.

3.- What are the most important challenges that AI and Machine Learning bring to data governance?

Without a doubt, the greatest challenge posed by AI and ML is that of ethics. Therefore, I believe that we must give a strong impulse to the entry of more humanistic profiles that help the most technological companies when they are faced with ethical dilemmas. If we think of data governance as such, leaving aside ethics, the greatest challenges come from agility, flexibility and speed, on the one hand, and from quality, on the other.

You can read the full article at the following link: https://bit.ly/2tQU454

The other day, September 26, at the CDO Day event, I had the great luck to participate in the panel of experts entitled “The Challenge of the Newly Appointed CDO: How to Build a Data Governance Policy from Zero” together with Julio Valero from Banco Santander, Manuel Ferro from Abanca and Jesús Armand Calejero from Funidelia.

The debate that emerged was very enriching and the experiences that were shared will surely serve more than one of the attendees who have the difficult pathway ahead of them to implement a data strategy and governance model in their organization. For this reason, and also at the express request of several people who were unable to attend, I do not want to miss the opportunity to share the main keys given during the session, expanding on them and including my point of view in each of them.

I hope these lines work as ideas and guidelines for those who may be looking for answers to their questions within the data governance field.

Data culture and communication

With no doubt, the main point to take into account is the data culture that exists in the organization at all levels…

• “Are there specific departments or roles focused on data?”
• “Are data-related goals included in corporate and employee objectives?”
• “Are there policies and procedures focused on the use and treatment of data?”
• “Do senior management, middle management, and other employees consider data as a strategic asset of the organization?

The maturity of the organisation in terms of data understanding will be the starting point for our governance model definition and both the culture and profile of the people who make up the organisation will indicate how quickly we will be able to go on with change management or where we need to put effort when undertaking initiatives.

Like any initiative that starts from the culture of the organization and its employees, it requires a very important communication strategy, focusing on reaching all levels and giving visibility and importance to the changes that are undertaken, as well as involving every stakeholder.

Data management has 3 axes: technology, processes and people; of which the most important and complex element is undoubtedly people, since they are the ones who must be impregnated with the data culture we want to establish so that they do things the way the organization expects them to do on a daily basis to help achieve its goals.

Involvement of stakeholders

In line with what is said above, the people involved have to feel empowered to make decisions and see themselves as part of the process of change, perceiving the value of it while assuming a series of responsibilities around the data that perhaps they did not had until now.

It is very important to make stakeholders see that data governance is not about “putting sticks in the wheel” or “playing bad cop” but about providing the organization with the necessary capabilities to generate business value through better use of data. In order to do this, it is also essential to provide them with the necessary tools and resources because without them, their workload will increase and they will not see a quick return.

Formulas that may work to achieve this involvement can be the inclusion of objectives or bonuses in relation to data governance initiatives, participation in committees or meetings of importance, visibility to colleagues and managers or the granting of space for decision making.

Likewise, it is key to create a collaborative and interactive environment where data governance is built with the sum of all and not only with the involvement of a few roles and/or areas since this causes frustration in some and misunderstanding in the others.

Implication of the Top Management

As it cannot be any other way in any cultural change for an organization, the Top Management must be the first one involved in the initiative because at the end it is the one that is going to make the strategic decisions and priorities that are going to appoint where the resources are allocated to face them with guarantees.

In this case, it may be understood as one more stakeholder that has to have its presence within the whole governance model with its role and functions but also its support is essential since it has to provide the necessary power to those who require it and they are the first ones that should follow steps to expand that data culture to the whole organization.

It is not easy to measure the ROI of a data governance initiative in the short term but it is necessary to sell it properly to achieve the required involvement from stakeholders in case that Top Management does not decide of its own to launch it with all its effects. This participation is really necessary because otherwise we will not have the corresponding resources at our disposal to achieve our goals.

First steps: the assessment

How can I be able to come up with metrics that will make the internal sale of this kind of initiative if I don’t even know where I’m coming from?

The first step after we are clear that we want to implement data governance at all levels in an organization is to know where we are starting from.

Just as we must know the existing data culture or the resistance to change that we may find, it is very important to know what we have, what we want to achieve and which tools we have to reach it.

A good initial analysis will not only provide us with a detailed roadmap and with a greater probability of meeting our goals, but will also make us capable of measuring our KPIs before we start. Then, we may compare them with those measured at the end of the exercise, thus obtain improvement results.

The importance of the use case

Once we have landed an initial assessment and have the As-Is vs To-Be approach at a high level, the next thing we need to do is to select a use case and set a time limit for measuring results. This step is very important since it will allow us to get a fast time-to-market with a limited scope and we will be able to measure what we have achieved in order to sell it internally.

One of the keys to making our use case successful is to choose an appropriate and striking one. The choice of this use case is not trivial and depends on many variables that are conditioned to a greater extent by the culture and degree of maturity of the organization.

For example, we can propose the following scenarios for the choice of a use case:

• Select some of the reports that are most interesting to the Top Management and define an end-to-end governance model of the generation process and the data that is represented in it. This has the advantage that we can achieve a good internal sale since it is an important use case by definition and it is surely very extrapolated to other areas because of its cross-wise nature. However, we can also fall into the trap of having chosen a complex use case, with a too wide scope and that has too many participants, who are also going to be very exposed to the organization’s management.
• Select some area of a particular department, so that we reduce the scope and number of participants but we may also be falling into the trap of doing something very ad-hoc and that we cannot extrapolate later to other areas as well as choosing a use case that does not have much impact for the Top Management.
• Finally, we can look for something more innovative and jump on the bandwagon of a new strategic data initiative, be it the implementation of new technologies, the development of algorithms with advanced analytics or the capture and processing of new sources and types of data. Like all of the above, this one also has its pros and cons. The good part would be that we would have the support of the Top Management and we would get the involvement of the participants quickly as it is something strategic, besides that starting with the initiative from zero we could go hand in hand and achieve milestones quickly. The negative side would come from the difficulty of measuring the results obtained from the implementation of the governance model in the initiative, since we could not measure the status before and after and we would have to find another way to get these metrics.

Whatever the case of use, what is clear is that we need what we do to be as extrapolated as possible to other areas and cases of use in order to be able to extend this governance model to the whole organisation, impregnating it with that data culture that we talk so much about.

Metrics to measure results

We have commented it above and throughout the article but it is simply because of the importance of defining and measuring the metrics that will allow us to achieve an internal sale of our initiative.

The problem with metrics in data governance initiatives is that it is not easy to define them since in most cases we cannot obtain a direct ROI in monetary terms.

Only in those cases where data have an economic value for our business or directly impact our P&L, we will be able to get that ROI directly but in the rest of the situations the most common thing will be that we have to infer that economic impact from time reduction, errors decrease and costs savings and also taking into account the level of satisfaction of those involved with the new way of doing things.

In addition to the metrics that help us with the internal sale, it will also be very important to define the metrics corresponding to the follow-up of the initiative and goals achievement. These metrics have to cover mainly the following aspects:

• Degree of coverage of the governance model (completeness of implementation)
• Stakeholders involvement (in quantity and quality)
• Data quality (basic controls)
• Time invested in tasks performance (dedication in their day to day of each team)
• Identification of “bottlenecks”

Technological tools and solution

They are not something essential from the beginning and it is important to know when to incorporate them into our model but they will certainly help us to achieve our objectives and above all to achieve the involvement of every stakeholder as soon as we include them in the picture.

To achieve this participation it is essential that tools include a high degree of automation of manual tasks and that they offer a neat and intuitive UI & UX where the learning curve for their use is not an added problem.

In this sense, technological solutions have to be understood as accelerators and facilitators of change.

It is also important to understand that the implementation of a tool is not the solution by itself and that we will need to integrate it into our ecosystem, so it is also essential to choose it based on our variables and make a detailed study of the options available.

Variables such as configuration and adaptation capabilities, scalability and interoperability are essential in a data governance solution in the current era, where it is very important that we can opt for solutions that are totally agnostic to the technology we have for data processing and that we can adapt to our governance needs as they evolve over time.

With this scenario, it is reasonable to consider whether the best strategy is to opt for a specific market solution offered by a software vendor, to carry out in-house developments with our own or external personnel or, finally, to reuse tools available in the organisation with this approach. Normally, the choice of one of the options is not exclusive of the others and what is usually considered is an environment where different pieces of different types fit together to form a puzzle that integrates with our vision, which is ultimately what is important.

Conclusions

To sum up, one of the most remarkable aspects we could extract from the session was that there is no whitepaper or instruction manual fully applicable as the ones that can be followed by someone who wants to comply with a specific regulation or install a software. Rather, it is a collection of good practices, implementation guides and experiences that must be transferred to the specific scenario of each organization, trying to align what is applied to the corporate strategy.

In line with the above, it is also clear that there is a difference between companies in different sectors and of different sizes, mainly due to:

• The regulations and standards they have to face
• The amount of data they have at disposal and the ease of obtaining new data
• Investment in technologies capable of handling large volumes of data
• The importance of data in their strategy and the competitive advantage they can achieve by using it
• The direct margins obtained through the correct use of data within their particular business activities.

For a new CDO, implementing data culture and data governance in your organization is a major challenge and not easy, but there are more and more mechanisms and information that can help you achieve the proposed goals. I hope that this little article will serve to shed some light and guide the walkers lost in the forest as the anjanas do…