Q&A – Data Governance in the DAMA Framework

On 20 May, we presented our third webinar in the series “The DATA-DRIVEN webinar series”where Mario de Francisco next to Michele Iurillo from DAMA Spain and Synergo! discussed data governance and data management. Most attendees had questions that could not be answered during the event, so we have decided to publish this Q&A section to address the various queries related to the webinar: “The importance of Data Governance in the DAMA framework”.

• Do you know of any tools or techniques for linking a term from the business glossary to a specific field in a database table (technical metadata)?

Mario: Almost all data governance solutions on the market today offer this functionality; the difference lies in how they implement it, both functionally and technically. For example, in Anjana Data We have a technology-agnostic metamodel based on containers, objects, and relationships, which allows us to easily relate any object in the Business Glossary to any object in the Data Catalogue at any level. In addition, with advanced analytics algorithms running in the background without the need for user intervention, the application itself makes suggestions to the user regarding new relationships, similarities, possible duplicates, etc., so that the user can decide whether to accept or reject them. Based on user feedback, the algorithms themselves «learn» and improve the quality of the suggestions made. In any case, the aim is to facilitate manual work, but never to replace it, as too many assumptions would be made without knowing the context of the organisation. Based on data profiling, knowledge of metadata, pattern identification and natural language processing, possible tags can be identified and these suggestions made to the user. As the user interacts with the algorithm, it learns the context and makes better suggestions, but we can never rely on the total automation of these tasks.

• In my experience, the organisations I have worked for have focused on transactions rather than data. Would you agree? How can this issue be rethought? None of the companies I have worked for had database administrators, only developers.

Michele: Assets are not in transactions (it's very strong but it's true); they are in knowledge, and knowledge is data in context.

Mario: The organisation must be made aware of the importance of data in order for transactions to make sense and how good data management can improve decision-making, contribute to cost savings and generate new business. Traditionally, database administrators tend to have a very IT-oriented profile and think much more about technical aspects than the data itself, just as developers think only about their application from a software point of view. What is needed are much more cross-functional profiles, with IT knowledge but also business acumen, who do not think in terms of a database, an application or a project, but rather have a global vision of the organisation. Otherwise, governance will continue to have a siloed perspective, which is not ideal.

• Is DAMA authorised to certify directly in Spanish?

Michele: Only DAMA certifies. The official courses help you pass the certifications. But in theory, you can certify yourself if you study the entire DMBok2.

• DAMA certifies personnel, but which organisation certifies organisations?

Mario: There are three possible schemes for certifying maturity levels with regard to good practices in data quality management, data governance and data management: ISO, DMM and DCAM. In Spain, there is a model called MAMD v3.0 which is based on ISO 8000-61 and ISO 8000-62 and is certifiable by AENOR. The model can also be used as a guide for assessing and improving organisational maturity.

• The DMBOK also includes maturity level analysis and mentions the DMMA Framework as a way to measure maturity. Are there companies that measure based on this DAMA framework?

Michele: All those who decide to apply the framework usually have indicators and metrics suggested by the DMBok2.

Mario: Yes, but there are also many other assessments and frameworks that can be combined. Ideally, whatever you use as a basis, you should adapt it to your organisation.

• Some organisations indicate that the implementation of an MDM is part of the DAMA methodology. What is your opinion on this?

Michele: There is a specific area of knowledge in the Framework called Master Data and Reference Data.

Mario: As Michele points out, MDM has its own chapter within the DAMA framework and is also treated as one of the areas with the greatest impact on an organisation. From our point of view, MDM is still a use case of data governance, applied to the data that is most critical to an organisation, such as customers, products, contracts, operations, suppliers, etc.

• If a company does not have the capacity to create a CDO position, can the CFO be the best sponsor?

Michele: Yes, any data governance project/process involving C-level executives is doomed to failure. Without a CFO who understands the need for governance, it's game over.

Mario: Even if there is no CDO as such, their functions must be supported by someone. The organisation may not have sufficient volume to have different people in the positions of CIO, CTO, CFO, CMO, CRO, CDO, and a long list of other C-level positions, but the functions must be contained within one of the existing positions. In that case, it will depend entirely on the sector of the organisation and the importance it wishes to give to data within it. There are many organisations that do not have a CFO and others that do not have a CIO. In any case, it is advisable to appoint a CDO who has a real C-level position within the organisation in order to implement a data-driven strategy.

• The big challenge is cultural change. We have a strategy, we have a CDO, we have a data governance committee and an office of excellence, formerly the BI office, but very few people still believe in the framework. How do you teach the real value of dedicating all that time on a daily basis?

Michele: The government was implemented without an adequate change in culture. Culture is like the soil in which a company grows. If it does not exist, it must be created.

Mario: Cultural change is vital for the implementation of data governance in an organisation and can be achieved either through imposition (objectives, bonuses, functions, responsibilities, etc.) or through communication and training (portal, articles, workshops, events, etc.). Ideally, the second option should be used, although sometimes it is necessary to give it a push to make it happen. What is clear is that this cultural change must be sponsored by senior management, and without that sponsorship, we will not be able to get the rest of the organisation on board. How do we achieve this sponsorship? Recently, on LinkedIn, I gave this response to a similar question.

• What would be one of the best practices for not presenting oneself as an invasive data government?

Michele: Incremental projects by Business Unit. At first, there will be many opponents, but in the end, they will be the ones to contact the government before making a mistake.

Mario: Move from passive governance to proactive and preventive governance gradually, incrementally and iteratively, on a case-by-case basis, measuring results to demonstrate the value generated. It is also very important to set up collaborative governance, involving all stakeholders and empowering them to carry out their functions, using technology as an accelerator of change and to automate manual tasks, being transparent about everything that is done and having a very aggressive communication plan. And most importantly, speak the language of business but ground it in technology.

• C-levels don't complain because management buys into it even if they don't evangelise it, but they don't get involved either... In that environment, how do you change the mindset?

Michele: It's not that they shouldn't raise objections, they have to lead the process.

• I understand that in order to govern effectively, all C-level executives must be clear about this; otherwise, the organisation is not mature enough. How can we convey the importance of this to the steering committee and achieve the necessary maturity?

Michele: With a clear example of what they are missing out on in terms of: lack of initiative, risk of regulatory non-compliance, loss of opportunities that competitors could take advantage of.

• How can you monetise the value of data?

Michele: First, you need to assess the data. How much would it cost to reconstruct it? Second, you need to be proactive when drawing up scenarios for monetisation. If I know my customers, I can sell them more and better; if the data is consistent, I avoid losing money, etc.

Mario: This is too broad a question and the answer may be too long. What is clear is that in order to monetise data (convert it into currency or money), you first have to assess its value (give it a measurable value) and, in order to assess it properly, you first have to treat it as an asset and govern it appropriately (have an inventory, traceability, control its use, procedures for its management, etc.). There are different ways to value data (depending on its use, importance, risk of loss, availability, etc.), but if you want to monetise it, you have to look at the impact of that data on the company's bottom line, which is not easy because you have to know a lot about your internal and business processes and how your data relates to them. I recommend the book Infonomics, which discusses this topic at length.

• What areas of DAMA knowledge could we use to start our Data Governance programme?

Michele: The Government itself is an area of the Framework, but undoubtedly a good understanding of metadata and having quality data is important.

Mario: For me, data management undoubtedly has to start with a strategy, however minimal. Then, you need to come up with a data governance model and then look at metadata and architecture, but always led by business and grounded in technology. I recommend you read our white paper Data governance.

• In the data value chain, from the data itself (which is worthless) to its knowledge, I believe that monetisation is one of the challenges of Data Governance. What techniques or KPIs do you consider important when assessing data in terms of cost and time?

Michele: Atomic data is valuable. But its potential skyrockets when you put it in context. The KPI is the sum of what we have been able to achieve with good governance in terms of new product design, campaigns, conversion rates, etc.

Mario: To monetise it, look for the impact of that data on the income statement. To assess it, use formulas based on its use, its importance, its relevance to decisions, its relationship to regulatory aspects that may result in financial penalties, its relationship to infrastructure costs, etc. It should also be borne in mind that the value may depend on the consumer; a piece of data may have a different value depending on who consumes it and why.

If you have further questions regarding data governance solutions, let us show you how Anjana Data can help you with your data strategy by changing the vision of governance in your organisation. Order one demonstration!