Anjana Data, the data governance solution for the Big Data and Multi-Cloud era, has positioned itself among customers, consultants and experts as one of the best alternatives to the data governance solutions that currently lead the market due to its innovative and disruptive approach.

Currently, in addition to preparing for the launch of version 4.0, Anjana Data remains faithful to its philosophy of disseminating and sharing experiences and best practices for effective and efficient data management.

For this reason, and due to the great reception of its first season of webinars among the data community, the company has decided to launch Season 2 of its series “The DATA-DRIVEN webinar series”, maintaining the format of short 30-minute webinars every other Wednesday of the month, with special guests and topics related to the challenges organisations face in becoming data-driven.
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Mario de Francisco, CEO at Anjana Data, will kick off Season 2 on Wednesday, 16 September at 6:30 p.m. CEST with a webinar in Spanish entitled: Data Sharing Agreements: the key element in bridging the gap between business and IT in terms of data.

• Further information and registration here.

Data Sharing Agreements (DSAs) represent a new way of managing how governed information is shared and made available within and outside an organisation. But what exactly is a Data Sharing Agreement? Why is it a key element between Business and Technology?

In addition to this topic, the second season of the Anjana Data series will feature special guests who will share their experience and knowledge on key aspects of data strategy implementation.

On Wednesday, 24 June, the webinar was held. MAMDv3.0 as a reference framework for creating an operating environment for data governance, data quality management, and data management where we had one of our partners, DQTeam, as a special guest. Ismael Caballero, UCLM Professor & Training Head of DQTeam, spoke about the MAMDv3.0 model.

This model has been developed by a group of experts in government and data management, coordinated by the group. Alarcos of the University of Castilla-La Mancha in collaboration with the companies DQTeam and AQCLab. It consists of a total of 20 processes, and these in turn are grouped into 5 levels of maturity. 

The webinar covered various points, which we will briefly outline below:

• Data management: best practices in technology infrastructure management to meet business requirements.
• Data governance: best practices in designing organisational data strategies aligned with business strategies.
• Data quality management: best practices in activities aimed at optimising business data quality requirements.

The reference model MAMD v.3 (Alarcos Data Improvement Model 3.0) integrates data management, data quality management and data governance processes in a joint and coordinated manner, and enables the evaluation and improvement of the organisation's processes related to data management, governance and quality. 

MAMDV3.0, process reference model

Components: assess and improve the capacity of the organisation's processes related to data management, governance and quality.

Implementation of DM, DQM and data governance processes:

1. Identify the most relevant or necessary processes for your situation based on the level of maturity.
2. Adapt the definition of processes to the circumstances of your company, ensuring that the results of the processes are achieved.
3. Adapt the definition of processes to the degree of capability (process attributes) that you are willing to assume (with a view to certification).

“Until people consider it normal to resort to data processing how to drink coffee at 11 a.m., you cannot say that you have a data culture.

If you would like to know more about how Anjana Data can help you with your data strategy by changing the governance of data in your organisation. Request a live demonstration.

You can watch the full webinar video MAMDv3.0 as a reference framework for creating an operating environment for data governance, data quality management, and data management on our channel YouTube, where you will also find more videos related to data governance. You can subscribe to receive notifications of new videos.

On 17 June, the webinar «ASSESSING YOUR DATA MANAGEMENT MATURITY» was held, in which Irina Steenbeek, founder of Data Crossroads, spoke about the maturity of data management/governance.

In this webinar on «The DATA-DRIVEN webinar series»We cover a brief overview of existing data management and governance maturity models, describe a methodology for conducting a brief exploration of maturity in your organisation, and demonstrate the results of a review of data management assessment worldwide.

Here are some of the main points:

Maturity is a measure of an organisation's ability to undertake continuous improvement in a particular discipline.

• Three questions about data management maturity
  • • Why: Key reasons for conducting a data management maturity assessment
    • What: Definitions of data maturity and data management/governance
    • How: Challenges with existing models

• There are two fundamental reasons for conducting a data management maturity assessment.
  • • Define the steps to improve data management performance in your company.
    • Compare the results with those of your peers in the industry.

• There are two key perspectives on data management and its scope.
  • • DAMA-DMBOK2 – «broad» meaning: from the company's point of view on the life cycle of data circulating within a company.
    • DCAM v2.0 – narrow sense: from the perspective of the tasks to be performed by data management professionals.

• There are several challenges with well-known data management models and data management/governance maturity models.
  • • Fundamental conceptual differences.
    • Differences in definitions of data management terminology and the content of data management capabilities/functions.
    • Data management maturity models are difficult to map.
    • The results of the different maturity models can hardly be compared.
    • The metamodels of DM models and DM maturity models are not aligned.
    • Comparative assessment of the maturity of MDs is hardly possible.

• Regardless of the approach chosen, it is necessary to conduct an assessment of data management maturity.
  • • Specify the DM metamodel used in your company: definition, scope, and key components replaced.
    • Align and map the DM metamodel with the maturity model metamodel.
    • Specify maturity levels and define corresponding indicators (KPIs) to measure maturity.
    • Conduct the maturity assessment and specify the follow-up steps.

If you would like to know more about how Anjana Data can help you with your data strategy by changing the governance of data in your organisation. Request a live demonstration.

You can watch the full webinar video ASSESSING YOUR DATA MANAGEMENT MATURITY on our YouTube channel, where you will also find more videos related to data governance. You can subscribe to receive notifications of new videos.

>>>> Register for the following webinar: MAMDV3.0 as a reference framework for creating an operational environment for data governance, data quality management, and data management.

On 10 June, the webinar «Minimal Data Governance: is it effective and how to do it?» was held, in which Nicola Askham The Data Governance Coach spoke about the effective implementation of data governance with little or no investment.

In this webinar from the series «The DATA-DRIVEN webinar series»We discovered whether data governance can be effective on a low budget and how to do it. Here are some of the main points:

• To discuss minimal data governance, we must be clear about these concepts:
  • • Shoestring: A small or inadequate budget.
    • Data governance is very limited: A minimal approach to proactively managing data quality.
    • Minimal Data Governance: A minimal approach to proactive data quality management
• What does that mean?
  • • Is that enough to say that we are doing it?
    • Enough to get rid of the regulator?
    • Something that won't require much effort?
• Minimal Data Governance It must provide real value to the organisation, otherwise there is no point in doing it. Benefits:
  • • Improved efficiency – cost reduction
    • Accurate reports – informed decisions
    • Facilitating compliance
    • Protecting reputation with customers and suppliers
    • Enable the strategy
    • Provide a single version of the truth
    • Enables secure innovation at pace
• Design your own framework
• Select deliverables
• Can minimal data governance be effective? Just remember:
  • • A very focused scope
    • Do it properly
    • Planning for the future

Nicola Askham also shared the document. Free Data Governance Checklist  which provides a high-level outline of the activities you must complete to successfully design and implement a data governance framework.

If you would like to know more about how Anjana Data can help you with your data strategy by changing the way your organisation views data governance. Request a demonstration live.

You can watch the webinar Minimal Data Governance: is it effective and how to implement it? full version on our YouTube channel, where you will also find more videos related to data governance. You can subscribe to receive notifications of new videos.

The sector of activity and the motivation of the company determine the momentum, scope, and focus of data-driven transformation.

The webinar Data-driven companies: their importance in today's environment and the insurance sector from the series «The DATA-DRIVEN webinar series»The event was held on Wednesday, 3 June, where Mario de Francisco, CEO of Anjana Data, as the event's presenter, spoke with Juan Francisco Riesco, Data Director at Mutua Madrileña, about how the demands of the current environment make it necessary to be a data-driven company and how having degrees of freedom beyond regulatory requirements is key to demonstrating that it is a business issue.

 Why do we talk so much about data-driven companies?

According to a study by management consulting firm McKinsey & Company, data-driven companies with data-intensive and analytical users are:

• 23 times more likely to outperform their competitors in acquiring new users.
• 19 times more likely to achieve above-average returns
• 15 times more likely to deliver better value to your customers.

Data wrapping

Essentially, data wrapping involves incorporating or “wrapping” the products and services offered by a company with more data-based information in order to ‘delight customers and increase profitability’.

But what does it mean to be a data-driven company?

• • Data-driven companies: data-driven areas + Strategy + Innovation
  • Data-driven areas: Data-driven people & processes + Culture
  • Data-driven processes: Data + Tools + Data-driven people
  • Data-driven individuals: Data + Tools + Knowledge + Use

In data-driven companies, we need everyone in the company to use data and leverage it to perform their day-to-day tasks. We need data scientists with expertise in statistics, programming, and business to develop advanced analytics models for the company. We don't need everyone in the company to be a data scientist.

Being data-driven involves changes in: Organisation, Technology, Processes and Culture.

• Regulation: imposing targets and setting the pace of regulation
• Market: following trends but with limited or vague objectives
• Business: purpose aligned with company strategy

How does this translate into the Company's activity?

Business improvements: 

• Increased satisfaction and sales
• Process and cost optimisation
• New business models
• Increased synergies in partnerships

Operational improvements: 

• Better understanding of what happened
• Anomaly alert
• Predictive suggestions on actions to take
• Task automation

Is it necessary in the current context of insurance companies?

• Further information available: Open data and connectivity.
• More accessible capabilities: Advanced analytics and new technologies.
• More complex environments: More competitive markets and more demanding customers.

The insurance sector from a data perspective

Challenges and opportunities from a data perspective for the insurance sector with regard to the following points:

• Complex business.
• Risks and regulation
• Technological ecosystems
• Little digitisation

If you would like to know more about how Anjana Data can help you with your data strategy by changing the way your organisation views data governance. Request a demonstration live.

We remind you that the full video of the webinar Data-driven companies: their importance in today's environment and the insurance sector It is on our YouTube channel, where you will also find more videos related to data governance. You can subscribe to receive notifications of new videos.

>>> Register for the following webinar: Minimal Data Governance – is it effective and how to implement it?

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!

LADY – the International Data Management Association – elected its first board of directors in 1988. The first chapter was founded in 1980 in Los Angeles. From that moment on, it became clear that the most urgent need in a sector that was just beginning to take its first steps was to have a single theoretical framework. A language that all professionals could understand and that would serve as a reference when tackling any data management project.

In this third webinar in the series «The DATA-DRIVEN webinar series»We had the pleasure of chatting with Michele Iurillo, member of the board of directors of DAMA Spain and Founder of Synergo! who, together with Mario de Francisco, CEO of Anjana Data, resolved some questions about Data Management and the importance of Data Governance in the DAMA framework.

But… What exactly is data management? 

Quoting DAMA, DM is: the development, implementation, and oversight of plans, policies, programmes, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles. Since its founding in 1980, many things have changed by 2020. There are new technologies, and there are even companies whose assets consist almost exclusively of data, but the DAMA methodology remains the best foundation for professionals who want to focus their knowledge on information assets. DAMA defines Data Management as a set of 11 areas of knowledge and, thanks to the DMBoK 2 reference, has created a framework of excellence and best practices that is a benchmark worldwide. A volume of more than 700 pages that DM professionals use and cite almost as if it were the Bible. DMBoK 2 has created a framework of excellence and best practices that is a benchmark worldwide. This methodology identifies Data Management in 11 areas.

There are many frameworks, but DAMA resists changes in technology and paradigms because it is oriented towards a more semantic and philosophical side of DM. If it is true that someone says that “poets” are needed for Data Management, they surely have DMBok2 on their bookshelves.

Below are some important points from both presentations:

DAMA: A framework for Data Management

• It is data that allows us to move from the dimension of “how” to do things to the dimension of “what” makes sense to do, which is the real turning point of Digital Transformation.
• Data Governance, what is data governance? “Data governance is the exercise of decision-making and authority for matters related to data,” according to the Data Governance Institute's definition.
• If we continue with DAMA's approach to data management, data governance is the central element. There is no data management without it.
  • • Data governance prepares us for the digital transformation process.
    • Data governance assists us in making efficient decisions.
    • Data governance enables us to improve processes.
    • Maximise the revenue-generating potential of data and leverage data lineage.
• The DAMA methodology defines Data Management as a set of 11 areas of knowledge and, thanks to the DMBoK2 reference, has created a framework of excellence and best practices that is a benchmark worldwide.
  • • It is not possible to adequately control data and transform it into reliable information without addressing DM in its entirety.
      • • Data Governance
        • Data architecture
        • Data modelling and design
        • Data storage and operations
    • Security, integration, and management of documents and content.
      • Data Security
      • Data integration and interoperability
      • Document and content management
    • DW & BI, Master data, metadata and quality.
      • Reference data and teachers
      • Data warehousing and Business Intelligence
      • Metadata
      • Data quality
• Data governance begins with data management infrastructure. We will be able to set up a governance model as long as we have chosen an infrastructure geared towards it: 
  • • Decentralised organisation 
    • Organisational Network
    • Centralised organisation
    • Hybrid Organisation
    • Federated Organisation

Data governance in the DAMA framework

According to DMBoK2:

• • • Data governance is defined as the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.
    • The data governance function guides the other data management functions.
    • The purpose of data governance is to ensure that data is managed correctly, in accordance with defined policies and best practices.
    • Data governance vs. data management = supervision vs. execution.
    • Data governance requires planning, not only to represent organisational change but also simply because it involves many complex activities that need to be coordinated.
    • A scalable, interactive implementation based on limited use cases maximises the chances of success and reduces potential frustration among the teams involved..

And something else that is VERY IMPORTANT: Technology can facilitate and accelerate data governance, but it cannot meet basic needs on its own. It requires strategy, people, and processes.

If you would like to know more about how Anjana Data can help you with your data strategy by changing the way your organisation views data governance. Request a demonstration live.

We remind you that the full video of the webinar The importance of Data Governance in the DAMA framework It is on our YouTube channel, where you will also find more videos related to data governance. You can subscribe to receive notifications of new videos.

>>> Register for the following webinar: Data-driven companies: importance in the current environment and the insurance sector.

On Wednesday, 6 May, Mario de Francisco, CEO of Anjana Data, presented Peter Kapur, founder of True North Data as a webinar speaker Data Governance 2.0, Data Stewardship & Privacy: Best Practices to Operationalise Data Strategy, the second in the series “The DATA-DRIVEN webinar series”. 

In this webinar at English, Peter Kapur spoke about the reinvention of data governance after the 2008 crisis as an opportunity for data monetisation and revenue optimisation. Here are some of the main points:

• • Data governance – Historically, it has focused on compliance with rules and regulations. 
    • • Data governance 1.0 gained greater relevance after the financial crisis, satisfying regulators in the certification of financial data (BCBS 239, Sarbanes–Oxley).
      • The frameworks were creating integrated governance components (DAMA, DMBOK). Assessments/checklists were created (DCAM, DMM). Privacy and data protection were added to the framework to continue compliance with regulations.
      • The Data Governance Framework 1.0, with policies and procedures, was used across all industries, even those that were not directly affected in 2008, but which relied on keeping regulators at bay.
  • Data governance 1.0. Industry best practices – Limited and isolated business operations, data innovation & big data.
    • • The primary objective was not to provide commercial value or solve problems, but to satisfy regulators. It was assumed that once policies/standards/best practices were written, everyone would follow them.
      • While data management intensified in 2008, data acquired strategic value for companies that focus on data transformation by leveraging the big data for operational efficiency and revenue optimisation.
      • Initially, data management attracted information technology management professionals to support regulations, but the profession is now more diverse. There is tremendous opportunity in data governance innovation.
  • Data governance 2.0 – Operationalisation, adaptation and enabling of business innovation.
    • • Data governance is evolving from a «policy and compliance» perspective to delivering tangible value for business innovation.
      • Part of data governance 2.0 is already underway with companies integrating it into their data strategy and analysis.
      • Stakeholders (data producers and consumers) are essential to the creation of a «connected» data strategy.
  • Data administrators are the heroes of data governance.
    • Data managers must take a leadership role in building a data culture within the organisation.
    • Metadata and business terms must be involved in a holistic view of data assets.
    • They should participate in the maturation of the data asset catalogue within an organisation.

     

    

     

Within these topics, Peter Kapur presented a definition of data governance that is quite aligned with Anjana Data's vision: «Data governance is a system used to define who within an organisation has authority and control over data assets and how those data assets can be used. It encompasses the people, processes and technologies needed to manage and protect data assets.».

If you would like to know more about how Anjana Data can help you with your data strategy by changing the way your organisation views data governance. Request a demonstration live.

We remind you that the full video of the webinar Data Governance 2.0, Data Stewardship & Privacy: Best Practices to Operationalise Data Strategy It is on our YouTube channel, where you will also find more videos related to data governance. You can subscribe to receive notifications of new videos.

>>>> Register for the following webinar: The importance of Data Governance in the DAMA framework.