In this article you will read:
what is a framework in data governance;
what are the advantages of using frameworks;
the main framework models;
how to apply them.
What is a framework in data governance?
Framework is a word derived from English that means structure. Frameworks are, therefore, structures built with the aim of solving specific problems or optimizing processes in general.
Data governance is no different. Frameworks facilitate the creation of the first guidelines for governance and reinforce good management practices. There are several models, created and adapted to the user's needs, but it is possible to summarize the main ones.
Data governance
The value of an organization is currently determined, among other factors, by the business's ability to manage, protect, organize and produce strategic material and knowledge from the use of the data generated.
Data governance is responsible for controlling and managing large amounts of data , based on policies, rules, standards and laws.
With six focus areas, the management of this data allows for more strategic code number of philippines decision-making, reduces costs, increases process transparency and service quality.
They are:
Policies, standards and strategies;
Data quality;
Privacy/compliance/security;
Architecture /integration;
Data Lakes/Data warehouse and business intelligence;
Alignment between governance and business objectives;
With so many challenges, it may seem difficult to implement efficient data governance in your business. This difficulty creates the need to use educational frameworks to create the first data governance guidelines.
Advantages of using frameworks
Frameworks are designed to summarize and highlight the importance of data governance and ease the challenges faced by companies. This is because having an action plan makes it easier to identify gaps, needs, and opportunities along the way.
Furthermore, the tool also provides conditions for the data governance culture to be gradually installed in the company, avoiding communication noise, facilitating the division of work and bringing clarity to processes.
It is worth remembering that with effective data governance, the company can benefit, among others, from solid data, less risk and costs, more operational efficiency, improved decision-making and increased consumer satisfaction.
What are the main framework models in data governance?
There are four main framework models to be used in data governance. You can also adapt them to your company's needs or replace information to make the model more specific – the best thing to do, since everything must be in line with your business objectives.
Please note: if you are not yet familiar with data governance frameworks, we recommend that you seek out a partner who is knowledgeable in the subject. This way, the corporate culture can be adapted in conjunction with the implementation of new practices.
DMBOK DATE
The DATA DMBOK framework, one of the most widely used, was created by DATA ( Data Management Association ), which generated a DMBOK – Data Management Body of Knowledge. The framework was created in 2009 and had the participation of more than 120 professionals.
According to the framework, there are nine key processes for data management, each with its own goals and practices:
Data Management and Architecture
Data Development
Database Operations Management
Data Security Management
Master Data Management & Reference
DW and BI Management
Content Management and Documentation
Metadata Management
Data Quality Management
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DATA DMBOK is the framework we use here at MATH TECH . Due to its ability to synthesize the main areas of data governance and identify objectives in each area, the model creates didactic and effective guidelines to start an efficient data governance proposal.
GARTNER
The framework developed by Gartner focuses on data governance maturity.
This is because, according to their analysis, data and analytics leaders are finding it difficult to identify which aspects of governance need to improve, as they do not have a clear reference for best practices in key governance areas.
This is why Gartner created the 7 key foundations for Modern Data and Analytics Governance.
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Data governance aligned with business results, making business governance policies and standards, business process metrics and D&A metrics clear to all areas involved.
Maintain a model of responsibilities and correct decisions that guarantees the right people security and confidence in the decision-making process.
Move away from assumption-based decisions to trust-based governance that supports a distributed D&A ecosystem and recognizes different asset types.
Value digital ethics and transparency by establishing a digital ethics framework that can be implemented across the enterprise. Data governance and analytics operating procedures should demonstrate a clear audit trail highlighting decisions made, actions taken, related investments and expenditures, and compliance with digital ethics.
Risk management and information security with multidisciplinary teams that can make balanced decisions, giving the necessary weight to opportunity, risk and security, and keeping in mind the long-term interests of the company.
Implement governance training and education with well-defined and measurable goals for the data and analytics function. For example, completing specific training modules on data governance best practices could become part of employees’ annual goals.
Encouraging cultural change and collaboration. You need to figure out what needs to change culturally and explain to employees how data and analytics governance can address real challenges that lead to digital fatigue.
Such practical steps are critical to creating an effective foundation for data and analysis.
Microsoft
For Microsoft, data governance is the fundamental pillar of the corporate data strategy, as they consider data to be the new currency of digital transformation.
And to provide effective data governance, the manager needs to be equipped with important information such as: what data exists, whether the data is of good quality, whether the data is usable, who accesses it, who uses it, what they use it for, and whether the use cases are secure, compliant, and governed.
To achieve this, Microsoft has developed a more modern data governance strategy with five main objectives:
Reduce data duplication and sprawl by creating a single Enterprise Data Lake (EDL) for trusted, secure, high-quality data.
Connecting data from different silos in a way that creates opportunities to use that data in ways that would not be possible in a siloed approach.
Promote responsible data democratization at Microsoft.
Drive efficiency gains in the processes Microsoft employs to collect, manage, access, and use data.
Meet or exceed regulatory and compliance requirements without compromising Microsoft's ability to build exceptional products.
Do you need to implement good data governance practices in your company? And do you want to count on a solid partnership that will bring you the most up-to-date and efficient information, without losing sight of your business objectives?
We can help you! MATH TECH will develop a plan based on your business's pain points and challenges, addressing a specific model for your needs.