Whether you’re an accomplished business manager or a consummate data wizard, Data Governance can be a lot to wrap your head around. Luckily, this list of common questions and straight-to-the-point answers will help you get a better grip on the basics.
Here’s our breakdown of the most frequently asked questions in Data Governance.
1. What is Data Governance?
Data Governance is a strategic program for optimising the way a business deals with data. It aims to organise and improve the policies and procedures a company uses to define, collect, store, secure, manage, and monetise business data. Good Data Governance aims not only to avoid liability but to find new ways of generating value for a business.
2. What goes into Data Governance?
Data Governance involves people, processes, and information technology. It evaluates and redefines roles and responsibilities, augments policies to improve communication and sharing between departments, defines and expands access to business-critical data, and standardises data collection and handling practices to ensure the quality and consistency of your company’s data.
3. What are the business benefits of Data Governance?
Data Governance helps companies avoid liability, save time and money on bad data, improve customer relationships, and actively generate revenue. With accessible, well-defined and quality controlled data, organisations are 23 times more likely to acquire customers, 6 times as likely to retain customers and 19 times as likely to be profitable as a result.
4. Is Data Governance a program or a project?
Data Governance is a long-term strategic business program rather than a single short-term project. Implementing Data Governance requires making structural changes to a company’s existing data policies and practices, as well as redefining the roles and responsibilities of data handling personnel.
5. How do I help business managers understand the importance of a Data Governance initiative?
The best way to explain Data Governance’s importance to management is to focus on its benefits to the bottom line. Emphasise how Data Governance will advance corporate strategy and help achieve concrete business goals. It’s essential to communicate that Data Governance is as much about generating value as it is about avoiding liability.
6. How do you implement Data Governance?
The first step in implementing Data Governance is to develop a crystal-clear understanding of your company’s corporate strategy. Identify concrete business goals that can be achieved with data and define which specific data elements you’ll need to achieve them. With all this in mind, determine where improvements can be made to your existing data and procedures and begin drafting up new policies accordingly.
7. How do you measure Data Governance success?
Measure the success of your Data Governance program by establishing key performance indicators (KPIs) ahead of time. Be sure to tie your KPIs to your organisation’s particular corporate strategy and concrete business objectives. This will ensure that you measure success in terms that are relatable to management and meaningful for the business as a whole. KPIs might track improvements in enterprise Data Quality, growth in the number of terms defined by business glossaries, or reductions in the amount of time spent searching for, organizing, and cleaning data. Another way to demonstrate success is by documenting improvements to your organization’s Data Maturity level over time.
8. Why does Data Governance fail?
Data Governance programs usually fall apart when they fail to secure business buy-in. It’s very important to have a business sponsor who can ensure that both business management and IT remain actively engaged with the program. Data Governance programs are also vulnerable to overreach. It’s important to establish sensible short term goals that are well-aligned with corporate strategy.
9. What’s the difference between Data Governance and Data Management?
Data Governance deals broadly with organisational strategies, policies, and procedures. It provides executive oversight and dictates how data should be handled to advance business objectives. By contrast, Data Management deals with the tools and practices used to handle data and implement the policies outlined by Data Governance.
10. What is a Data Owner?
A Data Owner is an individual ultimately accountable for the quality of one or more data-sets. They are usually a senior-level employee equipped with the authority, budget and resources to define, clean and maintain the data they “own.” A Data Owner is usually not the same person responsible for managing the data day-to-day.
11. What is a Data Steward?
A Data Steward is an individual responsible for managing one or more data sets on a day to day basis. They report to a Data Owner and work to maintain the quality and security of the data. A Data Steward may or may not have any decision-making authority over their data.
A Data Owner is formally accountable for the quality of one or more data sets, whereas a Data Steward is responsible for the day to day management of the data sets themselves. In some organisations, the duties of both roles will be carried out by the same person. Larger organisations will assign these roles to multiple individuals to promote oversight and accountability, to reduce the workload on senior staff, and to encourage the participation of both IT and business departments in Data Governance.
13. What is Data Quality and how is it measured?
Data Quality deals with whether or not a particular data set is fit for purpose. In other words, data that can be used to achieve a particular business objective is considered high quality. DAMA International measures Data Quality in terms 6 key dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness.
14. What is Data Maturity and how do you measure it?
Data Maturity deals with a company’s ability to collect, manage and monetise data. Data Maturity is focused on an organisation’s capacity to utilise and manage its data, rather than on the quality of the data itself. Measuring Data Maturity doesn’t need to be a complex process. It requires looking at a number of different organisational practices and data-touch points. Find out more here.
15. What is Data Lineage?
Data Lineage deals with tracking the complete lifecycle of a particular data set or element; where it comes from, where it’s transformed or stored, and ultimately how it’s put to use. Data Lineage is especially useful for determining the trustworthiness of your data, as well as for running impact and root cause analysis on data errors.
16. What is a Business Glossary?
A Business Glossary defines the meaning, format and uses of an organisation’s Critical Data Elements. Business glossaries are essential for keeping everyone on the same page. By contextualizing and defining individual data elements, they improve business understanding, save time on searching for reports and prevent misuse. Business glossaries encourage better data-driven decision making and are an essential part of Data Governance.
17. What is the difference between a Business Glossary and a Data Dictionary?
Business glossaries define and contextualize critical data and reporting elements for the entire organization. They’re written in accessible, plain text and will often cross-reference terms for greater clarity. By contrast, a data dictionary is a table or set of tables serving as a centralized repository for technical metadata. Data dictionaries are rarely used outside of IT.
18. How do I build a Business Glossary?
The first step in building a Business Glossary is to map out your critical business processes. Ensure that each data element, business term, KPI and metric used in the process is documented and defined. Include definitions of how data elements are being formatted, who is managing them, and where they are being stored.
19. How do I prioritise Critical Data Elements?
Critical Data Elements (CDEs) are data points that directly advance your corporate strategy and concrete business goals. A good way to establish your CDEs is by mapping out your critical business processes and identifying which specific data elements are involved. You should also prioritise data elements used in critical reporting, either internally or externally for regulators.
20. When should I buy a tool to help govern my data?
You should develop the personnel and processes of your Data Governance program before investing in tools. Be sure to align your Data Governance policies with corporate strategy, identify concrete business objectives and clarify roles and responsibilities before you begin shopping for software. Tools should only be seen as a means of empowering your staff to achieve their goals more efficiently, not as a way to secure engagement for your Data Governance program. We advise waiting until you’re above a level 3 in the Data Maturity Assessment to begin browsing.
Still have questions? Want to learn more about Data Quality, Data Maturity, or the bigger picture of Data Governance? Not sure if your company is even ready for Data Governance in the first place? Cognopia is here to help.
With free Data Maturity assessments and a range of bespoke consulting services, Cognopia’s team of Data Governance experts will help you craft a bulletproof Data Strategy unique to your organisation’s needs. Drop us a note below and find out what Cognopia can do for you.