Data governance is a strategic business program that determines and prioritizes the financial benefit data brings to organizations as well as mitigates the business risk of poor data practices and quality – Michelle Goetz
We have been helping companies in sub-Saharan Africa implement data governance programs since 2007 – nearly ten years later there are strong signals that the discipline is maturing.
Some of these include:
Analysts are selling research focused on just data governance
The technology research and advisory industry – players such as Aberdeen, Bloor, Forrester and Gartner – make a living from producing and selling reports that give prospective clients advice on technologies to manage specific business problems.
Analysts have been delivering research on data management disciplines such as data integration, data quality and master data management for years. The first Gartner Magic Quadrant for Data Quality tools, for example, was released nearly ten years ago.
While Gartner’s first Magic Quadrant for Data Governance is still pending, analysts such as Bloor Research and Forrester Research have delivered reports recently. The 2014 Forrester Data Governance Wave positions various vendors based on their ability to provide a sustainable solution and bridge the IT/business divide
These analysts do not deliver reports for which there is no market. A 2015 Forrester Data Governance Wave is pending and is a strong indication that the market for data governance solutions is both growing and maturing.
2. More organisations globally are looking to implement data governance.
Data governance is no longer just for highly regulated industries – such as financial services. In our market, which is arguably less mature that Europe and the USA, we are seeing more and more interest in data governance principles from clients in manufacturing, hospitality, government and retail – amongst others.
This is being driven by the realization that poor data management practices cost money. IT spend in many companies is out of control – by which I mean that a large proportion of spend is poorly allocated. Companies that are beginning to measure the cost of rework, project delays and operational issues linked to poor data management have recognised that they can achieve massive savings by governing data better.
Big data is raising awareness of the value of better managed data.
Big data is exciting to executives. Big data promises to bring new insights to business users to improve customer profitability, loyalty and satisfaction. It does so by simplifying the data management problems associated with large, diverse data sets.
Yet, without governance and data quality, big data solutions struggle to scale. The executive focus on big data has extended into a focus on data – this is good for data governance and for data management in general.
3. Data-centric regulations are emerging globally
Good corporate governance is increasingly linked to sound information governance
Regulations and frameworks such as Sarbanes-Oxley (SOX) and South Africa’s King III require board level responsibility for data.
Privacy regulations, such as the South African Protection of Personal Information (PoPI) Act, also have a strong data governance element. This bill forces companies to govern how they capture, store, use and dispose of personal data. Simply identifying where personal data is stored and how it is used is a significant challenge for most companies.
Data governance teams can provide the frameworks for ensuring these regulations are adequately supported
4. Data governance is becoming practical
Where, previously, companies were focusing on structures and processes we are now seeing more focus on deliverables.
Many early adopters found that, after months of meetings and after building large teams to manage data, they had very little to show for their data governance efforts. In most cases, these programs were passive in nature – in effect they were waiting for problems to occur and then putting processes in place to resolve (or debate) the issues.
Now, companies want to get value from their governance programs quickly.
Governance programs must identify and manage data related risks before they become issues; must govern the documentation of data assets such as the business glossary, or reference data; must go beyond tracking poor data quality to implement sustainable improvements; and must provide auditors and regulators with the information needed to meet compliance requirements.
5. Data Governance related career paths and Certifications are becoming main stream
Roles such as the Chief Data Officer are emerging to take executive responsibility for data management. In many cases, their primary focus is to set up data governance and data quality initiatives, define data strategy, and bridge the gap between business and IT from a data perspective
Data Stewards are being employed to report into this structure and assist with governance tasks.
Accreditations such as the Certified Data Steward and Certified Information Management Professional (CIMP) in Data Governance provide these staff with professional recognition and training for these important roles.
6. Every technology solution is now a “data governance” solution
As a discipline matures suddenly everybody claims to do it. BI solutions, master data tools, metadata tools, even identity and access management are being positioned to support data governance.
Data governance and data management are very often still being confused.
Data governance provides oversight and direction for the various data management disciplines, including business intelligence, metadata management, master data management and data quality. But solutions that address other areas do not typically support the business implementation of governance.
In a post on the Forrester Research blog, analyst Michelle Goetz expands on why data governance is not data management.
While data governance is a maturing discipline, it is still not mature.
In her post, Michelle tracks the maturing data governance market and the emerging platforms that support data governance, not just data management. She suggests that the time is soon coming when data stewards will be able to do their jobs without resorting to complex data management technologies, SQL code and manual remediation.
They will be seen as the business professionals they are, not as a shadow IT function (or as clowns), and they will be enabled and empowered to focus on the processes and activities that they are expected to do – manage the performance of data against business goals.
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