Why is data governance important?

Effective data governance ensures that data is managed in a consistent and controlled way, in line with the business's goals, ethics and values. Most typically, data governance is about delivering trusted data, with business context, to create new possibilities and manage risks.

Who is responsible for data governance?

Data governance requires a leader to steer the team - more and more frequently this is a business role such as a Chief Data Officer. Responsibilities are cross-functional, including stakeholders from business, IT and data functions, and may use terms such as data owner, data steward, subject matter expert, data privacy officer, and more. Any knowledge worker that depends on data has some responsibility for data governance

What is the difference between data governance and data stewardship?

Data governance and data stewardship are tightly related, but they are not the same thing. So what is the difference? Data governance is the system of decision rights and accountabilities for information-related processes. Data stewardship, by contrast, is the implementation of data governance capabilities. These are the people, often in business, that lead a company’s information management program and are accountable for the data strategy, definition, requirements, and quality of the data under their stewardship.

What are some key components of a data governance program?

A comprehensive data governance framework should include policies, standards, procedures, guidelines, and tools for managing data across the organisation. It should also involve ongoing training and communication to ensure that all stakeholders understand their roles and responsibilities.

What are some challenges to implementing a data governance program?

Some common challenges include defining clear roles and responsibilities, gaining buy-in and support from stakeholders, ensuring data quality and consistency, and managing data across multiple systems and platforms. Effective communication and collaboration are key to overcoming these challenges and achieving a successful data governance program.

How does data governance relate to data privacy and security?

Data governance is an essential component of data privacy and security, as it provides the framework for managing sensitive and confidential data. This includes establishing policies for data access, handling, retention, and disposal, as well as implementing technical controls to protect against unauthorised access, theft, or loss of data.

How can I train my data stewards?

We offer specialist training to give you the skill you need for data governance. CIMP for CDOs and the data office, and Certified Data Steward(CDS) for data stewards

Data Governance: Maximizing Business Success with data

Q: What is data governance?

Data governance is an enterprise framework that aligns people, processes, and technology, helping them to understand data with the goal of transforming it into an enterprise asset. Data governance delivers visibility and understanding of the data. This strengthens accountability for data assets while enabling organizations to unlock analytical insights needed to improve business outcomes and fuel growth.

Why formalise data governance?

Ensure that the right people are involved at every step of the data management process - making decisions, understanding impact, supplying context, prioritising deliveries, and staying informed.

According to a 2021 report from Drexel University’s LeBow Center of Business Analytics governance programs for data enjoy strong business support.

However, poor awareness of the data culture, a lack of appropriate data governance tools, and a poor understanding of the right organisation approach remain obstacles to success.

When looking at training for data governance, which could help address cultural awareness and adoption obstacles, findings show that only 21% of respondents report a formal training program is in place in their organisation

Lean Data Governance

Our lean approach, based on a Precisely methodology, actively engages the right stakeholders, across business siloes and IT, to share knowledge, minimise unwanted impacts and build trusted data.

This approach is designed to help you adopt what you need and adapt as you see fit.

We encourage you to start small, address immediate concerns, and then extend and adapt based on lessons learnt. We consider your data culture and maturity before providing a roadmap to achieve your desired data management state.

Always remember the goal is to embed data-related decision-making into your normal business-as-usual processes.

Active Data Governance

Companies that practice active data governance - embedding data stewardship and curatorship activities into business-as-usual data processes - report significant advantages in their ability to use data effectively without compromising data privacy, improve data quality, build trust in data-driven decision-making, and deliver new data products.

Address the challenges

Our approach, leveraging the methodologies and approaches of our international partners and our local implementation experience, addresses the challenges identified by LeBow Research, including:

The LDIS+ cultural assessment, developed by Logicle Analytics, builds an understanding of cultural obstacles and increases adoption
In partnership with eLearningCurve we offer two formal training programs - the Certified Data Steward and the Certified Information Management Professional - to bring best practice understanding to local data stewards and data management professionals
We apply proven technologies to automate collaboration, sharing of information and the approval processes, accelerate the capture of metadata, and scale the governance capabilities in support of enterprise goals

Data governance implementation experience

Case Studies

Information Governance framework and implementation for a number of Enterprises over more than ten years, including:
- a number of banks in South Africa and beyond
- an international financial services organisation
- a leading insurance group,
- a major player in the hospitality industry
Definition and implementation of data quality rules and standards across a range of industries including financial services, manufacturing, utilities, and government.
Implementation of data stewardship tools and processes to support the above.

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What is Data Governance?

Information governance is the system of decision rights and accountabilities for information-related processes.

Governance is the foundation of data management - changing behaviour to ensure the delivery of trusted and valuable information.

We recommend a lean and active approach to governance.

This combination means that we apply governance efficiently to actively support day-to-day business and analytics operations.

Governance in the Age of Big Data

To be most effective in the evolving data landscape, not only does information governance need to be centralized, but it must also be business-centric.

Years ago, governance could exist largely as a concern of IT and compliance, but business today needs to leverage data and analytics to gain a competitive advantage and improve the bottom line.

Get the Lowdown on the When, Why and How of Information Governance with this whitepaper from data-integrity leader, Precisely.

Our Implementation Approach, Framework and Training Options

Save Time and Increase Data Accuracy

Save time and money "The project began in October 2019 and was finished by the end of December of the same year.

It was a quick, easy implementation that was up and running swiftly and with minimal business impact."

Manrich Kotze

Lead Data Governance Analyst, Capitec

Data curatorship and stewardship should be as easy as possible.

Once you have agreed on basic priorities and your approach, a business-friendly data stewardship platform can help to accelerate the adoption of new governance processes, reduce the workload and management overheads, and provide an enterprise knowledge base that will help you to find, understand and trust your data.

Most importantly, automation ensures that the right people are involved in all decision-making and changes to data, ensuring that changes are prioritised by the business and that negative impacts are avoided, laying the foundation to monetise data.

"Precisely has a unique capability to relate individual data elements to business process models and estimate the business value of data elements in monetary terms" - Stewart Bond, VP, IDC