Data Governance-Adding value to the New Currency

#data #datagovernance #itgovernance #frameworks #datastewardship

We live in a digital world. Surrounded by data that is growing faster than ever before. Our lives are stored in digital records in places that we cannot point out. Databases upon databases in data centers upon data centers all in the connected world economy. Data has become the new currency.?With the vast quantities of data, we see today, management of data has evolved into a serious discipline that encompasses multiple subsets. The concept of Big Data has become common talk with the rise of the 4th Industrial Revolution.

Data cannot be of any use without management and governance. Data management?is essentially the practice of gathering, organizing, protecting, sharing and storing data. Through this practice, it can be analyzed for business decisions. Data management has become essential in organisations that have vast quantities of data as it facilitates a mechanism to generate maximum value from the data available to the business. Data management is the overarching framework in which these policies and procedures are executed and implemented alongside other important data initiatives. Data Governance is focused on data assets.

Data governance is a subset of data management. The?Data Governance Institute?defines it as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”

Data Governance ensures that data is high quality by making it secure, available, usable and consistent. Data governance aims to create harmony between data across various business units and ensure data is used properly through the implementation of data governance policies and procedures for how data will be used and stored.

It must be remembered that Data Governance and IT Governance are not the same.

IT Governance uses formal and informal mechanisms to monitor and control key information technology capability decisions. Through this approach, the delivery of value to key stakeholders in an organization is achieved. Where IT Strategy sets the approach for the use of IT for business value, governance sets the direction. The IT Governance Institute (ISACA) defines IT Governance as follows, "...leadership, organizational structures and processes to ensure that the organization's IT sustains and extends the organization's strategies and objectives.”

Data governance and its importance have grown over time. With the developments of the 4th Industrial Revolution, the growth of so-called Big Data and Artificial Intelligence and the impact of the Covid pandemic, organisations have established some form of governance for individual applications and functions, even if these processes and responsibilities are informal. With the move to digitization, organisations are seeing every data transaction becoming a business transaction. The days of manual paper-based transactions are over. The simple act of ordering your take-out food over an App on your smart device – a data transaction, a business transaction.

By building a formal Data Governance capability within the business, you establish systematic, formal control over these processes and responsibilities. In addition, the business will remain responsive, especially as they grow to a size in which it is no longer efficient for individuals to perform cross-functional tasks.

Establishing a more formalized Data Governance function within the Organisation has numerous benefits. Clear rules for how to change processes and data will help the business and IT become more agile and scalable. Remember that IT Governance should be enterprise-wide. ?Effective data governance programs are aligned or mapped to business capabilities and value streams with solid buy-in from the most senior of executives. Reduction in data management costs through a central control mechanism that is able to understand the enterprise-wide needs of the business. Reusing processes and data will ultimately result in increased efficiencies. Through the documentation and awareness training of these, confidence in the data quality and its associated processes will improve across the business. Improving the compliance requirements that involve data will also result.

The Data Governance Institute (DGI) has highlighted eight (8) principles at the centre of all successful data governance and stewardship programmes:

1. ?All participants must have integrity in their dealings with each other. This is necessary for all discussions around drivers, constraints, options and impacts for data-related decisions.
2. Transparency is a prerequisite.
3. Data-related decisions, processes and controls subject to data governance must be auditable. Documentation is key.
4. Cross-functional data-related decisions, processes and controls accountability must be clear.
5. Accountability and responsibility for data stewardship activities must be clearly defined.
6. There must be check-and-balances between the business and technology teams and between those who create/collect, manage, and use information.
7. The programme must introduce and support the standardization of enterprise data.
8. The programmes must support both proactive and reactive change management activities for reference data values and the structure/use of master data and metadata.

For Data Governance to succeed is no easy task. Teamwork, investment and the correct mix of suitably qualified and experienced resources are needed. Adequate planning and ongoing monitoring form an essential part of the overall requirement. Strong executive leadership is required from the onset. This leader needs to provide guidance, and directly communicate with his/her colleagues at the C-suite and other leaders across the business (effectively ‘fight the battles’).?

The importance of involving a Data Steering Committee cannot be over-emphasized. The steering committee members’ responsibilities include setting the overall governance strategy with specific outcomes, championing the work of data stewards, and holding the governance organization accountable to timelines and outcomes. With representation from Senior Management and the C-Suite, this committee will have the right voice for data governance.

Appointing Data Owners and Data Stewards form an important part of the teamwork element. Data owners approve the data glossaries, ensure the accuracy of information across the business and direct data quality activities and issues. They also sit on the data steering committee.

Data Stewards are involved with the day-to-day management of data. They are seen at the SMEs (subject matter experts). An organization can have different individuals or teams play the role of?data stewards?in their organizational structure. They work with other data stewards, each responsible for their own data domain, in resolving data issues, data policies and reporting to the data owners.

Data Governance is not a revenue generator on its own. Hence, getting budgets approved for the work it needs to get done can be a challenge at times. Taking a ‘Big Picture’ view will facilitate a better understanding of the long-term benefits that Data Governance will bring to the business enterprise-wide which will indirectly lead to revenue generation - essentially leveraging data to generate revenue. Data has a way of becoming siloed and segmented over time, especially as lines of business or other functions develop new data sources, apply new technologies, and when business merge. The data governance program will assist in continually breaking these siloes.

CIO.com highlights six (6) best practices of Data Governance –

1. Identify critical data elements and treat data as a strategic resource.
2. Set policies and procedures for the entire data lifecycle (creation to destruction).
3. Involve business users in the governance process
4. Do not neglect master data management (MDM)*
5. Understand the value of information
6. Do not over-restrict data use.

* Master data management (MDM) is a set of disciplines, processes, and technologies used to manage an organization’s master data. Master data is data about business entities or objects (customers, suppliers, employees, products, cost centers, etc.) around which business is conducted.

Like all things in life, we need to have structure and direction. For Data Governance, we make use of a Data Governance Framework. A data governance framework is a structure that helps an organization assign responsibilities, make decisions, and take action on enterprise data. There are numerous such frameworks available.

Data Governance frameworks can be classified into three (3) main types (adapted from Imperva.com):

• Command and control – the framework designates a few employees as data stewards and requires them to take on data governance responsibilities.
• Traditional – the framework designates a larger number of employees as data stewards, on a voluntary basis, with a few serving as “critical data stewards” with additional responsibilities.
• Non-invasive – the framework recognizes people as data stewards based on their existing work and relation to the data; everyone who creates and modifies data becomes a data steward for that data.

A data governance framework should include:

• Funding and management support – a data governance framework is not meaningful unless it is backed by management as an official company policy.
• User engagement – ensuring those who consume the data understand and will cooperate with data governance rules.
• Data governance council – a formal body responsible for defining the data governance framework and helping to enact it in the organization.

With the rapid growth and importance of data in the digitally driven world, treating data like we have in the past needs to change. Data is the new currency and offers great value if properly managed and governed. Data Governance requires a structured approach with executive leadership buy-in and support coupled with the adequate allocation of resources, and frameworks to achieve the level of success that will facilitate Data Governance into driving revenue-generating opportunities for business data- leveraging data to generate revenue.

References:

Data Governance Institute

ISACA

CIO.com

Gartner.com

Imperva.com