Much ink has been spilled over the relationship between data and information. In the book “Business at the Speed of Thought” Mr. Bill Gates stated, “How you gather, manage and use information determine whether you win or lose”. With the advent of data warehousing for better reporting, master data management to integrate highly shared data, new technologies emphasizing user empowerment, and the explosion of data volumes, today’s organizations realize that data management is a vital and permanent function. Moreover, data is the primary element in today’s business world that creates market leaders.

Data is no longer just a tool for predicting and prescribing market movements for organizations. Instead, it is a powerful device that can transform and redefine the organization’s revenue structure. To maximize the value of data, market leaders must expand infrastructure that can harness its power most profitably.

For an organization, a data management strategy can be envisioned through the data life cycle within the company. The data life cycle involves the collection, validation, storage, processing, and sharing of data by various entities like governments, private firms, NGOs, and academic institutions.

Every data has a story that must be preserved for reference. To maximize the value of data, it is important to engage in an outcome-oriented and user-centric data management approach during its life cycle and most importantly outcome for the business. Data has the capacity to build an auxiliary business and alternative revenue sources for the company.

Creating a Data Governance Framework for Maximising Revenue

To ensure effective decision-making, data management should be guided by a data governance framework. In a structured data management system with a robust data governance framework, users can maximize the value of data in a secure manner and stay ahead in the market.

Data management systems and Data Governance require a focused approach of right intention, planning, monitoring, enforcement, and commitment. It covers a wide range of vital tasks ranging from decision-making to technical implementation.

Whilst the objective of data management is to ensure that an organization gets optimum value out of its data, Data Governance focuses on how decisions are made about data and how people and processes are expected to behave in relation to data for the benefit of the company and people at large.

The Data governance program is designed as per the company’s nature of business, but most programs do include:

Strategic planning

• Defining, communicating, and driving execution of Data Strategy and Data Governance Strategy.
• Balancing the use of data with safeguards against misuse, guided by the country’s social contract for data.
• Transforming general principles into actionable strategies, policies, and integrated data systems.
• Developing data strategies that align with the business priorities and translating them into action plans with clear targets.

Policy and implementation

• Setting and enforcing policies related to data and Metadata management, access, usage, security, and quality.
• Legislating and regulating to standardize and organize data throughout its life cycle.
• Setting standards for data production, storage, transfer, access, protection, and security to support interoperability and improve data quality and integrity.
• providing clarification and guidance to stakeholders on their expectations and ensuring a shared understanding of data governance.

Compliance:

• Ensuring the organization can meet data-related regulatory compliance requirements.
• Enforcing compliance with laws, regulations, standards, and norms on a day-to-day basis.
• Conducting regular and occasional audits to identify areas of non-compliance and improve rules.
• Arbitrating when rules do not address specific questions or issues.
• Remedying breaches or damage resulting from unauthorized data use, such as notifying data subjects or canceling identity credentials.

Monitoring and Evaluation

• Monitoring and evaluation to track performance and assess program or policy outcomes.
• Disseminating monitoring and evaluation frameworks and results in user-friendly formats to promote accountability and trust.
• Forward-looking learning and risk management through horizon scanning, scenario planning, and anticipatory governance.
• Identifying and responding to emerging or unforeseen issues before they become significant challenges.
• Adapting data governance regimes in response to the use of new technologies like artificial intelligence (AI) and big data.

In response to this rapidly changing environment and business need, A Data Fabric-ScikIQ plays an important role in facilitating timely assessments of what works in the newly evolving data environment and offers guidance on how to quickly adapt to change and excel in the market as true leaders.

References

Chisholm, Malcolm and Roblyn-Lee, Diane. Definitions in Data Management: A Guide to Fundamental Semantic Metadata. Design Media, 2008. Print.

Giordano, Anthony Davis. Performance Information Governance: A Step-by-step Guide to Making Information Governance Work. IBM Press, 2014. Print. IBM Press.

Fisher, Tony. The Data Asset: How Smart Companies Govern Their Data for Business Success. Wiley, 2009. Print.

Kring, Kenneth L. Business Strategy Mapping – The Power of Knowing How it All Fits Together. Langdon Street Press (adivision of Hillcrest Publishing Group, Inc.), 2009. Print.

Also Read: Automated Data Governance