Data is a means of representation. Data is both an interpretation of the objects it represents and an object that must be interpreted (Sebastian-Coleman, 2013). This is another way of saying that we need context for data to be meaningful. People often make different choices about how to represent concepts and they create different ways of representing the same concepts. From these choices, data takes on different shapes. Even within a single organization there are often multiple ways of representing the same idea. There is a need for Data Architecture, Modelling, Metadata and Data Quality management all of which help people understand and use data. Hence the need for industry-level data standards that can bring more consistency to data. As defined by Data Management Association DAMA, Data Governance is the exercise of authority and control over management of data assets. The industry simplifies this definition of data governance further as, the overall management of the availability, useability, integrity, and security of data used in an enterprise.
All organizations make decisions on data, regardless of any formal Data Governance function. Only those enterprises that establish a formal Data Governance program have the ability to exercise authority and control on data with a focused intention. Such organizations are well positioned to get the best value out of their data assets. As an asset Data is dynamic and used for multiple purposes in any organization. Data defines, informs, and predicts, controls cost, drives revenues, manages risk, penetrates new markets, and helps businesses discover the newer avenues. Hence to realize these benefits, data sets must be managed and governed scientifically.
Through strategic governance, business can identify business efficiencies, generate more competitive offerings, and improve customer trust and experience. Today’s data governance solutions can benefit from technological advances that establish a continuous, autonomous, and virtuous cycle. This in turn becomes an ecosystem, a community in which data is used rightly for good.
Data governance encompasses the ways that people, processes, and technology can work together to enable auditable compliance with defined and agreed-upon data policies. The success of a data governance program depends on the cooperation of people, processes, and technology, which is essential for any business to consider while planning. People to build the business case, develop the operating model, and take on appropriate roles. Processes that operationalize policy development, implementation, and enforcement. Technology is used to facilitate the ways that people execute those processes.
To achieve a successful data governance initiative, various key actions must be taken.
First, make a strong case for investing in data governance by identifying key business reasons and explaining how it helps manage data risks.
Next, document the basic principles of overseeing enterprise data and get approval from senior management. It’s crucial to have support from leaders and key stakeholders.
After that, create a plan for how the data governance council and data stewardship teams will work, covering policy creation and addressing data issues.
Establish a system for assigning responsibility for important data areas. Develop clear categories and definitions for organizing and protecting sensitive data. Once roles and processes are set, choose tools to ensure that everyone follows data policies and provides accurate compliance reports.
Finally, educate and train people on the value of data governance through materials and regular training sessions to encourage good practices.
As business generates data continually, it sets in motion a profound transformation in the landscape of data management. Given the changing dynamics of data management, business should think about importance of data governance. Executives aiming to make the most of data as a valuable resource and achieve positive results must reconsider how governance plays a role. They should embrace a modern and transformative approach known as Data Governance. Within this modern framework, Data Governance empowers executives to alleviate from pain points. By adopting this forward-thinking approach, businesses not only safeguard their data assets but also unleash their full potential for innovation and growth.
SCIKIQ’s data governance platform has evolved as per today and tomorrow’s requirements from a cost centric and compliance prospect to a key element in propelling business growth and innovation. Business leaders aiming to leverage the potential of data for successful business outcomes must embrace a modern and transformative approach, and SCIKIQ stands out as an efficient solution tailored for this purpose. The data fabric platform is based on a no code principle for quick integration and customization as per business needs. SCIKIQ ensures data is the cornerstone to any business’s resilience, elasticity, speed, and growth opportunity and not an afterthought.
References
Data Management Body of Knowledge, DAMA International Technics Publications, Basking Ridge, New Jersey.
Giordano, Anthony David. Performing Information Governance: A Step-by-step Guide to Making Information Governance Work. IBM Press, 2014. Print. IBM Press.
Chisholm, Malcolm and Roblyn-Lee, Diane. Definitions in Data Management: A Guide to Fundamental Semantic Metadata. Design Media, 2008. Print.
