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. But simply having data isn’t enough. The true power lies in understanding and interpreting Data it correctly. This is the ability to unlock the stories and insights hidden within.
There is a need for Data Architecture, Modelling, Metadata and Data Quality management all of which help people understand data and make use of it. 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.
Here are some key steps to take control of your data interpretation journey:
- Understand the Context: Before diving into numbers, consider the source of the data and the question it’s trying to answer.
- Visualize the Data: Charts, graphs, and other visual representations can make complex data sets easier to understand.
- Beware of Biases: Our own perspectives can influence how we interpret data. Be mindful of potential biases and seek diverse viewpoints.
- Look Beyond Averages: While averages are helpful, they can sometimes obscure important details. Explore the full range of data points (e.g., median, standard deviation).
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.
A study by Dunning-Kruger found that people with low data literacy are more likely to be overconfident in their ability to interpret information [source: Dunning-Kruger study]. This can lead to costly mistakes based on misreadings or misinterpretations of data.
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 which helps greatly in data interpretation, 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 interpretation. Data interpretation is a vital skill in today’s data-centric world. It transforms raw data into actionable insights, driving better decision-making and strategic planning. By mastering data interpretation techniques, individuals and organizations can unlock the full potential of their data, leading to enhanced performance and success.
Importance of Data Interpretation
Enhanced Decision-Making: Data interpretation helps in making informed decisions by providing a clear picture of the current situation. For businesses, this could mean understanding customer behavior, market trends, and operational efficiency.
Identifying Trends and Patterns: By interpreting data, organizations can identify trends and patterns that may not be immediately apparent. This can lead to discovering new opportunities or potential issues before they become significant problems.
Improved Problem-Solving: With accurate data interpretation, businesses can pinpoint the root cause of issues and develop effective solutions. This leads to better problem-solving and a more strategic approach to challenges.
SCIKIQ’s data interpretation 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.
