Companies are struggling to achieve the expected level of trust in their analytics foundation and data governance process. So, building the right base for reliable data analysis should be one of the top priorities for firms.
With the technology maturing in today’s information age, data has turned to be the most potent resource whose potential should not be missed by enterprises. Businesses have swiftly embraced digital transformation, multiplying the risks associated with their brand name and reputation. Thus, extracting important insights from the enterprise data remains the most easily missed but essential factor. Although decision-makers try to implement AI and data analytics to drive businesses into the future, many end up underestimating pitfalls of poor data governance.
But, it’s crucial to analyze that if the company itself cannot trust its organizational data, how can stakeholders and customers trust themselves to be in good hands?
Businesses prominently prioritize data access and analysis over governance, as they help in instant analytical insights. Leaders fail to be confident in this case about their decisions, which are actually rooted deeply into the trustworthiness of the data.
The biggest drawback is that businesses get confused that adequate data security is equivalent to good data governance. At the same time, this is not an absolutely truth. Even if they are loosely linked, each requires its own attention. To remain ahead of the competition and be compliant, companies need to have a solid plan for both.
Below are the ways to balance them out and build a trusted analytics foundation:
Safeguard the business with a stout data awareness and governance strategy
Professionals across the departments, especially marketing, want to place data at the center of their decision-making processes to mitigate missteps and enhance their competitive advantage.
The ideal data governance strategies are listed below:
Gather data silos to get an overall picture for the organization
Data sources are unbelievably exponential. With the expansion of IoT data and the continuous progression of digital channels, adequate unique data types are now there to provide in-depth insights to enterprises. The problem is that the individual sources are frequently funneled using different business segments, each representing a comparatively smaller portion of a unique buyer profile. Collating such individual pieces together pushes the need to connect data from multiple sources across the industries. However, collecting all data in a common location isn’t always useful. Dispensing multiple types of heterogeneous information into a commonplace can resist insights to come out. To get meaningful data, accessing the right dataset is necessary.
[ Know More Is Data Security a Shared Responsibility? ]
Create a catalog for faster access
It is also preferable to build a ‘singular source of truth’ as the initial step to creating a long-lasting advantage. Even though the data is in a simplified landscape, one requires knowing where to find it. Data repositories should be considered as well-organized libraries. They could be stocked with thousands of varied pieces of information. Automated systems and users would never know where to look for them without thousands of individual components. So, setting up a cataloging system allows professionals to easily track down the hunting information.
Prepare the ever-evolving data landscape with continuous management
Most of the Chief Data Officers today are being charged with discovering new revenue streams for data. This clearly indicates that they are held accountable for driving better data management and focusing more on data quality assurance.
Today’s data landscape calls for regulations, technology, compliances, and constant evolution to keep up with the pace of market change. It is more complex for a business that operates across multiple countries, works in highly regulated industries, or conduct business with widely distributed data partners around multiple cloud eco-systems. It’s surely pivotal to plan and remain flexible for tomorrow’s technologies and potential regulations while planning and creating the enterprise data governance strategy.
Data governance also promises a competitive advantage. In the era of AI, unequal division or mismanagement of data between organizations will become more glaring, switching from competitive edges to crucial business advantages. Firms that have not dominated their focus on data governance will eventually find their AI based on unsound insights.