Data is today the main asset of companies and it is not only about storing it, maximizing the value of it to have a profitable business also implies maintaining healthy data; which is equivalent to having quality of these, free of errors, thus generating the confidence to rely on them in decision-making.
But how to get to have clean and clear data in a company? Keyrus, the global consultancy specializing in innovative technology solutions for the digital and data realms, talks about the six steps of the healthy data management process that improve business management and performance.
“If we stop to review, what is the difference between people who lead a healthy lifestyle and those who don’t; I really don’t know if they are clear on how to be healthier, or if they just prioritize diet, sleep and exercise in their daily life or not, this makes their healthy style moot. The same goes for company data: if they don’t have the infrastructure that supports customer initiatives with a 360 view, after deep insight into the customer, initiatives too, as in the healthy style example, become totally debatable” highlights Carlos Eduardo Díaz, Vice President of Data Nola Keyrus.
key steps
As you begin to prioritize the state of your data, you’ll want to incorporate these key steps into your fundamental data management process:
– Identify risk factors: the best way is to prepare for the future. This can include internal risks, such as applications and processes, employees, as well as external risks partners, suppliers and customers.
– Prevention programs: good data hygiene requires good practices and discipline. Labeling facilitates the entry and control of data, making it easy to understand.
– Proactive inoculation: Machine learning can train your systems to recognize bad data and suspicious sources early.
– Regular monitoring: the sooner a data health problem is detected, the better the chances of an effective intervention. Create continuous data profiles, evaluate incoming data, and batch check regularly.
– Protocols for continuous forecasting: the details of each intervention will continually evolve and improve.
– Efficient Treatments: Good data professionals know how to balance the trade-offs between things like security and efficiency for the net benefit of the business and its customers.
The data
Companies should treat their data health as a discipline that places importance on three dimensions of data health practice:
– Precautionary measures: pre-identifying and resolving data challenges.
– Effective Treatment: systematically improve data reliability and reduce risk.
– Solid Culture: establish an organizational discipline around the care and maintenance of data.
There may not be a single universal final state for the state of data. However, data state can be made a way of life by taking conscious steps at each stage of the data lifecycle, from before it enters the pipeline to when it is used by analysts and applications. . By having a preparation with the best technology, people and practices, healthy data can be maintained.