Effective data management to convert data into a valuable asset is the prime goal of nearly all CIOs in every company.
The role of a CIO is evolving to focus less on system uptime and more on leveraging data to drive the business forward. The trick is to gather data lying in a disparate set of systems and create an impactful, leverageable capability.
Creating useful data remains “a multi-dimensional challenge,” and below are few foundational steps that IT leaders should take, to succeed.
Establishing a data-oriented culture
The company’s culture should inspire excitement about leveraging data in innovative ways;
then, it gets easier to turn this data into an asset. But it’s only a tip of the iceberg; the trick is to create a data-oriented culture, developing a team that has the curiosity and ability to use data strategically. People should understand how disparate data sources can interact to solve various existing business problems. The overall concept is to develop a community where information and methods flow freely to where they are most needed.
Create interest and impact
Every company needs data scientists, and this is creating a tight talent market. To counter this ever-increasing challenge, data scientists want to focus on satisfying work that creates a significant impact. This is possible when they have access to advanced tools and methods for it. It is critical to see the effectiveness of the implemented models, which helps the business partners and customers to use the data in a productive and impactful way. The challenge of generating structured data from unstructured data requires a lot of interest from the data science community.
Know the rules
It is essential to assure that all employees understand how the contracts and regulatory issues dictate the use of data. If employees don’t understand their limits on their data usage, they might become too nervous about innovating. The teams need to have a thorough understanding of appropriate data usage to avoid either overreach and misuse data or go to the other extreme and just retract and do nothing. Understanding the contractual and regulatory regulations around the data and defining them in a way that people can easily comprehend, is the main challenge for data analytics.
After the company has developed a culture that is hungry for data and the data scientists are on board, the last thing they want to do is to lose all of that momentum by making data accessibility cumbersome. Flexibility remains the key to the success of business models, as data scientists across verticals have their own favorite tools and technologies.
Rather than locking in on specific database technology, it is preferable to stay flexible.