Data Enrichment, Transform Your Unstructured Data into an Asset

Effective data management to convert data into a valuable asset should be the prime goal of CIOs in every company.

A CIO’s role is evolving to focus less on system uptime and more on leveraging data to drive the business forward. The core is to gather data stored in a disparate set of systems and create an impactful, leverageable asset. 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, making it easier to turn this data into an asset. The trick is to create a data-oriented culture, developing a team with the curiosity and ability to use data strategically. Teams should understand how disparate data sources can interact to solve various existing business challenges. 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 the ever-increasing challenge, data scientists want to focus on work that makes a significant impact. This is possible when they don’t just have access to advanced tools and methods and see the effectiveness of the implemented models, which helps the business partners and customers use the data in a productive and impactful way. 

Also Read: How the Role of Data Scientists evolved amid COVID-19

Know the rules

It is essential to ensure that all employees understand how the contracts and regulatory issues dictate data use. If employees don’t understand their data usage limits, they might become too nervous about innovating. The teams need to have a thorough understanding of appropriate data usage to avoid 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.

Staying flexible

After the company develops a data-hungry culture, the last thing they want to do is to lose all of that momentum by making data accessibility cumbersome. Flexibility remains the key to business models’ success, as data scientists across verticals have their own favourite tools and technologies. Rather than locking in on specific database technology, it is preferable to stay flexible.