The need for data has never been greater than it is today.

Dynamic market economics, shifting consumer patterns, and new-age digital native companies have driven the demand for data and insights.

Increasingly, the data divide has taken on a new meaning between the haves and the have nots.

Organizations that are on the right side of the data maturity curve have recalibrated their supply lines, customer services, and cash flows by tapping into data insights.

The pandemic has further accelerated data and digital transformation. Grocery retailers for instance are tapping into data & analytics to discover new shopping behaviors, increase regional sourcing, adjusting store planograms, reducing SKU assortments while reorienting their workforce.

The story has however, been remarkably different for organizations that look to make decisions with limited visibility of the mostly uncharted data within enterprise silos. This leads to the principal question: How do you unlock the hidden potential of data assets?

Our customer interactions have resulted in some insights on this topic.

Business leaders unanimously agree on the need for organizations to:

• Navigate data islands to drive enterprise KPIs.
• Connect vast data estates to power digital transformation.
• Enhance data security and privacy.

They have, however, called out a few roadblocks for organizations to truly transform into intelligent enterprises. These start with defining a plan to:

• Increase data literacy and promote the role of data jockeys.
• Drive data democracy while breaking data silos across the organization.
• Leverage datasets which might not necessarily be consistent across various lines of business.
• Influence culture by transforming mindsets from being intuitive to data and insight driven.
• Treat data as a product and set up product owners to drive vertical capabilities.

Driving a mind shift toward a data-driven culture requires an appreciation of the multiple levels of consumption maturity within an organization.

These, typically, include the need for:

1.       Fundamental visibility and intelligence on core business operations:

• How much am I producing?
• How much am I selling?
• Who is buying?
• Where is my product?

2.       Finding the right question to ask using AI/ML

• What should be the next product I launch?
• How do I dynamically price my product to stay competitive for profitable margins?
• What design trends should I plan?
• Which customers are going to churn?

3.       Monetizing data to build new revenue streams

• Establishing data syndicates by opening enterprise data assets for business partners.
• Supplying business insights to customers and partners for operational efficiency and customer experience

Servicing the consumption needs within an enterprise requires setting up secure and robust data foundations that can scale. This has been the chief catalyst to move legacy enterprise databases, and on-premise data platforms to the cloud. Native platform-as-a-service capability of cloud platforms like Amazon Web Services begin to play a significant role here.

In summary, organizations need to consider building strong data foundations that can cater to multiple levels of consumption maturity while addressing traditional data ownership and stewardship challenges.