In late 2018, digital readiness ranked as the top concern in enterprise C suites in a survey conducted by North Carolina State University’s Poole College of Management.

That’s not a surprise considering that enabling digital readiness is more than just digitizing information. Work processes must be retooled to work in a digital environment, employees must be trained–and digitized information must be trusted.

SEE: Digital transformation in 2019: A business leader’s guide to future challenges and opportunities (Tech Pro Research)

What best approaches and projects should IT leaders pursue to facilitate digital transformation? See seven ideas below.

1. Encourage a move toward a customer-centric organization

CRM, sales, products and service systems should be cross-linked so that personnel in customer-facing departments have complete and consistent information on customers. This enables a salesperson to see that a customer recently had a problem with an order, and might not be willing to reorder without an incentive. It also enables a service person to see that the company’s largest customer is calling, and that premium service is expected. If employees throughout the company’s customer-facing functions don’t have up-to-date customer information, they will be unable to stand in the customer’s shoes and relate–and they might lose business to competitors with better systems.

2. Remove data silos

To facilitate a 360-degree view of customers and other corporate business functions, silos of information that were sequestered in different company departments must be broken down and consolidated into a single data repository so that everyone works with the same information.

3. Choose your data

With IoT and other forms of data pouring in, organizations can’t afford to house and steward all of the data. The time for IT to sit down with end users and determine which data stays and which data goes is now.

4. Get IT and end users onboard

When systems get digitalized work processes change and so do system configurations and IT architecture. If end users and IT are not onboard with the changes brought about by digitalization, projects will fail.

SEE: IT leader’s guide to achieving digital transformation (Tech Pro Research)

5. Determine data quality

The old adage of “garbage in, garbage out” still holds true for both structured and unstructured data. Digitalizing garbage doesn’t clean it up, so data cleaning automation processes, and in some cases manual efforts, must be used to assure data is high-quality.

6. Improve data security and stewardship

With the growth of digitalized data and systems, organizations face greater cybersecurity threats than ever before. New pressures will be on IT security analysts and database administrators to ensure that data is safe and access is secure.

7. Monitor AI and analytics processes

Machine Learning is imperfect, human learning is imperfect, and AI is imperfect. Therefore analytics results that business elect to act on may be biased or inaccurate. IT should formulate a means of monitoring AI systems for accuracy, along with a way of manually intervening when AI systems fail. After all, AI and analytics are still emerging technologies that have yet to achieve the level of reliability that comes with mature systems.

See also