Data modernization is key to reaping expected digital benefits. Just be sure you first identify the business driver behind the data modernization effort.

Business leaders are ready to embrace the game-changing innovations enabled by a range of advanced technologies, from artificial intelligence and machine learning, to blockchain, natural language processing and 5G. In our recent study, all of these technologies—and more—had been adopted by more than half of respondents or were in the planning stages of adoption.

At the same time, most respondents didn’t think they’d yet realized significant value for most of the technologies on our list; even for the widely adopted technologies such as cloud migration and data analytics, just 56% of respondents were realizing significant value.

While many factors are at play, we believe one of the major reasons for this finding is that so many businesses pursue their digital initiatives without fully addressing their data. The fact is, if you don’t tackle data modernization first, chances are your business won’t reap the expected benefits from any digital advancement.

Until recently, data modernization was an arduous and highly manual task. But today, it’s more seamless and manageable than ever due to a variety of technological innovations that have made it more accessible and cost-effective for companies to upgrade their data infrastructure. Automation and AI, low-code/no-code technologies and open-source software are just some of the advancements that make data modernization more accessible and achievable now.

At the same time, no data modernization endeavor should move forward until the organization has clearly identified the business driver behind the data modernization effort. The quickest path to wasting money is not to establish a clear, achievable business case before making the investment.

Business reasons to modernize data

We’ve helped clients across industries take on data modernization projects that encompass a range of business use cases. Depending on the business driver, these companies used an arsenal of data modernization approaches and tools, including master data management, customer data platforms (CDP), data mesh and data fabric architectures, and cloud migration with rigorous data governance, security and privacy guardrails.

Here are but a few of the business use cases our clients have identified and a brief idea of the approach taken:

• Reducing time to market for goods and services. A large telecom provider needed to differentiate itself by bringing innovative products to market quickly enough to beat competitors to the punch. We worked with the company to build a cloud-based unified data platform using a product-centric design approach, as well as data management frameworks like data mesh, data fabric and data in motion.
  
  Using our Neuro Cognition toolset, we had access to a group of accelerators with extensive automation of common, repetitive tasks across the data lifecycle, as well as everything we needed to deliver a modern data platform in an accurate, consistent, fast, optimized and cost-effective way.
  
  
• Creating a superior customer experience. Modern customer experiences are predicated on a unified data set that enables customer support agents to provide proactive and even predictive service experiences because they have all the relevant data and insights available to them in real-time on a single screen.
  
  We worked with a large UK bank to connect siloed data to enable a complete view of the customer, including all accounts across mortgages, checking, savings and home equity lines. Support agents can now provide a data-driven, personalized experience and act on cross-sell and upsell opportunities that are presented from the data. The bank will also be able to use its new data foundation for its compliance efforts, as well.
  
  
• Enhancing business agility. A global bank wanted to remove all the inefficiencies in its data management and business intelligence areas in order to improve overall business productivity. We worked with the bank to improve productivity by 30% to 60% with reduced total cost of ownership.
  
  We did this by moving the bank’s existing legacy ecosystem into a cloud-based platform using the right architectural principles around data mesh, along with our Neuro Cognition toolset to accelerate the migration process.
  
  
• Increasing revenue. A global automaker sought to increase revenue by improving auto sales across regions. We established a modular marketing and customer interaction system and a unified data platform across over 21 markets worldwide for 1:1 marketing. This resulted in 45,000 new leads generated, with improved sales of 10,000 cars sold in 15 months, along with forecasted 50% growth in the prospect base over three years and 40% improved conversion rate.

It starts with the business case

In all these cases, the businesses focus was to drive value by defining a clear future direction, and then identify inefficiencies and challenges across the data ecosystem to spotlight where improvements were needed most urgently.

We then carved out an engagement that would return value quickly. Each engagement began with an assessment of the existing data infrastructure, revisiting/revalidating the business’s data strategy to focus on data democratization across people, process, technology and architectural components, and a prioritized list of the use cases with the highest and fastest return.

It’s never been easier—and more important—to pursue a data modernization project. Businesses that take this step will no doubt see full value from their digital initiatives.