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Digital Without Data Leads To Blindness

Forbes Technology Council

Executive Director, Institute for Experiential AI at Northeastern University, Founder and Chairman, Open Insights.

The Covid-19 pandemic brought about rapid change that demonstrated the range of human resilience and propelled digitization forward at unprecedented rates. According to a McKinsey Global Survey of executives, in only months, the digitization of customer and supply-chain interactions at companies accelerated by up to four years, while digitally enabled products accelerated by seven years. But digitization itself is not a new concept.

Historically, digitization was primarily motivated by replacing manual tasks with robotic automation, but that wasn’t the result. Instead, digitization elevated the value of work performed.

For example, the accounting field evolved from manual ledgers, abaci and good penmanship to computers that work faster and more consistently. And with the U.S. Bureau of Labor Statistics predicting a 7% job growth rate for accountants and auditors from 2020 to 2030, digital transformation shows no signs of slowing that workforce down.

For consumers suddenly isolated at the onset of the pandemic, digitization felt like a lifeline, a way to stay connected to their families, social circles, favorite brands, teleworking and homeschooling their children. But for businesses, there were unintended consequences with lasting effects whose positive or negative impact depended on their place in the digital life cycle when the pandemic hit.

Many companies that showed a lot of reluctance and risk aversion to digitizing their businesses were suddenly pushed into a do-or-die situation, forcing them to overcome historical hesitations. It got the nay-sayers to realize that digitization actually works.

Companies whose roadmaps already marked significant commitment to digital transformation didn’t balk at the transition and experienced hypergrowth during the pandemic. History also has powerful examples of companies dealing with the struggle to digitize.

As a pre-pandemic example, JCPenney had losses that doubled between 2015 and 2019 while they were stuck in the “thoughtful” stage of digital transformation. But its competition embraced online transactions and invested appropriately.

The Covid shutdowns forced the company to face a reality that it wasn’t ready for, and the 118-year-old company declared bankruptcy. It was a similar story for 124-year-old Kodak, the company behind the digital camera, focusing more on its early flaws than its potential. Unfortunately, the brand declared bankruptcy in 2012.

On the other hand, many companies prepared, invested and embraced the digital transformation and prospered by capitalizing on data obtained through online transactions.

According to its own reports, Capital One conducts more than 80,000 big data experiments per year with a greater than 70% prediction accuracy. The company saw an 83% cost reduction in customer acquisition in two years and increased its customer retention by 87%. In a 2021 J.D. Power National Banking Satisfaction Study, Capital One came in first for customer satisfaction, edging out competitors.

Another great example of how adopting digitization can deliver transformative business value is Discover Insurance’s Vitality program. By integrating with over 100 wearable devices and other means of incorporating financial data on consumer spending habits and memberships, Vitality reported a 40% cost reduction in hospital admissions, 14% cost reduction per patient and 25% reduction in hospital stays.

A digital world leads to the loss of human intimacy.

One of the main faults of digital transformation has been a primary focus on automating workflows. Automation promises to reduce the inefficiency, inconsistency, high cost and error-prone nature of manual, human operations.

With this focus, digital transformations can introduce unintended problems because efforts focused on digitizing workflows tend to overlook the importance of collecting data to help understand how digitized processing is performing. When in-person processes and interactions are automated, businesses often lose the valuable information and connections that previously helped them to understand what’s happening in their organization.

“Manual processes” allowed employees to gauge what customers wanted, see how products performed and monitor service levels in a hands-on, contextual setting. These manual processes also allowed companies to learn why customers were leaving and where they were going once they left. This loss of visibility is how companies that go digital without data are essentially left in the dark.

Understanding The Digital Channel

Data serves as a means to enable companies to know each customer individually by substituting for the information implicitly gathered by humans. Data gathering moves the needle from a transactional view of the workflow to intimate intent understanding.

Consider a standard insurance scenario where Kelly buys home insurance coverage with their partner. The insurance agency knows of Kelly’s purchase and that she’s in good standing with the agency, which usually marks the end of the “transaction.”

Had the agency synthesized various interactions and built an understanding of Kelly’s intent, they could have known that Kelly had just got married, started a new family or might be thinking of buying a bigger car. In turn, the insurance company could have offered home-and-auto insurance bundling offers or better rates for cars through particular dealers.

So Much Data

In a world that grows more digitally connected by the day, companies would be wise to embrace digital transformation, and data is a key way for companies to know and understand their customers. To make the most of this digital asset, companies must have a story for data capture, management and its use, and then apply AI on top.

It is hard to imagine having too much of a good thing, but the truth is that the amount of data organizations collect is overwhelming. Gartner estimates that 80% to 90% of data in any organization is unstructured. Yet the majority of companies only know how to deal with structured data. So not only do organizations that aim to play in the new economy of interactions need to deal with much larger volumes of data, they need to consider other challenges with this data.

For most businesses looking to use AI to make sense of their data, it’s important to have the proper data first. AI can’t work without the proper data. Machine learning needs granular and high-quality training data to produce accurate, predictable results.

One of the most effective ways to compensate for the loss of customer intimacy that comes with digitizing interactions is to collect the right data to track and understand the interactions. Since data is often an after-thought, change can help make digital transformations more insightful and intelligent.


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