How Do We Leverage Data to Drive a Faster Economic Recovery?

As growth leaders, we will be waking to a world with fewer resources and businesses desperate to grow again once we get past the coronavirus pandemic. However, in our struggle to regain our financial footing we will have a very valuable resource that previous generations did not: data and data science.

As growth leaders, we will be waking to a world with fewer resources and businesses desperate to grow again once we get past the coronavirus pandemic. And despite the global hardships that will be felt by many, in our struggle to regain our financial footing we will have a very valuable resource that previous generations did not: data and data science.

When used well, data science will help direct scarce resources to the right opportunities and efficiently drive growth. I am convinced this will be a big differentiator versus previous recoveries of this magnitude.

Over my career, I have consistently encountered inefficient and counter-productive practices in data-driven decision management and have written about them often. They are paralleled in the crisis today. Below are three issues I would like us all to think about when we leverage data science to rebuild the national and world economy.

1. Customer Data Hoarding

Companies collect so much data that many are “drowning in data.” If you have no idea of the value of what you are collecting, then it is digital garbage.

We were led to believe that AI and data mining would help make sense of the data. It does to some extent, but more often it leads to head-scratching conclusions. We can’t leverage what we can’t understand.

As a data-driven consultant, I am amazed at how much time is spent sifting through data just trying to make sense of it all before any valuable insights can be generated. Going forward we cannot afford this luxury. If there are 10 gallons of fuel in the tank, we can’t spend five gallons trying to figure out if the engine works. However, when it comes to mining company data, we often do.

2. It’s About Qualitative, Not Just Quantitative

We can’t be slaves to the data we have. Collecting the right data is often cheap and easily done, if time is taken to plan. This means that measurement strategy can’t be a retrospective exercise. Too often, I have engaged clients in the post-mortem analysis of very important projects. In many cases, my team is often limited to the data that is available and not the data that was needed. Critical answers are sometimes left unanswered. This is a waste of time, resources and most importantly, valuable information.

3. Data Is Not the Solution, It’s the Tool

We must regularly remind ourselves that data does not solve problems or create opportunities. Rather, brave decision making solves problems and creates opportunities. Data is a valuable tool that can only inform the decisions we need to make. It can help lower the risk and provide valuable insights. Sometimes, collecting more data before acting can be wise. Other times it can also be the delay in action that leads to disaster.

What is happening today has no parallel in recent memory. While the 1918 flu pandemic had similar infection rates, the world was a different place then. Today, we have advanced tools and technology to aid our recovery.

Data science will be one of those important tools, especially if we collectively decide to use it to its true potential. As a result, I am hopeful that we can come out of this faster than we realize.

The Decline of Sears Is a Story About Narrow-Minded Analytics

I am a data-driven marketer, but I also talk about the dangers of using analytics for narrow-minded goals at the expense of long-term advantages. The story of Sears and its eventual bankruptcy is very illustrative of what I mean about narrow-minded analytics — used for short-term gains at the expense of longer-term goals.

I am a data-driven marketer, but I also talk about the dangers of using analytics for narrow-minded goals at the expense of long-term advantages. The story of Sears and its eventual bankruptcy is very illustrative what I mean about narrow-minded analytics — used for short-term gains at the expense of longer-term goals.

I know, because early in my career, I had spent several years at Sears. More importantly, I was there when Sears was bought out by Kmart holdings.

In 2004, Sears was already in decline. But it was still a force to be reckoned with. Despite the fact it had struggled to improve its soft lines (apparel, textiles, etc.) performance, it was still the go-to retailer for hard-line goods, such as appliances and tools. Management was also trying new formats and new product lines to rejuvenate the Sears brand.

Then the announcement came. Sears will be bought out by Kmart Holdings and ESL investments, run under the leadership of Eddie Lampert. The feeling among Sears employees was immediate demoralization. It was as if an old but proud ship was under attack by a ghost pirate ship under the flag of a cursed and dead brand.

Sensing the fear, senior management began preaching the benefits of a more efficient, data-driven management mindset that ESL investments would bring. Along with more resources, the data-driven culture would reward “smart risk-taking.” By better leveraging data, Sears would climb out of its slow descent to once again become a dominant leader in retail.

In this spirit, I became involved in an aftermarket pricing project, where we leveraged pricing and sales data to determine the optimal price of thousands of parts used in the repair and maintenance of hard-line goods. The project netted over $10 million in the first year alone, and the team was recognized with the “making money” award (Yes, that was the name of the award). As more price optimization projects came online, tens of millions of dollars in bottom-line revenue were being realized quarterly.

While the pricing initiatives were a brilliant use of analytics, senior leadership didn’t take advantage of the analytical talent to address the issue of the declining top line. Where was the data-driven strategy for top-line growth? Were we simply collecting cash for the big transformation? Was something already in the works? As we tweaked and re-tweaked algorithms to squeeze more profits, the brand atrophied. Long story short, you have what Sears is today.

However, this story is not an indictment of the transformational powers of data-driven thinking. Rather, as I have written in previous articles, such as here and here, this is an indictment of management’s ability to exercise visionary, data-driven thinking. Analytics is a powerful tool, but it doesn’t replace courage and visionary thinking.

Sears was so busy picking up loose change off the floor, it forgot to look up at the bus barreling toward it.

With analytics, this is easy to do, because it is exceptionally good at optimizing for your current environment. Changing the rules, however, requires the blend of analytics and courage.

Some argue that Eddie Lampert and ESL investments always planned to juice and kill the Sears brand. Eddie Lampert has denied this from the beginning. I believe him, because there was a time when Sears’ coterie of store brands (such as Kenmore and Craftsman) still carried immense market value. That was the time to begin stripping Sears.

This is simply a story where the potential and power of data-driven thinking was advertised as an opportunity for transformational change, but was frittered away picking up loose change.