Former First Lady Jacqueline Kennedy’s aunt Edith Bouvier lived in a 28-room house with 300 cats. The home was so cluttered that 26 of the rooms were uninhabitable, leaving Big Edie and her daughter Little Edie to live in only one bedroom. When sanitation workers eventually gained access to the residence, they found mountains of empty tin cans littering the floor and fecal matter everywhere. During the cleanup, they hauled away more than a thousand bags of trash. Ida Mayfield Wood, a NY socialite, moved into a seedy hotel and never went outside again. When she died, her two rooms were filled with bags, pots, and shoeboxes stuffed with cash – at today’s equivalent, more than $11 million – and a diamond necklace hidden in a box of crackers. The Collyer brothers shared a residence for 50 years. Even though nearly everything the men collected was worthless junk, they set traps to protect it from thieves. Their favorite devices were 100-pound bundles of newspapers used as deadfalls. Police found Homer’s body first. It took them two hours to find Langley’s even though it was lying only 10 feet away. Langley had been killed by one of their booby traps and Homer, who was not able to walk, starved to death. 

Some hoarding is stockpiling so you don’t run out. 

Squirrels hoard nuts for the winter. Earlier this year, tens of thousands of frightened people emptied store shelves of toilet paper. Taken too far, hoarding becomes a disorder. The Mayo Clinic says hoarding disorder is the persistent inability of people to discard possessions they never use because of a perceived need to save things and distress at the thought of getting rid of them. Signs and symptoms include procrastination, avoidance, and poor planning and organizing skills.

Most of the data collected by businesses goes unused. 

TRUE Global Intelligence conducted research with more than 1,300 business managers to learn how their organizations collected, managed, and used data. Their State of Dark Data Report tells us that in spite of business executives recognizing the value of their data, most don’t even know where it exists or how to find it. Tim Tully, chief technology officer for Splunk, says “Data is (sic) hard to work with because it’s growing at an alarming rate and is hard to structure and organize.” His is one of hundreds of companies selling data management and machine learning solutions, but tools aren’t enough.

Only 7% of companies have people who can turn data into action.

Harvard Business Review says 93% of executives responding to their Big Data and AI Executive Survey say they’ve got the tools, but don’t have the people who know how to use them. What good is your $12 million Formula 1 race car without someone who knows how to drive it? Collecting data is easy – analyzing it and applying what is learned to solve business problems is hard. What the C-suites need are people who understand how to use information and insights to produce meaningful results for the business. They need people who know how to make many different things work together in harmony, much as architects weave the practical, the technical, and the aesthetic together to meet clients’ needs.

They need someone who can intertwine Data, Information, and Insights.

Plug-and-play tools are intended to work perfectly when first used, without reconfiguration or adjustment by users. Because AI and machine learning are not the simple solutions executives thought they were, decision-makers find they need someone to plan, design, and oversee their data and their tools, sort of architects of information standards, practices, and protocols. The predicament remains:

• Businesses have a high need for people who can organize enterprise-wide knowledge management processes.
• There are very few people who have the soup-to-nuts credentials to pull everything together.

This doesn’t stop job applicants from making AI claims. Indeed posts 15,000 positions with AI in the job description. Monster has 42,000, and LinkedIn has 139,000. The only thing most of these applicants know about AI is to include it so they won’t get bounced by resumé scanners.

Dark data.

“Dark data is like all of the photos on your devices,” says Sky Cassidy, CEO of MountainTop Data. “Most of your photos will never be used or even viewed again, but they are there. Dark data is all the information companies collect in their regular business processes, don’t use, have no plans to use, but will never throw out.” Gartner defines dark data as “the information assets organizations collect, process, and store during regular business activities, but generally fail to use for other purposes.”

How much dark data are companies hoarding but not using?

Lucidworks says 7,700,000,000,000,000,000,000 databytes are generated worldwide each day, enough to fill one hundred thousand Libraries of Congress. So much of it goes unused that dark data is already drowning most companies and the situation gets worse every day. Lucidworks predicts data volumes will double and double again in the next five years.

Raw data are like raw ore.

Facts and figures are valuable resources that need to be extracted, assayed, and refined before they can be used to produce a profitable end product, just like gold. The scale is equally mind-boggling. Modern mining operations produce 50 million ounces of gold a year and have to handle 350 million tons of rock to get it. It takes seven tons of ore to produce one single ounce of gold.

Computers need data. Humans need information.

To convert data into information, raw data must be refined (processed, sorted, interpreted, organized, and presented) to make them meaningful and useful. Only then can these raw (data) and refined (information) materials be converted into insights that lead to actions that drive business success. Since machine learning needs to be “trained,” it requires huge amounts of properly labeled data to be able to perform complex tasks and produce accurate information. These labeling efforts require a commitment to investing in qualified and talented human resources, so few companies bother.

Three out of four business leaders agreed the organizations with the most data are going to win.

They should have said the organizations able to use the most data are going to win, because just having it isn’t enough. Winning comes from having the best data only if you also have people who actually know what to do with it and not just say they do. Most companies like to claim they are “data-driven,” but half of the executives surveyed admitted it is a lie in their organization. I don’t know about you, but I was astounded by two things when I read that part of the report:

• How few companies are data-driven in a world drowning in data.
• How many executives admitted it.

What does corporate culture have to do with dark data and machine learning?

The shared values, standards, and beliefs that characterize an organization are its corporate culture.

• Three of every four executives reported their corporate culture is not fact-based.
• Two of three admitted their business is not data-driven.
• Companies without fact-based and data-driven cultures will drown in their data.

At an executive breakfast HBR organized and hosted to discuss their survey results, chief data and analytics officers agreed that senior leaders who strongly advocate for data and analytics are incredibly valuable, but more the exception than the rule. None of them expected rapid improvements in their company’s data cultures.

1. Most of your competitors do not have fact-based cultures, data-driven organizations, or the right people to architect and manage their data-information-insights processes.
2. They don’t treat data as a business asset because they don’t have the expertise needed to turn data into action.
3. Their AI can’t make sense of their unlabeled data.
4. Waylaid by their own deadfalls and at great cost, they’re filling their warehouses with dark data at a pace that dwarfs the puny little efforts of Big Edie, Ida, and the Collyer brothers.
5. They have no strategy to bring their dark data into the light.

Don’t take my word for it.

Invest a few hours in following some of these links and exploring on your own. You’ll find many of the same things I did and much, much more. When you’ve read enough to start thinking about the challenges you will face on your way to becoming a fact-based, data-driven corporate culture, I’d be delighted to help you get started. Together we’ll write the set of blueprints your organization needs to build your data-driven vision.

Anyone who doesn’t see here a golden opportunity for an inspired leader to gain enormous competitive advantage can call me a monkey’s uncle while I eat my hat.

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