By Lloyd Marino

Your Hero’s Journey

People start businesses for any number of reasons, but I can summarize all of them simply enough: They want their business to grow and thrive. They’re looking to ensure their company’s health, and these days, rightly or wrongly, we measure health with wealth. Such “wealth” equally entails security and market space, self-confidence and growth, good PR and respect. And, we must never forget, pure profit. But over the past 25 years alone, the business environment has undergone a massive iteration greater than all the changes of the past 5,000 years combined. Yes, fundamentally, we’re still all working in a marketplace where we must succeed to sell our wares, struggle against competitors, serve our customers, and support our products. But the ways that we conduct all those operations have changed dramatically.

The most significant change boils down to something I call “Data Ecology.” Industry leaders across the board are agile, adapting quickly to changing conditions resulting from updated information stemming from their individual business environments, along with the latest global trends. Nowadays that means collecting and mining through mounds of data—some internally generated, some externally available.

The problem is there’s so much data that it confuses our usual processes and machinery, challenging us to sort through things quickly and effectively, making wise decisions difficult, if not impossible. In fact, that’s how I think of “Big Data,” or any collection of data sets so large and complex that they become difficult to process using on-hand database management tools, traditional data processing applications, and available human resources. This means either you can collect all that data but don’t have the means to process it—or you don’t even know that you can collect it in the first place using the resources you do have. And then there’s this problem: Assuming you do collect and store data, do you know what to do with it? How do you determine what information to save and what to toss?

The Data Blind Will Not Survive

Take a simplified example. Imagine a shop owner who sells amber trinkets in a family-owned business. He’s learned from the example of preceding generations what’s needed to sell his wares to any number of different customer types. His agility depends upon his familiarity with his customers’ cultural preferences, his careful study of their apparent wealth and status, his knowledge of the psychology of buying, and so on. And he might conduct an occasional reconnoiter of his competition, adjusting his products and prices depending on what else is on offer nearby, and the relative placement of his brick and mortar shop in a vast marketplace of similar businesses. This shop owner can achieve what seems to be relative success. Ironically, this fellow doesn’t know he’s flying blind.

Now imagine one of his competitors has instant access to trillions of bytes of information regarding all the transactions that take place at the bazaar and beyond. He receives wise counsel on how to sift out the meaningless mass of metadata, and hone in on only the relevant information he needs to succeed. The data let this shopkeeper know that his average customer has a relatively high net worth ($250K+), purchases 70 percent of his jewelry online, and routinely visits artisanal gold jewelers’ Facebook pages. Furthermore, although 75 percent of the visitors to the nearby trinket shops are men traveling on business, 95 percent of those men are buying gifts for significant women in their lives, and more than half for anniversaries and birthdays. Among the anniversary shoppers, about two thirds are purchasing for “silver,” “golden,” and other major anniversaries. Lastly, this seller learns that even among the remaining 25 percent of tourist visitors to the shops, split evenly between male and female, the women make the vast majority of the buying decisions.

This shop owner responds by ordering a line of more expensive gold, silver, and semi-precious jewel necklaces, and displays them prominently in his shop. He modernizes, and sets up an online shop that focuses on slightly higher-end pieces of jewelry, featuring anniversary metals and birthstones. He also spends a little money on links to his updated social media sites.

But where did this “data” come from? It didn’t magically appear out of thin air. This shopkeeper understood that instinct is important, but information is critical. He collects small amounts of information from his customers, not enough to cause a nuisance, and then spends time analyzing it. He also confers with his competitors. He scours the Web for larger data sets (the countries from which most tourists travel to his country; the average amount they spend on their trip, their disposable income, their buying histories, etc.). He carefully reviews his sales history. He also studies all the internal data he can collect. How long do his salespeople, on average, spend with customers, and how often do those interactions translate to sales? He knows the problem he wants to solve – find a higher end product and sell more of it – knows what information he needs to collect to make that happen, and what to look for amid that data. Then he acts on it. He decreases the number of salespeople on his floor, from five to four, eliminating the least productive. He trains those remaining to spend more time and energy on certain customers—perhaps the women making the purchasing decisions—on certain products, and on certain kinds of pitches.

The key to this shopkeeper’s success is his ability to determine what insights are actionable. Organizations see a potential advantage in such actionable Big Data-derived insights, not only to sell more widgets and services, but also to better manage healthcare, stop the flow of counterfeit drugs, locate terrorists, and maybe even track an entire nation’s cell phone calls and emails. Hence, it’s a given that Big Data isn’t inherently good or evil. It’s a problem-solving tool. How you use it is what makes it valuable. If your bottom line is about increasing profitability by better serving customers and eliminating waste, then you need to know what information will be necessary to make that happen—and you need to make it happen.

To be the data hero in your space, you should be able to answer my top ten Data Hero Questions:

  1. Who should be in charge of this data job? (The hero should)
  2. What are your key goals?
  3. What data can you collect using the tools at your disposal?
  4. What data should you collect based on its relative value?
  5. What help/consultation do you need?
  6. How will lead and manage your I.T. resources?
  7. What new technologies, if any, will you need to make this happen
  8. How will you filter this data effectively to avoid the fluff, and focus on the valuable?
  9. How do you analyze the meaning of the data?
  10. What decisions will you make based on this analysis?

Image By Olu Eletu

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