How big data can reap big rewards

Being clear on what you want is key

Data is exceptionally valuable for companies making decisions concerning reducing costs and increasing revenues. Though it’s seemingly simple to run a data report, getting to the right actionable insight can offer both opportunities and missteps.

Being crystal clear on what you want from the data is critical. This means understanding the specific business problem being addressed and the data needed to reach your goal. Willingness to tweak the business questions to better address the problem along the way is also critical. Data analysis should be considered a journey of discovery versus a black-and-white, requirements-driven process. It often takes turning the data, like a kaleidoscope, before the right data pattern takes form.

Let’s look at how to use the right data to reach your business goals faster:

  1. Have the right people in the room.
    It’s a mistake to only let the data scientists answer the question. Conversely, you don’t want your business partners charging off with self-service reports to answer a new business question on their own. That’s because both party has the full picture, and data can be misinterpreted. Combining data and business strategy at the “big data” level is complex and nuanced, requiring the combination of business knowledge, open-mindedness, and broad experience from many industries. Often this right-brain, left-brain glue must come from outside of your organization. Procter & Gamble’s former CEO, A.G. Lafley, calculated that for every researcher P&G employed, another 200 researchers outside of the company had talents it could utilize.              
  2. Base your business question on your goals and desired outcomes.
    Be very clear on the problem you are trying to solve and the goal you are trying to achieve. State the problem broadly to allow for iteration. For example, are you trying to:
  • Improve customer experience?
  • Expand into adjacent markets or products?
  • Reduce expenses?
  • Create efficiencies in your supply chain?


  1. Determine what data points help solve the business question.

Don’t retrofit data points to create the question. Instead, ask: How can data support our decision-making? When data gaps exist, look for accessible third-party data such as weather, credit scores, or other key factors that can improve the quality of your insights. For example, field service calls can use data to reschedule a truck roll when inclement weather is forecast within 2.5 kilometers of a customer’s address. The customer doesn’t lose a day of work and the company saves the cost of redundant service calls.


  1. Crunch the data, fast.

The competition is coming! Traditional ways to explore (yet another proof of concept) and deploy (yet another 2-year IT project) are too slow. Alternative models, such as analytics or insights as a service, exist, but too often, they are the same old rusty jalopy with a fresh coat of red sports-car paint. Many data providers can help you crunch data efficiently. IBM Watson is one of the larger players to consider, but smaller, more nimble alternatives exist as well.


  1. Iterate fiercely.
    Get to the next best action as soon as an insight surfaces … and do it again. Multiple run-throughs of this process are often needed to get really, really clear insights before making large strategic investments.

Let’s look at a real-world application. A Fortune 100 company used big data to improve customer retention and maximize marketing and promotion dollars. Historically, renewal offers remained consistent across its customer base. The company wanted to know how to maximize marketing dollars to retain the highest-value customers.

The company used internal data to identify upcoming renewals by location and credit history. Then it layered in third-party data, including competitor offers and the macroeconomics and psychographics influencing its customers by geographic area. The company segmented its highest-value customers and tailored offers by economic and competitive factors, by region, with a high degree of accuracy and cost efficiency. The result: The company improved upon its current churn model by 3 percent—nearly unheard-of in its industry.

Big data can reap big rewards if you use the proper analytics process. While your internal data is invaluable, using detailed rather than summarized third-party data can often expedite your path to profitable outcomes. Some say big data is all hype, but the reality is no matter what you label it, consumers expect businesses to tailor services and messaging. If you don’t, your competitor will.

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