Big data requires big decisions

  • Organizations that embark on data management initiatives without coalescing first on business decisions court failure
  • Instead of lauding how much big data can help retain and improve customers’ wallet share, begin first with the decision

Big data requires big decisionsRECENTLY we’ve seen the explosive growth of big data news, all spewing wondrous use cases and promising great things to come. Like all new paradigms, some elements remain the same while others require leaps of faith.
In this this article, I’d like to focus on the required fundamentals regardless of which area of data management you’re in, be it data warehousing, business intelligence, master data management or where we are today – big data.
Foundation laid by business decisions
Organizations that embark on data management initiatives without coalescing first on business decisions court implementation failure; and both the business and IT will be doomed to experience the purgatory of endless information optimization, data modeling, data extraction, cleansing exercises and yes, data blamestorming.
To achieve the business goal of eliciting real value and impacting business performance, the following lines of questioning serve as an aid:-

  1. Do you know and understand crucial business decisions that need to be made?
  2. Do you know how frequently these decisions are made?
  3. Do you understand the information that is required to support the decision?
  4. Do you trust the information provided?
  5. Do you have the information?

Big data requires big decisions
Figure 1 (click to enlarge): Representing the fundamentals versus activity and effort required
The figure above attempts to capture the relative importance and effort required to attain business success from data management.
A lot of what I am saying may seem like common sense, but think about this: We’re already in the second decade of the 21st century and yet financial numbers are still published on a quarterly basis and businesses still lose billions of dollars before realizing that they having an arterial bleeder.
Why is that? Well, what is uncommon sense in business is the ability to:

  • Ask the right questions,
  • Accurately project business outcomes based on business decisions,
  • Decide!
  • Reflect on the smaller decisions leading up to the big failure/success, and as mentioned,
  • Attain the required information to not only make the decision; but trigger course corrections and even reversals …

This is even more important as large Malaysian firms intend to take their businesses to the next level of performance and growth. Big numbers means bigger failures!
So here’s a simple wrap-up by reframing the discussion: Instead of lauding how much big data can help retain and improve customers’ wallet share, let’s first begin with the decision.

  1. Do you have the available options to improve customer wallet share? Who will be making the decision? How best should the information be presented for the decision maker? Do you know how to measure its efficacy? To be able to relate causality with effect – note: correlation is NOT necessarily causality.
  2. How often do you need to keep tabs on these actions? Having it in real time sounds cool, but is it practical? So what’s practical? Or rather, what is the longest duration possible or you may as well hire a bunch of bean counters.
  3. What kind of information do you need to support the best option?
    1. Is it a product feature advantage? (Do you have customer behavioral information to determine how they use your product; click stream analysis perhaps?)
    2. Is it a consumption experience differentiator? (Do you have service level and/ or customer satisfaction information? Note: Nothing beats eating your own dog food)
    3. What is the information required to determine the impact of each options? Is there a precedence, historical or competitor data?
    4. Yes, what are the competitors doing?
    5. When and why did it not work through what means?

In short, have you modeled, sliced and diced how all this information is correlated, tagged and aggregated with one another within a time scale?

  1. To what level of accuracy and currency do you need it? You can’t be nitpicking all through the next quarter, but you can’t have a ‘garbage in, garbage out’ situation either.
  2. Finally, where’s the information?
    Do you even have it in the first place, and if not, do you build it (customer surveys, lucky draw polls, built-in indicators, queue monitors, machine sensors, mobile apps sensors) or buy it (get it from IDC, Gartner, Ovum, Nielsen, whoever; just go get IT!)

Bernard Sia is head of strategy at Mesiniaga Alliances Sdn Bhd. His opinions here do not necessarily reflect the views of Mesiniaga.

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