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Analytics: Five Rules to Cut Through the Hype


Cut through the analytics hype by asking the right questions, discerning between value-add analytics, considering in and out of house solutions, forming an iterative analytics process, and making sure your organization uses it.



By Lana Klein (Co-founder 4i), Jan 2015.

Analytics today is at the point of high awareness and very little understanding.

Just a few years ago word “analytics” conjured up an image of a nerdy math guy in thick glasses. Now, it’s a buzzword, with companies scrambling to figure out their analytics strategy much in the same way they are trying to figure out Big Data strategy. But with all the hype about Analytics -- "competitive advantage of Analytics", "game changer Analytics", "Analytics-enabled decision process" -- many executives are still unsure about the basics:

What does it do? Where can it be helpful? How can use it to make decisions? What decision can it help me make? Does it really work? Can I trust it? Should I trust it more than my experience or business sense?


Here are five rules to guide you through analytics maze:

  1. Start by asking "What are the most important decisions  I make?",
    "What questions am I trying to answer?".  
    To assess whether analytics can be useful in making these decisions ask yourself:
    • do your decisions involve considering many factors and drivers?
    • are there many different things happening at once, making it difficult to understand impact of individual drivers/factors?
    • are there a lot of uncertainties about future behavior of these drivers/factors?
    • is there a lot of data – quantitative or qualitative – about these drivers/factors?
    • does it take a significant effort to collect in one place information about all drivers/factors?
    The more times you answered “Yes” to these questions, the more helpful Analytics can be.
  2. Understand that word “analytics” encompasses a wide spectrum of solutions with different complexity and “value-add.”  There are many ways to show the spectrum, but in general it looks something like this. Foresight Analytics bring the highest value, but are also the most complex and require serious expertise to implement.

Complexity vs. Value
  1. Decide where you need outside help and what you want to create in-house. On the less complex end of the spectrum, Data Analytics have many off the shelf visualization solutions. Building high value-add Predictive Analytics requires highly trained resources and solutions that often need to be custom-build or at least customized to your firm.
  2. Don't try to design a "perfect solution"  -  start with building out main functionality, then iterate and add features. Think of it as Agile Analytics development.
  3. Remember that the most critical thing is not building analytic solution but making sure that your organization starts using it: that means creating buy-in, working to build adoption,  educating and training, redesigning processes to include analytics. Give it time, be persistent, improve and results will follow!
Analytics progression over time

A co-founder of 4i, Lana Klein leads the firm’s Growth Foresight practice. She is a recognized industry expert in developing unique client solutions combining advanced predictive analytics with deep business knowledge. Focused on the CPG and Healthcare industries, Lana has more than 20 years of experience advising clients on a broad range of analytics solutions.

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