Solving the data conundrum: How to leverage tech and ‘big data’ for impact

The big problem of #BigData: incomplete, fragmented data sets and knowledge at NGO’s yet start right by asking the right questions on how to improve organizational performance.

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Inspiring NGO Big Data Examples:  The big problem of Big Data is incomplete, fragmented data sets & knowledge. Yet start an analytics project right by asking the right questions on how to improve organizational performance.  


Example:  Drill down from high-level “what do you need on a Monday morning” to “How am I performing?” which would then be refined to, “In which areas of health am I failing the most, as of one month ago, and which specific clinics are contributing the most to my nonperformance?” 

Extracting Insights from Vast Stores of Data

Here’s how Amazon Prime, Heineken, and BuzzFeed do Analytics.

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Do you ever see an article you wish you wrote? Well HBR article is it for me as I believe in chasing the business problem vs only looking at only the data & anticipating that it may provide insight. HBR agrees & counters the common wisdom of looking at data first to “find” insights and instead states that “Companies that have been successful in harnessing the power of data start with a specific business problem and then seek data to help in their decision making.”  It then provides 3 examples; Amazon Prime, Heineken and Buzz Feed. A short yet powerful read!

6 Ways To Make Your Company Data-Driven – InformationWeek

Rich Wagner, president and CEO of Prevedere, shares six guidelines he’s developed based on his own experiences seeing good data left to waste at major enterprises, including the Fortune 500 chemical company where he once worked.

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Leaders are launching Analytics and Big Data projects to help organizational performance. Why? The Economist Intelligence Unit found data-driven companies rate themselves substantially higher in terms of financial success than others do


Rich Wagner, President and CEO of Prevedere provides insight how do to take early moves:
Look forward, not backward: Go solve a business problem as “Executives needed to know what was going to happen, not what had already occurred.”
Determine the question: Be clear on what questions are needed to understand or solve the Business Problem “Before searching for answers, it’s critical to know what key questions your data should answer.”
Rethink your data sources: Many familiar initiatives are internal data yet “I have found nearly 85% of a company’s performance is dependent upon external factors” so how do you acquire this info?
Don’t go it alone: Partner with vendors who have solutions.
Automate: If the experiment was a success, optimize to make it available as a real-time system.
Mind your presentation: Make answers “part of an existing process” rather than a new awkward bolt-on.


Project Teams can work to manage business disruption by following these guidelines and help their organizations transition into data-driven enterprises.

How to Make Data Experiments Powerful

Easy Implementation Steps for Analytics Project Management

This article provides two clear examples of how managers can move their projects and organizations to become data-savvy organizations. The fun of this Analytics article is that an example is from an industry that you wouldn’t think of being open to IOT and Big Data!


Experimentation is powerful when it deepens managerial intuition. The first example asks teams to runs lots of tests and to ask how each one impacts the organizations key performance indicators (KPI’s.)  So the team has to look at all the “touchpoints, the task completion, metrics, more deeply than have they had in the past. Managers can quickly test their insights, either validating their thinking or sending them back to think more, then swiftly bring changes to scale.This really changes the team culture and how they look at analytics.


Experimentation is powerful when the organization has unique data. The next example looks at a laundry operations that has a unique set of data and opportunities for introduction of IOT to collect data. “This organization has made it easy, both technically and culturally, for managers to test ideas and learn from them.”


This is an easy read and a big win for project managers looking for examples of how to implement Analytics Projects.

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