Getting Big Impact from Big Data…

Does the actual benefit of big data still elude you? Want a clear path to start utilizing them successfully?

Well you’re not alone – that’s for sure 😉

Big Data is definitely one of the “en vogue” buzz words across several industries, however actual results & benefits are quite scarce and hard to find. This blog has been a big fan of the subject and this ongoing “Holly Grail” crusade for impactful use of Big Data, as attested by several previous posts (take a look at “The Big Data Playbook“, “OK, Big Data is important. How do i get started?“)

The good folks at McKinsey have just released an interesting paper called “Getting big impact from big data” on the subject.

To cut a long story short, they propose a gradual approach, across two phases, which they deem necessary in the long journey from Big Data to Big Business Impact. The key elements are highlighted in the diagram below:

BigDataBigImpact_exhibit01Speaking from personal experience i couldn’t agree more! The notion that you can swoop in and introduce valuable usage of Big Data in a “big bang” fashion is a common misconception & the cause of many failed attempts to ride the Big Data bandwagon 😉

Let me elaborate a bit on the aforementioned phases and propose some insights, stemming from actual implementations I’ve been involved in:

  1. Take advantage of advancements in Analytics: You can indeed start small and even experiment with “Big Data” without investing substantially in the beginning. Two of my favorite tools are Tableau & Microstrategy and both offer the ability to start your journey for free (for a complete view of available options see the Business Intelligence Magic Quadrant by Gartner)! It is essential that you start digging into your data sources with a more powerful tool than the – old & trusted workhorse – Excel. This will allow you to identify discrepancies as well as omissions in the existing data-sets as well as open up your appetite for more. Furthermore you suddenly have a playground to test your ideas & even present them as a working prototype to colleagues & management to initiate the long process of stakeholders buy in 😉
  2. Mobilize the organization: Assuming that the previous step was successful it’s quite important to exercise restraint!!! Don’t try to change everything at once. My suggested steps during that phase are:
    • Clearly identify your focal areas (my golden rule is no more than 3)
    • Go through a due diligence of shorts and map the current workflow at hand ( i always prefer to do this in a diagram )
    • Sit back and identify whether you can add value to existing tasks with enhanced information derived from Big Data
    • Examine the possibilities for disruption via the use of Big Data capabilities. You might be able to completely eliminate tasks or severely alter the way they are currently executed
    • Create a proper execution plan based on the previous steps
    • It’s time to involve the CIO / CTO / IT team now. They need to make a proper selection of the final corporate tool(s) you will use. They should also be responsible for integrating the various data sources & continuously maintaining them. It’s also a good idea for them to be involved in the creation of some dashboards BUT
    • You MUST involve business stakeholders as power users!!! This is critical for the impact of Big Data to have a long lasting and potentially self replicating effect within the organization. Otherwise it will be a static project which at best will continue to provide only the original impact and nothing more…
  3. Big Impact from Big Data : My only suggestion here is MEASURE, MEASURE, MEASURE!!! You need to quantify the benefit – both to prove beyond any reasonable doubt the value of Big Data – and pave the way for future projects – as well ass establish a benchmark for future reference!

What are your experiences from Big Data projects? Do they deliver true value or are just the next big hype?

For additional reference you might also want to check out another Mckinsey paper on the “Views from the frontlines of the data-analytics revolution

 

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