My twitter profile currently includes that I am “passionate about #BigData”.  Recently, someone spammed me with a direct message “What is the big whoop about #BigData”?  Typically, I dismiss DM spam but my brain kept whispering “This is a c-suite challenge”.

Today, cutting edge corporate superpowers are heavily investing top-management muscle into profoundly altering their business landscape by using the power of data analytics on the frontline.  They use big data to create new lines of business, improve existing revenue, cut costs and boost productivity by making significant changes in mind-set.  

Bootstrapping…  More than a few years ago, with the commoditization of big data technologies like cloud infrastructure, Hadoop, MapReduce, etc. plenty of forward leaning folks started buzzing about the technical horizon of collecting, storing and managing extremely large sets of data.  Aggregating multiple data sources, annotating data sets and deriving new views make the technical challenges even more exciting and interesting.  Lots of available data and tons of tools, cutting edge stuff is always exciting for the technical community.  Companies are emerging, smart folks are gathering and producing tools, products and platforms.

No kidding Chip, obviously you need more than a mountain of data – you need to mine some trends.  Who are my best customers?  What do they like to buy most?  How often do they visit my business?

This post is about more than turning on the miner’s helmet flashlight and breaking out the pickaxe.  There are plenty of posts, studies and surveys that are pointing out that many doomed companies are leaving these transformative matters to the IT department, building infrastructure and mining.  Designing an advanced analytic strategy and culture to earn a leadership position is more challenging than collecting data and mining.  Transformative change to business operations all the way to the front line requires authority during conflict, tough trade off decisions, company wide knowledge and consistent leadership commitments.  The c-suite challenges of “Big Data” include:

Perspective pivot: Typical of all new things, the entire senior team must acquire knowledge about what is possible with data analytics and embrace that data is core to the business so that significant improvements can be delivered from analytics.  Without this pivot in perspective by leadership, it isn’t reasonable to expect the organization to adopt a consistent change in behavior.

Data analytics strategy: Um, no strategy equals random results – so maybe you hit the lottery or maybe you dump large sums of money in the toilet.  And while even a blind squirrel sometimes finds a nut, if you want to improve your chances of your big data investment then define a data analytics strategy.  Seems obvious, but you might be surprised by the lack of course setting.  Again, not easy to do with new stuff – you can’t copy your friend’s homework.  You’ll need the usual strategy inputs – priorities, expected results, leadership, decision making, etc…

Acquire expertise: The rapidly changing data analytics landscape currently includes cloud infrastructure and open products supporting large data sets that combine internal and external sources in easy to use forms.  The c-suite needs to prioritize creatively hiring and retaining talented statisticians who can create and optimize predictive models.

Build or Buy:  Collecting large sets of data, building advanced analytic models and rolling out change to the front line can be a very large resource commitment.  Typical to many build vs. buy decisions, do you expect results or have proprietary intellectual property that justify the time and cost of development and ownership or is your strategy better served by partnering with external vendors that can quickly build out infrastructure, data, tools and expertise.  This is the single challenge that most companies start with and end with when investing in big data.

Rapid Iterative Mobilization: To lower the failure rate, a company’s leadership must commit to breaking down the usual barriers to change and mobilize cross functional support to help front line manager in making use of advance analytic models.  It is super important to involve the frontline team while identifying the opportunity to impact performance with data analytics and to quickly iterate the entire workflow of collecting data, modelling, mining and rollout to the frontline.  The senior team must be held responsible for sustained frontline change by prioritizing and measuring that the sophisticated analytics solutions that statisticians and technologist devise are embedded in frontline tools in simple and engaging ways so that frontline employees will be eager to use them daily.

In the end, I’m thinking that I should amend my profile to say that I am passionate about data analytic strategy and culture in a big data world.  Why?  Because admittedly, big data is just the trendy tip of the iceberg, the buzzword, a misnomer and doesn’t really capture the real story.