In good writing, apparently, someone needs to die in the first line and big data is a sensational, front-page victim. Some trends indicate that the “big data hype” has already peaked. Regardless of whether this is true and a post-hype hangover ensues, organizations will need to take real-life, pragmatic steps towards capitalizing on big data. Simply stocking huge amounts of data will not automatically result in actionable insights: Hope is not a strategy.
The variety, volumes and velocity of big data streams will inevitably exert influence on the way organizations are run. However, I propose that the sheer AUDACITY, both in terms of business impact and statistical underpinnings of the proposed analysis, is what will put the BIG into big data value. Let's look at a definition from the Free Dictionary:
n. pl. au·dac·i·ties
1. Fearless daring; intrepidity.
2. Bold or insolent heedlessness of restraints, as of those imposed by prudence, propriety, or convention.
3. An act or instance of intrepidity or insolent heedlessness.
I like that first definition better than the second. For statistics and big data analytics, we need to be fearless and bold, but certainly stop at being insolent or heedless of restraints. It is a fine line.
Let’s think about real-time credit scoring for credit offer optimization. Banks wish to offer competitive, timely credit offers to their customer that are in line with the organizational risk appetite.
Doing this in a real-time context, the scope of the analytics which need to execute is truly AUDACIOUS:
- Score on the current applicant’s current credit worthiness.
- Optimize no less than three (mostly competing) objectives: MOST competitive loan conditions, BEST communication channel, BEST exposure coverage (commensurate to portfolio balance)n
- Respect defined organizational and external compliance business rulesn
That’s a whole lot of analytics (predictive modelling, operations research, and lest we forget sound, consistent business rules) looking to run in the blink of an eye. Enterprise Decision Management, in combination with new analytics architectures such as in-memory and in-database, is making this kind of AUDACIOUS analysis possible.
How about putting together a next-best offer or recommendation engine for a retail customer? Retailers also need to offer competitive, timely and relevant offers to each customer. Each offer needs to be organizationally aligned with stock levels, promotions and cash-flows. That’s hundreds of thousands of operational decisions with impacting no less than four key departments (supply chain, marketing, finance, and of course sales) during a multi-channel discussion with the entire customer base.
In a real-time context, this proposed scope of operational analytics is AUDACIOUS:
- Score the current customer on purchase propensity for hundreds (or thousands, THINK BOLD) of products or product groups.
- Optimize competing offers on competing objectives: Respecting contact policies, profitability, supplier agreements.
- Centralize enterprise decisions to ensure a coherent customer dialogue.
Again, a whole lot of analytics (predictive modelling, operations research, decision management) looking to run at the speed of business.
How about usage based insurance (UBI)? This is where telematics data (specialized GPS technology that records driving events such as turning, acceleration and braking) pushes analytics into the Internet of Things. Insurers will seek to find competitive pricing advantage by allowing the driver’s demonstrated habits determine risk, and ultimately insurance premiums.
Telematics will be capable of generating huge amounts of data, but is all of it relevant for UBI? Analytics will first play a role in determining what data telematics devices need to generate, what data is actually worth the (processing) time and cost to store and eventually analyze. A whole range of analytics will be essential to the solutions emerging.
- Filter and aggregate the data at the source using event-stream processing technologies.
- Incorporate into driver profile status.
- Alert customer when changes for the better (less risky driving, lower premiums) or for the worse (more risk, higher premiums) present themselves.
These are three cases where audacious analytics are helping organizations to shape data-driven, operational decisions that are consistently in line with organizational strategy. There are plenty more out there and organizations that find the next sweet spot will enjoy competitive advantage. That’s AUDACIOUS and that’s where analytics will prevail.
For more on how statistics and audacious analytics will help organizations tackle big data challenges, check my other blog postss:
- Statistics on big data: Take it easy, but do take it
- Statistics in the era of big data and the data scientist