Vince Jeffs Vince Jeffs Director, Product Marketing - Customer Decision Management, Pegasystems
Big Data – Big Waste?

Big Data – Big Waste?

Adopting a refreshingly bottom-line approach to Big Data, Vince Jeffs, Director at Pegasystems outlines the key challenges in analytics, and considers how you can extract the most value in the context of ever-expanding data sets and unprecedented analytics capabilities.

Did my headline get your attention? Let’s face it, simply wiring to, and capturing big data does nothing except add cost if you don’t glean insight and take action on it. It’s no different than any asset – if it’s idle, it’s sucking energy and providing no return. Like a big data black hole, where data enters but no insight can escape. How do you combat that?

You need to have an action plan based on customer decisions you want to improve, so you can investigate data required, and constantly test and refine that plan. This plan dictates what data you need and how to leverage it. In other words, work backward from desired outcomes.

What is big data? As a participant in the business intelligence revolution, I’ve seen massive databases used for years for decision purposes. So what’s new?

First, customer data was historically captured, matched and restored into on-premise structured databases. This led to the data warehouse with the so called “360 degree view” of the customer. These systems required expert intervention to add new data elements, and latency rendered the view stale by today’s standards. Market expectations now mandate streaming data reflecting a holistic view of the customer.

Second, since it required a target structured store, unstructured data, which is massive, became difficult to assimilate into one structured data warehouse.

And third, the variety of structured and unstructured data sources has grown, and using an old approach of trying to codify and blend all the data into one mart does not meet flexibility, agility, and timing requirements needed to make better decisions.

Ok, I need a plan. What next?

What if you could identify and sway vocal, influential customers? What if you could proactively identify customers at risk, take actions to keep them, then turn them into ardent supporters?

Again, work backwards. Call it your big data blueprint.

Do you know who the most influential customers are? If not, start there. Conventional wisdom suggests it’s those with the biggest network of followers or highest NPS score. Perhaps more important are customers that frequently refer, versus ones who say so. Working backwards, you need data like mentions and referral codes. Determine the particular outcome, then concentrate on connecting to the data you need to track those actions – re-tweets, re-posts, forwarded links, reference events. Rate your customers on that basis – building a Clout Score. The higher the score, the more clout they have with others and the more they refer you. This score reflects their actual behavior.

Likewise, to find customers at risk, don’t use a one dimensional approach like identifying a major service interruption and simply running a query that finds customers impacted by the disruption. Customer retention is usually more complicated. It’s likely a behavioral model would work better – one that considers various risk factors (service disruption patterns, social sentiment, clout, customer loyalty, competitive options, and switching costs). Test that model against real churn outcomes to calibrate its effectiveness.

Having sponsors is vital because some aspects of your plan will involve capturing and leveraging data not readily available. Thus, sustaining funding and resources to be successful requires champions.

What technologies can help me get to my happy place?

We live in a complex world. Either accept that or get left behind – but we do expect technology will help us hurdle our challenges, so seek the most powerful solutions.

You need many technologies. Factor that into your approach. Select applications based on vendors that are open and have exceptional integration both with sister products and the outside world. Consider firms with a strong reputation for training, partners, and professional services.

Big data systems involve storage and retrieval of unstructured information - data that has not been codified from its raw form. For example, data in free-form text such as blog post comments or data collected from website clicks. Big data is also real-time coming from sensors that are always on, and stream data (often 24x7) such as devices that report precise location of objects (e.g., mobile phones). Partner with a vendor that has innovative, open technology, and has it in one unified platform. Beware of companies that have slick messaging to veil multiple platforms merely stitched together. This requires more custom coding, plus more time and resources, among other pitfalls.

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