Myth Busters Continued: The Foils of Big Data

Continuing from yesterday’s edition of Myth Busters (Big Data Edition), I now present you with myths 5-8. You can’t believe everything you hear…

Big Data Myth #5: Big data means that analysts become all-important
It is often said that Big Data will see the rise of the analysts, “the new gods of the Information Age”, as O recently heard someone call us. But the rise of the analytics team is exaggerated. The dramatic increase in data velocity means there’s no time to “brief the analytics team” now. Actually, what is required are tools to cope with velocity, volume and granularity of the data quickly. What’s really needed are analytics tools to empower marketers to do their own analyses. The intersection of technology and analytics means that Big Data is not about a shed full of analysts working away. It is all about a small group of master-analysts leveraging technology to empower marketers to do more of their own analytics and scenario-modeling and decision-support.

Big Data Myth #6: Big Data gives you concrete answers
Ambiguity is the dominant characteristic of Big Data. Multiple sources of data (e.g. transaction, customer acquisitions and media) can lead you in different directions of what the evidence is telling you. Different data analyzed incorrectly can give you conflicting evidence to reconcile. Which data do you believe? Different math and algorithms applied to the same data sets can give different conclusions. Different streams of data can lead to conflicting conclusions. Big Data requires human judgment to intervene and resolve seemingly conflicting evidence, and that’s where the skilled analyst comes in.

Big data helps reveal truths. The more data you have, the more likely you are to have contradictions and ambiguities that require resolution. Big Data is not all-powerful. Quite the opposite, in fact. More data gives you more witnesses, but doesn’t get you closer to the truth until you leverage experienced human judgment to reconcile conflicting evidence. Witnesses to a car wreck all see different things. Multiple data sources and different algorithms will tell the court that different culprits are responsible. The future of analytics is all about combining, weighing and judging multiple sources of information and different analyses.

Big Data Myth #7: Big Data is a magic 8-ball
Well, yes, but you need to ask the question in exactly the right way. A bit like when a genie gives you your three wishes. You have to phrase your wishes very carefully. Applying analytics with a lack of precision or detailed hypothesis creation in advance, when applied to complex data sets such as cell phone or calling network data, can actually lead you astray and give an incorrect answer. You need to ask your questions very carefully of the “Big Data” crystal ball.

Big Data Myth #8: Big Data can create self-learning algorithms
False positives from rogue data (e.g.: call center call volume prediction from direct response TV ads) mean there are quite a lot of limits to the marketing purposes of automated models. Rogue data from a Super Bowl weekend could distort an auto-update algorithm.

There are some exceptions, of course, to the points that I’m making. There are some great examples of auto-analytics. Cell phone operators have demonstrated good use of non-marketing data for marketing. They know who you friends are, they can guess your age, they know the parts of town where you hang out, they know what websites you visit, what apps you use, and when. Insurance companies can use telemetrics for obtaining data for marketing, not just underwriting. For example, I can quote Matthias Mohler, Head of CEM and e-business at Daimler AG, “There is amazing new data to harness for sales and marketing. ‘Intelligent cars’ will offer the opportunity for the central sales departments to directly interact with drivers at any time. And sales departments are currently working to ensure that there will be no legal hurdles regarding the usage of this data in their department.”

The options are seemingly endless for the usage of Big Data in sales and marketing – as long as you avoid the risk of believing the myths.