The Wrong Big Data
Nalpeiron Technology Blog, August 2013
Do you have the right data to make data-driven-decisions?
When big data is just wrong data.
During the July 2013 nomination proceedings for the FBI Directorship, James Comey said: “Just because we have the ability to collect huge amounts of data doesn’t mean we should be doing it.” It might be a great idea for tech businesses to adopt this mantra, make a big sign and paste it over the office water cooler! A slight tweak might be - Just because we have the ability to collect huge amounts of data doesn’t mean we are collecting huge amounts of the correct data.
So what is the correct data?
In the past, the correct data was what would be most persuasive to move our customer’s IT Department to decide to make a purchase. We would make the sale, sign the contract, the deal was over, and we were on to the next customer. But cloud computing has completely altered the landscape for our customers. It is no longer the IT Department that controls the sale. In 2008, the YMCA of San Francisco invested in the top of the line fundraising software for its 16 branches. Within 2 years the fundraising staff had staged a mutiny, and the IT Department was forced to withdraw the software, conduct focus groups, and invest another $2 million into providing a new - top-of-the-line-be-damned - simpler and more user-friendly product. A product that provided real success for its end-users in their business goals.
This scenario is being replayed on a larger and smaller scale across the globe. From this moment forward, tech service businesses no longer need to focus solely on that initial sale, but on the follow-up service with the end-user.And this is where the “big data” needs to focus. What is the end-user actually using? Where is the end-user having difficulty? What is the end-user ignoring? Are they ignoring that feature because it needs to be better framed/marketed? To be simplified? Does the feature need to be eliminated? Where are the opportunities to sell more add-ons? A more sophisticated monthly plan?
The data being collected needs to focus on the needs of the end-user, how to improve satisfying those needs, and to consistently collect data asking the customer’s satisfaction with the product. TaskRabbit, a popular job-sharing software asks users very directly, and after every completed transaction: How upset would you be if you could never use TaskRabbit again? And Airbnb asks for a review of the guest, of the host, and of the site, again, after every transaction. Not only is customer satisfaction rating built into the transactions, but ratings are what the business is based upon. Compare this to the seller ratings on eBay.
So where in the past there may have been a hard wall between Engineering and Marketing, we will now need to soften those demarcations. Where Marketing has the skillset to test market a particular advertising campaign, or run a focus group, these skills will be teamed up with Engineering to have products and features tested by the end-user. Each user’s information is stored immediately in the cloud, and it is possible to assess, in real time, how successful - or not - they were with that new product. Products can be tailored to different markets or test marketed with market segments. The cloud provides immediate feedback. This is where the focus needs to be with big data. On improving consumer satisfaction to develop opportunities to upsell and for greater investment in the product.
This is where the opportunity is in the cloud: customer satisfaction.
As long as the focus of data-mining is on the end-user’s ease of use of a product, success for their business needs, and ability to add on new and more useful apps or upgrades to monthly level of service, then tech businesses will be successful in the cloud economy. Tech service businesses will see more and more that their success and growth is intimately tied to the success and growth of their customers.