BIG data is suddenly everywhere. Everyone seems to be collecting it, analyzing it, making money from it and celebrating (or fearing) its powers. Whether we’re talking about analyzing zillions of Google search queries to predict flu outbreaks, or zillions of phone records to detect signs of terrorist activity, or zillions of airline stats to find the best time to buy plane tickets, big data is on the case. By combining the power of modern computing with the plentiful data of the digital era, it promises to solve virtually any problem — crime, public health, the evolution of grammar, the perils of dating — just by crunching the numbers.”
For a number of years Big Data has been touted as one of many next big things. The ability to analyze and provide meaning to huge data sets through sheer brute force of computational power allows companies to derive insights and trends never previously available. Retailer, hospitality and B2Cs have been leading the charge by using advanced analytics to predict product demand and dynamically adjust pricing models. Inspirational examples are everywhere, Gartner famously trotted out 55 in 55 minutes and there are some here http://www.crmsearch.com/retail-big-data.php. But for the rest of us big data hasn’t delivered too much. So what’s gone wrong and what could change?
Big data has two defining characteristics for mainstream use:
1. It’s expensive.
2. You need to know what you’re doing.
Both of these place a pretty large obstacle in the way of the average business. Point 2 effects every business, even the biggest companies with the largest budgets find use cases difficult to imagine.
A further problem with point 2 is the ability to correlate something with something else and prove anything you want. Let’s face it, we all know we can find the answer we’re looking for if we look hard enough. Want to prove sales of anti-bacterial wipes change with the pollen count? check. Want to link the spread of Zika to the Rio Carnival? You’ve just done it (to be fair, that might be accurate). Few of us need more self-fulfilling prophecies than we already have. Especially not me.
Despite criticism there’s an inevitability to how big data will change our lives, we’re all going to be effected by it. It’s not a matter of if, it’s about when.
This week Salesforce bought artificial intelligence start-up MetaMind (https://www.metamind.io/salesforce-acquisition). The objective is to integrate MetaMind’s AI technology into Salesforce tools to help predictive analytics from marketing, sales and service data.
Salesforce COO Keith Block was interviewed by Fortune (http://fortune.com/2016/04/07/salesforce-exec-ai-service/)
The problem for many companies right now, Block told Fortune, is that they’ve been collecting huge amounts of data, but they’ve failed to do much with it. He cited a recent meeting with the CEO of a large oil and gas company. “They have upstream and downstream devices collecting data but they’re not taking any action on it. They’re looking for ways to interpret that data and get to the next step,” he noted.
From personal experience I’d agree. Even at the most basic level data analytics is more of an art than a science and the results often collect metaphorical dust very quickly. Maybe we avoid metrics because we don’t want to see bad news. If we don’t look at them surely they don’t exist?
Despite a few poster children, the largest companies aren’t certain about how to effectively harness the data at their disposal. If they can’t, with big budgets and armies of employees to figure this out, what hope has everyone else? For me, the hope is the big vendors build artificial intelligence capabilities into their products that help us make sense big data. The Salesforce acquisition is a great example of how big data could work for the masses by putting the correct jigsaw pieces together.
There’s also a further dynamic for big data which is customer service. We also know from Forrester Research this week that customers increasingly prefer digital support for first line service and support over speaking to a fellow human being. Two takeaways from the report are:
Web Self-Service Unseats The Phone Channel In Customer Service. The number of channels through which customers engage companies for customer service is increasing. For the first time, the phone channel has been unseated from the top spot by web self-service, and electronic live-assist channels are also rapidly increasing in popularity.
Customers Use Self-Service Channels As A First Point-Of-Contact. Consumers are increasingly impatient and want quick answers to their questions. They use self-service channels as a first point-of-contact and escalate the more complex questions to live agents. This means that live agent interactions are increasingly important in building customer relationships
Does a combination of big data, artificial intelligence and the odd human being give us a truly valuable combination for understanding our own organisation and delivering great customer service ?
When someone gets this right they deliver the benefits of streams of truly useful data to ourselves and our customers. But that’s a big task and even bigger responsibility. From the recent experience of Microsoft’s Tay we know that machine intelligence can backfire in spectacular fashion. While we go through a transition to rely on the predictions from computational algorithms a healthy dose of skepticism may be required to provide an important reality check. Just as long as that skepticism doesn’t defeat a technology that promises so much.