Customers want their experiences to flow smoothly all the way downstream to happy outcomes. And you want that too, of course, as long as their personal outcomes sync up with your business’ outcomes: retention, sales, profits and so on.
Customer experience professionals are everywhere these days, or
Your customers really don’t care how smart your data scientists are. Customers don’t spend much time contemplating how much work those data scientists might have put into tuning the analytic models that power your channels. And they probably wouldn’t listen if you tried to impress them with the
Data science’s learning curve is formidable. To a great degree, you will need a degree, or something substantially like it, to prove you’re committed to this career. You will need to submit yourself to a structured curriculum to certify you’ve spent the time, money and midnight oil necessary for
We can argue till we’re blue in the face on the issue of whether a true data scientist must have academic credentials. But no one doubts that credentials mean little if you can’t actually do the work.
You can call yourself a data scientist in good conscience only if you can master the methodology.
“Next best action” is a hot focus area in customer-facing business processes, especially marketing, sales and service. But it has just as great a potential in back-end business processes, and, in fact, ensures that many companies operate smoothly.
Next best action, in the broadest perspective, is
Customer engagement is a bit of a game, because, deep down, it’s a form of haggling and bargaining. Let’s be blunt: everybody has an ulterior purpose and is manipulating the other party in that direction. The customer is trying to get the best deal from you, and you’re trying to hold onto them and
For the past 2 months, a LinkedIn discussion group has been debating the burning question "Do You Need a PhD to Analyze Big Data?" Always itching for fresh chat, yours truly has stepped into the fray with a humble opinion or two. And I got flamed in no uncertain words. In fact, one PhD who didn't
Here are the quick-hit ponderings that I posted on various LinkedIn big data discussion groups this past week. I opened up one new theme–Big Media (which I'd introduced a few weeks back at this IBM big-data-relevant site) –and extended my existing discussions of peta-governance (going beyond what
James Kobielus recaps last week's quick-hit ponderings, covering meaty metadata, proofs of concept, the role of behavioral analytics in recommendation engines, decision scientists, and the speed of thought.
developerWork's Scott Laningham interviews IBM Big Data Evangelist, James Kobielus, on why big data is so important, the role of Apache Hadoop and IBM BigInsights in making sense of big data, the evolving role of the data scientist, and and where data warehousing and big intelligence fit in.
One of the myths I deflated a few blogs ago was this prevailing notion that, as I expressed then, "data scientists are just an elite bunch of precious eggheads."
Well, maybe I was slightly exaggerating the myth for comic effect, but that statement, in spirit, sums up how traditional business
Smarter business is a game of incremental improvements. It depends on your ability to produce a steady stream of innovations in your operational processes.
Incremental tweaking is not usually a glamorous activity. Minute process adjustments don't usually call attention to themselves. And that's a
Game-changing analytics applications don't spring spontaneously from bare earth. You must plant the seeds through continuing investments in applied data science and, of course, in the big data analytics platforms and tools that bring it all to fruition.
Big data is not just about scaling your data analytics processing platforms to keep up with the onslaught of new information. Just as important, big data is about bringing together your best and brightest minds and giving them the tools they need to interactively and collaboratively explore rich