Nobody doubts that companies everywhere will continue to ramp up their hiring, recruitment and training of data scientists. But there seems to be a growing alarm that we won’t have enough data scientists to go around.
Will the big data revolution screech to a halt due to a shortage of data
What's sexy about data science? It has been dubbed the "sexiest occupation" of the 21st century, but you don't see hordes of autograph-seekers and paparazzi flitting around many data scientists. James Kobielus looks at why data science is hot.
Prediction markets are where data scientists will attain superstar status. It’s no coincidence that the current age of the “superstar” in professional sports began in the 1970s, when the legal constraints that had prevented the most accomplished athletes from seeking top dollar on the open market
If you think “data scientist” is a pretentious title, think again. Nothing could be more fundamental to science, to engineering, and to the continuous optimization of modern business processes.
So, you may ask, what is true science? And what exactly is a scientist? How can data scientists live up
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.