Best Argument: No
Audience Favored: Yes (56%)
Cutting edge requires cutting edge skills
People are all in favor of change except for the change part. The change here is that big data requires new skills and new ideas to fully exploit it.
Whether we embrace this or fear it probably has more to do with our individual natures than the fact of big data and data science. The name we give these people is much less important than the societal value of their skills.
Perhaps, someday, the Watson Mk. XXXXII will be able to ingest huge data sets and make sense of them. But until then the cutting edge of big data will require people with a wide skill set to mine the economic value of big data.
Specialists don't scale
Big data technology is powerful, and it keeps getting better. But the technology does, right now, require niche specialists to derive the greatest business value from it. These specialists have to be renaissance people – possessing a combination of technology, mathematics and business skills, and knowledge. It’s not clear that being so clever and versatile makes these specialists into “scientists,” but it does make them rarefied.
Nonetheless, for big data and analytics implementations to grow and become truly mainstream, having such diverse skill set requirements for them is not a sustainable situation. Market need is going to drive evolution in the technology such that the barrier to entry will not be nearly so high as it is now. If for some reason that didn’t happen, then adept use of big data would continue to be an option open only to a relatively small group of customers.
So the solution to our problem isn’t legions of new data scientists. Instead, we need self-service tools that empower smart and tenacious business people to perform big data analysis themselves. The specialists will still have an important role, but they won’t be the thing that scales big data across industries.
Big data for the masses
This argument boiled down to timelines in many ways. Today, we need more data scientists, but in the long run big data has to be for the masses of workers.
Specialists only go so far, argued Andrew Brust. Robin Harris argued that we need more data scientists. In the end, Brust had a better argument. Data scientists are important, but you may not need an army of them to boost the analytics revolution.
Ultimately, we're going to need data scientists, innovative user interface and a lot more data literacy from average workers.