To help vendors, app developers, and other interested parties sell big data products, services, and ideas to the government, I asked a panel of federal government agency leaders to share what government agencies are interested in buying and to reveal how best to sell to them.
Much has been said about the challenges in using big data, from data scientists having to spend too much time on data janitor duties, to adoption resistance stemming from corporate culture. Still, the biggest hurdle by far is in the Business-IT divide. Until business and IT reach a point where they truly understand each other and work as a team, big data and other initiatives will continue to falter and fail.
Yes, data scientists are the sexiest beings on the planet but that doesn't mean they get the superstar treatment they deserve--frustrations are high. Everyone wants big data but they don't understand it, yet they try to manage the data scientists. It would be funny if it weren't so darn destructive.
While many industry watchers, myself included, have long sounded the warning on the dangers of taking data security too lightly, it appears that good advice is less effective than massive lawsuits in potentially rectifying the situation. Health insurance giant Anthem may end up the poster child in mega-lawsuits for data security negligence. Suits were filed in Alabama and California just one day after the breach.
There's lots of terms for these elusive professionals who possess a unique mix of tech chops, business acumen, cunning intuition, and innovative problem-solving abilities--data artists, data diviners, data creatives. Whichever term you prefer, these are the pros you need to take your big data projects to the next level.
A new survey conducted by the Economist Intelligence Unit (EIU) and Teradata finds a huge gap in how CEOs and other executives see big data. They found that it is CEOs who now wear the rose-colored glasses. Other executives, especially lower-level managers, have a bleaker view.
CES 2015 showed us that the notion of personal privacy no longer exists in mainstream product production. The focus is keenly on the consumer not as customer but as product. And, big data users learned that a flood of useless minutiae from the IoT is headed our way to clog our pipes and create bottlenecks in analysis. So now what?
But let's assume that big data analytics spit out truths (aka knowledge, unimpeded by human biases and perceptions). Will we, as individuals, companies, and groups embrace the results and change our way of thinking? Or, will we continue to cling to the comfortable, albeit usually incorrect or only partially true, information we crave?
As we look forward to the New Year, here's a glimpse of what's to come in one area that will be further fueled by big data and machine learning: augmented reality (AR). Yes, a big part of data collection in the very near future will be capturing data from our own senses such as smell and touch in order to include such in AR.
Quite a bit of attention is given to improving data management and analysis at the data scientist level. But I submit for your consideration today an often overlooked area that needs our utmost attention: the cogs and wedges in both data entry and action execution.