Not Just Google:
Some business leaders have watched with a mix of envy and consternation as the technology firms, and large retailers, have taken advantage of big data. Until now, smaller firms have not been able to afford significant data science operations. However, predictions are that this year and next will be the year when Small to Mid-sized Enterprises (SMEs) in many industries will begin to use and understand their huge data troves.
Manufacturing, healthcare, traditional media, and many other enterprises will become data-driven. The result of all the data-
crunching will be better customer service, additional product or service choices, higher quality, and lower costs.
It is hard to imagine any industry that won’t be touched in some way by the move to ubiquitous data analysis. Even the trades will find it hard to ignore the benefit of knowing their customer’s needs, or when it might be time to schedule preventative maintenance, or perhaps when it is time to offer an upgraded product.
According to Nicos Savva, Associate Professor of Management Sciences and Operations, three factors are converging to drive data mining down the food chain to SMEs. Cloud computing is the first and the most obvious of them. Substantial computing power is available through services such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
A second factor is the availability of new user-friendly tools that make collecting, analyzing, and using data much easier. It no longer requires a Ph.D. in data science to make use of all the data we are collecting.
Third, our employees are becoming much more sophisticated. Data focused MBA courses, and master degrees in data science are much more common now than they have been in the past. Even those employees not involved directly with data are much more sophisticated in technology than previous generations.
Companies are delegating more decisions to automated data-driven systems. That, of course, means that companies must become more concerned about the social impacts of the decisions they have automated. We are learning that lesson from the tribulations that Facebook is presently experiencing. Moreover, we are seeing the liability of companies collecting personal data when hackers breach their systems and use the data gathered for nefarious purposes.
We are also learning that much-vaunted algorithms will not save us from our own biases. Recent studies have shown that the human-directed algorithms incorporate the biases of the developers. It isn’t on purpose; it is simply a matter of the assumptions that developers make as they construct the algorithms.
Every new technology creates a new set of risks. Data science, as I mentioned above, is no different. Identifying and addressing the risks in a timely and equitable fashion will be what makes our investment in data analytics profitable.
Business leaders will have to learn all they can about the security requirements around data, as well as make sure their organization takes advantage of all the good that data science can provide. I have heard it said, more than once, that there are two kinds of companies in the world—those who have been hacked and know it, and those who have been hacked and don’t know it. Assuming you’ve been hacked, how will you close the door and make sure your data doesn’t leave the warehouse again?