Machine learning has been the stuff of science fiction for decades and more recently has become a rampant buzzword in business media headlines. If you read business or tech publications, you've probably heard about the 'explosion of data in the business world'.
It is true that the internet, digitization, storage, and other technologies have contributed to data becoming far more abundant in a 2017 business compared to a similar shop in 1997. What's more, today's workers are far more technologically inclined than their predecessors, and have more digital tools at their disposal.
Talking about data has itself become more pervasive and ingrained in corporate culture. The good news is that we are rapidly approaching a future where AI-based tools actually will be able to drive more intelligent and meaningful use of data in the workplace.
We have optimized everything about the way data is stored, processed, and accessed. An executive, who wants data, has data. But that last, crucial mile - from data to the revered actionable insight, is still far from effective. It still relies on line-of-business workers who usually have little to no formal training in data to look at the numbers, identify the ones that matter, and understand how they correlate.
There is yet another elephant in the room: all of these data tools tend to come expensive, and you still won't get very far with them if you don't know what you're doing. However, what AI can do, is take on some of the heavy duty of creating complex statistical models based on a large amount of data points. It can apply learning algorithms to adjust the weight given to specific sources of information, and it can potentially identify patterns and correlations that are independent of the biases the user come in with.
By embedding intelligent technologies directly into currently used solutions and processes, businesses can innovate successfully, openly, and collaboratively. More importantly, they can experiment with interesting use cases to take these technologies from the realm of science fiction to a pragmatic reality that sets the foundation for their transition into intelligent enterprises.
The next step would be adding external data: whereas currently the business world is having enough trouble getting a handle on the data it already has, a well-designed predictive analytics model would not shy away from adding new data sources, and in fact would try to 'ingest' as much as it possibly can. This opens new possibilities for combining external and internal data for more sophisticated analysis. An executive or even a business analyst can typically do very little with this type of data; but by applying artificial intelligence algorithms, companies like IBM Watson are using this data to drive smarter analyses in specific scenarios, based on a wealth of internal and external information.
While the present of 'data-driven' is far from the ideal picture that others might try to paint, the future looks more promising. Once pointed in the right direction, AI can potentially bridge the gap between the questions a non-technical individual has about his or her business, and their ability to actually answer these questions using the data at hand; and it could enrich business decisions with a stream of data from external sources.
For now, we shall wait, see, and beware of anyone promising an easy fix when it comes to data.