Machine Learning Created A Big Data Problem And Only Machine Learning Can Fix It

September 12, 2019

Companies heavily invest in machine learning algorithms, but they soon learn that the algorithms are not magic and do not deliver the desired business insights. Data scientists are then employed to handle junk data and “fix the problem,” but they hardly get to use their skills appropriately. The bigger problem, said Silicon Angle’s article, “The Real Big-Data Problem And Only Machine Learning Can Fix It” is that businesses do not employee machine leaning algorithms from the onset. Instead they concentrate on the end result and data quantity over quality, most of which is useless.

Tamr Inc. CEO Andy Palmer and its chief technology officer Michael Stonebraker believe that smaller startups offer more scalable big-data solutions for companies than the legacy companies. Tamr Inc. assists companies to use machine learning to unify their data silos. Palmer and Stonebraker have worked for years to share the truth about big data. It is better to use machine learning for the menial labor, so that the data can be cleaned and organized before it’s analyzed, marketed, or anything is sold with it.

Becoming entirely machine learning is another problem, but it has more to do with a company’s culture than anything else:

“Machine learning isn’t a silver bullet, Stonebraker conceded. Becoming truly data-driven requires both technological and cultural adjustments. In fact, 77% of surveyed executives said business adoption of big data/AI initiatives is difficult for their organizations, according to a NewVantage Partners LLC study. That’s up from last year despite plenty of new software flooding the market. These executives cited a number of obstacles holding back adoption, 95% of which were cultural or organizational, rather than technological. ‘Organizations … need a plan to get to production. Most don’t plan and treat big data as technology retail therapy,’ Gartner Inc. analyst Nick Heudecker has said.”

The culture is one reason why data scientists are forced to spend much of their time sifting and sorting the data. It also means replacing humans with machine learning. Will organizations have the knowledge to make this type of shift in an informed manner?

Whitney Grace, September 12, 2019

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