As the enterprise struggles to incorporate machine learning into data analytics processes, it is becoming clear that the biggest machine learning challenges do not stem from the algorithms at the heart of these technologies, but rather from an inability to use them effectively.

In large part, this is due to the continued reliance on manual operation of the three basic steps that data scientists use to derive meaning from raw data, which not only adds time and complexity to the process, but also leads to errors that can vastly diminish the value of final results.

These steps include:

  1.     Define the specific prediction problems.

The specific prediction problem that needs to be addressed might involve consumer behavior, logistics, manufacturing or any other business need. A company, for instance, might need to figure out how to increase its customer behavior by calculating how much an individual is likely to spend over the next month, what they might purchase next or what marketing messages they may be most receptive to. Each of these use cases contribute to the same business goal, but they are implemented in different ways.

  1.     Perform feature engineering.

Feature engineering is the process of extracting the explanatory variables that are used to predict an outcome. To accomplish this, the data scientist must comb through troves of data, such as past purchases, browsing history and the like, and then manually extract and brainstorm key data points like spending patterns and possible triggers that led to a decision not to buy.

  1.    Apply ML to learn rules from variables to outcomes.

Once the above variables have been identified, feed them into an analytics engine, where a machine learning algorithm can learn rules that map the input variables to the label outcomes. After deploying the model, we simply provide the input variables and the model will apply the rules it learned to predict the outcome of interest.

All three of these processes can be automated, but the crucial step is the feature engineering, which by nature is time-consuming and tedious to do manually. Automated feature engineering can more quickly identify and extract the key variables that are critical to building a successful and accurate analytics model. If a variable does not have the appropriate predictive signals to properly train a machine learning algorithm, the resulting model is not likely to be accurate enough for business goals. And as mentioned above, automation reduces errors in the scripting pipeline, which are primarily responsible for performance discrepancies between dev/test and production environments, which in turn cause many data users to lose confidence in the models they are employing.

But perhaps the biggest benefit from automating the analytics process is that it will democratize what is currently a highly specialized field. By equipping all data users with the tools that take on the complexities of data science, the enterprise vastly increases the value of its data and can potentially unleash a wave of innovation to tap new revenue streams, create new markets and perhaps remake entire industries.

Data is, in fact, the lifeblood of the enterprise, but simply owning a lot of data is not enough. The true key to success is in how you use it.

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