Predictive analytics is now a strong force in the business world. Still, there are issues that remain. With so much data going around, it's impossible to have enough data scientists trained to glean factual and actionable information out of the various statistics and raw code coming in. As Forbes noted, there's already a shortage of properly credentialed experts in the field, and the nature of business changes as the potential of data analytics shifts away from IT thanks to custom BI solutions. With this in mind, there's a greater demand for alternatives, and the democratization of data is one path, creating the possibility of a citizen data scientist.

Data for the people
Many see the advent of data democratization not as a complete replacement, but rather a complement to skilled data science with support from trained scientists. With greater advances in BI technology, there's more automation happening at the ground level in terms of data processing. Moreover, key points concerning the data can still be gleaned without necessarily training a person on all the aspects associated with it. Gil Press at Forbes noted that automated processing already has a place in many companies, helping data scientists overcome much menial labor overall.

"One way to democratize data is through advanced data modeling."

In order to give laymen analysts a greater degree of control and power over the data they receive, different processes are under way. One is the creation of advanced models by data scientists, which staffers can then use to better assess certain trends or behaviors. This form of forecasting benefits analysts because they can process information without requiring expertise to glean critical points. Moreover, business professionals don't need complete training just to understand how to make the data models work, and more experienced professionals can tweak the models to accurately reflect certain trends.

Another process worth considering is creating data lakes. TechRepublic defines them as unstructured information sets analysts can assemble themselves for assessment. This solves the major problem of too much data: Rather than process every single data point, professionals can hand to scientists what they're actually looking for. This helps improve efficiency in many ways by allowing scientists to focus on what is most important to the business. Finally, companies don't even need analysts to gather the key data sets. All that's required is an understanding of the field a staffer in marketing or sales – to give examples – possesses, knowing what metrics are relevant. Such methods can help unburden data scientists and give firms more control over their information.

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