If you’ve been following the world of big data, then you’ve probably heard that 90% of all data in the world has been created in the past two years. 2.5 quintillion bytes of data is created every single day—that’s a lot of data. Companies need to a way to grab hold of this recent data explosion and use it to extract critical information which can help them make better business decisions for the future.
Unfortunately, while big data is growing exponentially, there’s actually a shortage of data scientists in the market who can analyze it. In 2011, the McKinsey Global Institute wrote a report on big data which stated that the U.S. would have a shortage of up to 190,000 skilled data scientists by 2018. In addition, there would also be 1.5 million managers unable of making informed decisions based on data analytics.
Well it’s 2018 now, and this shortage is even greater than expected. According to an infographic created by the University of California, Riverside, the number of data scientist jobs in the U.S. will exceed 490,000 by the end of this year—with less than 200,000 data scientists available to fill these positions.
With the demand for data scientists greater than the supply, businesses need to find ways to get their analytics from different sources.
Democratizing Advanced and Predictive Analytics
One solution to this problem is democratizing advanced and predictive analytics capabilities so they may be used business-wide. This means giving all employees of a company access to data and predictive analytics software to generate models, allowing them to make critical business decisions and gain business insight on their own.
However, businesses cannot just ‘give access’ to their employees and expect them to become analytics geniuses. They need to train their employees. Data scientists often get asked business questions that could easily be answered with basic training, which could be taught either through in-person sessions or online tutorials.
By teaching employees how to use and interpret predictive analytics software, companies can create their own armies of citizen data scientists. It might take some time, but eventually they will be able to create their own predictive models and answer their business questions, rather than asking someone else. This would ultimately free up time for the specialized data scientists and allow them to take on more demanding projects.
According to the 2018 Advanced and Predictive Analytics Market Study Report conducted by Dresner Advisory Services, citizen data scientists are the fourth most likely users of Advanced and Predictive Analytics. BI experts, business analysts, and statisticians/data scientists, make up the top three respectively. But as we’ve seen that the demand is growing faster than the supply of data scientists, we may very well see citizen data scientists take that third-place spot within the next few years.
What’s Needed for Successful Democratization
In order to successfully democratize predictive analytics, it helps if the data is all in one location—such as on the cloud. Having a centralized database ensures all employees are using the same up-to-date data to create their predictive models, which increases transparency throughout the company.
Another requirement is that the company uses the right tools for the right people. The software for citizen data scientists should have a simple and user-friendly interface that does not require a large learning curve. It should enable self-serve analytics that anyone with basic training can use.
With features such as SAP Analytics Cloud’s Smart Predict, all the user needs is a clear understanding of their data and the questions they want answered. Smart Predict then trains a predictive model to provide answers for business questions. This makes it easier for non-data scientists to start their dive into the realm of analytics and ultimately drive better insights for companies.
Some companies may worry that if non-data scientists create their own models using predictive analytics software, they may misconstrue the data and create inaccurate predictions. With proper training, this shouldn’t be a problem. But one solution to this concern would be to have the data scientists overview the models right before they are distributed and used. Having this final quality assurance check still takes less time than if the data scientists had to create the models on their own but ensures that models are accurate to what they are trying to answer.
So, What’s the End Goal?
With the growth of big data and shortage of data scientists in the market, businesses need to obtain some of their data analyses from other sources. Democratizing predictive analytics and training employees to use these software can very well solve this issue by creating citizen data scientists who can do much of the less-intensive analytics work. When more people are creating models and finding hidden trends and information, companies can gain greater perspectives on their business performance, all while adding value to their employees.
Read the Forrester Consulting Study – Powering the Intelligent Enterprise with AI, Machine Learning, and Predictive Analytics