According to Gartner, companies are shifting from looking into the past of the business to finding what lies in the future.
The survey results show companies are motivated from the outcomes that predictive analytics successfully brings for them. The motivation comes from one Gartner research that estimates a 20% growth in revenue for companies who utilize predictive analytics on top of big data.
The rise of predictive analytics
Most commonly data analytics is used as a technology that looks into the past data to reveal how well the business has performed. Predictive analytics is more of an advanced form of analytics. The aim is to use past data to see how a business will perform in the future.
Interestingly, as a business owner can use predictive analytics to find how much revenue the company will earn in future, more targeted questions allow customization of services.
For example, a company can find out if a customer is going to repurchase a product. Or how many customers are going to leave.
And so, the information provided by predictive analytics could be used to create targeted marketing content to attract existing customers as per their interests.
How predictive analytics works?
Gartner defines predictive analytics as the use of complex statistics such as regression and classification to study data in a new way which simple analytics tools fail to do.
In simple terms, predictive analytics transforms a business question into two or more variables and uses statistics to create a mathematical equation that states the relationship between these variables.
Once the equation is formed, one can use it to predict a future outcome should one variable change.
A simple example could be the scenario predicting the revisit of a customer at a hospital. While one can include more than one variable, for this case consider customer feedback as a deciding factor for revisit. Using past data of customer feedback and regression analysis, the relationship between feedback and revisit is found as direct. So if the feedback is positive, the customer is more likely to visit. Hence, in this way the relationship can predict the future outcome.
Predictive analytics helps in decision making:
The study of past data to predict future outcomes is an effective way to drive decision making at a business. A business analyst uses this knowledge to make strategies that work in the favor of or against the prediction. This allows business owners more control over their future position.
For example, knowledge of future outcomes helps navigating the competitive landscape via planning finances, product design, marketing strategies and production.
What it means for data science career?
Predictive analytics is performed by experts known as data scientists. They collect, clean, and use data to find relationships between different datasets.
As Gartner showed, companies realize the importance of advanced analytics more than ever. Therefore, an increase in the demand of data scientists is certain.
A data scientist is skillful in areas such as statistics, machine learning and business knowledge. Statistics is required to build data models, machine learning helps a machine perform the analysis tasks and business knowledge is required to understand and solve business problems.
That sure sounds a bit complex however, fortunately there is an easy way to become a data scientist. Gartner introduces another term ‘citizen data scientist’ that is a data science role who are non-experts.
The role of a citizen data scientist allows the use of easy data science platforms that require minimum technical and mathematical knowledge. So, you don’t have to learn coding such as Python and SQL as well as advanced statistics to become a data scientist.
If you want to explore more on how to become a data scientist, we have two comprehensive reads for you:
Skills required to become a data scientist
