There are a myriad of predictive analytics techniques and predictive modeling algorithms and you can’t expect your business users to understand and use them. If you are taking on an advanced analytics initiative and you want to leverage data democratization to cascade data analytics and data literacy throughout the organization, you will need a data modeling solution that takes the guesswork out of the process.
As business organizations fight for competitive advantage, funding for projects and large expenditures can fall by the wayside. In today’s competitive business market, every senior executive looks at risk, value and calculations like return on investment (ROI) and total cost of ownership (TCO) before approving a budget.
As the need for advanced analytics increases in organizations, enterprises large and small struggle to find and sustain the professional resources they need to meet their requirements for data, analysis and strategic direction.
Many organizations have grown comfortable with their business intelligence solution, and find it difficult to justify the need for advanced analytics. The advantages of advanced analytics are numerous and those advantages are based on the ability to further improve the business, increase user adoption (and therefore user empowerment and accountability) and, best of all, improve the bottom line and the accuracy of predictions and forecasts that will dictate the success of the business in the future.
Some people hear the term ‘assisted predictive modeling’ and their eyes cross. They immediately presume that we are talking about something complex and certainly NOT for them. Nothing could be further from the truth. This seemingly complex term actually describes a technique that is designed to be suitable for business users with average technical skills and, with these tools, the average user can enter the age of advanced analytics and make educated, confident business decisions about forecasts and predicted results.
When someone says ‘plug n’ play’, a lot of people think of the idea of plugging in an electrical appliance and having it run instantly. I think plug n’ play analysis should be that simple as well!
Predictive Analytics used to involve a crystal ball but, today, there are other options and they are more widely accepted in the business community! With the right predictive analytics tool, your business can hypothesize, test theories, discover the effects of a possible price increase, discover and address changing buying behavior and develop appropriate competitive strategies.
The process of predictive analytics has come far in the past decade. No longer is this process the sole responsibility of data scientists or IT staff. Today’s self-serve predictive analytics and forecasting tools are designed to support business users and data analysts alike.
Predictive Analytics is no longer limited to data scientists. Today, predictive analytics is, and must be, accessible to business users, if your enterprise is to grow and respond to the need for data democratization and increased productivity within the enterprise and to the rapid changes in the market, competition, resource and supplier needs and customer buying behavior. Every business user must have the tools to analyze data and make accurate, timely predictions and decisions.
There was a time, not so long ago, when predictive analysis, business forecasting and planning for results involved guesswork and lots of unscientific review of historical data. But, today’s competitive business landscape and rapidly moving markets demand more than guesswork.
