Augmented Analytics is the Secret Weapon for Competitive Advantage!
How do you compete? There are probably other businesses in the market that do what you do but your product or your services must stand out. How do you take all the data you collect on a daily basis and use that data to your advantage to point you in the right direction?
When businesses take on the task of forecasting and planning, it is important to provide the tools to go through this process at all levels with data integrated from the various systems and databases across the enterprise and to provide simple tools for all users that can be adopted and used to take on the various tasks and responsibilities involved in planning and accurate forecasting.
Assisted predictive modeling! The term sounds complex and intimidating, doesn’t it? But nothing could be further from the truth. The word ‘assisted’ is the key! It is true that without the skills and knowledge of a data scientist or a business analyst, predictive analysis can be a daunting task.
Predictive analytics is more refined, more dependable and more comprehensive than ever. The foundation for predictive analysis is a great predictive analytics tool, and features and function that include assisted predictive modeling.
When we use terms like ‘predictive analytics’, it sometimes puts off the general business population. Team members might envision everything from a crystal ball to complex charts and graphs with unreadable numbers and conclusions. While predictive analytics techniques and predictive modeling does include complicated algorithms.
To be successful in business, every organization must find a way to accurately forecast and predict the future of its market, and its internal operations, and better understand the buying behavior of its customers and prospects. With the democratization of business intelligence and the advent of self-serve business intelligence tools, organizations hope to encourage and create Citizen Data Scientists and enable the average business user to leverage sophisticated predictive algorithms and BI tools without the expertise and skill of a trained data scientist. This approach allows users who are not statisticians, data scientists, programmers or analysts to leverage self-service tools to confidently make business decisions, share data, and reports and present data in a way that allows the organization to identify trends, patterns, opportunities and challenges.
