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.
As one of its Strategic Assumptions, Gartner predicted that ‘By 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.’ Gartner also predicts that the number of Citizen Data Scientists will grow five times faster than the number of expert data scientists through 2020, and that by 2019 the amount of advanced analytics produced by citizen data scientists will surpass that produced by data scientists.
Smart data discovery is not dependent on a few geniuses sitting in a science lab somewhere! With appropriate data discovery tools your team members can leverage their knowledge of their role and domain, and the needs of their division, team or business unit to find the data and insight that is most meaningful to them.
When a start-up or small business looks at its budget and resources, it often concludes that, while data analytics is important to its business, data analytics tools may be too complex, too sophisticated and too expensive to include in its initiatives and programs.
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.
We all understand the value and innovation inherent in natural language processing (NLP). Think about the ease of searching for the answer to a question on Google. Users with average skills can ask a question and get an answer, a list of search results that address their interests and support and assistance to take the next step.
When you hear the term ‘Extract, Transform and Load’, does it make you want to run in the opposite direction? ETL, as it is called, refers to the process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.
How does an organization help the self-serve advanced analytics model grow and thrive? Responsibility lies in a number of places within the enterprise.
I hope you would agree that every team member in your business is an important and valuable resource. When we talk, in the tech world, about self-serve apps and the concept of cascading analytics to everyone in the organization, managers and team members start to get nervous.
