Assistive Predictive Modeling allows business users to leverage a self-serve advanced analytical tool and to enjoy complex, sophisticated forecasting and business predictions in a simple, user-friendly dashboard environment – all without the skills of an analyst, data scientist or IT professional.
Assisted predictive modeling is no longer the sole domain of data scientists and IT staff. With the right predictive analytics tools, your business users can accurately plan and forecast results and share data to build a dependable picture of the future for your organization, and for each team, division and individual.
Today, enterprises recognize the critical value of advanced analytics within the organization and they are implementing data democratization initiatives. As these initiatives evolve, new roles emerge in the organization. The newest of these analysis-related roles is the ‘analytics translator‘. As the enterprise considers the relevance of this new role within the business, it is important to understand the responsibilities of an Analytics Translator, and how this role might help the organization to achieve its goals.
You may think your business is unique (and it is) but in lots of ways, it is just like mine. Here’s what I mean. Every enterprise, including yours, desperately needs dependable, accurate data to support decisions and planning processes. So, in that way, all businesses are the same. Whether data analysis requirements are ongoing or specialized to support a focused initiative, the challenges of advanced data analytics and data science efforts can be daunting.
Anything that can help your business users to understand, interpret and analyze data is a help! Users have many tasks to perform in a given day and while analysis may not be their forte, they definitely need clear, concise data to share, to make decisions and to see opportunities, challenges and patterns.
ElegantJ BI, an innovative vendor in Business Intelligence and Augmented Analytics, is pleased to announce that ElegantJ BI is named as Representative Vendors in the Gartner ‘Market Guide for Traditional Enterprise Reporting Platforms‘ report published September 28, 2017.
Predictive analysis is very important to your organization. If you can dependably predict and forecast results, you can stay ahead of the competition, address changing customer needs, capitalize on opportunities and solve problems before they negatively impact your enterprise.
Every enterprise is talking about Business Intelligence and Advanced Analytics. Every enterprise has considered the benefits of implementing self-serve analytics across the organization and involving business users in the process. Panel discussions at technology conferences and hallway conversations among executives and IT staff encourage the trend and create anxiety within the organization that has not yet embraced advanced analytics at the grassroots level.
Oh, the confusion of advanced analytical terminology. Read a report, attend a conference and your head is swirling with terms like ‘assisted predictive modeling’, plug n’ play predictive analysis, smart visualization, augmented data discovery and augmented data preparation. Add to that, the sophisticated concepts of auto-suggest, auto-recommend, time series forecasting, causation and prediction and classification techniques and you may feel that you need a degree in data science to do your job.
This is a small note on small data. I hope it has a big impact. The common understanding of the world is that one should use predictive and prescriptive data on big data. A vast amount of data, classified and grouped, running analytics to predict what will be the next event that one or more elements of the group will take. Predictive analytics like this allows pushing of right products to e-commerce shoppers. I am sure you all have experienced this on the large e-commerce site and enjoyed it.
