Necessary Components of an Advanced Analytics Solution?

Business markets and competition are moving much more quickly these days and predicting, planning and forecasting is more important than ever. It is also important to ensure that every team member is a real asset to the organization and can contribute their knowledge and skill with full Insight into the effects and outcome of activities and processes and the ability to correct the course and make recommendations using clear, concise information. Advanced analytics is the logical tool to help a business optimize its investments and achieve its goals.

But, when an organization is ready to consider the implementation of an Advanced Analytics solution, it is difficult to know what it needs to ensure that it can satisfy current and future requirements and ensure user adoption. The current course of advanced analytics is taking businesses into the area of augmented analytics – tools that allow business users to leverage sophisticated algorithms and analytical techniques without the help of data scientists, analysts or IT staff.

If a business wants to assure that it has full coverage for its Advanced Analytics needs and can leverage all the benefits of advanced analytics, it should consider a solution with the following capabilities:

• Assisted Predictive Modeling provides predictive analytics capability assisted by auto-recommendations and auto-suggestions so users can apply predictive analytics to any use case using forecasting, regression, classification, clustering and other algorithms to analyze an infinite number of use cases and address customer targets, cross-sales opportunities, pricing, risk assessment and promotional targets and buying behavior.
• Smart Data Visualization allows users to view and analyze data to identify a problem and clarify a root cause and to interact easily with data discovery tools and analytics software to build a view that will tell a story using guided visualization and recommended data presentation so there is no need for assistance or delays. Guided recommendations are made based on data type, volume, dimensions, patterns and nature of data.
• Self-Serve Data Preparation allows users with average skills to perform data prep and transform, shape, reduce, combine, explore, clean sample and aggregate data without advanced skills In other words business users can perform data extraction, transformation and loading (ETL) without help – ETL for business users!
• Advanced analytics with Natural Language Processing (NLP) gives users a familiar Google-type interface to compose and enter a question using common human language, so they don’t need to scroll through menus and navigation. Click less search analytics allow users to enter a search query in natural language and the system will translate the query, and return the results in natural language in an appropriate form, such as visualization, tables, numbers or descriptions.

The augmented analytics advantages far outweigh the considerations for time and cost of implementation and the right advanced analytics tool will provide timely, cost-effective implementation and data integration to get the organization up and running quickly and efficiently.