Whether you know it or not, you are familiar with Natural Language Processing. Applications of NLP include search techniques used in Google and other popular tools. The use of natural language processing in self-serve advanced analytics solutions can mean the difference between a simple, yet sophisticated, business user tool and a tool that is only suitable for a data scientist or professional analyst.
Data analytics software used to be reserved for data scientists, analysts and IT staff but not today! Talk to any business colleague or pick up any technology analyst article and you will find plenty of discussion about the current use of data analytics tools and impressive predictions about the growth of this market. There is a reason for that popularity and growth! Businesses have discovered the value of business analytics and the benefit of taking the guesswork out of planning, problem solving and decision-making.
Whether you are a consumer or a business user, today’s technology users are savvy and they are used to having easy-to-use tools and features that make them more productive and allow them to quickly complete tasks. The same holds true for users of advanced analytics users. These users are also consumers when they are off the job and they are used to simple search technology like Google and other search methodologies that allow them to think, search and get results in a way that is fitting for normal speech and language.
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.
Data visualization may not seem important, but the way you see data can provide additional insight or it can muddle the picture to the point where you will miss critical issues or opportunities. The importance of data visualization is even more evident when that data is being analyzed by business users who are not likely to see data in an analytical way and probably do not have the knowledge or skill required to change visualization techniques to accommodate a particular type of data.
Imagine a world where data science and predictive analytics tools are created for business users! That world exists today with the evolution of sophisticated, yet easy-to-use tools that include predictive analytics for business users, visual analytics software and tools, and self-serve data preparation.
In today’s world, advanced analytics is for everyone. There is no reason to limit access to data preparation software or advanced predictive analytics to data scientists, analysts and IT staff. The evolution of augmented analytics and intelligence augmentation has created some impressive tools and solutions that allow business users with average skills to use self-serve data prep and complete data preparation using auto-recommendations and suggestions to work through the process easily and with no confusion.
Data literacy is a very popular idea these days. As business users adopt and embrace data and advanced analytics, features like predictive analytics for business users make it easier for a user with average skills to leverage data to make decisions and share information and, in so doing, to become more literate about data analytics.
If you want your business users to function and perform at their highest level and you want to optimize all resources, your data discovery solution must provide the guidance and ease-of-use that a business user requires to analyze and share data and make confident decisions that will improve the business bottom line.
There are many variables to the success of an advanced analytics solution but the primary pitfalls for this type of project can be traced back to poor planning. If the organization is not ready for self-serve advanced analytics, the initiative will fail. Organizational readiness is, in fact, one of the most overlooked issues in any type of software, system or technology introduction. If the enterprise wishes to succeed and achieve its goals for self-serve advanced analytics it must have a clear vision of what it wants to accomplish. It needs to have a comprehensive understanding of its requirements (technical, infrastructure, processes, tasks, locations, team members, etc.) The enterprise also needs to assess its need for external vs. internal assistance in implementation and training.
