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.
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.
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.
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.
As business organizations fight for competitive advantage, funding for projects and large expenditures can fall by the wayside. In today’s competitive business market, every senior executive looks at risk, value and calculations like return on investment (ROI) and total cost of ownership (TCO) before approving a budget.
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.
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.
If Data Science was once the sole domain of analysts and data scientists, Augmented Data Science represents the democratized view of this domain. With Augmented Data Science, the average business user can engage with advanced analytics tools that allow for automated machine learning (AutoML) and leverage sophisticated analytical techniques and algorithms in a guided environment that uses auto-recommendations and suggestions to lead users through the complex world of data science with ease and intuitive tools.
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.
