If your business is considering an advanced analytics solution, your IT and management team has probably already done some research and concluded that the concept of augmented analytics designed to support business users is the right way to go. To democratize data, improve data literacy and transition business users to the Citizen Data Scientist role, the business must select the right solution and plan for success.
Small and medium sized businesses (SMEs) are often challenged to satisfy all the roles and responsibilities in the organization and most team members wear more than one hat. That feeling of being overstretched is typical of growing businesses and, in an increasingly competitive market with businesses fighting for skilled resources, it is difficult to meet budget and scheduling goals and get it all done.
Natural Language Processing (NLP) may not be a term that everyone is familiar with but all consumers and business users are certainly aware of its power. You use natural language processing every day when you perform a search in Google using questions written in the same way one would speak or write to another person. Whether you know it or not, you are using NLP to process, interpret and return results that meet your criteria.
What is Natural Language Processing (NLP)?
Natural Language Processing utilizes artificial intelligence to translate computer code and language into real world, human language. While the goal is to simplify human interaction with computers, NLP is a complex mix of computational linguistics and computer science. When a business is considering an augmented analytics solution that leverages natural language processing, it need not concern itself with the complicated underpinning of code and design, but should rather consider what NLP can do for its users and for its business results.
The focus of this article is on Data Democratization within the business enterprise, but the concepts and approaches involved in implementing this type of initiative are worth considering for any kind of major change within an organization.
Perspective is everything. You can stare at numbers and columns all day and never see the one nugget of information that will give you insight and help you solve a problem or find that one opportunity to drive the business to the next level. When you and your business users can leverage augmented analytics tools, without worrying about complex algorithms or writing code or designing reports, you can find those elusive nuggets of information and use them to improve your business results.
Every business owner and manager understands the problem of limited resources. Today, you have fewer team members and you must do more to compete in the market. To enhance productivity and collaboration and ensure that every team member is making better decisions, it is wise to implement augmented analytics within your organization.
If you are a business manager who wants to enable a Citizen Data Scientist environment, but you find yourself up against resistance, it is often because your business users believe that advanced analytics is just too hard for them to learn and that the use of these types of techniques and systems will slow them down and confuse their otherwise familiar processes.
When it comes to the new world of analytics, the augmented analytics approach allows business users with no data science background to readily access and use analytics in an intuitive way. There are some important aspects of this approach, including auto machine learning, natural language processing (NLP) and intuitive search analytics.
If your business is trying to incorporate data analytics into the fabric of day-to-day work, you will need to get your users to adopt analytical tools. The way forward is not all that complicated. The solution you choose must take an augmented analytics approach, one that includes simple search analytics, ala Google search. Natural Language Processing (NLP) and NLP Search tools are key to this approach as they allow business users to ask simple questions and get answers. If a user does not have to create complex queries or look to business analysts or data scientists or IT professionals, that user can incorporate data into information sharing, reporting, presentations and recommendations to management.
