The days of data silos and exclusive data access are over. Data Scientists and business analysts are still an important component of the business and IT teams are still crucial to the success and stability of the organization BUT if you are thinking that these roles are still responsible for day-to-day analytics and delivering every bit of data analysis to the enterprise, your business strategy needs an upgrade!
A recent report revealed that by, 2028, the Augmented Analytics market is projected to reach USD 46.26 billion at a CAGR of 24.30%. The business intelligence solution market has evolved into augmented analytics and these solutions designs have progressed to support average business users with simple solutions that combine sophisticated analytical techniques and algorithms with easy navigation, reporting and insight.
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.
All of your business users have a favorite software application – an app they value because it helps them do their job more easily, or helps them get crucial information. These are the applications they have learned and they are used to leveraging them on a day-to-day basis to perform tasks. When you introduce augmented analytics into your business environment, one of the most critical factors is whether you can expect user adoption. Finding and implementing the right augmented analytics solution is just the first step. If you can’t get your users to USE the application, your return on investment (ROI) will be poor and your total cost of ownership (TCO) will be high.
No matter your reason for investing in that business application, the investment was meant to improve the business, to make team members more productive, to act as a repository for important business data and to somehow improve the bottom line. But, the effectiveness and success of a software solution depends on more than its features and functionality. Yes, one must consider its ease of use too, but that’s not the point of our discussion today.
What Are the Benefits of Augmented Analytics? Where Do I Start?
Those who know me are probably tired of hearing me talk about the benefits of Augmented Analytics. To them, I say, ‘I am sorry’. I am about to talk about it yet again. The reason is simple. Most businesses are either considering the addition of augmented analytics to democratize data, improve data literacy and create Citizen Data Scientists OR they are still unconvinced and feel that they are just fine. Either way, your business professionals and managers can use a primer on the benefits of augmented analytics, whether it is used to support their decision or to convince the team that augmented analytics should be pursued!
Business Intelligence is mandatory! Without intelligence you have no insight into your customer buying behavior, your competition or your organization. In order to achieve business intelligence in today’s environment, you need modern BI tools and augmented analytics that is suitable for your business users.
Today, business planning is harder than ever. No one knows what is coming next and considering the changes in customer needs and expectations is just one of the issues. How about competition? What about new technology and how it will affect your products and services? What about the need for skills and new training for your team members and candidates? How about access to capital and investors?
Preparing data for analysis used to be a long, difficult process performed by IT or data scientists. Needless to say, that process meant that business users could not get the job done. They were forced to make a request, wait for the process to be completed and then, more than likely, for the same team to actually perform the analysis. If the data prep was not accurate or was incomplete, the process starts all over OR produces poor results and, in many cases, those poor results are not obvious to the business user. What does that mean? It means that the business is using information and results that they do not know are wrong!
The use of business intelligence and business analytics is growing in every industry, business function and in companies of every size. 48% of small and medium sized business CIOs responding to a Gartner survey revealed that business intelligence (BI), data and analytics is one of the technology areas that will have the largest amount of new or additional spending.
