One of the most important capabilities of a Business Intelligence Solution or BI tool is the ability to provide reports, both for the user who is leveraging the tool and for teams, managers and executives who want to share or make decisions based on fact-based information.
Want to Improve Data Literacy and Data Democratization? Use the Right BI Tools!
When you want to democratize data and help your business users function at a higher level; when you want to improve the quality of decisions; when you want to optimize the analytical and data scientist resources you have and make them more productive, you have to look at the business intelligence solutions that are designed to support every team member. Most of your team members are professionals with specific knowledge and that knowledge should be giving you a market advantage and should be dedicated to doing the job and filling the role you need the team member to fill.
What is Augmented Analytics and Can it Benefit My Business?
What is the difference between analytics and augmented analytics? The simple answer is this: augmented analytics provide sophisticated analytics tools in an easy-to-use environment that offers guidance and recommendations, and a natural language processing (NLP) interface that makes it possible for every business user to leverage the solution, no matter their technical or analytical skills or experience.
When a business is considering an advanced analytics solution, it must sell its management team and investors on the value and benefit of that solution and the merits of implementing such a solution or upgrading an existing solution.
A few months ago, I was talking to a friend (someone who has worked, for years, in the IT industry) and happened to mention data democratization. In this age of modern business intelligence, augmented analytics and Citizen Data Scientists, one would think that every IT professional would know the term ‘data democratization’ and its crucial importance to the business environment and success. But, my friend did not know or understand the term or its importance. In fact, she was even a bit skeptical about the potential and the movement toward self-serve business intelligence and analytics.
Businesses can no longer afford to make decisions based on gut or opinion. Today’s management teams and directors insist on measurable results and metrics that clearly demonstrate what is working, what should be changed or updated and how to get to the results they want and achieve the objectives they have set.
Business intelligence is not a dry, robotic reporting mechanism. Rather, it is (or should be) a source that is based on enterprise wide data, integrated to help users see results, find the source of a problem and delve into opportunities that offer business potential.
As one of its Strategic Assumptions, Gartner predicted that ‘By 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.’ Gartner also predicts that the number of Citizen Data Scientists will grow five times faster than the number of expert data scientists through 2020, and that by 2019 the amount of advanced analytics produced by citizen data scientists will surpass that produced by data scientists.
Business Intelligence isn’t new but the way we gather, analyze and digest this intelligence is definitely changing. In the past, business intelligence was delivered to senior executives by IT and/or business analysts. The combination of a fast-paced business environment and a limited budget and limited resource reality means that most organizations must optimize resources and time to produce results. There are few businesses today that have the luxury of waiting for information, data or reports.
Let’s talk about data preparation! That often cumbersome, always complex process by which data scientists and IT professionals gather and prepare data for analysis. Data prep involves the extraction and transformation of raw data to prepare that data for analysis. It includes cleaning and reducing that data by removing outliers and standardizing data formats, among other things. Without data preparation, data analysis can often be misleading and incorrect – garbage in/garbage out, if you will.
