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.
When you consider data democratization and rolling out a BI tool or analytical solution to your team members across the enterprise, you have to think about how to ensure that team members can easily adopt analytics and use them in their day-to-day tasks with a clear understanding of the results.
It is a great idea to get your business users involved in analytics and to encourage and build data literacy in your enterprise but one of the things that often stops an organization from implementing self-serve analytics is the need to gather and prepare the data for analytics. How can IT or data scientist staff turn over day-to-day analytics when data is spread across the enterprise and it is so difficult to integrate that data and prepare the data for analytics?
If your business is planning to implement self service business intelligence, you may be focused on cascading data analysis and fact-based decision-making throughout the enterprise and, hopefully, your focus includes improving the data literacy of your business users and team members.
If a business is trying to implement self-serve business intelligence tools, it must look at its organization, processes and culture to determine whether it is prepared to roll-out data democratization and provide direct access to business intelligence tools. Changes in the environment and the expectations can help to empower business users and hold them accountable and to encourage data popularity and data sharing to work toward transformation of business users to Citizen Data Scientists.
There are many benefits to self-service business intelligence and business analytics. Many (in fact most) enterprises are going to a self service BI environment that will increase the value of each team member by providing them with easy-to-use BI tools that they can leverage to make decisions, to understanding issues, to share data and plan for the future and to find business opportunities.
‘R’ is not just a letter in the alphabet! R is a programming language, offered in a free software environment that supports analysts, IT and data scientists in doing tasks that require statistical computing. The R language is very popular and is used in many organizations around the world to support statistical analysis and data mining. When a business implements a business intelligence solution, IT staff, data analysts and data scientists are often put into a quandary. The enterprise is actively using and benefiting from R but the BI tool the business selected forces them to choose between tools to get the job done.
Today’s businesses and business users are more demanding than ever. No one has patience these days for tools, applications or products that offer no value or are frustrating or restrictive. So, when you hear the term ‘self-serve business intelligence’, you had better be sure you know whether the claim is valid before you choose a product.
An enterprise that employs restrictive business intelligence tools is losing competitive advantage and crucial business opportunities every day. To get the most out of business intelligence software, an organization needs a self-serve business intelligence dashboard that will allow business users to access data integrated from multiple sources and analyze that data with ease, using business intelligence reporting that incorporates sophisticated features in a simple, interactive environment.
There is a new business role on the horizon and, at first glance, it may seem very much like a role that was introduced a few short years ago. This new enterprise role is known as an ‘Analytics Translator’ and, while there is some confusion regarding the distinction between this role and the newly minted Citizen Data Scientist or Citizen Analyst, there are some subtle but important differences. In a previous article (What is an Analytics Translator and Why is the Role Important to Your Organization?), we discussed the definition of an Analytics Translator. Here, we will discuss the role of Citizen Data Scientist and Analytics Translator and how they differ. To understand these roles, let’s look first at the somewhat more familiar role of Citizen Data Scientist (AKA Citizen Analyst).
