July 21, 2020
In the fourth in a series of blogs focusing on the six steps organizations should take to become data-driven, we look at the importance of making analytics easy to consume.
This series began by examining the importance of a modern data architecture, followed by the role of automation in data integration and, most recently, the need to provide data-as-a-service. In this blog, the way business users access and interact with analytics is the focus.
It may seem obvious that for business users to make the most of data, they need information that is easy to access, easy to interact with, and user interfaces that that blend seamlessly into their work environment. One organization that has achieved this is Pilot Flying J. David Clothier, VP of Finance, Treasurer, and Controller, says, “Our lifeblood is the front line of the retail store. By providing our front-line workers with reporting at their fingertips, sales and customer service have improved. All our employees, including executives, look at the same numbers and make better decisions as a result. All of this value has definitely contributed to a positive ROI.”
Firstly, information must be delivered to users in a variety of different ways that are tailored to their skill level, data knowledge, type of analysis being performed, and location. For example, a business user may need guided analysis provided by a dashboard, which allows them to instantly access important key performance indicators (KPIs) and drill into the detail. They may then want to perform some pure-ad-hoc analysis or data discovery, asking questions that are not covered by standard pre-built dashboards. They may also want to see highly-formatted operational reports that are produced on a scheduled basis or required for regulatory reporting.
What is clear is that one size does not fit all in the world of analytics, yet it is common to see organizations standardize on one type of analytics. We discussed in the previous blog the considerable increase in the use of desktop data discovery in recent years. Yet, this type of analytics is better suited to knowledgeable data analysts and not at all geared to the needs of a less technically skilled business user. Similarly, relying entirely on dashboards for analytics would not meet the needs of data scientists or developers looking to use APIs to connect applications to data. Discover more about Infor Birst’s adaptive user experience in this brochure.
Finally, there is enormous value in embedding analytics in business applications where users spend most of their day. It means that front-line workers can readily see insights as they work within actual business processes. A further advantage of embedded analytics is that the business application software providers can deliver pre-built data models, dashboards and reports that are role and industry-specific, saving months of work and dramatically speeding up time to value from analytics.
Learn more about how fundamental easy access to analytics is, in our best practice guide on the six steps to becoming a data-driven organization
In the next blog on the topic of becoming a data-driven organization, we look at how designing analytics with business outcomes firmly in mind is critical to success.