When it comes to analytics, most people focus on the technology side of the house. Yet soft skills have emerged as the unsung heroes of successful business analysis projects. And one of the top soft skills in demand is the ability for technical teams to help business users become data driven. A key enabler of data-driven users is modern analytics technology that enables them to foster their data curiosity and engage in an ongoing conversation with data. But, even with a modern analytics platform, IT still needs to educate users—who, whether from frustration or ignorance, aren’t delving into data on their own—how to identify, manipulate, and leverage key data in order to make better business decisions.
- Analytics user experience, not the requirements
- Think generically, not specifically
- Understand that tables and charts go together
- Ensure your analytics solution lays a fopundation for future artificial intelligence
- Track your analytics usage to make smarter future investments
Analytics user experience, not the requirements
The status quo right now in analytics is for a user to request a report and IT to deliver it. But that approach rarely yields true business insight. That’s because business users often can’t articulate what they want because they simply don’t know what’s possible. Or—even worse—they’ve been conditioned not to ask for anything exemplary because it’s too hard, if not downright impossible, to deliver it.
Instead of focusing on what users think they need, users need to first ask themselves how what they need plays into the analytics user experience. And, as IT, you need to stop the mindless task of building only the reports requested by users. With this new approach, you’ll be able to foresee what the user’s next request might be, prevent problems about how to slice and dice the data, and create improved analytics processes that eliminate the need for users to manually manipulate the data you deliver—such as by joining together multiple, disparate reports in Excel.
- What business problem is the user trying to solve? What’sthe business context for the information?
- What are users doing right now to try to get the information they need? What isn’t working with that approach?
- How will the information be used?
- How familiar will users be with the report?
- Do users need visual indicators, such as labels, on the report?
- Do users need training materials within the report?
Think generically, not specifically
It may seem counterintuitive, but you need to think generically, not specifically, when it comes to modern analytics. Legacy analytics tools fostered an environment where users were conditioned to request and then stitch together their own separate reports. This process is neither accurate nor scalable, and it creates redundant work resulting in mismatched data.
Instead, help users understand what they need the end result to be, so you can connect data for them in such a way that multiple variations of the same analysis don’t have to be built.
For instance, ask them:
- Can we create a larger data view of this information? Is there a way we can set up this analysis to provide the most data and have access to the right things?
- Can we implement security to make one generic model—a semantic description of the data set— to be used for multiple things?
- Can we develop one master dashboard that uses filtering capabilities to let people slice and dice the data however they want, for self-service analytics?
- What’s the next question or questions the information is likely to elicit?
Understand that tables and charts go together
Two core types of visual analysis are used today: tables and charts. Tables display actual numbers, but the eye has a hard time spotting trends in tables and correlating those numbers with large data sets. Charts, on the other hand, focus on visual storytelling but often don’t give the level of detail required because they don’t let users dig into the numbers behind the graphics.
What’s really needed is a happy merger of the two— much like peanut butter and jelly: pictures that help users understand aggregated data, coupled with the ability to drill into the row level transactions that make up the chart.
With this approach, you:
- Help your audience quickly and clearly understand the data and insights;
- Let users quickly identify and discount outliers from their analysis, or dig deeper to understand and learn from the outliers; and
- Tell a more powerful story because you deliver at-a-glance understanding of high-level data via visualization capabilities while enabling users to validate and gain additional context into specific data.
Ensure your analytics solution lays a fopundation for future artificial intelligence
Even if your organization doesn’t yet know what it might do with AI, it’s important to plan for it. That means investing only in modern analytics technology that can support future AI needs, rather than relying upon old tech stack capabilities. Lay the foundation now, so you can do what you need to do in the future.
Track your analytics usage to make smarter future investments
It’s the norm in analytics to build and deliver reports yet have no insight into if they’re actually valuable or used regularly. Looked at another way—do the people who use analytics outperform others?
Unfortunately, reaching this level of analysis is a luxury most organizations can’t afford. That’s because most can’t even answer the basic question of which reports are run or used, and they’re stuck in an endless cycle of unmet requirements and delivery expectations.
The questions they really need to answer surround analytics usage and the resulting benefits, so they can hold business users accountable to the report usage. With this approach, you can force business users to prioritize the reports they want delivered and their timelines, as well as the reports they should retire to keep the environment clean and productive
Ask questions like:
- Does the report people reference five times per day actually make a difference?
- Can you say people who use dashboards outperform those who don’t by X percent?
- How do you justify the dollar amount and time invested in analytics technology?
- How do you demonstrate the massive impact you bring to the company to convince leadership to invest more money into analytics?
Implementing this new approach will help your budgeting and planning exercise for the next fiscal year, so you can make smarter future investments. It also will require both leadership and business users to assume accountability for their report requests. As a result, both business and IT leadership will thank you for it and together will unlock growth opportunities within the organization.