We all want to achieve the greatest return from our investments, and data discovery platforms are no exception. But, when it comes to analytical solutions, many companies struggle to achieve user adoption and measurable ROI. Some have invested in multiple analytical platforms, each promising to provide new and better insights, but often falling short of expectations. Why?
Even when the platform itself is easy to use, user adoption and ROI are driven by providing solutions that help solve real business problems. Far too often, companies focus on replicating existing reports, starting with the data – extracting, centralizing, cleansing – then focus on designing analytics that can be created from the data. Don’t get me wrong, good/clean data is important and necessary. But what drives adoption is excitement in finding new insights, which means doing something more than seeing existing data in a new format. Case in point:
A large retail organization has licensed multiple business intelligence software platforms. Each time, executives were promised dashboards that would help them better manage and grow their business. But, at the end of each project, the dashboards reflected exactly the same information, just in a different way. Each time the platform was blamed and the executives were advised to look at another option that promised what they need. And on and on it went.
So, how can you stop the “cycle of disappointment”? Here are three important steps.
1. Design solutions that solve real business problems for real users
Before you dive into cleansing data and decomposing KPI calculations, take time with end users to learn what they do with their reports. Ask questions about the decisions they make from their data. Ask for examples of times they could have taken advantage of an opportunity if they had visibility to that one anomaly in their data. Ask what’s next for their business and what KPIs are missing now.
Don’t worry if you don’t have all the data. Often new analytical solutions identify missing data elements – most of which, are remedied by enforcing existing processes or using fields readily available in their ERP systems. Users are far happier getting some of the insights they want while they wait for the rest of the data, rather than thinking the platform failed.
2. Provide users examples of other analytical applications for similar business scenarios
Maybe start with examples from other divisions or departments, or utilize application templates. Either way, seeing how others use visual analytics will help your users see the potential of how they can use them in their daily life. Then draw up wireframes or prototypes, and walk through them as if they are using them in real life. Ask questions to follow how they are thinking about what they are seeing. Refine the design to better follow their thinking and decision making process.
3. Create the data model to fit the requirements
NOW you can start focusing on data. Map out what is needed to fulfill the requirements, assessing where there are gaps or differences between data sources for key fields. Identify where the business may need to shore up their internal processes or ERP systems. But, most importantly, create the data model to fit the business solution need.
As technical, analytical professionals our nature is to believe we can save time and money by diving immediately into the data because we know that data is the key to good analytics. Or is it?
For more information on how to maximize the value of your Qlik solutions, please visit qlik.com/consulting.