A rise in the use of mobile devices and applications has heightened the demand for organizations to elevate their plans to deliver mobile analytics solutions. However, designing mobile analytics solutions without understanding your audience and purpose can sometimes backfire.
I frequently discover that in mobile analytics projects, understanding the purpose is where we take things for granted and fall short—not because we don’t have the right resources to understand it better, but because we tend to form the wrong assumptions. Better understanding of the “mobile purpose” is critical for success and we need to go beyond just accepting the initial request at the onset of our engagements.
The Merriam-Webster dictionary defines the purpose as “the reason why something is done or used: the aim or intention of something.” Although the reasons for a mobile analytics projects may appear obvious on the surface, a re-evaluation of the initial assumptions can often prove to be invaluable both for the design and longevity of mobile projects.
Here are a few points to keep in mind before you schedule your first meeting or lay down a single line of code.
Confirm link to strategy
I often talk about the importance of executive sponsorship. There’s no better person than the executive sponsor to provide guidance and validation. When it comes to technology projects (and mobile analytics is no different), our engagements need to be linked directly to our strategy. We must make sure that everything we do contributes to our overall business goal.
Consider the relevance
Is it relevant? It’s a simple question, yet we have a tendency to take it for granted and overlook its significance. It doesn’t matter whether we’re designing a strategy for mobile analytics or a simple mobile report—relevance matters.
Moreover, it isn’t enough just to study its current application. We need to ask: Will it be relevant by the time we deliver? Even with rapid deployment solutions and the use of agile project methodologies, there’s a risk that certain requirements may become irrelevant if current business processes that mobile analytics depends on change or your mobile analytics solution highlights gaps that may require a redesign of your business processes. In the end, what we do must be relevant both now and when we Go Live.
Understand the context
Understanding the context is crucial, because everything we do and design will be interpreted according to the context in which the mobile analytics project is managed or the mobile solutions are delivered. When we talk about context in mobile analytics, we mustn’t think only about the data consumed on the mobile device, but also how that data is consumed and why it was required in the first place.
We’re also interested in going beyond the what to further examine the why and how. Why is this data or report relevant? How can I make it more relevant?
Finding these answers requires that you get closer to current or potential customers (mobile users) by involving them actively in the process from day one. You need to closely observe their mobile interactions so you can validate your assumptions about the use cases and effectively identify gaps where they may exist.
Bottom line: Focus on the business value
Ultimately, it all boils down to this: What is the business value?
Is it insight into operations so we can improve productivity? Is it cost savings through early detection and preventive actions? Is it increased sales as a result of identifying new opportunities?
What we design and how we design will directly guide and influence many of these outcomes. If we have confirmed the link to strategy, considered the relevance, and understood the context, then we have all the right ingredients to effectively deliver business value.
In the absence of these pieces, our value proposition won’t pass muster.
Stay tuned for my next blog in the Mobile Analytics Design series.
You may also like the Mobile BI Strategy series on IT Peer Network.
Connect with me on Twitter @KaanTurnali, LinkedIn and here on the IT Peer Network.
A version of this post was originally published on turnali.com and also appeared on the SAP Analytics Blog