Mobile BI has come a long way. From the time of receiving automated text messages that signal failure of a batch process or breach of a critical threshold, today we have arrived in the age of interactive BI content delivered via mobile devices.
Most organizations today are either planning for a mobile BI strategy or have already framed one. The mobile BI strategy could either be a subset of the overall mobile strategy for the organization or an independent piece.
Forrester claims that mobile BI is "no longer a nice-to-have" and BI will soon fully catch up with mobility. Gartner predicts that by 2015, 50% of BI functionality will be consumed via hand-held devices.
What are the elements that need to be considered when drawing up your mobile BI strategy? How important are they?
What are the use cases for your organization to adopt a mobile BI strategy? Most of the current usage of traditional BI is for strategic purposes by people residing in the upper echelons of the pyramid. Mobile BI to perform strategic analysis does not really make sense, unless there is a reason why your C-level executive cannot open his/her laptop. The real use cases for mobile BI lie in operational decision making. Gartner believes that "The biggest value is in operational BI — information in the context of applications — not in pushing lots of data to somebody's phone". For example, a sales person on the road may want to know the “Next Best Product” to be sold to a customer. A valid use case could also be in integrating device specific features in BI. For example, a door-to-door service agent may be advised on the order in which to make the house visits based on multiple parameters including proximity (GPS!), criticality, aging etc. Irrespective of the use case, it is important to be clear on it and set expectations appropriately.
The other important factor that helps finalize the use cases is by tracking the RoI. Mobile BI investments span the cost of devices as well as software, security, development and maintenance costs. So it is important to ensure that your use cases provide tangible returns. The RoI can be quantified as Revenue Enhancement, Margin Enhancement, Cost Reduction, Cost Avoidance or Capital Cost Avoidance. For example, an iPad containing drug performance comparison across patient profile can help increase quality face time for a medical representative with a physician by x%, your sales head would surely be comfortable promising a x/5% increase in sales. That, by itself, could fund your entire project.
Typically, the 70% of users who don’t access traditional BI are the representative audience for mobile BI. Hence it is important to tailor your strategy to the new cross-section of people who will be your consumers. They may be less techno-savvy, more impatient to see results and have narrower but very specific needs. The real value of mobile BI is when users can fully interact with BI content delivered to mobile devices – there needs to be a distinction from informative email or text messages.
Mobile BI should complement your existing BI solution – it need not cover all the bases. A desktop dashboard is meant for deep analysis while a mobile BI is designed for quick and easy consumption. A mobile BI solution is meant for the “mobile” folks in your organization: executives, sales personnel, line managers on the shop-floor etc. Tailor your mobile BI solutions to suit the needs of these people (not those of your research analyst and financial accountant).
Don’t ignore existing devices when framing the mobile BI strategy and attempting to arrive at organization standards. BYOD (Bring Your Own Device)/heterogeneous systems have their drawbacks especially in terms of consistency, but the task of converting iOS fanatics to Android (and vice-versa) is not a battle worth getting into. There might also be a challenge in utilizing device specific capabilities and this is another negative that needs to be considered. HTML5 is helping us bridge the divide between browser specific and device specific strategies.
There is a tendency in BI projects to say “we are just doing some reports on the mobile; let us roll it out in a couple of weeks”. Beware! Mobile BI projects are governed by the same fundamentals that traditional BI projects are - just even more stringent. Garbage in is still garbage out. Visualizations that don’t make sense will kill adoption. Security is even more critical. User Types are a lot more critical. The mobile BI strategy should therefore be planned and executed in a well-thought out manner and not rushed into. Ensure it doesn’t get fast tracked.
A critical part of the mobile BI strategy is managing expectations. No one should expect feature parity with a traditional BI solution (drag and drop etc.) but there are some cool new things that can be integrated (GPS, gyroscope, touch screen etc.). Telling people what they can get and what they cannot is an integral part of the strategy via trainings etc.
Security architecture needs to be revisited for mobile BI implementations and should be a critical part (if not the most critical part) of the strategy. Key business information needs to be delivered via mobile devices; else the utility of the BI project is compromised; however this carries security considerations that need to be carefully handled. All mobile BI projects transmit data from an internal firewall through a DMZ and via an external firewall before hitting the end device. It is critical that the mobile BI strategy lays out the approach for handling a whole gamut of security considerations including:
Some other factors that need to be kept in consideration when framing your mobile BI strategy include bandwidth (system and resource), mobile UX and design considerations (templates, interactivity, device features), ability to reuse existing investments, strategy to reuse design for both traditional and mobile BI etc.
Mobile BI is something that can only be ignored at your own peril; but don’t just jump into the waters without some thorough preparation.
Source : Article published by the Marlabs BI Team
Ravindra Pilli, Test Architect at Marlabs, wrote an article in Tools Journal about testing mobile business applications. There's an excerpt below and you can read the full article on the Tools Journal website at the link below.
"The phenomenal growth of mobile devices has opened up avenues for organizations to integrate them into the mainstream computing environment. Today’s mobile applications deliver Major functionality on platforms that have limited resources for computing. Yet, unlike the desktop -based environment, the mobile environment comprises excess of devices with diverse hardware and software configurations and communication intricacies. - See more at: http://www.toolsjournal.com/testing-articles/item/2268-mobile-business-a... "
Ravindra Pilli is currently the Mobile Test Architect at MARLABS and has more than 8 years of experience as a Test Engineer, Senior Test Engineer, Testing TL, and Mobile Test Architect. Ravindra is part of the Testing CoE which works on Functional, Automation, compatibility, Performance, Network, & Field testing.
Business Intelligence enables greater transparency, and can find and analyze past and present trends, as well as the hidden nature of data. Business Intelligence tools analyze only historical data—data about what has already happened. However, past and present insight and trend information are not enough to be competitive in business. Business organizations need to know more about the future, and in particular, about future trends, patterns, and customer behavior in order to understand the market better.
Predictive analytics is used to determine the probable future outcome of an event or the likelihood of a situation occurring. Predictive analytics combines business knowledge and statistical analytical techniques that can be applied on business data to achieve insights. It is the branch of data mining concerned with the prediction of future probabilities and trends.
Predictive analytical models are built by data mining tools and techniques. Data mining tools extract data by accessing massive databases and then they process the data with advance algorithms to find hidden patterns and predictive information. Data mining, also known as knowledge-discovery in databases, is the practice of automatically searching large stores of data for patterns.
Data mining is widely used in Banking and Insurance for Risk Estimation & Management, Retail & Consumer Product Goods (CPG) for Consumer behavior, Pharma science for drug testing and Six Sigma for Process Improvement
Tools which are widely used to data mining are:
[Adapted from ElegantJ BI White Paper]