Having recently attended the Gartner BI and Master Data Management Summits in London, it is clear that now is an exciting yet confusing time for data discovery and analytics. Of course, they have always been exciting areas for analysts, but now the industry as a whole is turning to self-service business intelligence (BI) to deliver critical information and data analytics to a much wider audience. This can only be done, however, by “operationalising” insights to bring together employees, the supply chain, and customers.
What do I mean by this? The issue at the moment is the market need to deliver self-service information and analytics to a broader audience. Businesses need to look at simpler ways of doing this than using complex dashboard tools. Visualisation and data discovery tools are, at their heart, still an analyst’s best friend but leave the average employee scratching their heads.
Businesses need to stop deterring staff from using BI and analytics by offering ease of use and high functionality. This requires an app-based approach to easily and quickly view corporate data, as a next step in truly bringing big data to the masses. The average person does not have the time or inclination for formal training, and would much rather download an app that delivers analysis directly to their mobile device. Advanced analytics tools provide a mechanism for analytics to build sophisticated predictive and statistical models, but the ultimate value will come when we embed these models and their outcomes into consumable apps for operational decision-making.
Law enforcement is a great example of where self-service apps make a big impact, with analytics available at the tap of a mobile device to help the police force to work more efficiently. The amount of available data on crime grows day by day, and harnessing this to gain useful insights is an extremely powerful tool. Significant value can be derived from historical crime data, which helps predict and prevent crimes based on variables. It’s not about individuals, but more about populations and environmental factors; weather, traffic, events, seasons, and so on. It sounds a bit like sci-fi, but it’s actually very accurate. Think of it this way – how much crime would you expect at a London football derby, which happens twice yearly? Or in the rough part of town on payday? Or at a packed annual summer festival on a particularly humid day? By offering data on how likely it is for a crime to happen, these insights can help with prevention and more importantly help police forces accurately plan resourcing for such variables and events.
Self-service apps can make this predictive model easily accessible to ‘bobbies on the beat’. Even a cop on their first day can access the same level of insightful knowledge as a veteran officer through their mobile device to make smarter decisions in real time. An app to find vehicle licence plate numbers, for example, is just one way to speed up police procedures, saving police time and resources and ultimately making them more efficient.
However, this isn’t the only place where an analytic app could add value. Real time data in an easy-to-use format will have a massive impact on all customer service professions. Providing customer-facing staff with access to key data about customers allows them to deliver a more personalised service to customers. Staff would be able to better understand complaints as they’d be able to quickly access their previous experiences or purchase history, in real time.
The added benefit of using an app-based approach means you can gather data from many difference sources and combine it. For example, you can combine various types of enterprise data with other data available in public and private clouds such as weather services, to pull in variables. This comprehensive combination provides an accurate, collective view which delivers self-service for daily and operational decisions – in real time. This approach is the future of self-service for the masses via an easy-to-consume app for the hungry user.