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Business Intelligence: What’s Next?

Insight into trends and developments in business intelligence
By Gavin Sheehan, General Manager: Business Intelligence, Decision Inc.

  1. Cloud Analytics

The year 2016 saw an increasingly large number of businesses putting their data (and trust) into the cloud. Cloud analytics proved to be an agile and highly accessible platform to its users.

Cloud analytics facilitates intelligence as a service, allowing for low cost subscription-based services at price points that are difficult to match with traditional on premise platforms. This allows Business Intelligence (BI) to reach a larger audience than before, empowering its user base through information and transparency.

Adoption inhibitors still exist around data privacy, security and bandwidth constraints on performance. The challenge 2017 will face is dispelling these perceptions and limitations.

  1. Predictive Analytics and Data Modelling

With the complexity of R, historically there has been a steep learning curve within the area of predictive analytics.  Users require a formal mathematical background which creates an aura of elitism and exclusivity.

There has, however, been a recent proliferation of analytical tools seeking to harness the power of R but within a framework that is understandable to a non-analyst user.

In doing so, predictive analytics and data modelling are no longer the preserve of data scientists and mathematicians, they have become accessible to users and consumers of more traditional BI streams.

This trend towards simplification is quickly removing the technical road blocks that once existed and has firmly positioned predictive analytics as a must have in every BI roadmap.

  1. Closer alignment of Business and IT Objectives

Traditionally, a polarity has always existed between business and IT.  With IT being process and governance driven, and business self-service and insight orientated, there has always been the perception that IT is not able to support business timeously and is a stumbling block to information.

With BI platforms and self-service analytics, the divide is being bridged. IT still manages data integrity, but business has faster more comprehensive access to information that enables them to make better decisions, faster.

  1. UI (User Interface) and Data Visualisation

Users have become more sophisticated and demanding and are no longer impressed by the same simplistic visualisations of five years ago.

Accuracy and relevance of data used to be determiners of success on BI projects. This has now become a mandatory requirement and is equally as important as how you visualise this information.

User adoption and buy-in is intrinsically linked to visualisation as users are more likely to use or adopt a dashboard or model they find aesthetically pleasing and engaging. This is primarily the reason why the trend towards using infographics, as an example, has been so effective.

  1. ROI (Return On Investment)

With the current economic climate and many business’s experiencing budget cuts, a cautionary approach has been applied to BI implementations and project approvals.

It has become critical that, for BI to compete for budget, it must now show a clear value proposition. All projects or initiatives must have a clear goal from the outset of what it they trying to achieve whether it be driving revenue, cost reduction or time efficiencies.

This return on investment (ROI) creates a compelling business case and provides a target on which to benchmark and measure your project success.  It is imperative that these measurements are tangible, quantifiable and not subjective. 

  1. Data Mobility and Accessibility

In an age where access to information is no longer the preserve of a privileged few, users are seeing self-service as critical to having data on demand, anywhere, anytime.

Mobile analytics has really stepped into the spotlight and is no longer perceived as merely an extension of desktop analytics. With BI platforms putting more emphasis on mobile, it has become a focused analytics platform in its own right, and is a huge driver behind information portability.

There has been a shift from data centralisation to rather integrating disparate data sources in order to support quick data collection and information dissemination. This is no more prevalent than in the Internet of Things (IoT) where the emphasis will shift from data collection to practical use cases in 2017.

The end result of all this is a seamless integration of BI into all information streams and an entrenchment of BI into every day decision making.

The line will start to be blurred as to where BI starts and ends as it becomes more sophisticated and embedded in all applications.