Lack of good, consistent quality data is cited as the number one challenge organizations face to realizing the full potential from analytics (A.T. Kearney’s “2015 LEAP Study - Leadership Excellence in Analytic Practice”). Excessive time and resources are needed to manipulate and “roll-up” data before business analysts can start to use it for reports, analytics and insights.

Often these challenges are compounded when analysts create work-arounds that drive “shadow” data bases and ad hoc data management processes that undermine confidence in the data. Strong business intelligence can become the data syndication traffic cop and data clearing house for enterprises that need to make better, faster decisions using good quality data and insightful analytics.

Help is here in the form of three big technology trends:

Intuitive, Business-Centric BI Tools

There is continued growth in the market for BI and visualization tools that allow organizations to syndicate and share data in a “self-serve” fashion for the end business users.

Tableau and TIBCO have emerged as potential leaders in business intelligence and visual representation of analytics. Several of these emerging players provide flexible, easy-to-use interfaces to manipulate and visualize data without requiring a data science degree – i.e., the democratization of analytics.

These tools are available in both on-premise and cloud-based flavors. Although the latter is preferred for speed-to-deployment and the variabilzation of IT costs. Traditional players like Microsoft and SAS have had to “up their game” so as not to be left behind.

Business Intelligence Plus

Convergence of BI tools with robust analytics tools has given rise to the next generation of decision support platforms. These platforms have the ability to model data and run advanced analytics such as simulations, optimization and sentiment analysis by making better use of the “passive” data resident in traditional BI tools.

These platforms can also be used to quickly interpret and synthesize recommendations using the reporting and visualization capabilities of the traditional BI tools. AIMMS for optimization, Arena for simulation and Alteryx for predictive modeling are some category leaders in this space as well as the traditional players such as SAS and Microsoft.

BI EDW and Hadoop Integration

Business intelligence and analytics models are only as good as the quality of the underlying data. The proliferation of data sources in terms of sheer volume, diversity and real-time availability means that enterprises are trying to make sense of more and more complex and unstructured data.

BI tools still provide the best way to serve up the unstructured, messy data in a “structured” format for further reporting and analysis by business users. However, there is a growing recognition to offload large data volumes and heavy processing to big data platforms and then move the aggregated, high-value results back into a traditional OLAP or data cube structure to display using BI tools.

Before jumping headlong into making capital investments in these technologies, organizations must first develop a portfolio of use cases that link the business value proposition to these technology trends. For example, business-centric BI tools will enable use cases focused on syndicating data to business users so they can report on marketing campaign performance and operating or financial metrics.

Alternatively, investments in new big data components such as Hadoop are required if the need is to integrate social media or real-time IOT sensor data streams to existing transactional customer data.

(About the author: Khalid Khan is a partner with global management consulting firm A.T. Kearney where he leads the Analytics Practice in the Americas.)

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