Innovative Business Intelligence Trends to Incorporate in the Year 2016

By CIOReview | Wednesday, July 6, 2016

2016 has so far witnessed enterprises tapping into the existing and emerging Business Intelligence (BI) methods to augment workflow efficiencies. This crusade validates the insatiable thirst of businesses to shift from the traditional siloed, client-specific analytics methods to a more generalized big data approach. Industries are now at the crossroads of data analytics, mobility, and cloud computing technologies, which holds the promise to unlock new doors of opportunities. But, most of the BI tools available this year are either the existing ones or those which have been modified to meet the customer demands. As the year reaches halfway through, we dig deeper into some of these innovative BI methodologies and trends that can transform the business world as we know it.

Predictive and Prescriptive Analytics

Companies leverage predictive analytics to extract insights from existing data sets, scout for patterns, and eventually forecast the future probabilities, what-if scenarios, and risk assessment. Prescriptive analytics, on the other hand is slightly different; it not only predicts the future for businesses, but also analyzes the data to suggest recommendations to achieve business targets. It assists in highlighting business decisions which might affect the future, so that they can be modified or dealt with in time. This type of analytic models help companies optimize scheduling, production, inventory, and supply chain design.

Visual Data Discovery

The volume and scope of Big Data has grown to such an extent that establishing relationships between data elements across multiple data sets has become an overwhelming job. Visual data discovery tools are leveraged for finding patterns among those impenetrable data sets. This approach provides an edge to companies in a way that they can arrive at reliable data insights, identified in real-time. Enterprises study the insights and respond swiftly to eliminate risks and enhance profits.

Business Intelligence as a Service

Analogous to the use of visual data discovery tools, companies make use of self-service BI tools to harvest data sets in the presence of limited IT support. Business intelligence as a service (or BI-as-a-Service) solutions, this year has moved a notch higher with the inclusion of outsourcing services. Companies these days usually rely on analytics packages where they can outsource their data. The service takes into account every possible intricate variable to make the most appropriate decision for the companies.

Innovative Data Virtualization

Integrating the surplus amount of data in real-time could become an uphill battle for IT teams as the data remains scattered across various sources, formats, internal, and external locations. 2016 has seen significant efforts being laid down in this road to connect these disparate data sets with more agile tools and methods. This trend, commonly referred as data virtualization, can allow personnel working in a company to easily access the data they require. Furthermore, this method eliminates data replication and consolidation problems, which otherwise hinders business processes.

Data Storytelling and Data Journalism

The use of data streams has escalated significantly in the current technological context. Glancing through that overwhelming amount of data or its complete analysis becomes a copious task in itself. The shift from written to visual communication has implanted the idea of switching to infographics for advanced data analysis because this is a peerless way for exchanging a complex set of data. The use of data visualization techniques has escalated this year as more users realize that attractive visuals foster decision-making capabilities.

IoT Data

Enterprises today leverage IoT data extensively to address their BI requirements. This in turn is enabling data scientists to make use of the unstructured data coming from different sources. Moreover, we have already noticed the initiatives being taken on this front with the interconnection of physical world and sensor-based systems. Smart sustainable homes, wearables, supply chains, traffic management, water distribution, waste management or urban security are some common applications which make use of the IoT data.

Cloud Analytics

Cloud technology and cloud-based services reside at the very heart of Business Intelligence. The ever-growing number of cloud-based tools in the market has pushed companies to jump on the cloud bandwagon to support their business needs. The cloud adoption can enable entrepreneurs to streamline their business workflows and provide them with the necessary elements like data sources, data models, processing applications, computing power, analytic models, and data storage.


This versatile process involves a mechanism where a complex analysis is built from a simple starting point. The process comprises gathering samples from the same data set continuously for verifying user’s knowledge about the entire data set. This BI approach is employed over impenetrable information, but the process usually gets more sophisticated with every step of execution.

These trends clearly indicate a growing shift towards data analytics being at the centre of a business structure. The amount of data enterprises have to encounter is not overwhelming anymore as more companies emerge in this scenario and furnish the tools necessary to make use of the data. But this is not the end or limitation to the potential of this technology as companies take one step ahead to bring in techniques for democratization of data product chain. This will turn decision-making process a swift affair, because with democratization, data can be easily accessed by people across any enterprise.