5 Hot Trends in Business Intelligence and Big Data Analytics

Andy McCartney's picture
 By | juni 13, 2017
in iWay , WebFOCUS, analytics, big data, big data analytics, Business Intelligence, hot trends, iOT, iot analytics, webfocus
juni 13, 2017

Our industry is evolving and innovating at an astonishing rate as more data and ways to generate insight emerge. This post takes a look at 5 trends that we see out there in the world of business intelligence, analytics and big data management – enabling smarter, faster, and ultimately more data-driven business.  
 

1. Big Data’s Value Delivered via Contextualization

Only recently has consistent ROI been delivered by big data initiatives, thanks significantly to contextualization. Big data can be challenging to decipher or interpret in meaningful ways, without ingesting, cleansing and integrating it with other data assets that provide bigger picture context.  For example, we have customers increasingly leveraging our iWay tools to enable big data sources to be contextualized into integrated data sets that support downstream business analysis and processes. The use of these tools maximizes value from big data sources like IoT. They eliminate the challenges of fragmented, inaccurate, or incomplete data, and ensure that the resulting information is comprehensive, and of the highest quality at all times.  With the big data analytics market estimated to be $203 billion in 2020, ROI is a key driver, and contextualization is key to empowering users to leverage big data in meaningful and relevant ways.
 

2. Smart Analytics Opens New Doors

Smart analytics (including smart data discovery) is a fast emerging sector, driven by the competitive need to derive as much value as possible, and as fast as possible from an ever increasing combination of data sources.  For example smart analytics can help a non-technical user generate immediate value from data via a spoken request (e.g. “How were our sales last month, where should we improve?”) that constructs an analysis to provide business conclusions and recommendations.  Smart analytics can leverage machine learning and artificial intelligence to automate the exploration of data models to identify new trends, opportunities and predictions. Smart analytics also includes self-service data management, BI asset search, and other innovations that open the door for more data-driven insight to be achievable by more stakeholders, in new, easier and clever ways.  Keep your eyes on this space!
 

3. The Scope of Self-Service BI Broadens to ‘Everyone’

Not long ago self-service BI was hailed as the Shangri-La of decision making, where non-technical users could discover answers to business questions without the assistance of IT.  Well that has proven true for some, but no more than 10-20% employees have the skills, time or inclination to learn data discovery tools in order to achieve this.  BUT, self-service is highly desirable, in the same way an ATM or airport check-in machine provides easy-to-use autonomous value.   We now see organizations providing data-driven insight to the 80-90%, by delivering a combination of self-service tools, 'InfoApps' (information applications), and 'In-Document Analytics' (interactive documents). The key is having a technology that offers the flexibility to deliver many forms of self-service, whether via pure discovery, tailored consumption, or analytical documents.
 

4. Embedded BI is a Key to Higher Adoption

Embedded BI is the integration of BI and analytics content into commonly used business applications, which enables your users to benefit from actionable insights in the context of their usual workflow. We are seeing tremendous interest in both internal and external embedded use cases. Internal:  organizations are increasing their BI adoption levels by embedding BI inside off-the-shelf and custom applications, especially for operational users. Many users are not even aware they are interacting with BI software as they optimize their daily tasks. External:  organizations can provide their customers access to analytics embedded within portals or web applications.  For example banking customers who access their statements online can explore their spending trends or run what-if scenarios with investments.  The key to success is to leverage an underlying technology that provides seamless integration, ease of branding, out-of-the-box content options, comprehensive security features, and scalability on a single, unified platform.
 

5. New Revenue Streams: Monetizing Your Data

Perhaps the most obvious way to generate more money from data is to sell it. Gartner’s Doug Laney stated, “Companies in a variety of information-rich industries are already generating entirely new revenue streams, business units, and standalone businesses out of the data they hold.” Significant financial value is locked away in your enterprise systems - the process of realizing it is known as data monetization. There are actually two primary ways to monetize your data:  you can use it to generate more revenue, or leverage it to uncover hard-dollar cost-savings.  We are seeing an upsurge as more organizations are monetizing their data to either drive revenue, increase productivity, or reduce expenses.  The market is driving all organizations to look at their data in a different, broader sense and to evaluate the feasibility of data monetization. More and more companies are investing in our out-of-the-box capabilities that transform analytics from a cost center to a profit center. 

We also created a related infographic with links to additional resources HERE

Thanks for your time today - see you next time!

Andy McCartney