Looking at the Shift in Analytics Adoption Through Trends in Business Intelligence

Lyndsay Wise's picture
 By | december 22, 2017
in analytics, data monetization, Internet of Things, Big Data
december 22, 2017

It is the time of year when analysts and vendors make predictions about upcoming trends in technology adoption and analytics use. I usually stay away from making my own predictions. My years as an analyst covering the mid-market have taught me that sometimes the trends being discussed apply to early adopters, or that, although organizations are leveraging upcoming and popular technologies, many struggle with how to leverage these technologies to gain quantifiable business value. At the same time, the recent trends identified by Jake Freivald highlight important ways in which organizations are taking advantage of analytics and better overall data management.

The trends identified by Jake were:

  • Internet of Things (IoT)
  • Embedded Analytics
  • Predictive Analytics
  • Artificial Intelligence (AI)
  • Data Monetization

The interesting thing about each of these is that they represent the expansion of analytics in general. Organizations are finally leveraging their data beyond looking at analytics for reporting and trends analysis. Now they want to ensure visibility into their operations and tie these deployments to better efficiencies, higher profit margins, and new/enhanced business opportunities.

Data monetization takes embedded analytics to the next level. Some of the consulting projects I've worked on were a combination of the two, basically leveraging embedded analytics internally and externally to gain visibility into customers for employees, and providing customers with value-added services that could be tied to revenue. Both leverage the benefits of embedded analytics by white-labeling analytics and providing solutions within other applications. The benefits of this type of analytics expansion is that analytics is no longer a separate entity but provides continuous visibility into the business to help monitor and act upon daily operations.

Predictive analytics and artificial intelligence support organizations with mature analytics deployments that want to identify patterns and develop recommendations to enable proactive business practices. Depending on the specific business need, industry, and analytics environment, AI and predictive analytics involve complexities that require high levels of data and a deep understanding of that data.

In some ways, IoT represents an anomaly when one looks at analytics trends strictly as the collection and availability of sensor-related data. IoT extends beyond analytics and includes several vertical markets. Anything from smart cities and transportation to saving people’s lives through healthcare-related device monitoring are included in IoT. With big data infrastructures, this information can be stored. The value is being able to leverage this data for analytics and to support better healthcare outcomes, manage cities and utilities more effectively, and ensure better product quality.

All of these trends require an understanding of an organization's data infrastructure and business needs. This means that organizations need to be aware that although they may want to apply predictive models within their companies or leverage IoT analytics, many technical and business requirements are needed to get there. Sometimes the hype of being able to take advantage of trends creates the opinion that actual adoption is an automatic extension of current use. The reality for many organizations, however, is that taking advantage of these trends requires some level of business intelligence maturity or, alternatively, the right infrastructure, to be able to benefit from these trends.