AT A GLANCE

  • In 2015, there was a shift towards businesses leveraging self-service analytics data; this year, some experts believe the focus will be on information on demand
  • Prescriptive analytics goes a step further than predictive analytics to help determine what decisions should be made and what steps should be taken to achieve an intended goal
  • In 2016, experts expect more busienss users to seek inclusion and self-reliance through data analysis, especially as more digitally-native millennials enter the workforce

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Mar 01, 2016

What’s Trending in Business Intelligence?

The business intelligence (BI) landscape is in flux. Big Data, cloud services, predictive analytics, and data science are continuously innovating spaces that feed into BI, and constantly changing the role it plays within enterprises. In 2015, there was a shift towards businesses leveraging self-service analytics data. This change is driven by fast-moving technology, and by new techniques to derive value from data. Organizations began to realize the value of data as an important tool towards strategic planning and productivity. This year, some experts believe that the focus will be on information on demand.

Predictive and Prescriptive Analytics

Predictive analytics is defined as the practice of extracting information from existing data sets to forecast future probabilities. It indicates what might happen in the future with an acceptable level or reliability, including some alternative scenarios and risk assessment. In terms of business application, predictive analytics is used to analyze current data and historical facts to better understand customers, products, and partners, and to identify potential opportunities and risks for an organization.

Prescriptive analytics goes a step further. It examines data or content to determine what decisions should be made and what steps should be taken to achieve an intended goal. Often, it is characterized by techniques such as graph analysis, simulation, neural networks, recommendation engines, and machine learning. The goal of prescriptive analytics is to see what the effect of future decisions will be, which helps to adjust decisions before they are actually made.

Visual Data Discovery

Visual data discovery is used to find patterns or structures in data sets that at first glance seem complex and opaque. Using different data visualization tools helps to discover the relationships between data elements across multiple data sets. This allows users to arrive at data insights, and gives them the ability to respond quickly to reduce risk, enhance profits, or jump on opportunities.

Additionally, there will be more emphasis on providing user-friendly tools for business users. Experts predict that business analytics will slowly move away from traditional compliance reporting that involves rows and columns to visualization snippets such as dashboards for browser-based applications and simple graphical elements on mobile devices.

Self-Service Analytics Tools

In 2016, experts expect more business users to seek inclusion and self-reliance through data analysis, especially as more digitally-native millennials enter the workforce. Rather than just consuming information, users want to engage in data preparation and processing. The demand for self-service data preparation tools is expected to grow, as people demand to have the ability to respond quickly to shifting priorities.

Cloud Analytics

In 2015, business users began to embrace the cloud, realizing that storing data in the cloud was easy and very scalable. This year, they are realizing that cloud analytics will allow them to be more agile. More and more companies are planning on using cloud analytics to analyze data faster. Some examples of cloud analytics products and services include hosted data warehouses, SaaS BI tools, and cloud-based social media analytics.

Bootstrapping

Bootstrapping is a term that is used in many different contexts and has different meanings for entrepreneurs and for programmers. In terms of business intelligence, bootstrapping is defined as a the process of building a complex analysis from a simple starting point by taking samples from the same data over and over again to estimate how accurate estimates about the entire data set are. The analysis is gradual and progressively more advanced as the model learns from previous data.

“2016 is the year of modern BI and Analytics [BI&A] platform. The BI&A market is in the final stages of a multiyear shift from IT-led, system-of-record reporting to pervasive, business-led, self-service analytics. Organizations will continue to transition to easy-to-use, fast, agile, and trusted modern BI&A platforms deployed across the enterprise to create business value from deeper insights into diverse data sources.” ~Rita Sallam, Research Vice President at Gartner

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