5 Major Trends Shaping the Evolution of Analytics and Business Intelligence: Gartner

The Gartner, Inc. report “Hype Cycle for Analytics and Business Intelligence, 2019” reveals five key trends impacting the analytics and business intelligence (BI) market.

“As intelligence is at the core of all digital businesses, IT and business leaders continue to make analytics and BI their top innovation investment priority,” said Jim Hare, research vice president at Gartner. “This Hype Cycle helps data and analytics leaders make the transition to augmented analytics, to build a digital culture and operationalizing and scaling analytics initiatives.”

Figure 1. Hype Cycle for Analytics and Business Intelligence, 2019

Source: Gartner (October 2019)

The five key trends are:

1. Augmented Analytics

Augmented analytics uses machine learning to automate data preparation, insight discovery, data science, and machine learning model development and insight sharing for a broad range of business users, operational workers and citizen data scientists.

As it matures, augmented analytics will become a key feature of modern analytics platforms. It will deliver analysis to everyone in an organization in less time, with less of a requirement for skilled users, and with less interpretative bias than current manual approaches. As the technology develops, there will be more citizen data scientists. Gartner predicts that, by 2020, citizen data scientists will surpass data scientists in the amount of advanced analysis they produce, largely due to the automation of data science tasks.

2. Digital Culture

Developing an effective digital culture may be the first and most important step an organization takes in its digital transformation journey. “Data literacy, digital ethics, privacy, enterprise and vendor data-for-good initiatives encompass digital culture,” said Mr. Hare.

Any organization that aims to derive value from data and is on its journey towards digital transformation must focus on developing data literacy. Gartner analysts expect data literacy to impact all employees by becoming not just a business skill but a critical life skill.

Concerned by the rise of artificial intelligence (AI), digital society and “fake news,” individuals, organizations and governments are increasingly interested in digital ethics. Data and analytics (D&A) leaders should sponsor discussions about digital ethics to ensure information and technology is used ethically to gain and retain the trust of employees, customers and partners.

Gartner predicts that, by 2023, 60% of organizations with more than 20 data scientists will require a professional code of conduct incorporating ethical use of D&A.

3. Relationship Analytics

The emergence of relationship analytics highlights the growing use of graph, location and social analytical techniques to understand how different entities of interest — people, places and things — are connected. Analyzing unstructured, constantly changing data can provide users information and context about associations in a network and deeper insights that improve the accuracy of predictions and decision-making.

Many of the highest value applications are focused on discovery, where the questions to be answered are not known in advance. For example, relationship analytics based on graph techniques can identify illegal behavior and criminal activity. By analyzing formal and informal networks of people, law enforcement agencies can identify money laundering and other criminal activities. It becomes easier for them to distinguish between malignant and benign behavior within networks.

4. Decision Intelligence

D&A leaders draw on a wealth of data from ecosystems that are in constant motion. This requires them to use a multitude of techniques to manage data effectively. The unpredictability of the outcomes of today’s decision models often stems from an inability properly to capture and account for the uncertainty factors linked to these models’ “behavior” in a business context. Decision intelligence provides a framework that brings together traditional and advanced techniques to design, model, align, execute, monitor and tune decision models.

5. Operationalizing and Scaling

The number of use cases at the core of a business, on its edges and beyond is exploding. More people want to engage with data, and more interactions and processes need analytics in order to automate and scale. Analytics services and algorithms are increasingly activated whenever and wherever they are needed. Whether to justify the next big strategic move or to optimize millions of transactions and interactions gradually, analytics tools and the data that powers them are showing up in places where they rarely existed before. This is adding a whole new dimension to the concept of “analytics everywhere.”