August 20, 2019 • Richard Neisz

The Future of Business Intelligence: The Trends You Need to Know for 2019 and Beyond

We hear a lot about data these days. From big data to data analytics, the importance of data to the success of an organization is undeniable. As a result, business intelligence (BI) and its role within organizations has changed drastically in the past decade. In the early days, BI was confined to spreadsheets full of numbers and information that often felt disconnected from the everyday reality of the business. Now, we have the cloud and new ways of gathering, storing and analyzing data that allow for insightful visualizations and immediate action. Business intelligence is no longer just for the business analysts.

Today, companies don’t ask if they need business intelligence analytics, but what BI solution is best for their needs and goals. At Smart Resources, we worked with our expert BI consultants to assemble a resource designed to take a business through the stages of developing a comprehensive and competitive business intelligence strategy. Our eBook, Your Start-to-Finish Guide to Implementing Vital Business Intelligence Strategies, can be downloaded here.

However, as data continues to grow and technologies evolve, it’s important to keep up on the emerging trends and changes in business intelligence. For many BI experts, topics like data quality management, artificial intelligence, and actionable analytics are top of mind in 2019. Let’s examine the current trends shaping BI efforts and what the future of business intelligence holds.

1. Data Quality Management and Curation

With the rise of diverse and numerous data sources and the importance of data in driving decisions across organizations, the management and quality of data have become crucial components of BI. Data Quality Management (DQM) refers to the process of acquiring data, implementing advanced processing, distributing data effectively and ensuring sufficient oversight. DQM forms the foundation from which all other business intelligence efforts proceed, and thus data management is a key priority for organizations in 2019.

While data management has become a critical piece of modern BI deployments, organizations have also begun to focus on data curation. According to Tableau’s 2019 Business Intelligence Trends, curation refers to the way a business captures, cleans, defines and aligns different data, creating a link between disconnected data and real-world applications. Data curation has also led to the rise of important new processes such as data catalogs, semantics and natural language processing, which allow regular users to communicate with BI platforms in more streamlined ways and quickly arrive at new, useful insights.

Moving forward, Gartner predicts that data management will be augmented thanks to machine learning and AI, which will automate many of the manual tasks involved in DQM and database management. This in turn allows less technical users more autonomy when using data and frees highly skilled tech talent to focus on other areas. Similarly, advances in data curation and semantic search will help businesses to move beyond simple data analysis to examine broader business questions.

2. AI and Continuous Intelligence

The rise of artificial intelligence and machine learning is impacting how people live and work across industries and organizations. In the area of business intelligence, augmented analytics is changing how data is analyzed, consumed and shared across organizations. Augmented analytics is the use of automated algorithms to explore data and hypotheses that data scientists alone don’t have the capacity to study. With machine learning however, analysts can explore greater amounts of data and uncover additional business insights.

In addition to augmented analytics, another important AI trend affecting business intelligence is continuous intelligence. Relying on real-time analytics and integrating them with business operations, continuous intelligence uses current data to improve business decisions. Using multiple technologies, continuous intelligence can help companies make smart, real-time decisions using live dashboards instead of static reports. Seen by experts as the ultimate in operational BI, continuous intelligence and the augmented analytics that support it both offer great opportunities as well as challenges for BI professionals.

3. Putting Data into Context

Today, organizations have access to huge amounts of data and the ability to automate, calculate and analyze this information more accurately than ever. But all of this is useless if data analysts and experts are unable to communicate the information to others in ways that make an impact. As a result, another important trend dominating business intelligence is actionable analytics and data storytelling to communicate current data discoveries and share future predictions.

Before we consider actionable analytics, let’s first explore the use of predictive and prescriptive analytic tools to forecast future business. Predictive analytics, an extension of data mining, involves taking information from existing data sets to evaluate, with some reliability, what could happen in the future. Using either Artificial Neural Networks or ARIMA methods, predicative analytics can help businesses understand their customers and products, while eliminating risks and taking advantage of new opportunities. Prescriptive analysis goes even further by considering the effect of future decisions before they are made. Both these analytic methods help improve decision making by taking future outcomes into consideration in the present.

In order to make these current and future analytics actionable, it’s important to have data and actions in the same place. Current BI platforms are beginning to respond to this need by putting core business operations and processes through mobile analytics, embedded analytics, dashboard extensions and APIs. This allows workers to get the insights they need without switching to another application and lets them take immediate action to make changes based on the data they are receiving.

The final piece in producing actionable, sharable data is data storytelling. Usually defined as part data visualization and part explanation of the process that led to the insight, data storytelling is less about noting a singular conclusion and more focused on nurturing a broad conversation around the data itself. From a business intelligence perspective, visualization technologies and tools like natural language processing will aid in the expansion of storytelling formats and, ultimately, help shape how companies use data to inform decisions and test ideas that benefit the bottom line.