Business Intelligence Trends 2020

January 13, 2020
News, Technology
12 min read

Business intelligence today is in a unique position to meet the demand for the advanced analytics that enterprises are requiring to stay competitive, identify opportunities, optimize operations, and leverage trends among their buyers and suppliers. While the capabilities in BI are rapidly evolving, the ability for organizations to make full use of business analytics cannot be satisfied by the existing pool of skilled technical resources, creating an incredible opportunity for growth in BI solutions. Thus, BI software is becoming multi-faceted, and the technology is taking big leaps so that BI not only assists with collecting and storing data, but also with automation, advanced AI, operational analysis, and big-picture analytics.

In 2019, business intelligence trends saw BI providers and their products becoming more influential and useful in business decision making, both in long-term strategy and on short-term opportunities and corrections. Some BI offerings solidified their position as an irreplaceable necessity for day-to-day business activities through a two-prong approach: by advancing the capabilities of BI solutions to be more accessible and consumable by the average, non-technical business users who need them and through educating the general market on the role BI can play in improving operations and overall strategy.

In terms of accessibility, the development and demand for ‘cloud computing’ is a fast-moving trend that opens-up new possibilities for BI and its consumers. By having BI offered through cloud-based analytics solutions, providers of business intelligence can support the delivery of analytics through software that no longer has to be downloaded on a device and manually managed by scarce technical resources allowing more organizations to leverage BI’s vast abilities in a landscape where data is the only universal currency.

Some movements began to emerge in 2019, but have not become as widely consumable yet, such as automated data science tasks, data governance, and natural language processing (NLP). These are worth noting as influential BI trends for 2020 because they hold an incredible amount of promise into the new year as they continue to develop. For instance, natural language processing ensures easy accessibility to analytics through vocalizing requests to the software. But NLP is still under development to build vocabulary and rid complications with understanding broken sentences and extracting semantic meanings, hence it has not realized its full potential. Additionally, data governance ensures the BI back-end consisting of the data warehouse and cubes contain accurate and reliable information, arguably the most crucial foundation and component to BI practices overall. However, standardization on roles, accountability, and approach leave a wide range of optimization for data governance practices in the coming years.

Business Intelligence Trends

This article will review emerging BI trends for 2020, what tech is enabling BI software, and how these progressions will help businesses around the world.


As mentioned before, using NLP as part of the BI workflow was a trend that indeed advanced in the last year. The technology for NLP – composing of the combination of machine learning and linguistics that enables you to speak to your device like it is a person – has made incredible progress recently. NLP is also commonly paired with visualizations for users to interact with their data naturally.

Natural language processing will continue to play a vital role in allowing valuable data insights for companies in 2020 as its use can bring the benefits of data analysis to every level of user. It effectively eliminates barriers for accessing data within a BI product because the user doesn’t require a deep understanding of the data structure or much preliminary knowledge on that tool itself. Consequently, according to Gartner, upwards of 50% of analytics queries in 2020 will be made via search, natural-language query, voice, or auto-generation.

Natural language is not only proving useful in accessing optical workflows, benchmarking reports, and data visualization dashboards from your data warehouse, but it is also becoming a data-collection source. Thanks to AI and NLP, known voice-activated assistants like Siri and Alexa will even start transcribing language and converting it into structured data to be analyzed.


With cloud services and platforms becoming an integrated part of everyday life both for individuals and for businesses around the world, many organizations have already adopted cloud-based BI services and solutions to benefit from the reduced maintenance and cost advantages it has to offer. MLAAS refers to several machine learning tools as part of cloud computing services. These include data modeling APIs, machine learning algorithms, data transformations and visualizations, and predictive analytics. There is wide-spread attraction and demand to get started with machine learning tools quickly, without having to install software or provision on in-house servers, so the cost-benefit relationship is favorable for the multitude of services garnered.

Data visualization, NLP, and predictive analysis are forecasted to become the most in-demand MLaaS services in 2020, underlining their relevance for companies that have no time or resources to invest in complicated data analysis tools.


While most employees understand the importance data plays in the success of any modern-day business, the significance of proper metadata administration is still unfamiliar to most. Metadata means ‘data that provides information about other data,’ or ‘data about data,’ and it is actually not a new term at all. However, it is becoming increasingly more well-known and crucial in commerce considering the scope of data to which it applies.

‘Big Data’ was the ‘big buzzword’ some years ago. By definition, it emphasizes the magnitude of data sets that are collected in totality by enterprises today. Consequently, the more volume, velocity, and variety of data that keeps increasing, the more we need metadata to help us make sense of it. Metadata becomes particularly essential for business intelligence tools, which rely on structured data sets to help users analyze information.

Metadata also plays a critical role in the data warehousing and data governance process of business intelligence. A well-organized metadata set becomes the inventory list that informs what data is stored in the warehouse, and it will search back to the sources of the data and piece together if any imperative data is missing or broken. Metadata consolidation is an effort designed to reduce rework, eliminate corrupt data, and prevent the loss of user confidence.


Where traditional BI would mostly collect, aggregate, and store data, embedded analytics picks up where this leaves off as the integration of analytic content and capabilities within business process applications. In practice, embedded analytics enables employees to use the BI software delivering analytics in their current workflow integrated into the apps they already familiar with and using.

Embedded analytics have been around for quite some time, but what makes the concept more relevant now is how embedded analytics can integrate with AI functions that facilitate the ability to understand and interact with data organically. Even for large data sets, embedded analytics unlocks the possibility to collect and analyze all the relevant data to deliver a recommendation or insight only when needed in the employee’s daily workflow. Furthermore, AI will soon be able to notice unusual or valuable pattern-changes by itself, regardless of the size and complexity of data sets. Therefore, businesses will only need to enter raw data to get actionable insights in return, reducing the barrier to entry for self-service BI.

The delivery of business insights with embedded analytics will provide individualized and relevant information in a familiar and easy-to-understand format, but only when it is useful or requested from the user to minimize ‘data overload,’ which was often the result of embedded analytics in previous applications. Embedded analytics can provide personalized and contextual information, enabling and guiding any user in the organization to the next steps in their workflow by quickly reviewing recommendations and taking action – a valuable tool in a fast-paced world.


Data monetization is officially here to stay. The world’s leading brands are already profiting from the data collected from their users, and many organizations are looking at new ways of using their data to position themselves in an increasingly competitive marketplace. Savvy businesses will start putting a more realistic value on the data they keep. Come 2020, expect both data monetization by selling and indirect data monetization to become standard. Businesses will use indirect data monetization to identify relevant marketing data from their information inventory to determine new revenue opportunities with much better accuracy.

According to, 17 percent of companies have already established data monetization initiatives, the most common being the provisioning of data via benchmarking and reporting. This effort, of course, requires well-implemented BI solutions. Business intelligence software, tools, and dashboards that deal with packaging and visualizing historical performance data to support decisions are hugely valuable in the day-to-day management of operations to monetize data. Not surprisingly, the most common obstacle that businesses encounter in their data monetization objectives is data quality, which is why the sophistication of an organization’s BI technology will greatly influence their ability to convert data into monetary value. Data mining, or slicing and dicing on past performance information to support tactical decisions, are prerequisites for proper data monetization.


When business intelligence and analytics play a huge role in better understanding your customers, optimizing your operations, and making actionable business decisions, it is paramount that the data you are using is guaranteed to be correct. The focus on having confidence in the quality and credibility of your data is a significant trend in business intelligence that will continue into 2020.

A BI tool is only as good as the data on which it relies. Failing to maintain accurate data will bear high costs in the future, especially as AI and BI tools continue to develop, and the risk of actioning from incorrect answers increases exponentially. Unfortunately, not many businesses organize or control their data, and this is where master data management (MDM) comes into play. MDM is a favored approach to preparing data for BI and analytics. It uses master data to establish a uniform framework across your organization, focusing on providing rules and standards for presenting your key business entities cohesively to deliver a dependable foundation for analytics trusted by employees.

Accurate data lies at the heart of every business’s operational steering. The goal of master data management is to bring together and exchange master data such as customer, supplier, or product master data from disparate applications, entities, or data silos. Quality data and the processes to manage it should be a priority for any company employing a BI solution as it directly correlates with an organization’s ability to make the right decisions.


Cognitive computing (CC) is an up-and-coming technology worth monitoring in the coming years. CC uses artificial intelligence, neural networks, machine learning, natural language processing, sentiment analysis, and contextual awareness to mimic the human thought process. Cognitive computing can ingest massive amounts of structured and unstructured data with text, video, audio, images, social media content, emails, website server, and application logs, then rearrange it into manageable content.

The technology is very complex, and according to Gartner, cognitive computing will disrupt the digital sphere, unlike any other technology introduced in the last 20 years. The software’s ability to gather predictive analytics tools, data discovery, and most recently, AI under one roof shows great promise. Analyzing emerging patterns, spotting business opportunities, and taking care of critical process-centric issues in real-time can help simplify processes, reduce risk, and pivot according to changing circumstances. However, technology this advanced has some hurdles in its path to success. Early adoption is set back by flaws in security and human hesitance, such as the fear CC will replace human work. Software this extensive also needs quite a bit of change management for a successful introduction within an organization, which can be pricey and time-consuming.


BI software unifies data, performs accurate analysis, and mobilizes employees with the intelligence they need to make informed decisions. As time passes, BI becomes faster, more sophisticated, and more accurate, while data analysis becomes more robust, and forecasting grows in importance.

As we revealed through the emerging BI trends for this coming year, the future holds an incredible amount of promise for organizations using business intelligence software to unify and control data outputs, perform sophisticated analysis from anywhere, and eliminate the barriers for accessing advanced analytics so every user, and contributor, can be empowered with informed decisions. With the amount of data collected by enterprises growing exponentially every year, using these rapidly advancing business intelligence solutions to ensure proper data management, quality, and translation for meaningful answers to everyday questions, will be imperative to improving the outcome of business performance and surviving the competitive landscape.

By staying aware of the upcoming business intelligence trends, and partnering with a trusted analytics solution provider like Tigunia, businesses can set themselves up for success this year and into the years to come. We believe that real results come from going beyond merely implementing the latest in technology and rely on more on a dedication to matching the BI solutions and services that fit your objectives and your organization’s needs. BI is far from one-size-fits-all. Unfortunately, many businesses end up spending too much time, resources, and dollars chasing BI trends or dashboards they don’t need or deploying them in a way that doesn’t meet the individual business requirements or goals. The good news is, there are cost-effective business intelligence solutions that can easily integrate with your existing ERP, CRM, and other industry data sources. And Tigunia can help you find them.


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