Over the past decade, business intelligence has been revolutionized. Data exploded and became big. We all gained access to the cloud. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. Suddenly advanced analytics wasn’t just for the analysts.

2019 was a particularly major year for the business intelligence industry. The trends we presented last year will continue to play out through 2020. But the BI landscape is evolving and the future of business intelligence is played now, with emerging trends to keep an eye on. In 2020, BI tools and strategies will become increasingly customized. Businesses of all sizes are no longer asking if they need increased access to business intelligence analytics but what is the best BI solution for their specific business. Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. It will also be a year of collaborative BI and artificial intelligence. We are excited to see what this new year will bring. Read on to see our top 10 business intelligence trends for 2020!

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Let’s Discuss These 10 Business Intelligence Trends

1) Data Quality Management (DQM)

The analytics trends in data quality grew greatly this past year. The development of business intelligence to analyze and extract value from the countless sources of data that we gather at a high scale, brought alongside a bunch of errors and low-quality reports: the disparity of data sources and data types added some more complexity to the data integration process.

A survey conducted by the Business Application Research Center stated the data quality management as the most important trend in 2020. It is not only important to gather as much information possible, but the quality and the context in which data is being used and interpreted serves as the main focus for the future of business intelligence.

*Source: Business Application Research Center*

Accordingly, the rise of master data management is becoming a key priority in the business intelligence strategy of a company.

Today, most companies understand the impact of data quality on analysis and further decision-making processes and hence choose to implement a data quality management (DQM) policy, department, or techniques. DQM is indeed reckoned as the key factor in ensuring efficient data analysis, as it is the basis from where all the rest starts from. According to Gartner, poor data quality is estimated to cost organizations an average of $15 million per year in losses. The consequences of bad data quality are numerous; from the accuracy of understanding your customers to constructing the right business decisions. That’s why it is of utmost importance to start with utilizing the right key performance indicators – there are numerous KPI examples that can make or break the quality process of data management.

DQM consists of acquiring the data, implementing advanced data processes, distributing the data effectively and managing oversight data. We detailed the benefits and costs of good or bad quality data in our previous article on data quality management, where you can read the five important pillars to follow.

Data quality management is not only uprising in the BI trends 2020, but also growing to a crucial practice to adopt by companies for the sake of their initial investments. Meeting strict data quality levels also meets the standards of recent compliance regulations and demands. By implementing company-wide data quality processes, organizations improve their ability to leverage business intelligence and gain thus a competitive advantage that allows them to maximize their returns on BI investment.

2) Data Discovery/Visualization

Data discovery has increased its impact in the last year. The already mentioned survey conducted by the Business Application Research Center listed data discovery in the top 3 business intelligence trends by the importance hierarchy. BI practitioners steadily show that the empowerment of business users is a strong and consistent trend.

An essential element to consider is that data discovery tools depend upon a process, and then, the generated findings will bring business value. It requires understanding the relationship between data in the form of data preparation, visual analysis and guided advanced analytics. “The high demand for data discovery tools reflects a huge shift in the BI world towards increased data usage and the extraction of insights,” the Research Center emphasizes. Using online data visualization tools to perform those actions is becoming an invaluable resource to produce relevant insights and create a sustainable decision-making process. That being said, business users require software that is:

Easy to use

Agile and flexible

Reduces time to insight

Allows easy handling of a high volume and variety of data

Discovering trends in business operations that you didn’t even know were there or enabling immediate actions when a business anomaly occurs have become invaluable tools in effectively managing businesses of all sizes.

Since humans process visual data better, the data discovery trend will find increment as one of the most important BI trends in 2020.

3) Artificial Intelligence

This is one of the major trends chosen by Gartner in their 2020 Strategic Technology Trends report, combining AI with autonomous things and hyperautomation, and concentrating on the level of security in which AI risks of developing vulnerable points of attacks. Artificial intelligence (AI) is the science aiming to make machines execute what is usually done by complex human intelligence. Often seen as the highest foe-friend of the human race in movies (Skynet in Terminator, The Machines of Matrix or the Master Control Program of Tron), AI is not yet on the verge to destroy us, in spite the legit warnings of some reputed scientists and tech-entrepreneurs.

While we work on programs to avoid such inconvenience, AI and machine learning are revolutionizing the way we interact with our analytics and data management while increment in security measures must be taken into account. The fact is that it is and will affect our lives, whether we like it or not.

Businesses are evolving from static, passive reports of things that have already happened to proactive analytics with live dashboards that help businesses to see what is happening at every second and give alerts when something is not how it should be. Solutions such as an AI algorithm based on the most advanced neural networks, provides high accuracy in anomaly detection as it learns from historical trends and patterns. That way, any unexpected event will be immediately registered and the system will notify the user.

Another feature that AI has on offer in BI solutions is the upscaled insights capability. It basically fully analyzes your dataset automatically without needing an effort on your end. You simply choose the data source you want to analyze and the column/variable (for instance, revenue) that the algorithm should focus on. Then, calculations will be run and come back to you with growth/trends/forecast, value driver, key segments correlations, anomalies, and what-if analysis. That is an incredible time gain as what is usually handled by a data scientist will be performed by a tool, providing business users with access to high-quality insights and a better understanding of their information, even without a strong IT background.

Time gain is also present in the form of AI assistants. Tools have started to develop artificial intelligence features that enable users to communicate with the software in plain language - the user types a question or request, and the AI generates the best possible answer.

The demand for real-time online data analysis tools is increasing and the arrival of the IoT (Internet of Things) is also bringing an uncountable amount of data, which will promote the statistical analysis and management at the top of the priorities list. However, businesses today want to go further and predictive analytics is another trend to be closely monitored.

Another increasing factor in the future of business intelligence is testing AI in a duel. To illustrate, one AI will create a realistic image, and the other will try to determine whether the image is artificial or not. This concept is called generative adversarial networks (GANs) and can be used in online verification processes, like CAPTCHA technology. When the dueling happens several times, the AI can become smarter to evaluate and break that kind of online security systems. Tech giants use AI in many different ways that will alternate the machine learning process and we should keep an eye on this process in 2020.

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4) Predictive And Prescriptive Analytics Tools

Business analytics of tomorrow is focused on the future and tries to answer the questions: what will happen? How can we make it happen? Accordingly, predictive and prescriptive analytics are by far the most discussed business analytics trends among the BI professionals, especially since big data is becoming the main focus of analytics processes that are being leveraged not just by big enterprises, but small and medium-sized businesses alike.

Predictive analytics is the practice of extracting information from existing data sets in order to forecast future probabilities. It’s an extension of data mining which refers only to past data. Predictive analytics includes estimated future data and therefore always includes the possibility of errors from its definition, although those errors steadily decrease as software that manages large volumes of data today becomes smarter and more efficient. Predictive analytics indicates what might happen in the future with an acceptable level of reliability, including a few alternative scenarios and risk assessment. Applied to business, predictive analytics is used to analyze current data and historical facts in order to better understand customers, products, and partners and to identify potential risks and opportunities for a company.

Industries harness predictive analytics in different ways. Airlines use it to decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect on any given night in order to adjust prices to maximize occupancy and increase revenue. Marketers determine customer responses or purchases and set up cross-sell opportunities, whereas bankers use it to generate a credit score – the number generated by a predictive model that incorporates all of the data relevant to a person’s creditworthiness. There are plenty of big data examples used in real life, shaping our world, be it in the buying experience or managing customers’ data.

Predictive analytics must also become accessible for everyone, and in the year 2020, we will witness even more relevance that will cater to that notion. Self-service analytical possibilities are becoming a criterion for BI vendors and companies alike; both can profit from it and bring more value to their businesses. The predictive models, in practice, use mathematical models to predict future happenings, in other words, forecast engines. Users simply select past data points, and the software automatically calculates predictions based on historical and current data, as shown in the example:

**click to enlarge**

Among different predictive analytics methods, two are quite popular among data scientists: artificial neural networks (ANN) and autoregressive integrated moving average (ARIMA).

In artificial neural networks, data is being processed in a similar way as in biological neurons. Technology duplicates biology: information flows into the mathematical neuron, is processed by it and the results flow out. This single process becomes a mathematical formula that is repeated multiple times. As in the human brain, the power of neural networks lies in their capability to connect sets of neurons together in layers and create a multidimensional network. The input to the second layer is from the output of the first layer, and the situation repeats itself with every layer. This procedure allows for capturing associations or discovering regularities within a set of patterns with the considerable volume, number of variables or diversity of the data. ARIMA is a model used for time series analysis that applies data from the past to model the existing data and make predictions about the future. The analysis includes inspection of the autocorrelations – comparing how the current data values depend on past values – especially choosing how many steps into the past should be taken into consideration when making predictions. Each part of ARIMA takes care of different sides of model creation – autoregressive part (AR) tries to estimate current value by considering the previous one. Any difference between predicted data and real value is used by the moving average (MA) part. We can check if these values are normal, random and stationary – with constant variation. Any deviations in these points can bring insight into the data series behavior, predicting new anomalies or helping to discover underlying patterns not visible by the bare eye. ARIMA techniques are complex and drawing conclusions from the results may not be as straightforward as for more basic statistical analysis approaches. But once the basic principles are grasped, the ARIMA provides a very powerful tool for predictive analysis.

Prescriptive analytics goes a step further into the future. It examines data or content to determine what decisions should be made and which steps taken to achieve an intended goal. It is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning. Prescriptive analytics tries to see what the effect of future decisions will be in order to adjust the decisions before they are actually made. This improves decision-making a lot, as future outcomes are taken into consideration in the prediction. Prescriptive analytics can help you optimize scheduling, production, inventory, and supply chain design to deliver what your customers want in the most optimized way.

5) Collaborative Business Intelligence

Today, managers and workers need to interact differently as they face an always-more competitive environment. More and more, we see a new kind of business intelligence rising: the collaborative BI. It is a combination of collaboration tools, including social media and other 2.0 technologies, with online BI tools. This is developed in a context of enhanced collaboration addressing the new challenges the fast-track business provides, where more analyses are done and reports edited. When talking about collaborative BI, the term “self-service BI” quickly pops up in the sense that those self-service tools do not require an IT team to access, interpret and understand all the data.

These BI tools make the sharing easier in generating automated reports that can be scheduled at specific times and to specific people. For instance; they enable you to set up business intelligence alerts, share public or embedded dashboards with a flexible level of interactivity. All these possibilities are accessible on all devices which enhances the decision-making and problem-solving processes.

Collaborative information, information enhancement, and collaborative decision-making are the key focus of new BI tools. But collaborative BI does not only remain around some documents’ exchanges or updates. It has to track the various progress of meetings, calls, e-mails exchanges, and ideas collection. More recent insights predict that collaborative business intelligence will become more connected to greater systems and larger sets of users. The team’s performance will be affected, and the decision-making process will thrive in this new concept. Let’s see how it will be developed in the business intelligence trends topics of 2020.

6) Data-driven Culture

We have mentioned the importance of data-driven decision making in businesses, but next year, creating a data-driven culture in the whole organization will be one of the top priorities for BI professionals and business managers – one of the trends in data analytics that will certainly be most discussed. Making a decision without relying on data could lead to potential damages that will be hard to recover from, but implementing the data culture across departments can prove to be beneficial across the board: the mentality of employees will change, data will be stored on the cloud where is easily accessible, accurate market segmentation will become a standard, and the costs will significantly decrease.

The agility that a data-driven culture will provide a company with is unmeasurable – the response to market changes will be easy to detect and quicker to implement. There is simply too much data available today and companies need to find solutions that will ensure their competitive advantage. By empowering each and every employee to work with data and to base their decisions on what information they can derive from that data, each business has a chance to thrive in our cutthroat digital environment, no matter the industry. Building advanced analytics models that can optimize outcomes is one of the latest BI trends that will shape the future of BI.

Not just predictive models will affect data management of various businesses but also connecting data to a single point of truth with the help of numerous data connectors. Multiple sources are no longer frozen in one department but easily accessible by everyone in a company, and these emerging trends in business intelligence will not be just a cool “science experiment,” but first an exercise to make better decisions, and later on a business standard.

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7) Augmented Analytics

We’re continuing our list of trends in business analytics with the augmented properties that have entered the analytics world in recent years and next year will be even more focused on changes in analytics. Connected with our trend in creating a data-driven culture to be able to make better decisions, augmented analytics is, according to Gartner, on place no.1 for data analytics trends in 2020. Automating findings and optimizing decision-making will certainly impact businesses of all sizes.

The central notion of augmented analytics is that uses machine learning automation and AI techniques to “augment human intelligence and contextual awareness.” It drives less biased decisions and more awareness across the business and it will cause a new wave of analytics disruption in 2020. That doesn’t mean that the skills of a data scientist or advanced analytical capabilities will completely vanish. They will be augmented to the point that analytics could be used by everyone in a company without the need to study complex mathematics or computer science but utilizing them with the help of modern software. It will bring the possibility that even average business users will be able to build analytics models and take advantage of complex formulas in a more simple and approachable way.

The augmented analytics market is estimated to reach USD 13 BN by 2023, with the CAGR of an astonishing 24%. This data certainly gives the industry more room to develop with technologies such as machine learning and artificial intelligence. Let’s take the manufacturing industry, for example. Augmented analytics can identify meaningful relationships between certain metrics and various other business variables, generate a dashboard and present the data story in a simple, non-technical manner. Another useful utilization would be in the form of digital assistants that are capable to listen and respond to voice commands of production workers. Manufacturing analytics is just one part of the industries affected by these changes and we will yet to see how it will develop in the future as well.

8) Mobile BI

Mobile business intelligence is becoming more incorporated into BI solutions and next year the trend will certainly not lose its importance. In fact, it is one of the most prominent emerging trends in business intelligence identified by almost 3000 professionals in the industry by the research we mentioned at the beginning of the article.

A few years ago, mobile BI was considered a huge swirl in the BI and analytics community. The market penetration is still growing, although slowly, but next year we will see even more vendors and BI solutions that will have this option within their software such as modern mobile dashboards. But not just vendors, companies will also implement mobile solutions and actively use it since it will provide them with numerous benefits: accessing your information at any time, and any place – while riding on a train or relaxing on a beach. Physical presence at an office site is less necessary each year and this is certainly affecting the BI industry as well. Mobile BI enables companies to have access to their data also in real-time, ensuring faster reactions to any business occurrences and giving more freedom to users that are currently not in the office but need to access their data.

This is one of the business intelligence market trends that is not going to vanish anytime soon. Since it has been evaluated at USD 6.18 BN in 2018, it is also predicted to grow with a CAGR rate of 22.43% by reaching 2024. While there are challenges that are affecting companies’ decisions to implement mobile BI such as limited screen size and design of the interface to ensure the best possible usability, mobile is undoubtedly going to stay as one of the trends that will be considered by companies in 2020.

9) Data Automation

Business intelligence topics wouldn’t be complete without data (analysis) automation. In the last decade, we saw so much data produced, stored, and ready to process that companies and organizations were seriously looking for modern data automation solutions to tackle massive volumes of information that has been collected. Gartner predicts that next year more than 40% of data science tasks will be automated, hence, this is one of the trends in business intelligence that we need to keep an eye on.

Dozens of tools and disparate sources are still part of the bottleneck that businesses are facing today. BI has come to the solution to enable users to consolidate all the data that a company manages and provides methods to discover, analyze, measure, monitor and evaluate large scale data. We have mentioned hyperautomation in our article for the top 10 IT buzzwords for 2020 which Gartner predicts will explode in the next year, and we certainly agree.

Business intelligence has brought many automation possibilities and in 2020, we will see even more. Long-standing barriers between data scientists and business users are being slowly mixed into a one-stop-shop for any data requirement a company might have – from collecting, analyzing, monitoring and reporting on findings. A scenario might include intelligent reporting – predictive analytics and automated reports increase the business users’ capabilities to automate data on their own, without the help of the IT department. On the other hand, data scientists still will manage complex analysis where manual scripting and coding is necessary.

Let’s now tackle the last of our BI and analytics trends 2020!

10) Embedded Analytics

When data analytics occurs within a user’s natural workflow, embedded analytics is the name of the game. Businesses have recognized the potential of embedding various BI solutions such as KPI dashboards or reports into their own application and thus improving their decision-making processes and increasing productivity. Formerly strangled by spreadsheets, companies have realized how utilizing embedded BI enables them to provide higher value within their own applications. In fact, according to Allied Market research, the embedded analytics market is projected to reach $60.28 BN by 2023, with a CAGR of 13.6% from 2017, and this is one of the business analytics topics we will hear even more in 2020.

Whether you need to create a sales report or send multiple dashboards to clients, embedded analytics is becoming a standard in business operations and in 2020, we will see even more companies adopting it. Departments and company owners are looking for professional solutions to present their data without the need to build their own software. By simply white labeling the chosen application, organizations can achieve a polished presentation and reporting which they can offer to consumers. This is one of the trends in analytics that can be implemented immediately since many vendors already offer this opportunity and ensure that the application works seamlessly and without much complexities.

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What Are The Analytics & Business Intelligence Trends For 2020?

We’ve summed up in this article what the close future of business intelligence looks like for us. Here are the top 10 analytics and business intelligence trends we will talk about in 2020:

Data Quality Management Data Discovery/Visualization Artificial Intelligence Predictive and Prescriptive Analytics Tools Collaborative Business Intelligence Data-driven Culture Augmented Analytics Mobile BI Data Automation Embedded Analytics

Become Data-driven In 2020!

Being data-driven is no longer an ideal; it is an expectation in the modern business world. 2020 will be an exciting year of looking past all the hype and moving towards to extract the maximum value from state-of-the-art online reporting tool.

If you’re ready to start your business intelligence journey, or keep up with the 2020 trends, trying our software for a 14-day trial will do the trick! And it’s completely free!