Over the past decade, business intelligence has been revolutionized. Data exploded and became big. And just like that, 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.
2021 was a particularly major year for the business intelligence industry. The trends we presented last year will continue to play out through 2022. But the BI landscape is evolving and the future of business intelligence is played now, with emerging trends to keep an eye on. In 2022, 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, especially with the help of modern BI dashboard software. 2022 will be the year of data security 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 2022!
Let’s Discuss These 10 Business Intelligence Trends
1) Artificial Intelligence
We will start our analysis of what is new in business intelligence with AI. This is a trend that is wildly being covered by Gartner in their latest Strategic Technology Trends report, combining AI with engineering and hyperautomation, and concentrating on the level of security in which AI risks 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 of 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.
It is expected that in the coming year’s AI will evolve into a more responsible and scalable technology as organizations will require a lot more from AI-based systems. According to Gartner’s Data and Analytics research for 2021, with COVID-19 completely changing the business landscape, historical data will no longer be the main driver of AI-based technologies. In change, these solutions will need to work with smaller datasets and more adaptive machine learning while also being compliant with new privacy regulations. This concept is known as ethical AI and it aims to ensure that organizations use AI systems in a way that will not break the law. Many companies have faced legal issues for illegally collecting data from users. The Facebook and Cambridge Analytica scandal is a perfect example. We will discuss data security as our second BI trend for 2022.
Businesses are evolving from static, passive reports of things that have already happened to proactive analytics with 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 provide 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 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 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 and we will cover it later in this post.
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 system. 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 2022.
2) Data Security
Data and information security have been on everyone’s lips in 2021, and they will continue to buzz the world in 2022. The implementation of privacy regulations such as the GDPR (General Data Protection Regulation) in the EU, the CCPA (California Consumer Privacy Act) in the USA, and the LGPD (General Personal Data Protection Law) in Brazil have set building blocks for data security and management of users’ personal information.
Moreover, the recent overturn by the European Court of Justice of the legal framework called Data Privacy Shield hasn’t made software companies’ life much easier. The Shield was a legal framework that enabled companies to transfer data from the EU to the USA but, with recent legal developments causing the invalidation of the process, companies that have their headquarters in the US don’t have the right to transfer any of the EU data subjects.
Actually, a similar situation happened already in 2015 when the EU and the USA had no legally valid agreements on this matter for a while. Many US-based (software) companies argue that they use European servers, and there is no data transfer to the US at all. However, from a legal perspective, even this solution is questionable, as, in theory, the US judiciary could force US-based companies to reveal even data from EU-based servers. In essence, the data that is located in the EU needs to stay in the EU. In practice, that means that EU-based businesses that use in the current situation US-based software vendors that store any kind of data for them are taking hazards as they operate in a legal grey area. For companies such as datapine, this doesn’t represent a big issue since the registration, business, and servers are located in the EU.
Taking all this into account, businesses have been forced to invest in security to stay compliant with the new regulations, but also to protect themselves from cybercrime. In fact, it is expected that the global spending on cybersecurity products will reach $1.75 trillion in the next 5 years. This is not a surprise to the experts. During 2020 and the beginning of COVID-19, companies of all sizes were forced to mutate from physical to digital and, to accelerate the transformation, they relied on online services leaving a gap for cybercriminals to attack. According to the 2021 KPMG CEO Outlook Pulse survey, the cyber security risk is the greatest threat for CEO’s in the next 3 years. As seen in the graph below it increased from 10% in 2020 to 18% in 2021.
This concern in cybersecurity also presents a challenge for SaaS BI tools as they need to make sure they are offering a secure product that clients will trust with their sensitive data. Just like any other cloud solution, online business intelligence tools are also subjected to security risks. Some of them include processing data quickly to provide real-time insights that might be subjected to regulatory compliance, vulnerabilities when moving data from user’s systems to the BI tool’s cloud, or when the tool provides access to data from multiple devices that may be unsafe and exposed to attacks, among others. To avoid any of this happening BI software needs to have a clear focus on security.
One of the latest approaches to help SaaS BI solutions stay safe is cybersecurity mesh architecture. Cybersecurity mesh is a composable and scalable security control that aims to protect digital assets that reside in applications, in the cloud, IoT, and others. It seeks to establish a defined security perimeter around a person or a specific point, with a more modular approach, for example, enabling users to access data from their smartphones in a secure way. One of the cybersecurity predictions for 2021-2022 from Gartner stated that by the end of 2024, organizations adopting cybersecurity mesh architecture will reduce the financial impact of security incidents by around 90%. Since data breaches have been regularly in the news, buzzing industries, and average users, the demand for security products and services is understandable.
3) Data Discovery/Visualization
Data discovery has increased its impact in the last year. A survey conducted by the Business Application Research Center listed data discovery in the top 4 business intelligence trends by the importance hierarchy for 2022. BI practitioners steadily show that the empowerment of business users is a strong and consistent trend.
Essentially, data discovery is the process of collecting data from various internal and external sources and using advanced analytics and visualizations to consolidate all the information. This allows businesses to keep every relevant stakeholder engaged with the data by empowering them to analyze and manipulate the information in an intuitive way and extract actionable insights. To achieve this, businesses of all sizes turn to modern solutions such as business intelligence tools that offer data integration, interactive visualizations, a user-friendly interface, and the flexibility to work with big amounts of data in an efficient and intuitive way.
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.
Data visualization has evolved into a state-of-the-art solution to present and interact with numerous graphics on a single screen, whether it’s focused on developing sales charts, or comprehensive interactive reports. The point is that data discovery is a process that enables decision-makers to reveal insights and by using visualizations, teams have the chance to spot trends and major outliers within minutes.
For 2022 the dashboard will continue to be a major visual communication tool that will enhance collaboration between teams by being the analytical hub of a project. But more than just a visualization tool, KPI dashboards will take its interactivity features to the next level with technologies such as AI-based alarms and real-time data. Since humans process visual data better, the data discovery trend will find increment as one of the most important BI trends in 2022.
4) Data Quality Management
With so much information being produced every second, using quality data when performing analysis has become a critical element, and therefore, a relevant business intelligence trend to look out for in 2022. Considering that poor data quality costs single businesses between $9.7 and $14.2 million per year, it is impossible to ignore the importance of this trend as working with insufficient data can not only be a waste of resources but can harm businesses deeply. Some of the consequences of bad data quality can include wrongly generated marketing budgets, how accurately businesses understand customer behaviors, how quickly they can turn leads into sales, and even bigger business decisions such as wrong investment or resources allocations. With all this in mind, data quality management (DQM) has come as a relief for businesses to efficiently deal with their information.
Essentially, data quality management ensures that companies can make the right data-driven decisions by using the correct data for their analytical purpose. This means there is no definitive truth about the way businesses can measure the quality of the data as this solely depends on the context. That said, there are guidelines to follow in order to ensure a successful data management process, some of them include data being accurate, consistent, complete, timely, and compliant. Meaning, no duplicate or missing values, no outdated data that doesn’t represent the required timeline, and no data that is not consistent. A simple example of data consistency would be that the sum of employees in each department does not exceed the total number of employees in that organization.
The important takeaway from this insightful analytical trend is that is not going anywhere. Data has become a critical part of business success. Every day, companies are collecting more complex data from several sources that need to be carefully managed using the right tools and processes. With stricter compliance requirements being placed regularly, ensuring that everything is in place with the data available becomes even more important. That’s why data quality management will become one of the fundamental business intelligence industry trends for 2022.
5) Predictive & 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 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 that 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 2022, 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:
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 – the autoregressive part (AR) tries to estimate the 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, predict new anomalies, or help 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 are 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, and these are some of the emerging trends in business intelligence 2022 that we will hear more about.
6) Real-time Data & Analytics
The need for real-time data has tremendously evolved this year and will continue to do so as one of the data analytics trends for 2022. We have seen since the pandemic arrived, that the needs for real-time and accurate updates are critical in developing proper strategies to respond to such unfortunate situations. Some countries have used data to make the best possible decisions, and companies followed to ensure survival in these uncertain times. Real-time access to data has become a norm in everyday life, not just for businesses, but the general public as well, where we could see press conferences filled with the most recent information, graphs, and statistics that have defined some of the strategies against the pandemic. But not only; creating ad hoc analysis has enabled businesses to stay on top of changes and adapt to immense challenges that this year has brought.
In business is similar: forecasting and alarms will inevitably become used much more in developing proper business responses and strategies for future endeavors with more variables brought into the equation. Moreover, implementing live dashboards will help companies to immediately access relevant information regarding their business and react if any potential issues arise. Up-to-date data is becoming more important than ever before, and since the world has changed, companies need to adapt as well. High gear for data access is becoming the norm and is one of the reasons why some companies can survive, and others not.
Trends in business analytics will certainly have real-time data as one of the main drivers in 2022 and we will, without a doubt, see more of it in action.
7) 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 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, critical for today’s ever-changing environment. This is especially necessary now that the pandemic has forced businesses to shift to a home office dynamic in which collaboration needs to be supported by the right tools more than ever.
Collaborative information, information enhancement, and collaborative decision-making are the key focus of new BI solutions. 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.
In fact, it is expected that for 2022 collaborative BI will move further from just sharing insights and will start from earlier stages. Starting from data exploration and spreading across the entire analytical workflow for a more efficient decision-making process including every stakeholder no matter their location. Let’s see how it will be developed in the business intelligence trends topics of 2022.
8) Data Literacy
As data becomes the foundation of strategic decisions for businesses of all sizes, the ability to understand this data and use it as a collaborative tool that everyone in the organization can use becomes critical for success. That said, data literacy will be one of the relevant data analytics trends to look out for in 2022.
Data literacy is defined as the ability to understand, read, write, and communicate data in a specific context. This means understanding the techniques and methods used to analyze the data as well as the tools and technologies implemented. According to Gartner, poor data literacy is listed as the second-biggest roadblock to the success of the CDO’s office, and it adds that by 2023 data literacy will become essential in driving business value.
Even with the rise of self-service tools that are accessible for everyone, data literacy continues to be the foundation of a successful data-driven culture. Business leaders are responsible for providing the needed training and tools to the entire organization so that everyone will be empowered to work with data and analytics. To achieve a successful data literacy process, a careful assessment of the skills of employees and managers needs to be made in order to identify weak spots and gaps. Gartner recommends starting by identifying fluent data users that can serve as “mediators” for non-skilled groups as well as identifying communication barriers where data is failing its purpose. With all this knowledge in hand, the creation of targeted training instances will become an easier task.
In the long run, with the proper training and the right tools, users from all levels of knowledge will be able to perform advanced analysis and use data as their main language. With technologies such as predictive analytics becoming accessible for regular users, data science will no longer need to be performed by experts- shifting these professionals to focus on other advanced tasks such as Machine Learning or MLOps. In fact, according to Gartner, it is expected that by 2025 the shortness of data scientists will no longer be an obstacle for businesses to adopt advanced technological processes. That said, data literacy will be a regular topic in the BI industry during the coming year.
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. A survey by KDNuggets predicts that in the next decade, data science tasks will be automated, hence, this is one of the trends in business intelligence that we need to keep an eye on since we don’t know when it will exactly happen.
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 which Gartner predicts will explode in the next year, and we certainly agree. This new trend refers to the action in which businesses automate as many processes as possible by using multiple tools and technologies such as AI, machine learning, low-code, and no-code tools, among others.
Business intelligence has brought many automation possibilities and in 2022, 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, reporting, and sharing 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 are necessary.
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 components such as 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 dashboards 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 $77.52 BN by 2026, with a CAGR of 13.6% from 2017, and this is one of the business analytics topics we will hear even more in 2022.
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 2022, 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.
More than just embedding a dashboard or BI features to an application, embedding analytics allows for collaboration by keeping every single stakeholder involved. By providing clients and employees the possibility to manipulate the data in a well-known environment you facilitate the extraction of insights from every area of your business. This makes it one of the fastest-growing business intelligence trends from this list.
Business Wire recently published a report called “Global Embedded Analytics Market (2021 to 2026) – Growth, Trends, COVID-19 Impact, and Forecasts” in which they mention that “organizations are deploying embedded analytics solutions to realize significant gains in revenue growth, marketplace expansion, and competitive advantage”. They also add that embedding analytics is expected to significantly grow in the healthcare industry in the coming years. Taking into consideration the massive amounts of data that hospitals collect, which got even bigger with COVID-19 and telemedicine interactions, leads healthcare businesses to “switch from paying for service volume toward service value”. By using a powerful healthcare analytics software that can be embedded, hospital managers can extract valuable insights that will help them to optimize processes from a clinical, operational and financial point of view.
This is one of the trends in business analytics that can be implemented immediately since many vendors already offer this opportunity and ensure that the application works seamlessly and without much complexity.
What Are The Analytics & Business Intelligence Trends For 2022?
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 2022:
- Artificial Intelligence
- Data Security
- Data Discovery/Visualization
- Data Quality Management
- Predictive And Prescriptive Analytics Tools
- Real-time Data And Analytics
- Collaborative Business Intelligence
- Data Literacy
- Data Automation
- Embedded Analytics
Become Data-driven In 2022!
Being data-driven is no longer an ideal; it is an expectation in the modern business world. 2022 will be an exciting year of looking past all the hype and moving towards extracting the maximum value from state-of-the-art online business intelligence software.