Top 10 Business Intelligence and Analytics Trends for 2022
Business intelligence has changed dramatically during the last decade. Data multiplied and grew in size.
Suddenly, we had all obtained access to the cloud. Spreadsheets have finally given way to interactive business dashboards and actionable and intelligent data visualizations. The democratization of the data product chain was aided by the advent of self-service analytics. Advanced analytics was no longer reserved for analysts.
The year 2021 will be remembered as a watershed moment in the business intelligence industry. The patterns we discussed last year will continue until 2022. However, the BI landscape is changing, and the future of business intelligence is being played now, with new trends to watch. BI tools and techniques will become more personalized in 2022. Businesses of all sizes are now asking what is the ideal BI solution for their specific organization, rather than if they need more access to business intelligence insights.
Companies are no longer debating if data visualizations improve analysis, but rather how to best communicate each data story, especially with the support of contemporary BI dashboard tools. Data security and data discovery will be the focus in 2022, with clean and secure data mixed with a clear and compelling presentation. It will also be a year of collaborative business intelligence and AI. We’re looking forward to seeing what this new year has in store for us. Continue reading to learn about our top ten business intelligence trends for 2022.
Let’s Have a Conversation About These 10 Business Intelligence Trends
1) Artificial Intelligence (AI).
We’ll begin by looking at what’s new in business intelligence using AI. Gartner’s current Strategic Technology Trends study focuses on this trend, which combines AI with engineering and hyper automation, and focuses on the level of security in which AI risks establishing susceptible areas of attack.
Artificial intelligence (AI) is a branch of research that aims to make machines do things that complicated human intelligence would normally do. Despite the legitimate warnings of certain reputable scientists and tech-entrepreneurs, AI is not yet on the verge of destroying the human race (Skynet in Terminator, The Machines of Matrix, or the Master Control Program of Tron).
While we work on programs to avoid such inconvenient situations, AI and machine learning are transforming the way we engage with our analytics and data management systems, and increased security precautions must be considered. Whether we like it or not, it is and will continue to have an impact on our lives.
Organizations will demand a lot more from AI-based systems in the coming year, thus it’s projected that AI will progress into a more responsible and scalable technology. Historical data will no longer be the primary driver of AI-based solutions, according to Gartner’s Data and Analytics research for 2021, with COVID-19 fundamentally transforming the corporate environment. In order to comply with new privacy requirements, these solutions will need to work with smaller datasets and more flexible machine learning. This notion is referred to as ethical AI, and it tries to ensure that businesses use AI systems in a legal manner. Many businesses have experienced legal ramifications as a result of improperly gathering data from users. The Cambridge Analytica and Facebook controversy is a classic illustration. As our second BI trend for 2022, we’ll talk about data security.
Businesses are transitioning from static, passive reports of events that have already occurred to proactive analytics with dashboards that allow businesses to view what is going on in real-time and provide alerts when something isn’t working as it should. As it learns from historical trends and patterns, solutions like an AI algorithm based on the most advanced neural networks give great accuracy in anomaly detection. Any unexpected incident will be immediately recorded, and the user will be notified by the system.
Another advantage of AI in BI tools is the ability to provide upscaled insights. It basically analyzes your dataset in its entirety without requiring any effort on your part. You simply select the data source and the column/variable (for example, revenue) on which the algorithm should focus. After that, calculations will be performed and you will receive growth/trends/forecast, value driver, key segment correlations, anomalies, and what-if analysis. This is a huge time saver because what a data scientist would normally do will be done by a tool, giving business users access to high-quality insights and a better knowledge of their data, even if they don’t have a strong IT background.
AI helpers are another means of gaining time. Artificial intelligence capabilities have begun to emerge that allow users to speak with software in plain language: the user inputs a question or request, and the AI delivers the best possible response.
The demand for real-time online data analysis tools is growing, and the Internet of Things (IoT) is providing an inexhaustible amount of data, putting statistical analysis and management at the top of the priority list. However, today’s organizations want to go even further, and predictive analytics is another trend to keep an eye on, as we’ll discuss later in this post.
Testing AI in a duel is another growing trend in the future of business intelligence. For example, one AI will construct a realistic image, while the other will try to identify whether or not the image is real. GANs (generative adversarial networks) are a type of online verification system that can be utilized in CAPTCHA technology. When a fight occurs multiple times, AI can learn to analyze and break that type of online security mechanism. In 2022, we should keep a watch on how tech giants use AI in a variety of methods that will change the machine learning process.
2) Data Protection
In 2021, data and information security were on everyone’s minds, and they will continue to do so in 2022. Privacy rules such as the GDPR (General Data Protection Regulation) in the European Union, the CCPA (California Consumer Privacy Act) in the United States, and the LGPD (General Personal Data Protection Law) in Brazil have laid the groundwork for data security and management.
Furthermore, the European Court of Justice’s recent reversal of the Data Privacy Shield legal framework hasn’t made software businesses’ lives any easier. The Shield was a legal framework that allowed corporations to transfer data from the EU to the United States, but due to recent legislative changes invalidating the process, companies with headquarters in the United States no longer have the right to transfer any EU data subjects.
In fact, a similar situation occurred in 2015, when the EU and the US were unable to reach legally binding agreements on this issue for a period of time. Many (software) companies established in the United States assert that they use European servers and that no data is sent to the United States. However, even this option is problematic from a legal standpoint, as the US judiciary might theoretically require US-based corporations to release data stored on EU-based computers. In other words, data that is stored in the EU must remain in the EU. In effect, this implies that EU-based enterprises that utilize US-based software vendors to keep any kind of data for them are putting themselves in jeopardy because they are operating in a legal grey area. This isn’t a major deal for companies like datapine because their registration, business, and servers are all in the EU.
As a result of all of this, businesses have been forced to spend on security not only to comply with new legislation but also to protect themselves against cybercrime. In fact, in the next five years, global spending on cybersecurity solutions is predicted to surpass $1.75 trillion. Experts are not surprised by this. Companies of all sizes were compelled to transition from physical to digital between 2020 and the start of COVID-19 and to speed up the process, they relied on online services, leaving a gap for cybercriminals to exploit. According to the KPMG CEO Outlook Pulse study for 2021, the greatest threat to CEOs in the next three years is cyber security. As you can see in the graph below, it increased from 10% in 2020 to 18% in 2021.
This cybersecurity worry poses a difficulty for SaaS BI solutions, as they must ensure that they are providing a secure product that clients can trust with their sensitive data. Online business intelligence solutions, like any other cloud service, are vulnerable to security threats. Processing data quickly to deliver real-time insights that may be subject to regulatory compliance, risks while transporting data from user systems to the BI tool’s cloud, and providing access to data from different devices that may be hazardous and vulnerable to assaults are just a few examples. To avoid any of this, BI software must place a strong emphasis on security.
Cybersecurity mesh architecture is one of the most recent techniques to keep SaaS BI systems safe. Cybersecurity mesh is a modular and scalable security control that strives to secure digital assets in applications, the cloud, the Internet of Things, and other places. With a more modular approach, it aims to create a defined security perimeter around a person or a specific site, allowing users to securely access data from their smartphones, for example. According to one of Gartner’s cybersecurity predictions for 2021-2022, firms that employ cybersecurity mesh architecture will lower the cost impact of security events by roughly 90% by the end of 2024. The demand for security products and services is understandable, given the prevalence of data breaches in the news, bustling industries, and everyday people.
3) Data Visualization/Discovery
In recent years, data discovery has become more important. Data discovery was ranked as one of the top four business intelligence trends for 2022 in a poll performed by the Business Application Research Center. The empowerment of business users is a significant and constant trend among BI practitioners.
Essentially, data discovery is the process of gathering data from a variety of internal and external sources and combining it with advanced analytics and visualizations. This enables businesses to keep all key stakeholders engaged with data by empowering them to study and manipulate data in a natural way and derive actionable insights. Businesses of all sizes are turning to modern solutions like business intelligence tools that provide data integration, interactive visualizations, a user-friendly interface, and the flexibility to work with large amounts of data in an efficient and straightforward manner to do.
One thing to keep in mind is that data discovery tools are based on a process, and the results created will provide business value. It necessitates data preparation, visual analysis, and guided advanced analytics to comprehend the relationship between data. The Research Center underlines that “the increasing demand for data discovery tools represents a tremendous shift in the BI sector toward higher data consumption and the extraction of insights.” The use of online data visualization tools to carry out those tasks is quickly becoming a key resource for generating relevant insights and establishing a long-term decision-making process. Business users, on the other hand, require software that is:
- Simple to use
- Flexible and adaptable
- Reduces the time it takes to gain insight
- Allows for easy handling of a large amount of data in a variety of formats.
Discovering previously unknown trends in business operations or enabling prompt response when a business abnormality happens have become indispensable tools in effectively managing enterprises of all sizes.
Whether it’s for producing sales charts or comprehensive interactive reports, data visualization has evolved into a state-of-the-art solution for presenting and interacting with multiple graphics on a single screen. The point is that data discovery is a process that allows decision-makers to uncover insights, and teams may notice trends and large outliers in minutes by employing visualizations.
The dashboard will remain a primary visual communication tool in 2022, enhancing team cooperation and serving as the project’s analytical center. KPI dashboards will go beyond visualization to include technologies like AI-based alarms and real-time data, making them more than just a visualization tool. The data discovery trend will grow in importance as one of the most important BI trends in 2022, as humans digest visual data better.
4) Data Quality Control
With so much data being generated every second, employing quality data when conducting analysis has become a vital component, and thus a relevant business intelligence trend to watch in 2022. When you consider that poor data quality costs a single business between $9.7 and $14.2 million per year, it’s impossible to overlook the significance of this trend, as working with insufficient data is not only a waste of time and resources, but it can also be detrimental to a company’s bottom line. Incorrectly produced marketing budgets, how precisely firms understand client habits, how quickly they can transform leads into sales, and even larger business decisions such as incorrect investment or resource allocations are some of the repercussions of poor data quality. With all of this in mind, data quality management (DQM) has proven to be a boon to businesses looking to manage their data more efficiently.
Essentially, data quality management guarantees that businesses can make the best data-driven decisions possible by ensuring that they are using the correct data for their analysis. This means that there is no universal truth regarding how businesses may assess data quality because it is entirely dependent on the context. Data must be accurate, consistent, comprehensive, timely, and compliant, to name a few standards to follow in order to achieve a successful data management process. That is, there should be no duplicate or missing values, no obsolete data that does not represent the relevant chronology, and no inconsistent data. The sum of employees in each department does not exceed the total number of employees in that business is a basic example of data consistency.
The key lesson from this astute analytical trend is that it is here to stay. Data has evolved into a crucial component of corporate success. Every day, businesses collect more complicated data from a variety of sources, which must be carefully managed with the appropriate tools and methods. As compliance requirements get more stringent on a regular basis, ensuring that everything is in place and that data is available becomes even more critical. As a result, one of the most important business intelligence industry trends for 2022 will be data quality management.
5) Analytics Tools for Predictive and Prescriptive Analysis
Tomorrow’s business analytics is focused on the future and attempts to answer the following questions: what will happen? How are we going to make it happen? As a result, predictive and prescriptive analytics is by far the most talked-about business analytics trends among BI practitioners, especially now that big data is becoming the focal point of analytics processes used by both large corporations and small and medium-sized firms.
The method of gathering information from existing data sets in order to determine future probability is known as predictive analytics. It’s a subset of data mining that solely considers historical data. As software that manages enormous volumes of data today grows smarter and more effective, predictive analytics contains predicted future data and so always includes the chance of errors in its definition, though those errors are progressively decreasing. Predictive analytics provides a reasonable estimate of what will happen in the future, as well as a few alternative scenarios and risk assessments. Predictive analytics is a business tool that analyzes current data and past facts to better understand customers, goods, and partners, as well as identify possible dangers and possibilities.
Predictive analytics is used in a variety of industries. It is used by airlines to determine how many tickets to sell at each price point for a given flight. Hotels strive to forecast how many guests they will have on any given night so that they may modify prices to maximize occupancy and revenue. Marketers use it to forecast client responses or purchases and set up cross-sell opportunities, whereas bankers use it to calculate a credit score, which is a number calculated by a predictive model that takes into account all relevant data about a person’s creditworthiness. There are several real-world examples of big data being utilized to shape our reality, whether it is in the purchasing process or in the management of client data.
Predictive analytics must also become more accessible to the general public, and by 2022, we will see even more relevance that will support this idea. Self-service analytical capabilities are becoming a criterion for BI suppliers and businesses alike; both may benefit and add value to their organizations. In practice, predictive models, often known as prediction engines, use mathematical models to anticipate future events.
Data is processed in artificial neural networks in the same manner as it is in biological neurons. The technology mimics biology: data enters the mathematical neuron, it is processed, and the results are output. This one-time technique is turned into a mathematical formula that may be repeated many times. The power of neural networks, like that of the human brain, resides in their ability to connect groups of neurons in layers to form a multidimensional network. The second layer receives its input from the first layer’s output, and the situation repeats itself with each subsequent layer. With a large volume, a number of variables, or diversity of data, this approach enables for capturing correlations or detecting regularities within a group of patterns.
ARIMA is a time series analysis model that uses historical data to model current data and make predictions about the future. Inspection of autocorrelations – evaluating how present data values depend on historical values – is part of the study, as is determining how many steps into the past should be taken into account when making predictions. The autoregressive part (AR) seeks to estimate the current value by considering the previous one, while the other parts of ARIMA take care of other aspects of model building. The moving average (MA) portion uses any discrepancy between projected and actual data. We can use constant variation to see if these numbers are normal, random, and stationary. Any discrepancies in these points can provide insight into the behavior of the data series, anticipate new abnormalities, or aid in the discovery of underlying patterns that are not evident to the naked eye. Because ARIMA procedures are sophisticated, deriving conclusions from the results may be more difficult than with more basic statistical analysis methods. However, once the fundamentals are understood, the ARIMA becomes a highly effective tool for predictive analysis.
Prescriptive analytics takes things a step further. It looks through data or information to see what decisions should be made and what activities should be done to accomplish a goal. Techniques including graph analysis, simulation, complicated event processing, neural networks, recommendation engines, heuristics, and machine learning are used to define it. Prescriptive analytics aims to predict the consequences of future decisions so that they can be adjusted before they are made. This greatly enhances decision-making because future consequences are factored into the prediction. Prescriptive analytics may help you improve scheduling, production, inventory, and supply chain design to give what your consumers want in the most efficient way possible, and these are some of the new trends in business intelligence 2022 about which we will hear more.
6) Data and analytics in real-time
The demand for real-time data has exploded this year, and it will continue to do so in 2022, according to one of the data analytics trends. Since the outbreak of the pandemic, we’ve seen how important it is to have real-time and precise information for building adequate response measures. Data has been used by certain countries to make the best judgments possible, and businesses have followed suit to secure survival in these unpredictable times. Real-time data access has become the norm in everyday life, not only for businesses but also for the general public, as seen by press briefings packed with the most up-to-date facts, graphs, and statistics that have shaped some of the pandemics strategies. Not only that, but ad hoc analysis has allowed firms to keep on top of changes and react to the enormous obstacles that this year has thrown at them.
With additional variables in the mix, forecasting and alarms will eventually become much more important in formulating correct corporate responses and strategies for future initiatives. Furthermore, integrating live dashboards will enable businesses to quickly obtain pertinent information about their operations and respond if any possible difficulties occur. Data that is up to date is more vital than ever before, and because the world has evolved, businesses must adapt as well. High-tech data access is becoming the norm, and it’s one of the reasons why certain businesses will thrive while others will perish.
Real-time data will undoubtedly be one of the key drivers of business analytics trends in 2022, and we will undoubtedly see more of it in action.
7) Business Intelligence Collaboration
Managers and employees must engage differently nowadays as they face an ever-increasingly competitive environment. A new type of business intelligence is gaining traction: collaborative BI. It’s a mix of collaborative tools, such as social media and other 2.0 technologies, and online business intelligence tools. This is being created in the context of increased collaboration to handle the new issues that the fast-track industry presents, where more studies and reports are being edited. When it comes to collaborative BI, the term “self-service BI” comes to mind because self-service technologies don’t require an IT team to access, interpret, and comprehend all of the data.
These BI solutions make it easier to share information by generating automatic reports that can be sent to specified people at specific times. They allow you to create business information alerts and provide public or embedded dashboards with a customizable level of interactivity, for example. All of these options are available on all devices, which improves decision-making and problem-solving processes, which are crucial in today’s constantly changing world. This is especially important now that the epidemic has driven businesses to migrate to a home office environment, where communication is more important than ever.
The focus of contemporary BI systems is on collaborative information, information improvement, and collaborative decision-making. However, collaborative BI is not limited to document exchanges or updates. It must keep track of meetings, phone conversations, e-mail exchanges, and the gathering of ideas. According to more recent research, collaborative business intelligence will become more integrated to more systems and people. In this new concept, the team’s performance will be affected, and the decision-making process will flourish.
In fact, by 2022, it is projected that collaborative BI will have progressed beyond simply exchanging insights and will begin at an earlier level. Starting with data discovery and moving through the complete analytical workflow for a more efficient decision-making process that includes all stakeholders, regardless of location. Let’s see how it progresses in the 2022 business intelligence trends subjects.
8) Knowledge of Data
As data becomes the cornerstone of strategic choices for businesses of all sizes, the ability to comprehend this data and use it as a collaborative tool that everyone in the organization can use becomes increasingly important. In 2022, data literacy will be one of the most important data analytics trends to watch.
The ability to comprehend, read, write, and transmit data in a specific context is referred to as data literacy. This entails comprehending the data analysis methodologies and processes, as well as the tools and technologies used. Poor data literacy, according to Gartner, is the second-largest hurdle to the success of the CDO’s office, and data literacy will become important in driving business value by 2023.
Data literacy is the core of a successful data-driven culture, despite the rise of self-service tools that are accessible to everyone. Business leaders are responsible for providing all employees with the necessary training and tools to enable them to work with data and analytics. A rigorous assessment of the skills of employees and managers is required to identify weak spots and gaps in order to establish a successful data literacy approach. Start by identifying fluent data users who can act as “mediators” for less-skilled groups, as well as communication hurdles where data is failing to fulfill its purpose, according to Gartner. Creating focused training instances will be easy now that you have all of this information.
Users of various levels of knowledge will be able to undertake advanced analysis and utilize data as their primary language in the long term, given the right training and tools. Data science will no longer be required of experts as tools like as predictive analytics become more widely available, allowing these professionals to focus on more complex jobs such as Machine Learning or MLOps. Indeed, Gartner predicts that by 2025, the scarcity of data scientists will no longer be a barrier to firms adopting modern technology procedures. Data literacy, on the other hand, will be a hot subject in the BI business in the coming year.
9) Data Processing Automation
Without data (analysis) automation, business intelligence themes would be incomplete. We’ve seen so much data produced, saved, and available to handle in the previous decade that businesses and organizations have been looking for modern data automation solutions to deal with large amounts of data. According to a KDNuggets poll, data science chores will be automated over the next decade, therefore this is one of the business intelligence trends to keep an eye on because we don’t know when it will happen.
The bottleneck that businesses face today still includes dozens of tools and disparate sources. BI is a system that allows users to integrate all of a company’s data and provides ways for discovering, analyzing, measuring, monitoring, and evaluating large-scale data. Hyperautomation was featured in our piece on the top 10 IT buzzwords that Gartner believes will explode in the coming year, and we agree. This new trend refers to firms automating as many operations as possible utilizing a variety of tools and technologies, including artificial intelligence (AI), machine learning, low-code, and no-code solutions, among others.
Many automation opportunities have arisen as a result of business analytics, and we will see even more in 2022. Long-standing boundaries between data scientists and business users are gradually being dissolved, resulting in a one-stop-shop for whatever data demand a firm may have, including data collection, analysis, monitoring, reporting, and sharing. Intelligent reporting – predictive analytics and automated reports boost the ability of business users to automate data on their own, without the assistance of the IT department, in one scenario. Data scientists, on the other hand, will continue to manage complicated analyses that require human scripting and coding.
Let’s take a look at the last of our BI and analytics predictions for 2022!
10) Embedded Analytics
Embedded analytics is the name of the game when data analytics occurs within a user’s normal workflow. Businesses have realized the value of integrating BI components such as dashboards and reports into their own applications, hence boosting decision-making and increasing productivity. Companies have discovered that using embedded dashboards allows them to give more value within their own apps, which was previously suffocated by spreadsheets. According to Allied Market Research, the embedded analytics market will reach $77.52 billion by 2026, with a CAGR of 13.6 percent from 2017, and this is one of the business analytics topics we’ll hear about even more in 2022.
Embedded analytics is becoming commonplace in corporate operations, whether you need to build a sales report or provide several dashboards to clients, and we’ll see even more organizations using it in 2022. Departments and business owners are looking for professional data presentation solutions that do not require them to develop their own software. Organizations can achieve a polished presentation and reporting that they can offer to customers by simply white labeling the chosen application.
Embedding analytics allows for collaboration by keeping all stakeholders involved, rather than just adding a dashboard or BI elements to an application. By allowing clients and workers to alter data in a familiar context, you make it easier to pull insights from all areas of your business. As a result, it’s one of the list’s fastest-growing business intelligence trends.
“Organizations are using embedded analytics solutions to experience considerable gains in revenue growth, marketplace expansion, and competitive advantage,” according to Business Wire’s study “Global Embedded Analytics Market (2021 to 2026) – Growth, Trends, COVID-19 Impact, and Forecasts.” In the future years, embedding analytics is projected to increase dramatically in the healthcare industry, according to the authors. Taking into account the vast amounts of data that hospitals collect, which has grown even larger with COVID-19 and telemedicine interactions, healthcare organizations are moving away from paying for service volume and toward paying for service value. Hospital management may extract useful data that will help them enhance procedures from a clinical, operational, and financial standpoint by adopting robust healthcare analytics software that can be incorporated.
This is one of the business analytics trends that can be used right away because many vendors currently provide this service and ensure that the application runs smoothly and without a lot of complexity.
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