BI Market Trends

2022: How the business intelligence solutions market will develop. 10 forecasts

On December 5, 2022, the analytical company IDC published a forecast for the development of the global market for solutions in the field of business analytics. It is said that organizations investing in the relevant area will be more digitally stable, flexible and dynamic than their competitors.

Improving the efficiency of corporate analytics often requires concerted financial investment and action at several levels, analysts note, from data platforms (to provide greater openness, flexibility, scalability, and communications capability) to various processes (to achieve better and more consistent data processing). In addition, it is necessary to take into account various tools, decision-making methods and cultural aspects.

The forecast for the development of the global market for solutions in the field of business analytics was published

Corporate intelligence allows companies to thrive in any macroeconomic environment. The IDC forecast outlines key trends that will occur before 2028 and that should be known to executives looking to boost corporate development.

Forecast 1. By 2024, organizations with more advanced corporate intelligence will have a 5-fold advantage in terms of time to respond to the market environment, which will give a constant gain in the use of new opportunities.

Forecast 2. By the end of 2025, senior executives from the world's 2,000 largest public companies (Global 2000) will be investing 40% more in corporate and market analytics, helping them withstand a recession and overcome economic challenges.

Forecast 3. By the end of 2024, 30% of businesses using video surveillance technology will also apply video analytics to support operational decision-making that requires significant monitoring.

Forecast 4. By 2024, approximately 80% of Global 2000 listed companies will increase investment in threat intelligence systems related to external factors, such as supply chain failures.

Forecast 5. About 30% of organizations in the Global 2000 ranking will not be able to meet their corporate analytics goals by 2026 because they have not focused enough on developing a data culture.

By 2024, organizations with more advanced corporate intelligence will have a 5-fold advantage in terms of time to respond to market conditions

Forecast 6. By 2025, 90% of large companies will use real-time analytics to improve outcomes such as customer experience. Event streaming technologies will help.

Forecast 7. By 2027, 66% of large enterprises will invest in data management technologies that will help measure risks and reduce their impact through security and verification tools.

Forecast 8. By the end of 2025, more than 50% of organizations in the Global 2000 ranking will face difficulties if they do not use artificial intelligence to detect and automatically correct errors due to increasing complexity, instability and lack of resources.

Forecast 9. Faced with the growing demand for corporate analytics skills, 70% of large organizations will implement literacy and employee development programs by 2028, according to IDC experts.

Forecast 10. By 2026, approximately 30% of large companies are investing in artificial intelligence infrastructure for high-performance computing to solve the most complex problems with effective modeling methods. This will help improve performance and strengthen market position.^[1]

2020: Gartner names 10 trends in data collection and analysis

At the end of June 2020, Gartner presented the top 10 trends in data collection and analytics for 2020.

1. AI gets smarter, faster, more responsible

By the end of 2024, 75% of organizations will begin to implement AI algorithms, which will provide fivefold growth in the transmission infrastructure and data analytics. AI techniques such as machine learning (ML), optimization and natural language processing (NLP) are already actively used in various fields. Reinforcement training and distributed training create more adaptable and flexible systems for dealing with complex business situations.

Gartner Introduces Top 10 Trends in Data Collection and Analytics

2. Toolbar value drops

Dynamic data analysis aimed at the user will replace the "point and click" principle. This approach using advanced analytics or NLP means that the most important ideas will be shared with each user depending on the context, role or use of this data.

3. AI to support decision-making

According to Gartner forecasts, more than 33% of large organizations will have specialists in solution analysis, including their modeling. The new tools will enable you to design, model, align, execute, track, and customize business-context decision models and processes.

4. X-analytics

According to Gartner, X can be designated as any complement of a number of structured and unstructured data. Thus, text analytics, video analytics, audio analytics, etc. appear. X-analytics, combined with AI and other methods, will play a key role in predicting natural disasters and other crises.

5. Advanced Data Management

Advanced data management uses machine learning and AI techniques to optimize operations. The tool also converts metadata from auditing and reporting into powerful dynamic systems. Using existing workload data, these tools can optimize the configuration, security, and performance of business processes.

6. Cloud as the foundation of everything

By 2022, public cloud services will be required for 90% of data and analytics innovation. Data and analytics leaders need to prioritize workloads that can leverage cloud capabilities and focus on optimizing costs when moving to a cloud service.

Public cloud services will be required for 90% of data and analytics innovation

7. The Collision of the World of Data and Analytics

Data management and analytics have traditionally been treated as separate entities and monitored accordingly. Advanced analytics tools blur the differences between the two markets. As a result, the range of professions for working with these opportunities will also expand.

8. Marketplaces and Data Exchanges

35% of large organizations are expected to either sell or buy data through the official online marketplace. Marketplaces and data exchanges provide a single platform for consolidating such offers and reduce the cost of trading and acquiring third-party data.

9. Blockchain in Data and Analytics

Blockchain technologies solve two problems in the field of data and analytics. First, blockchain provides a full line of assets and transactions. Secondly, blockchain provides transparency in complex networks. These technologies will allow not only to draw up "smart" contracts, but also to audit data sources at the enterprise.

10. Relationships - Underpinning the Value of Data and Analytics

Graphical analytics is a set of analytical methods that allows you to investigate the relationships between objects of interest, such as organizations, people, and transactions. Such tools help specialists find unknown connections and analyze data that do not lend themselves to traditional methods.^[2]

2019: Top 10 trends in data management and analysis

On February 18, 2019, the analytical company Gartner presented 10 trends in the field of data management and analysis for 2019 and subsequent years:

1. Advanced Analytics

Advanced analytics is an approach to developing, using and disseminating analytical content based on machine learning and artificial intelligence. By 2020, advanced analytics will be the dominant driver in business intelligence and embedded analytics.

According to Gartner analysts, advanced analytics, continuous working intelligence and explainable AI are among the main trends in data management and analysis.

2. Advanced Data Management

Advanced data management uses machine learning capabilities and AI mechanisms to work with data, including to control its quality, integrate and configure database management systems. This approach automates many tasks and allows highly qualified professionals to focus on higher priority areas. By 2022, data management tasks will be 45% automated.

3. Continuously operating AI

By 2022, more than half of large business systems will use real-time contextual data to make the most effective decisions. Continuously running AI that integrates with business systems, processes available and incoming data, automating decision-making whenever possible.

4. Explained by AI

To gain the trust of users, application developers must show how their AI models work. To do this, the application can automatically generate natural language explanations in terms of validity, attributes, statistics of models and functions.

5. Columns

Graph-based analytics is a set of analytic methods that allows you to investigate the relationships between objects of interest, such as organizations, people, and transactions. Such analytical systems are able to effectively evaluate complex relationships between data.

6. Data Factory

The data factory provides trouble-free access and information exchange. It allows you to create a single and consistent data management structure that allows you to easily access and process data.

7. Natural Language Word Processing

By 2020, 50% of analytical requests will be generated using natural language processing technologies. The need to analyze complex data combinations and provide results in an accessible and understandable way will contribute to the wider adoption of these technologies.

8. Commercial application of AI and machine learning

By 2022, 75% of new end-user solutions using AI and machine learning technologies will be based on commercial products rather than open source programs.

By 2019, data processing and analysis technologies are increasingly being used in healthcare

9. Blockchain

The main value of blockchain is to provide a decentralized transparent system that unites unreliable participants. Potentially, blockchain can significantly change analytics, but it will be several years before several major blockchain technologies become dominant. Up to this point, end users will be forced to integrate with other customers or networks, but integration costs can outweigh any potential benefits. So far, blockchain is not able to compete with existing data management technologies, analysts said in February 2019.

10. Permanent Memory Servers

New permanent memory technologies will help reduce the cost and complexity of implementing architectures that support in-memory computing. Persistent memory can improve application performance, data availability, and analytics security while reducing costs. It will help speed up work by eliminating the need for duplication of data.^[3]

2008: BI - Gartner Market Trends

According to Gartner analysts, in difficult times, the first step towards business survival and development should be to increase its transparency, which will identify the main cost centers and more clearly build a management strategy. Therefore, even during the economic crisis and the reduction of IT costs, the demand for analytical tools remains high. However, the growth rate of the BI market will still slow down.

The "high" sales in 2008 are due in part to the fact that BI solutions became the first candidates for cross-sales after large vendors mastered the client base of the developers they absorbed. However, these processes are close to completion, and the first half of 2009 demonstrated this.

According to experts from Gartner, when evaluating future investments in BI, potential customers will pay more attention to the completeness of the proposed business application stack and interaction with integration platforms, abandoning the focus on best-of-breed solutions. However, according to analysts, this does not interfere with the development of smaller, niche BI developers, the pace of development of which in 2007 was even ahead of the general market.

There are other trends on the market that can partly level the situation and prevent the final market distortion towards a small number of major players. Among the main trends of 2008, analysts noted the "growing up" of the "open" segment, ON the development of Web 2.0 technologies and the steady growth in the field of BI-systems "on demand" (). SaaS

ERP systems, already implemented in many large and medium-sized companies, generate huge amounts of operational information. To make this information really valuable, it needs to be analyzed. In this sense, it is BI systems that can actually improve the validity of managerial, including strategic decisions. In addition to the already familiar reporting and OLAP functions, developers of BI systems have begun to offer tools such as dashboards, scorecards and other tools for visualizing and supporting strategic thinking.

In addition, the development of new technologies, such as data processing in RAM, the provision of software "on demand," the development of the infrastructure of "cloud" computing, service-oriented architecture and the development of search algorithms, make it possible to significantly facilitate the processes of implementation and use of BI systems, as well as make BI solutions more accessible and attractive to small and medium-sized businesses.

A significant part of management decisions belong to the category of operational, that is, they must be taken in a mode close to real time. Therefore, the demand for solutions for operational business analysis continues to grow. In addition, in addition to global BI solutions covering the company as a whole, demand for narrower solutions designed to solve problems within one division or business direction is likely to increase.

Some companies, normalizing processes in the field of operational management, are moving to solving the problems of improving business efficiency, both in the field of financial management and in regulating "non-cash" flows (managing the customer base or supply chains).

Another of the key trends in the development of BI, analysts consider standardization, which is both a "spur" and a deterrent to the development of the market. On the one hand, it provides vendors with a huge "front line of work," and on the other hand, it allows customers to streamline the existing IT infrastructure without resorting to buying new large solutions.

Business analysis solutions, according to analysts at AMR Research (BI) and for performance analysis (PM), businesses will integrate more closely with each other as companies become more aware of the relationship between operational and financial results, and this relationship will develop both broadly, encompassing all aspects of enterprise management and even relationships with partners, customers and suppliers, and deeply, providing a clearer understanding of cause and effect relationships.

According to the findings of experts from The Data Warehousing Institute (TDWI), the growing demand for BI solutions will force vendors to revise pricing policies. So, in addition to algorithms for calculating the cost of software based on the number of working users, new calculation methods will appear that are based on the number of servers used. On the other hand, startup companies entering the BI systems market offer a simplified all-inclusive pricing system as one of their competitive advantages.

A relatively new class of BI systems is gaining popularity - event-driven. Many platforms have already implemented the ability to continuously monitor and run predefined business processes when critical states are detected or events are detected. In addition, the tools for administration based on the analysis of user activity are being improved. Such capabilities are especially in demand with a significant increase in the number of users. And if earlier these tools were developed by individual niche players, now they are being built directly into BI platforms.

In terms of hardware, most BI implementations will use server clusters that have their own tools to protect against failures and restore data. In addition, data extraction and processing (ETL) processes will be parallelized, built on the basis of micro-packets or event triggers to make these processes as close to real time as possible and ensure their continuous operation in 24x7 mode.

In 2009, according to experts from TDWI, business analysis systems will be used by an increasing number of users within the company. So, if initially it was assumed that a maximum of several dozen qualified business users would be engaged in the development of individual reports designed to solve current problems, now it is obvious that almost all employees of the company will need access to such opportunities, the number of which can reach several hundred, or even more. Moreover, often these ordinary employees do not have sufficient knowledge to develop competent and clear requests that accurately answer their questions. And BI solutions will need to provide a flexible, efficient, and intuitive interface to deal with this problem. It is proposed to solve these problems not only by software methods, but also by creating specialized software and hardware systems.

A significant increase in the number of users will also lead to an avalanche-like increase in the volume of information processed. If earlier, analyzing historical data, users looked around a year ago, now they want to be able to analyze the data three, five and even ten years ago. In addition, the information is becoming more diverse - in addition to data from their own corporate applications, users want to analyze informalized data (for example, price lists of suppliers and competitors, various information from the Internet). All this data should not only be taken into account, but also interconnected. All this brings the amount of information processed simply to dizzying values.

The improvement of search technologies, according to Gartner, will be useful for the development of BI in two directions at once - for searching for ready-made, similar reports in meaning and structure (and in large organizations, such standard reporting forms are in the hundreds or even thousands), as well as for finding information that may be useful for analysis (among the data of other enterprise applications, that is, reusing already processed and structured information).

According to the latest Gartner forecasts for 2009-2012, in the near future, companies will expect significant assistance from BI systems in overcoming the economic crisis, planning business transformations, making strategic and operational decisions, as well as meeting the tightening requirements of shareholders and regulatory organizations to provide full and transparent reporting, including financial. However, most organizations still do not have sufficient tools to obtain such information.

Until now, the construction of the IT infrastructure has been mainly carried out by the IT divisions of companies. As a result, business users have lost some confidence in the solutions they create, in their ability to provide the information and tools they need to analyze it, content themselves with spreadsheets or standard analytical tools embedded in transactional business applications. Analysts estimate that by 2012, control over BI project budgets will be transferred to business units by 40%. The advantages of this situation are that they will be able to require the implementation of BI systems that contain the functionality they need for business performance analysis, online marketing analysis and forecasting, and not just for post-fact reporting. On the other hand, this can result in an imprudent acquisition of a set of disparate decisions that, in general, will not be able to provide solutions to the tasks set, and at the same time will create additional difficulties in the process of analyzing the information necessary for making important decisions. In this regard, the task of IT departments is to develop corporate standards that limit the spread of the BI systems under consideration.

According to experts from Gartner, by 2010, 20% of organizations will have industry analytical applications in their portfolio of BI systems, provided under the "on demand" model. Despite the fact that in the near future there are likely to be hundreds of companies on the market capable of aggregating information, however, only a limited number of players who occupy and monopolize their chosen vertical niches will be able to ensure the proper level of security and reliability.

Already in 2009, we can expect the emergence of a new category of software products that combine the capabilities of BI platforms and social networks, designed to support collective decision-making processes. Such software will allow you to abandon traditional decision-making algorithms ("top down") and develop new methods based on collective discussion of problems.

By 2012, about a third of all analytical systems will be organized as an integrated complex of heterogeneous applications. According to experts from Gartner, mega-vendors are experiencing significant difficulties in combining the products they purchased on a single platform. In addition, the focus on the use of single developer solutions narrows the possibilities for choosing "best-in-class" solutions and generally weakens the customer's position when discussing deal terms. On the other hand, few customers really pay due attention to the elaboration of the service-oriented architecture of their complex corporate systems.

Most companies now use enterprise portals to integrate individual business applications (transactional and analytical). However, they only allow users to conveniently switch between different systems. It should be replaced by the next stage of integration, when operational and analytical applications overlap due to the presence of a single system of indicators, metrics, requests, as well as a common user interface.