Business Intelligence and Business Analytics Trends in 2022

Data is an indispensable tool for modern business. It is used everywhere in business: from supporting the decision-making process itself to upgrade and improve products and services. 2021 has been a year of intriguing advancements in Business Intelligence and data analytics. The goal was to provide companies with a custom solution based on different technological approaches to make the most of their data. Given the dynamic pace of development in this area, new solutions can be expected in 2022. Consider the main trends in the development of Business Intelligence and data analysis.

  1. Data Factory and Hybrid Cloud

The Data Fabric is a cohesive, conceptual information management architecture that provides complete and flexible access to work with it. Data factories feature is the use of the approach and tools of Artificial Intelligence, Big Data and machine learning in order to organize optimal data management algorithms.

The latest development from Qlik is an example of such a concept. Qlik Forts is designed to connect all data regardless of its location in the cloud. Companies using a variety of private and public cloud platforms have the ability to easily connect and leverage all of the data for an efficient workflow. Moreover, the data is available for analysis to the user from anywhere in the world. In 2022, the development and bringing of analytical solutions to a new level.

  1. Automation and Machine Learning

Nowadays, most of the commonly used machine learning algorithms perform their functions well and smoothly. Specialists, in turn, are trying to improve developments and make them even smaller, faster and more efficient. The next year will be devoted to platforms and tools development that can be used by any user to automate any task.

  1. Small data

To solve the scaling problem mentioned in the previous point, it’s worth starting by changing AI and machine learning goal. It is advisable to process only the most important data, in other words, go to small data. However, this does not mean that big data is losing its value – it will always be needed. We can already see the success of implementing the «small data» approach in self-driving cars to quickly respond to potential road accidents. In 2022, we can expect new ideas emergence with the effective use of small data.

  1. From SaaS to iPaaS

One of the trends next year will be SaaS (software as a service). However, in 2022 there will be some changes, namely iPaaS. The tool is a cloud-based solution with the ability to easily scale and integrate large data amounts. Gartner defines iPaaS as a cloud services set that enable the development, execution and maintenance of integration flows that connect any combination of on-premises and cloud services, processes, applications, and data within one or more organizations. The goal of every company is to avoid data loss and information silos across departments and platforms. Therefore, a breakthrough in this area is expected next year.

  1. Planning and forecasting

Back in 2020, the predictive and prescriptive analytics market was projected to grow by 20% over 5 years. Now analytics is becoming more accessible to users. With the help of Qlik BI platforms, it is possible to easily integrate predictive analytics into CRM, ERP, etc. In 2022 and beyond, this area will actively develop.

  1. Information literacy

The final item on the trend list is information literacy. Knowledge is essential for the implementation and correct use of all technologies and innovations. Otherwise, all progress will be meaningless. Modern companies that strive for dynamic development should think about quality training for their team. End users require particular attention, as they often make data-driven decisions.

Qlik optimizes HR workflow

People are the main source of success for any business. The main HR professionals’ task is to maximize this resource value through a deep understanding of staff performance and predicting changes impact.

Analytics has never been the primary tool for HR professionals. However, they possess a large data amount that can be effectively used to develop business strategy and make decisions. Qlik HR analytics allows to effectively use data, hire the right people, provide them with a favorable climate and conditions for work and business prosperity in general.

Talent acquisition

Modern business requires modern approaches in everything, including in the recruitment process. Qlik Analytics helps to modernize and automate the talent acquisition process specifically:

Education and development

Another task of HR specialists is to provide conditions for the development of each team member. With the help of Qlik analytics, they can accomplish the following tasks:

With Qlik tools, HR professionals have the ability to combine data from the LMS, performance management system, and other employee data sources to create a holistic view of performance. This allows:

DataLabs is a Qlik Certified Partner. A high level of team competence and an individual approach allows us to find a solution in any situation. You can get additional information on the project by filling out the form at the link

Previous #fridaypost “Qlik helps IT specialists to undertake mission”

The main ways to monetize corporate data

Although data is the main asset of modern business, the issue of its monetization remains relevant. Business owners and leaders clearly understand that data is the key to effective operations and overall success. However, they have no idea what monetization opportunities exist.

Let’s consider 3 main options for data monetization to improve business performance.

  1. Transparency and understanding

The first way to monetize data is to use it for better market, trends, behavior and customer needs understanding. Data is a key tool for identifying current and future customer needs.

Regular work with customer data helps to create more relevant, quality and personalized products and services. The client gets the desired product – the business increases profit.

The result of this approach is building long-term relationships with clients, establishing their loyalty, which in turn increases the lifetime value of each client.

  1. Reduction of expenses

Any company seeks to increase revenue and reduce costs. The data can be used for: business processes optimization, workplaces automation using AI, reduction of transport, logistics, time and financial costs, marketing costs optimization, etc. It is important to study the data held by the company to understand exactly how to use it to optimize all processes, cost and increase profits.

  1. Data renting or selling

Of course, selling or renting data can be a great monetization option. Data uniqueness or inaccessibility will be the advantage in this case. Data is an independent asset of the enterprise? and its value is included in the total company value for a sale.

To implement this method of monetization, it is necessary to answer several questions:

Based on the answers and ideas, an effective monetization strategy can be drawn up.

Companies from different market area can use this method. For example, the American engineering company John Deere has created an intelligent farming system. Thus, the system collects data on weather, climate, soil condition to form recommendations for farmers what and when to plant, what fertilizers to use, etc. The company’s annual income from the data sale to farmers is about $1 billion.

Another example of using this method of monetization is the American financial services corporation Visa. The company sells data to retail businesses that need information about shopping patterns. Retailers may not always be able to see their customer’s entire purchase history. Thus. it makes impossible to get a correct understanding of shopping behavior. Visa, possessing this valuable information, sells it to companies and generates additional income of more than $1 billion a year.

The influence of BI on business performance

Until recently, BI applications were mainly used only by IT professionals. With the technology development, Business Intelligence has become the main tool for many business users from various fields of activity.

The main task of Business Intelligence is to extract important facts from structured and unstructured data, transform it into coherent information that allows to make effective business decisions, increase productivity and optimize the operational activities of the organization.

Business Intelligence is a workflow that, by scanning data and extracting facts from it, helps business owners, managers and other business users to see a clear picture of the current situation, analyze and develop an appropriate action plan. With the help of this tool, companies are able to collect information from internal business structures and external sources, that makes it possible to improve the internal business structure, identify market trends, and identify problem areas.

The wide information resources provided by business analytics enable companies to achieve their goals faster. Any interaction with customers (voice messages, testimonials, chatting or email communication) can be carefully analyzed to extract information about customer preferences, technical difficulties they face, their reactions to promotions, purchasing power, etc. Analysis contributes to the improvement of all business indicators.

A high-quality process of making effective business decisions is provided by Business Intelligence. The main business areas affected by Business Intelligence within the company:

  1. Increasing business productivity

The business analyst team is dedicated to extracting and interpreting data using BI applications. Thus, the company management can focus on the management of important resources and workforce. The result of this approach is financial and time costs improvement and business performance optimization.

  1. Extraction of critical information

BI provides business users with the ability to extract critical data by analyzing customer interactions and visualize them in a convenient and understandable way. The tool ensures that detailed and reliable reports are provided to all business users.

  1. Information availability

By incorporating Business Intelligence into the workflow, companies gain access to all the data they need to make decisions. Users can get such access at any time.

  1. Return on investment

The main BI advantages are the ability to reduce costs, increase revenue, and increase margins. It helps to improve ROI. BI also helps to improve indicators such as employee productivity, quality of decision making, customer satisfaction, business process efficiency, etc.

  1. Real-time reporting

A high-quality report based on reliable information provides the company’s management with a clear understanding and the ability to evaluate business processes. By providing reports on critical data (current and historical), as well as data on future trends, customer needs and preferences, companies are able to operate effectively.

Top 6 mistakes in data strategy development process

Any company, no matter the size, needs a data strategy. Only data can provide a business with answers to all questions, thereby improving operations and increasing profits. However, an ineffective strategy makes it impossible for a company to tap its full potential. Here are 6 the most common mistakes in data strategy development process.

  1. Outdated strategy

The world is rapidly transforming. The business strategy must fully comply with the requirements of the 4th industrial revolution. The data strategy, in turn, must support the business strategy and be relevant in the modern world. Investing in an outdated strategy is pointless. This not only does not work, but also leads to inappropriate costs.

  1. Ignore business goals and objectives

Not all existing methods of data analysis are important and effective in achieving the business goal of a particular company. Many companies develop a data processing strategy based on interesting cases. However, they do not take their own interests into account. It is important to define goals and objectives, figure out how the data can be used to achieve them, and only then begin to develop a strategy.

  1. Do not define indicators of success

How can you determine how successful or unsuccessful a strategy is? – determine the KPI. Only by defining the key performance indicators, it is possible to correctly track and analyze the progress. Any data initiative must have a business case with appropriate KPIs.

  1. Concentrate only on technical costs

Implementing a data processing strategy entails technical costs. However, do not forget that changes will be required in the corporate culture. Each team member must rely on data every time he makes a decision. To do this, it is necessary to organize a training process for collecting correct data, assessing their quality and analyzing.

  1. Consider only structured data

At the moment, data comes in different forms – photographs, sound recordings, text files, and much more. The data strategy must take into account structured and unstructured data to get the correct information. Don’t ignore external data sources from repositories, governments and data brokers as it can be useful.

  1. Ignore ethics, confidentiality and legal aspects of data

Any data project should start by defining aspects of the ethical and confidential data usage. Consumers must be confident in the confidentiality of their data usage, and also benefit from it.

The correct data strategy is a key element in the process of achieving business goals. It should be based on 3 to 5 use cases. The use cases for each business will be different, however, they should be consistent with the business strategy.

The main analytics levels in the automation process

Data by itself is not too valuable. What makes it valuable is analytics and insights. Data analytics helps to get the current situation, draw the right conclusions and make decisions that will lead to more efficient business activity and growth.

Let’s consider a car example. Often, we do not think about how it works, but we know that it can go wrong. It is possible to identify the causes and prevent them using analysis. It is enough to collect 2 datasets: the first is about car workflow and the second is about time when the car fails. Such dataset analyzing will help to understand what actions lead to failure and identify ways to prevent recurrence in the future.

Descriptive analytics

Data scientists call the analytics in the example above «descriptive». The result of this analytics type is the description of «what?», «when?» and «why?» happened. However, it does not offer possible solutions.

Now modern companies mostly use descriptive analytics. Specialists get access to the full amount of information in the form of graphs, charts, tables, reports that show the recurrence number of a particular event. This can be interaction with a client, a mechanical failure, sales, etc. Next, the specialist’s task is to analyze all information and develop the action plan.

A good example of descriptive analytics is Google Analytics. This tool perfectly visualizes indicators of site traffic, its load, user behavior on the site and much more. Descriptive analytics can be useful when used strategically.

Predictive analytics

The goal of many companies that adopt artificial intelligence and machine learning technologies is to enable predictive analytics.

An idea if predictive analytics is to analyze data about what has already happened and make predictions. Let’s go back to the car example. There is problems report from January to March. Prediction algorithm provides information on how and when a machine may fail in the future.

The result of predictive analytics is a set of probabilities that do not give 100% confidence that an event will definitely happen in the future. However, having such information, it is possible to prepare, check the availability of spare parts for repairs and develop a «plan B».

Predictive analytics is actively used by banks and lenders to assess risks. In consequence of analytics bank employees receive an estimate of the payment likelihood by a particular applicant and can compare it with risk thresholds.

Until recently, only advanced companies could implement analytical technologies and evaluate benefits. Now these companies are moving to «prescriptive analytics».

Prescriptive analytics

So, predictive analytics provides an answer to the question «what can happen», but does not answer the question «what to do to obtain the optimal result?» The next step in analysis is prescriptive analytics.

The predictive analytics system provides a number of possible outcomes. This can be effective when the specialist exactly knows the effective solution. For example, choose a specific action to increase sales. However, if the goal is to increase revenue in general, it is necessary to know the most effective measures package. This is where prescriptive analytics can help.

Let’s take autonomous vehicles as an example. The car must «know» that the fastest left/right turn can be lengthened by heavy traffic for a certain period. In this case, it is necessary to choose the best option and «register» it to the computer that drives the car.

With prescriptive analytics, the person driving the machine can know not only the cause of the failure, but also the best way to minimize such situations.

The 3 levels of analytics described above are fundamental to the automation process. Until recently, advanced predictive and prescriptive analytics technologies were prohibitively expensive for most companies. Now there are a large number of analytical tools and platforms that small organizations can afford. Gradually progressing through the analytics levels contributes to a successful digital transformation.

Big Data and Business Transformation

During the  time, all new technologies become simpler and more affordable for large-scale use. Now Big Data is going through this phase. As a result, different industries transformation is taking place. Here are some examples of key industries influenced by Big Data.


In recent years, selling and buying procedures have changed a lot. However, both online and offline store owners use the data to better understand customers, their needs, and comparison with the current offer. This approach ensures an effective operation and allows for huge benefits.

Data analytics is applicable to almost every step of the retail process. By predicting trends, it is possible to determine the demand for a product, optimize the price, determine the target audience, and gain a competitive advantage.

Health care

Big data in healthcare is helping to improve disease detection and treatment, improve life quality and reduce mortality rates. The main Big Data task is to collect as much information as possible about the patient and identify the slightest changes and illness signs at the earliest stages. It prevents disease development, provides a simpler and more affordable treatment protocol.

Financial services, Banking, Insurance

Big Data helps financial companies and banks detect fraudulent transactions. Insurance companies use Big Data to establish fairer and more accurate insurance premiums, improve marketing efforts, and detect fraudulent claims. British insurance company Aviva is offering a discount to drivers for being able to control their driving using smartphone apps and car devices. It allows insurers to observe how safe is driving.


The production process is changing dramatically with the development of robotics and the automation level. Sportswear, footwear and accessories company Adidas is actively investing in automated factories.

In traditional manufacturing, Big Data matters too. With the help of built-in sensors, it is possible to monitor the specific equipment performance, as well as collect and analyze data on its effectiveness.


Now, data is being collected about how people learn. This information is used for new ideas, defining strategies for a more effective learning process, highlighting ineffective areas of the learning process and ways to transform it. In one Wisconsin school district, data was used for almost everything from defining and improving cleanliness to planning school bus routes. The performance data analysis of a particular person in online learning mode leads to the personalized, adapted learning development.

Transport and logistics

There are cameras to monitor inventory levels in warehouses. With the help of data from the cameras it is possible to provide reminds about replenishment. Also, this data using machine learning algorithms can be transmitted to train an intelligent inventory management system. In the near future, warehouses and distribution centers will be almost completely automated and require a minimum of human intervention.

Transport companies collect and analyze data to improve driving behavior, optimize transport routes, and improve vehicle maintenance.

Farming and agriculture

Traditional industries also use data to generate new opportunities. American manufacturer John Deere has applied Big Data techniques and launched several services. They enable farmers to benefit from crowdsourcing real-time data from thousands of users.


The volatility of international politics complicates discovering and producing oil and gas process. Royal Dutch Shell has developed a «data-driven oilfield» with the aim of reducing the cost of its production.

Hospitality business

Recreational service providers use data to make their customers happier. The main goal is to ensure each room profitability, taking into account seasonal changes in demand, weather conditions, local events that can affect the number of bookings.

Professional services

The professional services like accounting, law and architecture are also changing as a result of advances in data, analytics, machine learning, artificial intelligence and robotics.

For example, accounting software allows to automatically import transactions, track digital receipts and taxes, and automate payroll calculations.

Big Data – big opportunities

Now it is too popular to discuss such term as Big Data. But not everyone clearly understands what it is and which value it has. Let’s figure it out in order.

So, big data is a huge and complex dataset from different sources and constantly growing in volume. 3 main characteristics of big data: high speed of reception, large volumes, variety. Big data is mainly used to solve business problems in consequence of information content depth and width. At the moment, many organizations already work with big data, reaping the full benefits of its usage.

10 main areas where big data is actively and successfully applied:

1. Customer understanding and targeting

At the moment, this ​​business area actively uses big data. The main goal is to understand better customers, their behavior and preferences. Companies gain a more complete picture of their customers by expanding information sets with data from social media, browser logs, text analytics, and sensory data. The main goal is to develop predictive models.

2. Business processes understanding and optimizing

Companies use big data to understand better operational processes and improve its efficiency. For example, companies can optimize their inventory based on forecast data, web search trends, and social media data.

3. Personal indicators assessment and optimization

Big data can be useful not only for organizations, but also for people. For example, a person who owns a smartwatch or bracelet receives certain data every day (number of steps, number of calories consumed per day, activity level, sleep pattern, etc.). The correct this data usage brings benefits to the user.

4. Health care system improving

Big data analytics can decode entire DNA strands in minutes, develop new medicines, and better understand and predict disease patterns. Also, it is possible to track and predict epidemic outbreaks, monitor newborns in specialized departments.

5. Athletic performance improving

Big data analytics are widely used among elite sports. At the moment, it is already developed IBM Slam Tracker for tennis tournaments. Video analytics is actively used, with the help of what it is possible to track individual football or basketball player’s performance during a match. Sensor technology of sports equipment helps to obtain data about the game and improve it. Smart technologies can be used to track the routine of each athlete: his diet and sleeping mode, his emotional state through messages on social media, etc.

The US National Football League (NFL) has developed a platform that allows to make effective decisions by analyzing the pitch condition, weather conditions, statistics of the individual players results.

6. Science and research development

Science and research field is empowered by big data. The European Council for Nuclear Research (CERN) conducts various experiments to reveal the Universe secrets, its origin and existence, generating huge data amounts. Big Data computing power can be applied to any dataset, discovering new opportunities and sources for scientists. Researchers can easier access census and other data to create more accurate picture of public health and social sciences.

7. Machines and devices performance optimizing

Big data analytics enables to create smarter and more autonomous hardware. Big data technologies are used to drive self-driving cars, optimize computers and data warehouses performance.

8. Security improvement

Big data is widely used in this area. Thus, the US National Security Agency (NSA), by analyzing big data, has the ability to prevent terrorist operations. Big data analytics is also used to detect and prevent cyber-attacks, to catch criminals, predict criminal activity, and detect fraudulent transactions.

9. Cities and countries improvement

Big data is used to improve many aspects of life in cities and countries. For example, optimize traffic by analyzing traffic conditions in real time. Big data analytics is also used to transform a city into a «smart» city, where transport infrastructure and utilities processes are combined.

10. Financial markets

Big data is widely used in high-frequency trading. Decision making processes take place using big data algorithms. Share sale is carried out using big data processing algorithms that consider signals from social media, news sites, etc. It allows to buy and sell shares in a matter of seconds.

Data analytics improves website performance

Successful business is based on information. Information quality and timeliness are the key of effective activity. Data analytics gives the business many opportunities: right goals setting and the most accurate result achievement, forecasts creating, trends identifying, strategy development, possible risks identifying and their consequences preventing or minimizing.

Among other things, analytics is also important in determining the company’s website effectiveness. Working with data on site performance and marketing strategy from multiple sources enables management to determine the company’s ability to meet expectations.

Website traffic check and comparative analysis with competitors’ websites provide a clear picture of business development on the Internet. The lack of this information makes it quite difficult to run business.

Let’s consider several reasons why analytics is important in the business management world:

The target audience is rarely stable and predictable, so its analysis is critical for strategy development, plans generation and goals setting. Such analysis may contain information about the age categories of users, their preferences, the platforms they use to browse the website. Tracking online users’ behavior makes it possible to timely identify various changes, new trends and promptly respond to them. Therefore, it optimizes the business marketing strategy and leads to effective operations.

There are many indicators, by determining which it is possible to say exactly how effective the site is. For example, a high bounce rate means «something» on the main page that prevents users from navigating further to other website pages. The reasons can be different: too much information on the main page, long page load time. Also, the reason may lie in incorrect marketing – the user’s request does not match the site information content. With the help of analytics, you can identify all the causes, eliminate them and set up correct and effective marketing.

Uncertainty and unexpectedness always provoke stress and frustration. Nescience and understanding lack of how to act can be a big problem for business owners. Failures can happen with a strategy and possession of all the information. However, understanding the reasons why a particular situation happened minimizes stress level and makes it possible to determine alternative development way. The analytics essence is to provide all the necessary tools to create an effective marketing and optimization strategy.

Analytics is the main tool for modern business management. Analytical tools implementation and usage make it possible to understand the business, track causal relationships and optimize the company’s activities.

Innovations that have transformed Business Intelligence

One of the most common questions that business owners ask themselves is «How to be competitive and outperform competitors?» A couple of years back the main characteristics for getting a competitive position were price, design and advertising. Now they are not the only ones. Understanding the client, his needs, willingness and ability to pay are the main characteristics that determine a competitive advantage. Data and BI are too important here because they can help to reply to these questions. The 3rd BI generation release gave a possibility to afford analytics tools to every business user and bring to the light the full data value. The current business intelligence scenario is driven by innovations.

Let’s consider some of them:

  1. Technology shift

Let’s come back to 2000’s. The appearing of a big number of users promoted the appearing of a big data amount. Such situation led to the purchase of high-performance desktops with CPU servers that had bigger memory and direct attached storage. The first 2 generations had a data-oriented stack as a design point. Unlike its predecessors, the 3rd generation is moving towards a network-oriented stack. The first important technology shifts began to emerge during of major vendors IBM and Oracle consolidation. Early BI solutions often were installed on desktops and it was difficult to deploy enterprise software products globally. Over time the Internet became the main design point. In consequence, web-based architecture was developed, and it offered easy installation process and faster deployment options.

  1. Data about data

Realizing that analytics is an enterprise-wide function and not limited to desktop, vendors strived to develop well-managed and secure products seeing the enterprise involvement. Products were built around a metadata layer. A metadata repository stores and manages metadata.

Metadata types:

  1. Storytelling with data

Earlier BI solutions made use tools focusing on reports and dashboards. However, architecture evolvement promoted the functional development of BI solutions. It will not be difficult for a qualified specialist to make evident insights looking at a specific data set. But it’s impossible to say the same about all business participants. Information only in the form of graphs or tables is not always fully understandable for users. For comprehensive understanding of the data meaning, solutions that allow to use the storytelling technique were developed.

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