business intelligence

BI tools to improve business results

Most businesses consider Business Intelligence to be a critical tool for their current and future business strategies. According to Forbes, 54% of enterprises are adherents of this opinion. BI tools help transform company-owned data into ready analytical formats (reports, dashboards, etc.). The range of issues that business analytics helps to solve is very wide (product prioritization, supply chain optimization, workflow optimization, etc.). All BI products are designed primarily for making informed and effective decisions to optimize business activities in general.

Consider the main tools of business intelligence

SAP BusinessObjects is a flexible, scalable information infrastructure for discovering, exploring, and presenting data to make better business decisions. The key features of this system are:

Datapine is a one-stop platform for data visualization and business intelligence. The tool allows data scientists and other business users to use different data sources, perform advanced data analysis, create intuitive business dashboards, get answers and draw the right conclusions.

Microstrategy is a modern business intelligence software (integrated reporting, analytics and monitoring). The tool combines fast logical and factual capabilities to provide real-time assessment with third-party data analysis. Microstrategy also allows to process unstructured textual information, that can be further analyzed by specialists using a text analytics platform.

Qlik Sense is a data visualization, exploration and monitoring tool, business intelligence platform with in-memory associative search with built-in ETL tools. The wide functionality of the tool allows to collect data from disparate sources into a single information system. The associative data model provides the ability to explore data in any direction, understand the relationships of data and see all the relationships between them. These and other Qlik Sense features have made it one of the best and most popular BI tools. DataLabs develops custom BI solutions based on Qlik, focused on effective business management. Detailed information can be found here.

Sisense is a software BI tool that combines dynamic and powerful text analysis features. This allows users to transform text into valuable insights.

Zoho Analytics is a platform for self-service BI and data analysis, as well as internal and external information discovery. The tool allows to download, synchronize data from different sources (spreadsheets, applications, etc.), create and share dashboards and reports. Zoho Analytics offers connectors for rational databases and NoSQL databases and connects to cloud databases.

Microsoft Power BI is a set of data analysis services that allow to turn unrelated enterprise data sources (databases, data from cloud sources, the Internet, text files, Excel files) into reports, presentations and business plans. Power BI includes: Power BI Desktop – data models and reports creating); Online service Power BI (SaaS) – reports publication; Power BI Mobile – reports viewing on mobile devices and tablets.

Tableau is a BI tool for data discovery and visualization. The tool provides an easy process for analyzing, visualizing and sharing data without the help of IT specialists. Tableau belongs to the «self-service» category, providing all the opportunities for users to work independently.

Oracle BI is a business intelligence software product (interactive and published reports, KPI monitoring, business process monitoring). The platform supports different data sources and combines data from these sources in one report.

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.

Business Intelligence vs Business Analytics

Business Intelligence and Business Analytics are the indispensable and most common tools of modern business to optimize workflows. To understand how each tool can benefit, let’s figure out the definitions.

Business Intelligence is the process of collecting, storing and examining data owned by a company to ensure smooth operations. BI includes various tools and software: spreadsheets, reporting system, monitoring software, data mining software, event management, online processing, dashboard development, etc. BI provides the ability to interpret big data, identify new opportunities and implement new development strategies. The ability to combine internal and external data is the BI advantage. This provides a clear current situation vision and understanding, that cannot be obtained using single data. Incorporating BI into the workflow helps organizations understand the business, market, customer needs and behavior.

Business Analytics is a statistical technology using quantitative methods for obtaining information, forecasting defects, and developing strategies. BA tools: predictive analysis, forecasting analysis, data aggregation, association and sequences identification, correlation analysis, factor analysis, data visualization, growth analysis, optimization, etc. BA usage involves the involvement of data processing specialists, as well as an additional specialized training. The decision-making process with BA occurs through numerical analysis methods (predictive modeling, analytical modeling, explanatory modeling).

What is the difference between Business Intelligence and Business Analytics?

  1. The main focus of BI is the analysis of historical and current data. BI provides a process for analyzing / comparing past events, the current situation and business development over a certain period. Using this tool it is possible to find the cause of the problem and determine the necessary actions to fix it. The main focus of BA is predicting future results. Using this tool it is possible to determine the consequences of an unresolved problem in the future, possible strategies and predict the results.
  2. BI works with past and current data to determine business needs. BA works only with past data to predict business needs, customer needs and drive productivity.
  3. The task of BI is to provide a vision of current business operations, information on current trends and customer interests. The task of BA is to provide information on changes in business operations, increased productivity, future customer needs.
  4. Basic BI tools: Qlik Sense, SAP Business Objects, TIBCO, PowerBI, etc. BA tools: Word processing, Google documents, MS Office tools, MS Visio, spreadsheets, etc.
  5. BI is a tool mainly for large companies whose goal is to ensure smooth running of business. BA is a tool for small companies to develop business, improve efficiency and achieve goals.
  6. BI uses more User Interface Dashboards for analysis and carrying out operations, BA uses more tools and software applications.
  7. BI methods and technologies for data analysis: reporting, real-time analytics, dashboards, online analytical processing, etc. BA methods: predictive modeling, data modeling, requirements modeling, SWOT analysis, etc.

BI and BA perform different tasks and roles in the business optimization process. However, both tools are an important and integral part of operational activities for better understanding the business, ensuring an efficient decision-making process, optimizing business processes, predicting trends and gaining a competitive advantage.

Top 10 advanced technologies that affect business

Now the world is going through the 4th industrial revolution, the main engines of which are data, Artificial Intelligence and the Internet of things. As a result, the surrounding world is transformed into one large information system. In addition to the large number of technologies that are involved in this revolution, an important condition for progress is the different ways all these technologies interacting. Technologies development continues and it will affect every field of activity and company anyway.

Let’s take a look at 10 major technology trends that have an impact on a company’s success.

  1. Ubiquitous computing

Today, a mid-range smartphone is more powerful than a super-powerful computer 10 years ago, microchips have become smaller, devices are smaller, lighter and more powerful. Computing advances in the future will come from software and algorithms, quantum computing, new forms of digital storage (like DNA storage).

  1. Connections

At the moment, the Internet of Things (IoT) is developing very actively. This gives the impression that each device can connect to the Internet as well as collect and transmit data. According to the forecast by 2030, there will be at least 50 billion IoT devices installed in the world. This ability to connect places and things to the Internet can change many areas (education, manufacturing, medicine, etc.). Working with IoT data opens a unique understanding of the real actions of the client and employee.

  1. World datafication

People generate huge data amounts every day without even thinking about it. Almost every human action leaves behind a digital footprint. This contributed to the data storage methods development. The challenge for companies is to ensure that information assets are properly protected and confidential. Business data can serve as the main source for an improved product or service creating, business processes optimizing, etc. However, this requires a strategy for transforming data into analytic data. Artificial Intelligence can become an important tool for improving data literacy.

  1. Artificial Intelligence

AI development is very dynamic that allows modern machines to perform different tasks instead of humans. AI can also improve internal business processes by automating or helping specialists perform specific tasks, as well as in the decision-making process.

  1. Augmented reality

Augmented reality (XR) is an umbrella term that includes a range of immersive technologies (virtual, mixed, augmented reality).

  1. Trust in digital technology

Digital trust is the trust that users place in organizations in building a secure digital world. Each user must be confident in the security, reliability and ease of transactions or other interactions.

  1. 3D printing

3D printing provides an opportunity to redefine the way things are made. Manufacturers also get another way of making a product in an unconventional way. This allows them to streamline their manufacturing process, create custom products, and cut costs and waste.

  1. Genome editing and synthetic biology

Uncovering genetic mysteries will help to find new ways to understand and control them. Gene editing techniques such as CRISPR can make significant advances in the fight against various diseases, improve the vitality of plants, and produce new synthetic substances, that can replace fossil fuels, plastics, animal products.

  1. Nanotechnology and materials science

Nanotechnology means controlling matter on a tiny scale (atomic and molecular). Nanotechnology can be used to manage and improve products and components.

  1. New energy solutions

Renewable energy sources (wind, solar) have now become more efficient and affordable. In the future, there are 2 new energy sources – green hydrogen and nuclear fusion. New sources can be an important, safe and environmentally friendly solution to energy needs.

Machine Learning & Big Data

Among other modern terms and concepts, the most relevant are machine learning (ML) and Big Data. These 2 terms are often used in conjunction, although they have a fundamental difference. And it is important to understand this difference during a data strategy development.

The similarity between machine learning and Big Data is that both terms refer to the field of theoretical academic research and practical data-driven business applications. It is a scientific discipline that studies information and use cases.

Data is the main engine of technological progress. It helps to create new tools and platforms to change the world through analytics, more accurate modeling and forecasting. The development of the Covid-19 vaccine is a great example of the data importance in today’s world. Usually, it took up to 10 years to develop a vaccine. However, over the past decade, the ability to collect and process data has expanded significantly. It has significantly accelerated the pace of vaccine development. If this pandemic had happened in 2010, it would have taken a lot longer to solve this problem, just because technologies for deep data understanding were in their infancy.

This situation is made possible by both Big Data and Machine Learning. Let’s make sense of the terms.

Big Data is a collective term that includes a huge amount of ever-growing information, as well as tools, methods and technologies that have been developed to work with data, including Machine Learning. With the Internet transformation into a daily use tool, Big Data has begun to be identified as a powerful tool. Big Data isn’t just about size. Data definition as big assumes the presence of 3 characteristics («3 V»):

Machine Learning is a type of computer algorithm. It can be viewed as part of Artificial Intelligence (AI). A fundamental aspect of intelligence is learning. Machine learning is involved in creating programs that help to perform better taking into account an ever-growing data amount.

It is important to understand the difference between supervised and unsupervised ML. Supervised learning is a Machine Learning technique that includes tagged learning algorithms that lets you know immediately how well an operation has been performed. Unsupervised learning is a method of Machine Learning, as a result of what the system under test spontaneously learns to perform tasks.

Big data and Machine Learning are intertwined. The best results are most likely to be obtained by using the most appropriate ML and Big Data processes.

However, if the business does not work with Big Data, Machine Learning is unlikely to be needed. Its main advantage is the extraction of value from datasets that are difficult for classical computer and statistical analysis. For example, for a static dataset that fits into an Excel worksheet, the ML implementation will not be justified. It is advisable to use this tool in the case of working with unstructured data that cannot be understood using tables (text, graphic, sound data etc).

Key points in data strategy development

The modern world is developing and becoming smarter. At the same time, data is becoming a key tool for gaining a competitive advantage. The quality of data and analytics use, and the latest technologies introduction directly affects business competitiveness.

Data is a valuable business asset that transforms workflows. Almost every company, no matter the size, should be involved in data-related activities. In this case, each such company must have a clear and effective strategy for working with data.

It is essential to start working with data with a strategy. At the moment, part of the business and their leaders are simply collecting as much data as possible, because of the hype around Big Data. However, they do not think for what purpose they are doing it. The second part of the business is so overwhelmed by the data provided opportunities that they deliberately move away from solving this issue.

At the stage of developing a strategy, firstly it is important to understand what the company wants to achieve and what data can help to do it. It does not matter at all what data is already collected and is being collected now, what data are collected by competitors, what type of data is available. Data can only be useful if it meets specific business needs, helps achieve a goal, and thus creates value. Therefore, to avoid collecting useless for a particular business data, it’s necessary to focus on the goals, problems and issues that need to be solved with the help of the data.

Many companies use data processing strategies in areas of business such as marketing and sales. However, this is not enough – a plan is needed for the entire company. There is also a perception among executives that data and analytics are IT department’s responsibility. That is, managers are shifting responsibility to IT specialists. In fact, it doesn’t have to be that way. The responsibilities of IT technicians include storage, ownership, access, and data integrity based on strategic goals set by business leaders.

Key points in data strategy development:

  1. Data needs. To understand what data needs to be collected, first of all, it is necessary to determine what it is for. Different types of data may be needed for each specific purpose.
  2. Data searching and collecting process. When goals are defined, it’s possible to start thinking about finding and collecting suitable data. At the moment, there are many ways to do this: buying external data, using internal data, introducing new collection methods.
  3. How the collected data is transformed into analytic data? To extract business ideas, it’s necessary to understand the process of applying analytics to data.
  4. Requirements for technological infrastructure. The next step in the strategy development process is choosing the hardware and software that will enable an efficient data management process.
  5. The level of data expertise within the company. Getting the most out of the data requires certain knowledge and skills. This issue can be resolved with the help of internal resources of the company or the involvement of specialists from outside.
  6. Data governance. Working with data, especially personal data, presupposes strict adherence to the rules and regulations. It is critically important to consider and ensure security, privacy and data integrity when developing a strategy.

Using Qlik promotes effective financial management

Business success depends largely on the work of the finance, accounting and reporting departments. The information provided by these departments must be accurate, reliable and in real time. Qlik helps to solve such tasks as reducing costs, managing risk, increasing profits, increasing transparency of processes and others. The main Qlik’s mission is to transform disparate financial and accounting data into powerful financial analytics.

Cost management

Company expenses optimization is possible due to the unification of disparate data and the provision of more complete and accurate information. It contributes to a deeper study of data on costs, purchases, contracts, which allows you to identify new ways to optimize business activities.

Financial analysis and planning

Qlik provides professionals with sophisticated analytics for forecasting, planning, and budgeting.

Benefits management

The list of financial management tasks includes monitoring income and profit indicators to maintain the company’s financial balance. Qlik’s advanced analytics greatly simplifies this task with improved cash cycle and balance sheet visualizations.

Risk assessment and regulatory compliance

With Qlik tools it is possible to solve complex challenges related to risk and regulatory compliance. These tools provide financial professionals with access to aggregated data from multiple sources to effectively align governance with business priorities, make informed decisions, mitigate risk, and prevent fines and fraud.

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 Analytics for the consumer goods market”

What’s the best way to start interacting with data?

High quality interaction with data is the market leaders’ hallmark. Data has become the foundation for groundbreaking concepts like Artificial Intelligence and the Internet of Things. The main goals of using data: products and services upgrade, internal processes optimization and performance improvement, understanding user needs and behavior, monetizing and additional revenue generating.

Everyone understands that it is necessary to work with data, but not everyone understands where to start. Any process should start with setting goals. Before you can start working with data and getting value, it’s necessary to establish long-term and short-term goals for the company (for example increase profits, scale, reduce customer churn and manufacturing defects, understanding customers and the market). Moreover, each team member must know and understand them.

Here are a few guidelines to help you identify a company’s capabilities using data:

  1. Use cases

An effective solution would be to familiarize yourself with existing cases and look at the other companies’ experience, how they used the data and what result was achieved. A great example is the American company Netflix, which has adopted data mining. The company uses the collected data on the behavior of their customers to form recommendations for films and shows, content, etc. With the help of data company monitors the quality of video playback and it helps to increase the customer service level. Also, Netflix monetizes the received data through advertising partners.

  1. Brainstorm

To solve the problem, it is necessary to gather all interested business participants and brainstorm the way. The purpose of this process is to combine business goals and possible use cases of data to achieve a result.

During the brainstorming process, it’s important to answer the following questions:

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.

AI provides more capabilities for small and medium business

There is a misconception that Artificial Intelligence (AI) is applicable only to large businesses. Artificial intelligence (AI) is the human thought process imitation for solving problems and making decisions using computers and systems.

However, small and medium businesses can also use AI successfully and benefit from it. Moreover, its integration into the main business processes and functions becomes more accessible, in order to greatly simplify the entire process of use.

The introduction of Artificial Intelligence into smaller companies’ operational processes is practically no different from the process of implementation into a large business. The first and important part of this process is to study the strategy, identify options for effective use of AI to better understand customers and optimize business activities in general.

Questions that need to be answered for a successful AI implementation:

  1. How can AI be used to develop more advanced and customized products?
  2. How and what business processes can be automated using AI?
  3. Is it possible to monetize the received data?
  4. What available data sources are appropriate to use (own or external)?
  5. What are the most effective ways to aggregate and analyze data?

A breakthrough in Artificial Intelligence for small businesses is its availability as a service. This has allowed many small companies to leverage AI, develop their own competencies, infrastructure, and benefit. They can also use the data collected by companies such as Microsoft, Amazon, Google and transform it into analytical information. It’s not necessary to set up own AI and it makes more affordable for small businesses.

Another use case for AI is using an Artificial Intelligence-based service. Companies that use cloud-based tools for accounting, HR, marketing, CRM are already an AI user.

Here are some practical examples of how AI can be useful for small businesses:

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