Why use business intelligence

Business Intelligence GuideWhat is Business Intelligence? How do companies benefit from it?

An overview of modern business intelligence

For the success of a company and its business model, it is important to gain deep and quick insights into the processes and company data. Ever increasing amounts of data require IT-supported systems and applications from the big data environment in order to extract, process and analyze information. Modern business intelligence systems offer companies appropriate solutions. The following article gives you an overview of important topics related to business intelligence. We define the term Business Intelligence and explain to you how it has developed and why it is so important for companies today. You will get to know the structure, architecture and central functions of business intelligence and learn how to develop a successful BI strategy. Finally, we will show you some exemplary applications of the BI systems in practice.

1) What is Business Intelligence (BI)?

Business intelligence is a term from business informatics. It describes tools and procedures for the systematic analysis of company data in order to gain insights into business development. Decision-making processes should be supported on a data basis.

The abbreviation for Business Intelligence is BI. In German, the word business analytics is sometimes used. It is a term from business informatics that describes tools and processes for collecting, processing and analyzing internal and external data of a company. The aim is to prepare the results with the help of reports, dashboards and data visualizations in such a way that executives, managers and other decision-makers from the various areas of the company can use them for well-founded, data-based decisions. Modern BI systems make the analysis results company-wide and thus also available to operational employees. The systems use big data technologies and collect data in so-called data warehouses. Business intelligence and competitive intelligence are sub-areas of business intelligence. While business analytics generally concentrates on the analysis methods, competitive intelligence has the objective of collecting and evaluating business data about competitors.

Applied correctly, Business Intelligence offers companies numerous advantages and opportunities: Decision-making processes are accelerated, forward-looking action is enabled, business processes are optimized, and the company's efficiency and success are increased.

2) Why is business intelligence important?

BI is important to companies today in many ways. As the amount of data generated and processed in a company increases, so does the importance of systematic business analysis. The following is an overview of the most important advantages of business intelligence:

a) It prevents you from getting lost in the data: Without BI, you run the risk of "sinking" into your data. Companies generate and process huge amounts of data and the number is increasing every day. Partly the growth has an exponential character. Mastering this enormous amount of data is a real challenge. On the other hand, the large amount of information available offers undreamt-of opportunities and possibilities to generate completely new insights into the company's activities. IT-supported processes and systems allow the automated collection, processing and evaluation of huge amounts of data.

b) It enables important insights into the company and the business processes: The use of BI concepts and solutions for your company allows you to generate a wealth of interesting and important insights. These help you, for example, to improve cooperation within the company, to solve problems, to improve internal and external communication, to optimize costs, to check the achievement of goals, to take the right marketing measures or to increase profitability.

c) It helps to precisely determine and monitor central key performance indicators (KPIs): Determining and monitoring the central key performance indicators is an important task in every company. If the KPIs are to be determined and monitored based on BI, this improves and accelerates your ability to act. Values ​​can be determined and visualized in almost real time and with a high degree of accuracy. Ad-hoc analyzes are possible, which generate enormous competitive advantages. Executives and managers no longer have to wait hours, days or weeks for the required indicators to be provided, but rather access the current values ​​directly via interactive dashboards.

d) It integrates different external and internal data sources into the analysis processes: Meaningful BI analyzes rely on data from many different data sources. A modern BI architecture allows the connection of a wide variety of external and internal data sources. They are aggregated with the help of data warehousing and are available centrally at any time for the analysis processes. Online and offline data as well as structured and unstructured data can be integrated.

e) It makes data and data analyzes available anytime and anywhere: In order to operate successfully in the market, the right information must be available at the right time. A modern BI system makes data and data analyzes usable anytime and anywhere. You can carry out analyzes and access the results at any time of the day and with any end device, even with mobile devices such as smartphones, laptops or tablets. There is worldwide 24/7/365 access to all the important information you need to make informed decisions.

f) It ensures meaningful data visualizations with effective storytelling: Stupid data series and huge tables filled with innumerable information make it difficult for executives and managers to make the right decisions. Appealingly visualized analysis results that make the most important information jump straight to the eye are much more effective. Modern BI tools enable meaningful visualizations. At the same time, the information presented can be presented in the right context using storytelling. A coherent environment is created, which ensures a better understanding of the data and facilitates decision-making.

g) It reduces the need for data specialists and data scientists: Whereas just a few years ago it was reserved for data specialists and data scientists to collect, extract, process and analyze data, modern business intelligence also offers users without specific data processing knowledge the opportunity to generate their own analyzes at the push of a button. The need for know-how is significantly reduced, as intelligent software relieves users of a large number of tasks. Self-service analyzes can easily be created by non-technical users. In addition, many tasks can be automated and manual activities are reduced.

h) It enables predictive analysis: Predictive analytics are possible on the basis of the large amount of historical and current data. Intelligent algorithms recognize patterns, trends and relationships from which they generate their predictions. This not only gives an overview of the current status of a company, but also makes predictions with a high probability of future developments.

i) It optimizes costs and ensures higher profitability: BI solutions allow fast planning and decision-making processes based on valid and well-founded information. These help the company cut costs and work more profitably overall. The analyzes can be used, for example, for sales planning, customer management, process monitoring, offer optimization, production planning, personnel planning, logistics and much more.

3) The history of business intelligence

Business intelligence, as it is used in today's parlance, seems to be a fairly modern term for many. However, it was used long before computers and IT systems were invented. Hard to believe, but Richard Miller Devens was already using the term Business Intelligence in 1865. Back then, Devens used it to describe a banker's recipe for success. It consisted of gathering information about the market and its conditions. In addition, the banker built a network of relationships through which he was promptly provided with all the necessary information. Thanks to this informational head start, he managed to make bigger profits and beat the competition.

Business intelligence experienced its first decisive boost in 1956 with the invention of hard drives by IBM. Hard disks made it possible for the first time to store large amounts of data and quickly access them again. It was only thanks to this technical progress that modern BI became possible, because a BI system requires a digital database. In 1958, the IBM employee Hans Peter Luhn published a document that described with remarkable precision how business intelligence will look and work in the future. It showed the challenges that companies have to face in order to make the right decisions based on large amounts of data. Hans Peter Luhn is therefore also referred to as the father of business intelligence.

On the basis of the theoretical foundations of Hans Peter Luhn, companies such as SAP began to provide BI systems in the 1970s. They enabled their customers to store information in databases and generate reports using this data. However, the flexibility, performance and functionality of these systems were still very limited.

In the 1990s, business intelligence finally achieved its final breakthrough thanks to the steadily increasing performance of IT systems. Powerful tools for analyzing company data and generating reports were developed. More and more software solutions changed from complex data analysis tools to easy-to-use self-service BI applications. Today there are a variety of applications, systems and processes available for evaluating and visualizing company information. Many of these are supported by artificial intelligence (AI) and machine learning processes.

4) BI systems - structure and BI architecture

The term BI architecture describes the linking of the various functions and components in order to implement BI systems for data analysis and data visualization with the help of computer-based technology. Each component has its specific task and is linked to other functions or components. One of the fundamental architecture components is the data warehouse (DWH). It takes care of the central availability and organization of the data. But how is the data warehouse connected to the other systems and components and how does the actual functionality of a BI system come about? Before we go into more detail about the data warehouse and the other components, the following graphic provides a good overview of the overall architecture. You can see how the components are linked and how the business analysis and intelligence process works.

The BI architecture consists of the following six functional elements:

1) Data Collection - 2) Data Integration - 3) Data Storage - 4) Data Analysis - 5) Data Distribution - 6) Response

1) data collection: The task of the first functional component of the architecture shown is to collect the data from the various data sources. The data sources can be very different. For example, it can be CRM systems, ERP systems, internal or external databases, files or interfaces to IT systems. Modern BI offers a variety of high-performance, easy-to-use data connectors. Intelligent collectors and engines work in the background to make the data collection process as smooth as possible. They communicate with the various systems and data sources and thus ensure that the distributed information is brought together. The data collection forms the basis for the further processing steps.

2) data integration: After collecting the data from the various sources, the next step is data integration. The data must be extracted and loaded into a data warehouse in a suitable form. This process is known as the ETL process (Extract-Transform-Load) and consists of three individual steps: extracting, transforming and loading. The ETL process has steadily gained in importance due to the enormous increase in the amount of data that arises. It can be largely automated and relieves IT departments of time-consuming, sometimes manual activities. After extraction, the data is transformed and adapted to the required formats and standards. The transformation step ensures that the data is in the correct form and that it is prepared for the next ETL step, which is loading into a data warehouse.

3) data storage: Now we come to the data warehousing mentioned at the beginning. The aim of data warehousing is to store all the data collected from the various sources in a central location and to keep it available for further processes. The DWH provides the data for the subsequent upstream functions and takes care of tasks such as data cleansing, metadata management, data distribution, storage management, data backup and data recovery. As a rule, users do not access the data in a data warehouse directly, but use downstream tools for data compilation, data analysis and data visualization. Working directly with a DWH usually requires specific IT and database knowledge and is reserved for IT professionals.

4) data analysis: The data analysis can only take place after the data has been extracted and transformed and the DWH has made it available after it has been cleaned up. The analyzes are carried out by special algorithms, applications and data analysis software. They offer a high level of flexibility and are easy to use in order to answer the business questions asked quickly and as precisely as possible on the basis of the available data. Modern tools allow ad-hoc analyzes and generate direct answers. Powerful self-service tools such as those from datapine allow users to compile analyzes using drag & drop and to visualize the results in an impressive manner with just a few clicks, without the need for special technical knowledge. With the help of these tools, the creation of reports and dashboards is greatly simplified.

5) data distribution: The process of data distribution is important when it comes to sharing the results generated in the data analyzes with all persons or departments involved in the decision-making processes with the help of reporting tools. Three different methods are used for data distribution:

a) Distribution by automated e-mailing: generated reports are distributed to selected recipients according to a defined schedule. They can be automatically updated and sent on a daily, weekly or monthly basis.

b) Distribution via dashboarding: Another option for sharing the analysis results with others is dashboarding. A selected group of people has access to the dashboards via a secure viewer environment. Although you cannot change the dashboards, you can interact with the dashboard by assigning filters for selection or by adapting the display. Furthermore, the KPI dashboards can also be easily shared via URL, which can be password-protected if desired.

c) Distribution by embedding: The analysis results can be embedded in other applications and thus distributed to a defined group of people. The information is available, for example, in internal applications, external applications or via intranet portals.

6) Reaction to analysis results: The final functional component of the BI architecture deals with the reaction to the analysis results and business insights provided. After the information is distributed in visual form, it forms the basis for the well-founded, data-driven decisions of managers, CEOs, department heads and other decision-makers. The answers to the business questions provided by the dashboards are easy to grasp by the respective target group and form the basis for meaningful, profitable business decisions.

5) Central business intelligence functions

A modern BI system offers a variety of different functions. The following is an overview of some of the key business intelligence functions:

1) Data integration and data connectors: Business analytics collects and integrates data from various external and internal sources. The data sources are no longer just databases. The information can be integrated from other systems, from business applications such as CRM systems, via APIs (interfaces) or from production and logistics processes. For this purpose, the software provides various data connectors to open or proprietary systems and applications. You connect the data sources to the BI system and implement the corresponding protocol and interface conversions.Even physical measurement data from sensors can be integrated. Users do not need any specific interface knowledge.

2) database queries: The basis for the business analyzes is formed by individual database queries with which the data required for evaluation are fed into the analysis processes. In the past, such queries had to be created and executed manually. It was not uncommon for extensive database know-how and in-depth knowledge of SQL to be necessary to create complex queries. Today modern BI software offers intuitive, user-friendly drag & drop interfaces. They also allow technically less experienced users to execute complex database queries by graphically compiling query parameters and conditions with just a few clicks of the mouse.

3) data analysis: Analysis functions carry out the actual evaluations of the available data. During the data analysis, patterns or trends are recognized and the knowledge gained is summarized. The analysis is a central function of every BI software and provides a deeper understanding of the company processes. Today, artificial intelligence (AI) and machine learning functions support the analysis process. Intelligent algorithms are able to analyze large amounts of data quickly and find patterns independently.

4) data visualization: The data visualization is used for the visually appealing presentation of the analysis results. The results of the data analysis should not be presented in the form of data series that are difficult to understand or extensive tables, but rather as easy-to-understand visualizations. A large number of different chart types allow you to always present the data in the best possible way.

5) Dashboards: Dashboards are a particularly effective way of visualizing and presenting the analysis results. These are interactive surfaces that present information such as KPIs in a condensed yet clear form. They allow the respective target group easy access to the information. Thanks to interactivity, data can be filtered according to certain criteria and valuable new insights gained. The creation of the dashboards does not require any programming knowledge and is made possible by the integrated dashboard software via a graphical user interface.

6) Reporting: The basis for an effective, modern reporting system are extensive reporting options. The most important goal of reporting is the effective distribution of analysis results and key figures for data-driven support of decision-making and management processes. Thanks to modern BI reporting functions, this process can be almost 100 percent automated.

7) Embedding: Embedding functions allow business intelligence to be integrated into external applications, processes or portals. Thanks to embedded analytics, applications receive functions such as reports, predictive analyzes or interactive dashboards. This transforms BI from a standalone application into a comprehensive, integrative solution. Users work with their familiar applications and at the same time use the intelligence of modern business analytics.

6) BI management & strategy development

The term BI management summarizes the activities of the technical, functional and organizational control of business intelligence in a company. BI management is an important success factor for the implementation, operation and use of corresponding solutions. In order to successfully introduce business intelligence in a company, a strategy and a roadmap are necessary. Only a sensible strategy enables you to find the perfect solution for the requirements of your company, to select the right product and to integrate the system into the operating processes. The business intelligence strategy takes into account all steps to be carried out for the implementation. In the following, we present eleven concrete steps on your roadmap to business intelligence.

Step 1: Approach the process with open eyes

With the right business intelligence solution, it is easy to identify trends, problems or opportunities at an early stage. But the implementation is not trivial. Even the best and most powerful software needs some adjustments and integration measures in order to reach its full potential. Therefore, approach the process with open eyes and the right attitude. Only then will you be prepared for the challenges you face, such as complex data problems, resistance in change management, dwindling support, hesitation in the IT department or user problems. You always keep an eye on the critical points and the implementation process is not interrupted unnecessarily.

Step 2: Identify the goals of everyone involved

Many areas of a company benefit from data analyzes and deep insights into business data. Nevertheless, you should identify your most important target groups and determine their respective goals. Collect the expectations of the different areas and prioritize the important points.

Step 3: choose a sponsor

Your strategy targets many stakeholders. Still, it is important to have a main sponsor for the implementation. While it is tempting to choose the Chief Information Officer (CIO) or Chief Technical Officer (CTO) as your primary sponsor, it is not the best solution. It should be someone with fundamental responsibility for the company who has a good understanding of the organization's strategy and knows its goals. The sponsor should know how to translate the company's mission into KPIs. For example, the Chief Marketing Officer (CMO) or the Chief Financial Officer (CFO) are good choices. No matter who is chosen as sponsor, he should be in constant contact with the CIO and CTO.

Step 4: Note that BI is not a purely tech initiative

As already mentioned, you need support from various company departments. IT should of course be included in the project. However, the implementation of business intelligence is not a purely technical project. The three most important user groups, the strategic, planning and operational users, are to be brought on board. Each group needs specific adjustments to the solution. You will only find suitable solutions if you know who is using the data and for what purposes.

Step 5: Set up a Chief Data Officer (CDO)

Larger companies or corporations in particular need a Chief Data Officer (CDO). Although he is not the main sponsor of the implementation, he still has a key position. The Chief Data Officer is involved in various activities in the implementation process. He takes responsibility for the project after the initial implementation has been completed.

Step 6: Assess the current situation

An important point for your strategy is the assessment of the current situation. After you have gathered all those involved in the project, the organizational structures, processes and software stacks must be analyzed. Since you don't want to start from scratch, you have to find out what is already working in terms of business analysis and reporting. Try to integrate what is already working into your strategy, otherwise you will upset many employees. Of course, you also have to identify everything that is not working (which processes are inefficient or which business questions cannot be answered?).

Step 7: clean up the data

Only a clean database allows exact analyzes. Cleansing your data is an important step in ensuring successful BI. Poor data quality causes unnecessary costs and leads to wrong decisions. It is therefore crucial to establish solid data quality management. While you will never achieve 100 percent data quality, the closer you get to that goal, the more you can rely on your data.

Step 8: create a data manual

Detailed documentation for software applications is rather uncommon these days. But you shouldn't do without it entirely. You need some kind of data manual with the central information. The most important data types, KPIs and business calculations are to be listed in this manual. This creates a broader consensus in the various areas of the company. For example, it could otherwise happen that the finance department and the sales department define parameters such as the gross margin differently. Misunderstandings and rejection of business analyzes are the result.

Step 9: Identify the Key Performance Indicators (KPIs)

KPIs are measurable values ​​that allow a company to monitor the achievement of goals. They show where things are going well or where improvements are needed. The central KPIs are an indispensable part of every BI strategy. It is necessary to examine which KPIs best represent the company's goals. Not every key performance indicator needs to be included in your solution, but the most important ones should be there.

Step 10: Select the right software and the right partner

Now that the requirements for your solution have been clarified, it is important to find the right software and the right partner. In this step, you compare cloud-based solutions with on-premises solutions, for example. Look for a flexible solution that meets the needs of all of your users. Use freely available demo and test versions, such as the one from datapine, and give yourself a lot of time in this step.

Step 11: take it step by step

Rome wasn't built in a day. The situation is similar with BI. A successful strategy takes a phased approach. Start with a few KPIs and create some sample dashboards. You then collect feedback and optimize or expand your solution. Keep asking users what is working well and where there are problems. Gradually, a solution is created that meets the requirements of the various users and company areas. Optimization and adaptation processes have to be repeated continuously and never completed.

7) Business Intelligence use cases

After so much information and theory on business intelligence, it is now time to look at practical examples. In the following, we present two BI examples each for specific company functions and sectors or industries.

Functional example 1: Human Resources (HR)

The human resources department fulfills fundamental functions in every company. Human Resources contributes to the productivity and success of the company by finding the right candidates for the various positions. The variety of tasks in the HR area such as managing employees or handling the hiring processes and the number of applications makes manual work processes almost impossible. Business analyzes and data visualizations help to optimize processes. Interactive dashboards provide HR employees with a good overview of the development and status of the HR department.

The personal dashboard shown focuses on the performance of employees in a given time interval. Key figures such as absences or work effectiveness are grouped by department. The observation period of five years quickly shows how the performance of the employees has developed over the years. It is possible to draw conclusions as to whether the workforce is satisfied and in which departments there may be problems. With the correct measures derived from this, the development of the key figures can be influenced.

Functional example 2: Marketing

Regardless of whether the company does the marketing itself or an agency takes on the task, it is extremely important for every company to continuously update and optimize advertising and marketing measures. With a professional marketing dashboard, valuable information can be collected and visualized. They form the basis for regular reassessments of the measures implemented and the assessment of their success. This information saves marketing specialists a lot of time.

The example above shows a CMO dashboard with metrics needed to plan strategic marketing initiatives. The dashboard focuses on high-level metrics such as Cost Per Lead (CPL), Marketing Qualified Lead (MQL), and Sales Qualified Lead (SQL). The values ​​show where the marketing department stands compared to the previous months and allow the appropriate conclusions to be drawn or adjustments to be made. The dashboard can be fully automated. The dashboard saves CMOs time because they don't have to spend hours generating new reports for a meeting or analyzing marketing success.

Industry example 1: logistics

Logistics is a very complex industry. Data must be constantly updated, supply chains monitored and large amounts of data managed in real time. Logistics dashboards are useful to ensure that all logistics processes run smoothly. They track all important data and provide insight into the current status of the processes in real time.

The dashboard example shown focuses on transportation management. It informs a defined group of people about the current status of the transport flows and enables performance to be optimized. The metrics shown relate to fleet management, delivery, loading times and utilization. They are updated in real time in the interactive dashboard and minimize the risk of unnoticed errors in the logistics processes. The information presented can be shared with everyone involved and can be used to automate the processes that were previously carried out manually.

Industry example 2: production

The manufacturing industry depends on a continuous and reliable flow of information. Even a single missing piece of information can lead to serious production downtimes, errors or damage in a certain production step. Many different areas have to work together in optimized processes in order to deliver the desired output. Business intelligence is an important source of information for structuring and optimizing error-free processes. Real-time analyzes provide information on the current status of production and allow predictions to be made about future processes.

Products are the heart of every manufacturing company. Knowing about the exact production volume, order quantities and the utilization of the production lines or the individual production machines helps to identify potential bottlenecks in the manufacturing processes. By dealing with the causes of the complaint or the return of products, strategies can be developed how such problems can be avoided in the future. As this example shows, dashboards do not have to be complex and expensive for production. Simple data visualizations are sufficient to achieve optimal results with the equipment and workforce used.

8) Summary

In this post we have explained to you what business intelligence is, why it is so important for companies and how a BI system is structured. You now know how BI has developed historically, what the architecture looks like, which functions are provided and what BI management and strategy are all about. Practical examples illustrate the useful use of modern dashboards.

If you would like to deal more closely with the topic of business intelligence, datapine offers you the right solutions. You have the opportunity to test modern BI software for 14 days free of charge and convince yourself of its performance. Take advantage of this offer and register now for a free 14-day trial. Carry out business analyzes yourself, create reports and visualize the results. You will quickly see how valuable such a solution can be for your company.

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