Today’s world is increasingly connected by virtual networks. All processes from planning to delivery of goods and services are getting faster, and consumers expect everything in real time while receiving the greatest possible experience.

Today’s world is increasingly connected by virtual networks. All processes from planning to delivery of goods and services are getting faster, and consumers expect everything in real time while receiving the greatest possible experience. The management of even a small company therefore cannot do without an enterprise IT solution, referred to as Business Intelligence (BI), which can include a set of processes, technologies, and systems. BI collects and analyses data to provide business insights for decision making and strategic planning. It automates processes to the maximum extent, and monitors and coordinates production, service and sales activities to help make them as efficient as possible. There are a number of different software solutions on the market and it is up to each organization to choose the best option that will satisfy the needs of users while offering the best performance at a reasonable price.



As regards selecting software tools, there is no single rule. There is no universal software with a single implementation template. A well-designed BI system will also vary by industry. A solution for the automotive industry will have different functionalities than that for retail, banking, public services, or healthcare. Not many organization are starting from scratch with an enterprise system these days. Most have some experience and an idea of what they expect from a new system. Adoption of new technologies needs to be kept on the right and sustainable track. Modern technology automatically brings benefits to everyone who buys it. According to the annual Business Intelligence 2020 study, which has been conducted regularly for the past twelve years by Dresner Advisory Services, nearly 60 percent of Business Intelligence projects fail to be deployed and used correctly. The main reason is insufficient preparation, where the objectives and requirements for the system are not clearly defined, there is no implementation plan with the scope and timeline, and the structure and competencies of users in the organization are not properly addressed. 

To ensure success of a BI project, you need to chart business processes, identify the needs of all departments, and arrange them by business function into a logical structure. This should reveal the different requirements of all stakeholders. The BI system should be implemented incrementally, starting from high-priority functions that have the greatest impact on the company’s business. Each step should follow after sufficient time to verify the functionality of the processes and data integrity, and to address any issues. Likewise, it is important not to underestimate the importance of training users and explaining the benefits of the new system to drive adoption.



To maximize the potential of BI, it is necessary to make it available to as many employees as possible. The interface needs to be tailored to their abilities and work tasks to maximize the benefits and minimize strain. They should use a central data warehouse in real-time mode, with individual rule settings. Users do not need to be programmers or know how to use an SQL database or Hadoop and must be able to accomplish tasks on heir own. Self-service BI is not reliant on a small group of IT professionals who manage inputs and interpret results for business users. Such a system is rigid and will never fully deliver on its promise.

The self-service BI analytical tools feature graphical interfaces with data visualization as the primary means of interaction for greater user convenience.  They are intuitive to use and allow users to work with the data as they need. They provide clear interpretation of the insights gleaned from the data, in terms of depth, scope and form of the results that are presented in a way easy to understand for different audiences. 



Organizations currently prefer powerful BI systems that leverage neural networks with elements of artificial intelligence. Advanced analytics is essential for real-time data mining, marketing, risk management, text and image recognition, prediction, simulation, etc. Vertical tools are grouped by product or solution. Data management integrates with Hadoop for big data analytics, including scoring. Reporting needs to be interactive and ideally offer mobile access.

In addition to analyzing past and current events, organizations are increasingly interested in predictive analytics for modelling “what-if” scenarios. Such modelling provides greater certainty when making decisions about changes in processes, business models, and so on. They also help manage risks and find preventive solutions to undesirable situations before they develop into costly problems. For a more illustrative interpretation of the analytical results, it is increasingly desirable to enrich the data with location details. Visualization in maps is easy to understand for most people. Up to 80 percent of enterprise data contains location information. And there are many insights about each location that can be easily contextualized for BI. Locally connected customers, IoT and other online connections can be defined in space, and their location is important for many types of business analysis. They are needed for calculations of catchment areas, driving distances, certain analytical techniques, such as clustering to improve customer segmentation, and for a spatial aspect in predictive and prescriptive analysis.



BI systems can serve all verticals, from manufacturing to services to government. They improve operations, boost performance, and save costs by enabling effective management. 

For example, at Czech Savings Bank, BI drives the entire direct marketing campaign process, from target group selection to fully automated evaluation, with a 38% increase in profitability and a 20% time saving compared to the previous solution. 

In e-commerce, where there is extreme competition among large players, such as Amazon and Aliexpress, small electronics retailer Conrad Electronics uses BI to analyze the current and historical behavior of online shoppers, determine the likelihood of purchase and show them relevant offers based on their preferences. Simple behavior-based banner display works for only 2 to 3 percent of identified customers. In contrast, advanced neural network technology that learns from all available data sources in real time is able to create a complete profile for 60 percent of unknown visitors.

American Honda uses BI to track vehicle maintenance and repairs in the dealer network. It processes data, including car type, which parts were replaced or repaired, how much the customer paid, the respective technician’s comments, and much more. Analysis results help assesses warranty claims, plan distribution of spare parts, and provide data for marketing purposes. By combining time series information with sales data, BI predicts where the biggest opportunities will be in the coming years.

BI also helps in healthcare. At Taipei University Hospital, BI analyses processes at the clinic, ward, doctor, and patient level. It provides comprehensive statistics on hospital operations, finances, and the nature and quality of medical procedures. Data visualization offers better insight - instead of looking at meaningless lists of numbers and values, people can see the factors behind them. Besides tracking and scheduling the number of attending physicians, outpatient care volume, and consumption of medical supplies, it also assesses patient-level data such as visit rates, causes, and outcomes.

A shift to new technologies with advanced analytics should be seen as an opportunity to get a better picture, regroup existing customers and win new ones. A thorough understanding of their needs and their use of products and services, enables the company to create and develop tailored products. An organization that successfully deploys modern BI and is able to process and analyze all available data consistently and in depth will gain a major competitive advantage. 


Kamil Mahdal
CEO, Analytics Data Factory
SAS Institute Silver Partner

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