BI, i.e. business intelligence, exists for the purpose of efficient business decision making. It’s a technology-driven process for analyzing data and presenting information that’s useful for executives, managers, and other corporate users to make positive business decisions. BI includes a wide range of tools, applications, and methodologies that enable organizations to collect data from internal information systems and/or external sources as a result of business processes (transaction databases – e.g. ATMs, remote readers of electricity, water, gas…), excel spreadsheets, and other forms of generated reports.
BI prepares data for analysis, and based on created queries over data, creates reports and visualizes data so that analytical results are available to corporate decision-makers, as well as operational workers. As a result, using a Business Intelligence solution to construct highly focused recipient segments enables deep data mining and multi-dimensional analysis.
Furthermore, integrating this knowledge with the power of email creates incredibly powerful marketing. Leading BI platforms, such as Sisense or Zoho Analytics, have built-in integrations with email newsletters, which can be customized to a brand’s needs and goals via VerticalResponse. This enables email campaigns to be generated automatically using segments of targeted data created on these systems. Platform users can also send an email campaign to the pre-built segment without ever leaving the platform.
Reasons and Needs for the Introduction of BI Solutions
- There’s a need to increase revenue, reduce costs, and operate more competitively. Gone are the days when end-users were able to make business decisions using monthly reports. BI accelerates and improves decision-making, optimizes business processes, increases operational efficiency, and influences the creation of new revenue and gaining a competitive advantage over competitors.
- The need to display small amounts of data, which are the result of processing and analyzing large amounts of data. BI data can include historical information stored in a data warehouse, as well as new data collected from a variety of sources that enable BI tools to support strategic and tactical decision-making processes.
- There’s a need to reduce IT costs. Initially, BI tools were primarily used by data analysts and other IT experts who conducted analyzes and produced reports with query results. However, business executives and employees are increasingly using BI platforms, thanks in part to the development of self-service BI tools.
- There’s a need for permanent monitoring of company performance. The company is required to clearly define its goals and plans, and Key Performance Indicators (KPI) are interpreted in this regard.
Business Intelligence Architecture
There are basically two types of data sources: internal and external.
- Internal data belongs to the company and is created as a result of generation through the transaction system, describing the activities that took place within the company: such as financial subsystem, sales subsystem, production, resource tracking, billing…
- External data is obtained outside the company, most often through specialized functions that deal with the collection and distribution of information. They are critical to strategic decisions because they help companies see favorable opportunities as well as threats. External data may relate to data on competitiveness (products, services, changes in competing companies…), economic data (currency fluctuations, political indicators, interest rate movements, stock exchange data…), professional (technological trends, marketing…), econometric (revenues of an individual group, customer behavior…), psychometric (customer profiling), demographic, and marketing data.
Integration services (extraction, transformation, loading)
Integration services are tools used to migrate a wide range of data. They use data transfer extraction, transformation, and loading (ETL). The integration service also has a wizard for importing and exporting data, which can work with different types of data without transformation.
DW – Data Warehouse
DW (DWH) is a data warehouse. Data must be integrated, consolidated, and cleaned using integration services before it can be stored in DWH. Data can be stored in smaller DMs (data marts), grouped by business processes: finance, production, warehouse, sales… which all represent subsets of company information.
Each data contains a timeline with a historical trace of the company’s business as well as data from the external environment. Predicting future events isn’t possible without knowing the past of the same or some other event. DW is designed to enable data retrieval, on-line analytical processing (OLAP), reporting, and supporting business decision-making processes.
Databases for analytical and transactional purposes
To understand the essence of the DWH concept, it’s of great importance to identify the basic characteristics, advantages, and disadvantages of OLTP (transactional) and OLAP (analytical) data processing.
- OLTP (On-Line Transaction Processing) is focused on details, with frequent updates by end-users, and continuous (permanent) business processes. OLTP systems are based on relational databases in accordance with the rules of normalization. Intricate data is broken down into the most basic structural columns. They are suitable for quickly updating data, and determining relationships while preparing complex reports can take a lot of processing time and impair database performance.
- OLAP (On-Line Analytical Processing) is a type of technology that allows analysts and managers to view data through fast, consistent, and interactive access to a large number of diverse reports, based on information obtained by transforming raw data. Here, the data is denormalized, the execution of queries over such data organization is much faster, and at the same time, the database scheme is simplified. This makes it easier to search for staff who aren’t technically savvy. The model is based on the methodology of multidimensional analysis, which means that data can be viewed through a number of filters. These systems don’t deal with data processing, but interpretation and analysis.
OLAP vs OLTP
|Attribute||Information processing and data analysis||Operational data processing|
|Focus||More complex queries||Large number of transactions, data entry|
|Users||Managers, analysts||Salesperson, technician, a multitude of different users|
|Database size||Gb – Tb of data||Mb – Gb of data|
|Access/number of users||Reading/hundreds||Reading – writing/thousands|
|Functions||Long-term analysis, summary, and revised data||Everyday operation (raw data/entry, changing…)|
|Data type||Historical data||Updated data, current|
|Database design||Star/snowflake||ER model|
Reporting and analytical tools
BI technology also includes data visualization software for charting and other infographics as well as spreadsheet tools, Dashboards that display visual data on business metrics, and KPIs in a simple way. The use of data visualization tools has become the norm in present business intelligence. Several leading manufacturers have defined BI reporting and analysis tool technology.
In addition to BI Managers, business intelligence teams typically include a mix of BI architects, BI developers, business analysts, and data management professionals. Business users are also often people who represent the business side, they aren’t directly interested in the details of DWH but are there to provide business logic in the BI development process.
BI platforms are increasingly being used as front-end interfaces for large data systems. Modern BI software usually offers flexible endpoints, allowing them to connect to different data sources. This, with a simple user interface, makes it a good tool for handling large amounts of data.
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