Business Intelligence Software Data Warehouse – All businesses operate with data – information generated from your company’s many internal and external sources. And these data channels act as a pair of eyes for executives, providing analytical information about what’s going on with the company and the market. In addition, any misconception, inaccuracy or lack of information can lead to a distorted view of the market situation or internal operations – followed by wrong decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even the ones you don’t think about. But how do you turn unstructured pieces of data into something useful? The answer is business intelligence.
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In this article, we discuss the actual steps to bring business intelligence into your existing business infrastructure. You’ll learn how to set up a business intelligence strategy and integrate the tools into your business workflow. What is business intelligence? Business Intelligence or BI is a set of practices for collecting, modeling and analyzing raw data to turn it into actionable business insights. BI considers methods and tools for manipulating structured data sets, compiling them into easy-to-understand reports or informative dashboards. The main purpose of BI is to support data-driven decision making.
What Is Embedded Business Intelligence (ebi)
Business Intelligence Process: How does BI work? The entire process of business intelligence can be divided into five main steps.
Business intelligence is a technology-driven process that relies heavily on input. Techniques used in BI to transform unstructured or semi-structured data can also be used for data mining, as well as front-end tools for working with big data. Business Intelligence vs Predictive Analytics The definition of business intelligence is often confusing because it intersects with other fields of science, especially
. Using descriptive and diagnostic analytics – or BI – companies can study their industry’s market conditions, as well as their internal processes. An overview of historical data helps in finding pain points and improvement opportunities.
Based on data processing of past and present events. Instead of creating overviews of historical events, predictive analytics makes predictions about future business trends. It also enables visual simulation and comparison. To make this possible, a complex data architecture with advanced ML techniques must be created by a professional data science team.
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So we can do it, Meanwhile, prescriptive analytics is the fourth, most advanced type that aims to find solutions to business problems and suggest actions to solve them. Architecture for business intelligence: ETL, data warehouses, OLAP and data marts
Is a broad concept that can include organizational aspects (data management, policy, standards, etc.), but in this article, we will focus primarily on technical infrastructure. Most often, it contains
We will now look at all the infrastructure aspects one by one, but if you want to expand your knowledge of data engineering, check out our article or watch the video below.
To begin with, the core of any BI architecture is the data warehouse. A warehouse is a database that holds your information in a predefined format, usually structured, classified and cleaned of errors.
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However, unless your data is pre-processed, neither your BI tool nor your IT department can query it. For this reason, you cannot connect your data warehouse directly to your data sources. Instead, you should use ETL tools. ETL (Extract, Transform, Load) as a data integration tool pre-processes raw data from primary sources and sends it to the warehouse in three sequential steps.
Typically, ETL tools are provided out-of-the-box with BI tools from vendors (we’ll cover the most popular ones further). Data warehouse After you configure data transfer from the selected sources, you must set up the warehouse. In business intelligence, data warehouses are specific types of databases that typically store historical information in tabular formats. Warehouses are connected to data sources and ETL systems at one end and reporting tools or dashboard interfaces at the other. It allows displaying data from different systems through a single interface.
But the warehouse usually contains extensive information (100GB+), which makes responding to queries understandable. In some cases, data may be stored unstructured or semi-structured, leading to a high error rate when parsing the data to generate a report. Analytics may require a certain type of data to be grouped into a single storage location for ease of use. Therefore, companies use additional technologies to provide faster access to smaller, more contextual pieces of information.
Recommendation: If you do not have a large amount of data, using a simple SQL warehouse is sufficient. Additional infrastructure such as data marts can cost you a lot without providing value. Data warehouse + OLAP Cubes Data stored in a warehouse has two dimensions because it is usually depicted in a spreadsheet format (tables and rows). Warehouse The process of storing data is also called a
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. It contains thousands of data types in a single database, so querying the data warehouse takes considerable time. To meet the needs of analysts to quickly access data, analyze it from different angles and group it when they need it, OLAP cubes are used.
OLAP or Online Analytical Processing is a technique for analyzing and representing data from multiple dimensions simultaneously. Forming your data into OLAP cubes helps overcome data warehouse limitations.
An OLAP cube is a data structure optimized for fast analysis of data from a SQL database (warehouse). Cubes are a small representation of the source data from the data warehouse. However, the structure of the data assumes more than 2 dimensions (row and column format of spreadsheets). Measurements are the key elements that make up a report, be it for a sales department for example
Cubes form a multidimensional database of information that can be customized to group them in different ways and create reports faster. A warehouse and OLAP are used together because cubes store and render relatively small amounts of data for ease of processing.
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Recommendation: The Data Warehouse + OLAP Cubes architecture can be used by companies of all sizes that require complex multidimensional analysis of information. If you don’t want to bombard your warehouse with queries, consider an OLAP architecture. Data Warehouse + Data Mart Technologies The warehouse is the first and largest element in building business intelligence. A smaller representation of warehouse datasets is a data mart that collects information dedicated to a particular subject area. With the help of data marts, specialized departments can access the required data.
Recommendation: Data warehouse + data marts are the second most popular architecture style. This enables consistent reporting or easy access to information without granting access rights to end users. Hybrid Architecture Enterprise companies may need multiple options for data management. Data marts and cubes are different technologies, but they are both used to represent small chunks of information from a warehouse. Data marts represent a problem-specific subset of a data warehouse, but they can be implemented differently. Implementation options include relational databases (a warehouse or another SQL database) and multidimensional, which are basically OLAP cubes. So you can use both technologies to organize and distribute your data across departments in the organization.
Recommendation: You can use both techniques because they support the same idea but serve different purposes. Data marts can be implemented as part of a data warehouse for security, data aggregation, or accessibility. Or you can use data marts to represent the various dimensions of an OLAP cube. Note that both Data Mart and OLAP cubes require separate database settings.
Now that we’ve covered what constitutes a BI infrastructure, let’s finally talk about how to implement it in your organization. Implementation of business intelligence
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The BI adoption process can be divided into introducing BI as a concept to your company’s employees and the actual integration of tools and applications. Let’s take a look at the main steps.
Step 1: Introduce BI to your employees and stakeholders To start using BI in your organization, first explain the meaning of BI to all your stakeholders. How you handle this depends on the size of your organization. Mutual understanding is essential here as employees from different departments are involved in data processing. Make sure everyone is on the same page and don’t confuse BI with predictive analytics.
Another objective of this phase is to impart the concept of BI to the key people involved in data management. You need to define the real problem you want to work on and organize the experts you need to start your business intelligence initiative.
It is important to mention that at this stage, technically, you will set the assumptions about the sources of the data and the criteria to control the data flow. You can validate your assumptions and specify your data workflow in the next steps. So, you need to be ready to change your data source channels and your team lineup. Step 2: Set goals, KPIs and requirements.
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