Creating Websites Utilising Business Intelligence Software. – All businesses run on 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 them with analytical information about what is happening in the business and in the market. Accordingly, any misconception, inaccuracy or lack of information can lead to a distorted view of the market situation and 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 make unstructured pieces of data useful? The answer is business intelligence.
Creating Websites Utilising Business Intelligence Software.
In this article, we discuss the actual steps involved in bringing business intelligence to your existing corporate infrastructure. You’ll learn how to set up a business intelligence strategy and integrate the tools into your company’s 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 transforming structured data sets, combining them into easily digestible reports or informative dashboards. The primary purpose of BI is to support data-driven decision making.
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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 manipulate 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 as it intersects with other fields of knowledge, especially
. With the help of descriptive and diagnostic analytics – or BI – businesses can analyze the market conditions of their industry as well as their internal processes. An overview of historical data helps find pain points and improvement opportunities.
Based on data processing of past and current events. Instead of creating overviews of historical events, predictive analytics makes predictions about future business trends. It also allows visual simulation and comparison. To make this possible, a professional data science team needs to create a complex data architecture with advanced ML techniques.
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So we can say that predictive analytics is considered the next step of business intelligence. Meanwhile, prescriptive analytics is the fourth, most advanced type, which aims to find solutions to business problems and suggest actions to solve them. Business Intelligence Architecture: ETL, Data Warehouses, OLAP and Data Mart
A broad concept that can include an organizational aspect (data management, policies, standards, etc.), but in this article, we will focus mainly on the technical infrastructure. Usually, it contains
We’ll look at all aspects of infrastructure 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 predetermined format, often structured, classified, and cleaned of errors.
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However, if your data has not been processed, it cannot be queried by your BI tool or your IT department. For this reason, you cannot directly connect your data warehouse to your data source. Instead, you should use ETL tools. ETL ETL (Extract, Transform, Load) or data integration tools pre-process raw data from primary sources and send 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). Data Warehouse After you have configured data delivery from selected sources, you need to set up the warehouse. In business intelligence, data warehouses are specific types of databases that typically store historical information in tabular formats. Warehouses are integrated with data sources and ETL systems on one side and with reporting tools or dashboard interfaces. 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 data to generate a report. Analytics may require certain types of data to be grouped in a storage location for ease of use. That’s why businesses are using 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 a data mart will cost a lot without providing any value. Data Warehouse + OLAP Cubes The data stored in a warehouse has two dimensions, as it is usually described in spreadsheet format (tables and rows). Warehouse The process of storing data is also known as a
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. It can contain thousands of data types in a database, so querying the data warehouse takes a large amount of time. To meet the needs of analysts to quickly access data, analyze it from different dimensions and group it when they need it, OLAP cubes are used.
OLAP or Online Analytical Processing is a technique that analyzes and represents data from multiple dimensions simultaneously. Framing your data in OLAP cubes helps overcome data warehouse limitations.
An OLAP cube is a data structure optimized for quick analysis of data from an SQL database (warehouse). Cubes are a small representation of source data from the data warehouse. However, the data structure assumes more than 2 dimensions (row and column format of spreadsheets). Measures are the main elements that make up a report, e.g., it might be for the sales department
Cubes form a multidimensional database of information that can be adapted to group them in different ways and generate reports more quickly. Because cubes store relatively small amounts of data and are useful for ease of processing, warehousing and OLAP are used together.
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Recommendation: The Data Warehouse + OLAP Cubes architecture can be used by companies of all sizes that require complex multidimensional information analysis. If you don’t want to bombard your warehouse with queries, consider an OLAP architecture approach. Data Warehouse + Data Mart Technologies The warehouse is the first and largest part of a business intelligence architecture. A smaller representation of warehouse datasets is a data mart that collects information focused on a specific subject area. With the help of data mart, specialized departments can access the required data.
Recommendation: Data warehouse + data mart is the second most popular architecture style. This allows for consistent reporting or easy access to information without granting permissions to end users. Hybrid Architecture Enterprise businesses may need multiple options for data management. Data marts and cubes are different technologies, but both are used to represent small pieces of information from a warehouse. A data mart represents a problem-specific subset of a data warehouse, but they can be implemented differently. The choice of implementation includes relational databases (a warehouse or any other SQL database) and multidimensional, typically OLAP cubes. So you can use the same technologies to manage your data and distribute it across organizational departments.
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 access. Or you can use data marts as a multi-dimensional representation of an OLAP cube. But note that both data mart and OLAP cube require separate database setups.
Now that we’ve covered what makes up a BI infrastructure, let’s finally talk about how to implement it in your organization. Implementing business intelligence
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The BI adoption process can be divided into the introduction of business intelligence as a concept for your company’s employees and the actual integration of tools and applications. Let’s look at the basic steps.
Step 1: Introduce business intelligence to your employees and stakeholders To start using business intelligence in your organization, first explain the meaning of BI to all your stakeholders. How you do this depends on the size of your organization. Mutual understanding is very important here because employees from different departments are involved in data processing. So, make sure everyone is on the same page and don’t confuse business intelligence with predictive analytics.
Another benefit of this phase is imparting the concept of BI to key people involved in data management. You need to identify the real problem you want to solve and organize the experts you need to start your business intelligence initiative.
At this stage, it is important to mention that you have made technical assumptions, data sources and standards set to control data flow. You can validate your assumptions and define your data workflow in the next steps. That’s why you should be ready to change your data sourcing channels and your team lineup. Step 2: Set Goals, KPIs and Requirements The big step after aligning the vision is defining what the problem is
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