Transcultural Travel Preparedness Business Intelligence – All businesses operate on data – information generated from multiple sources both inside and outside of your business. And these data feeds serve as a pair of eyes for executives, giving them analytical insights into what’s happening in the business and the market. Consequently, any misunderstanding, inaccuracy or lack of information can lead to a distorted view of the market situation and internal operations, followed by bad decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even those you may not be aware of. But how do you turn unstructured chunks of data into something useful? The answer is business intelligence.
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In this article, we will discuss the actual steps to embed business intelligence in your existing corporate infrastructure. You will learn how to set up a business intelligence strategy and how to integrate the tools into your company’s workflow. What is business intelligence? Business intelligence or BI is a set of practices for collecting, structuring and analyzing raw data to turn it into actionable business insights. BI considers methods and tools that transform unstructured data sets, compiling them into easy-to-understand reports or 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 business intelligence process can be divided into five main stages.
Business intelligence is a technology-driven process that relies heavily on input. The technologies used in BI to transform unstructured or semi-structured data can also be used for data mining, in addition to being advanced tools for working with big data. Business Intelligence vs Predictive Analytics The definition of business intelligence is often confusing as it intersects with other spheres of knowledge, especially
. With the help of descriptive and diagnostic analytics, or BI, companies can study market conditions in their industry as well as their internal processes. An overview of historical data helps to find pain points and opportunities for improvement.
Based on processing data from past and present events. Instead of creating overviews of historical events, predictive analytics makes predictions about future business trends. It also allows simulation and comparison of scenarios. To make this possible, a complex data architecture involving advanced ML methods must be developed by a professional data science team.
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So we can say that predictive analytics can be considered the next stage 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 marts
It is a broad concept that can include the organizational aspect (data management, policies, standards, etc.), but in this article we will focus mainly on the technological infrastructure. Usually, it includes
Now we will look at each element of the infrastructure individually, but if you want to expand your knowledge of data engineering, see our article or watch the video below.
To begin with, the central element of any BI architecture is a data warehouse. A warehouse is a database that maintains your information in a predefined, usually structured, classified, and error-free format.
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However, if your data has not been processed, your BI tool or your IT department cannot query it. For this reason, you cannot directly connect your data warehouse to your data sources. 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 a warehouse in three consecutive steps.
Typically, ETL tools are provided out-of-the-box with vendor BI tools (we’ll cover the more popular ones later). Data store Once you’ve set up data streaming from your chosen source, you need to set up a store. 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 on one end and reporting tools or dashboard interfaces on the other. It allows data from multiple systems to be presented through a single interface.
But a store usually contains a lot of information (+ 100 GB), which makes it understandably slow to answer questions. In some cases, data can be stored unstructured or semi-structured, which leads 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 storage space for ease of use. So companies are using more technologies to provide faster access to smaller, more topical pieces of information.
Recommendation: If you do not have large volumes of data, using a simple SQL store is sufficient. Additional structural elements such as data marts will cost you a lot without providing any value. Data warehouse + OLAP cubes The data stored in a warehouse has two dimensions, as it is usually represented in spreadsheet format (tables and rows). A warehouse method of storing data is also called a
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. You can include thousands of data types in a database, so querying a data warehouse takes a lot of time. To meet the needs of analysts to quickly access data, analyze it from different dimensions and group it as needed, OLAP cubes are used.
OLAP or Online Analytical Processing is a technology that analyzes and represents data from multiple dimensions simultaneously. Structuring your data into OLAP cubes helps overcome the limitations of a data warehouse.
OLAP cube is a data structure optimized for fast data analysis in SQL databases (warehouse). The source data in the data warehouse cubes is a smaller representation of it. However, the data structure assumes that there are more than 2 dimensions (row and column format in spreadsheets). The dimensions are the important elements that form the report, for example, for the sales department to be able to
Cubes are a multidimensional database of information that can be adapted to group it in different ways and create reports more quickly. A warehouse and OLAP are used together, because cubes store a small amount of data and serve to speed up processing.
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Recommendation: The data warehouse architecture + OLAP cubes 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 element of the business intelligence architecture. A small representation of warehouse data sets is a data mart that brings together information dedicated to a particular subject area. With the help of data marts, separate departments can access the required data.
Recommendation: Data warehouse + data marts is the second most popular architectural style. It allows constant reporting or quick access to information, without giving permission to end users. Hybrid Architecture Business enterprises may need multiple options for data management. Data marts and cubes are different technologies, but both are used to represent small pieces of information in the warehouse. Data marts represent a specific subset of the data warehouse problem, but can be implemented differently. The implementation option includes relational (warehouse or any SQL database) and multidimensional databases, which are usually OLAP cubes. Thus, you can use both technologies to manage your data and distribute it to organizational departments.
Recommendation: You can use both technologies 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 as a multidimensional representation of an OLAP cube. But note that both data marts and OLAP cubes will require separate database configurations.
Now that we’ve covered what BI infrastructure consists of, 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 the introduction of business intelligence as a concept to your company’s employees and the actual integration of tools and applications. Let’s review the main stages.
Step 1: Introduce business intelligence to your employees and stakeholders To start using business intelligence in your organization, first explain the meaning of business intelligence to all your stakeholders. How you do this will depend on the size of your organization. Mutual understanding is important here because employees from different departments will be involved in data processing. So make sure everyone is on the same page and don’t confuse business intelligence with predictive analytics.
Another purpose of this phase is to introduce the concept of BI to key people involved in data management. You need to define the real problem you want to solve and organize the specialists needed to launch your business intelligence initiative.
It is important to mention that at this stage, technically, you will make assumptions about the data sources and the established patterns to control the data flow. You can verify your assumptions and specify your data workflow in later stages. That’s why you need to be willing to change your data collection channels and the training of your team. Step 2: Set goals, KPIs and requirements The big step after aligning the vision is defining what the problem is.
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