Self-service Business Intelligence Tools For Natural Language Processing Techniques – All businesses operate on data – information from various internal and external sources within your company. And these data channels become a pair of eyes for executives, providing them with analytical information about what is happening in the business and the market. Accordingly, any misconception, inaccuracy or lack of information can lead to a distorted view of the market situation as well as internal operations, which can lead to bad decisions.
Making data-driven decisions requires a 360 ° view of all aspects of your business, even if you have not thought of it. But how do you turn a bunch of unstructured data into something useful? The answer is business intelligence.
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In this article, we will discuss practical steps to bring business intelligence into your existing enterprise infrastructure. You will learn how to build a business intelligence strategy and integrate the tool into your company’s workflow. What is business intelligence? Business intelligence, or BI, is a collection of practices for collecting, organizing, and analyzing raw data to transform it into actionable business insights. BI considers methods and tools that transform unstructured data sets and compile them into easy-to-understand reports or dashboards. The primary goal 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 stages.
Business intelligence is a technology-based process that is largely input-based. The technology used in BI to transform unstructured or semi-structured data is an advanced tool for working with big data and can also be used to generate data. Business Intelligence vs. Predictive Analytics The definition of business intelligence is often confusing because it overlaps with other areas of knowledge, in particular.
. With the help of descriptive and diagnostic analytics, or BI, businesses can learn about the market conditions of their industry as well as internal processes. Reviewing historical data helps identify pain points and growth opportunities.
Based on processing information of past and present events. Rather than producing a summary of historical events, predictive analytics makes predictions about future business trends. It also allows scenario simulation and comparison. To do this, a complex data architecture involving advanced ML techniques must be created by a professional data science team.
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So, we can say that predictive analytics can be considered as the next stage of business intelligence. Meanwhile, prescriptive analytics is the fourth, most advanced type, which focuses on finding solutions to business problems and recommending actions to solve them. Business intelligence architecture: ETL, data warehouse, OLAP and data marts
Is a broad concept that can cover organizational aspects (data management, policies, standards, etc.), but in this article we will focus mainly on technological infrastructure. Often included
Now we will explore all the infrastructure elements separately, 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 element of any BI architecture is the data warehouse. A warehouse is a database that stores your data in a predefined, usually structured, classified, and error-free format.
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However, if your data isn’t pre-processed, your BI tools or IT department won’t be able to query it. For this reason, you cannot link your data warehouse directly to your data source. Instead, you should use an ETL tool. ETL ETL (Extract, Transform, Load) or data integration tools will pre-process the raw data from the primary source and send it to the warehouse in three sequential steps.
Usually, ETL tools are provided with BI tools from vendors (we’ll see the most popular ones later). Data warehouse After configuring the data transfer from the selected source, you need to set up the warehouse. Data warehouse in business intelligence is a special type of database that typically stores historical data in a tabular format. Warehouses are connected to data sources and ETL systems on one side, and reporting tools or dashboard interfaces on the other. This allows data from different systems to be presented through a single interface.
But the storage usually contains a large amount of data (100GB +), which makes it reasonable to respond to queries. In some cases, data may be stored in an unstructured or semi-structured manner, which leads to a high level of error when analyzing data to generate reports. Analytics may require multiple types of data to be grouped into one memory location for ease of use. That’s why businesses are using additional technologies to provide faster access to smaller, more thematic sets of data.
Recommendation: If you do not have a large amount of data, a simple SQL repository is sufficient. Additional structural elements such as data marts will cost you a lot without providing value. Data warehouse + OLAP cube Because the data stored in the warehouse is usually represented in a spreadsheet format (tables and rows), it has two dimensions. Storage is also called the method of storing data
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. It can include thousands of data types in a single database, so querying the data warehouse takes a significant amount of time. OLAP cubes are used to meet the needs of analysts to access data quickly, analyze it from different dimensions and group it when they need it.
OLAP, or online analytical processing, is a technology that analyzes and presents data from multiple dimensions simultaneously. Structuring your data in an OLAP cube helps overcome data storage limitations.
OLAP cube is a data structure optimized for fast data analysis of SQL databases (warehouses). A cube is a data source from a data warehouse, a smaller representation. However, the data structure shows that there are more than 2 dimensions (rows and columns in spreadsheet format). Dimensions are important elements that make up reports, for example for the sales department can be
The cube forms a multidimensional database that can be customized to groups in different ways and generate reports faster. Warehouse and OLAP are used together because the cube stores a relatively small amount of data and serves for ease of processing.
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Recommendation: Data warehouse + OLAP cube architecture can be used by companies of all sizes that require complex multidimensional data analysis. If you don’t want to bombard your warehouse with queries, consider an OLAP architecture approach. Data warehouse + data mart technology The warehouse is the first and largest element of the business intelligence architecture. A smaller representation of a data set warehouse is a data mart that collects data dedicated to a specific area. With the help of data mart, individual departments can access the required information.
Recommendation: Data warehouse + data mart is the second most popular architectural style. This gives end users easy access to permanent report settings or data without permission. Hybrid enterprise enterprise businesses may require multiple options for data management. Data marts and cubes are different technologies, but both are used to represent smaller sets of data from a warehouse. Data mart represents some problem-specific data warehouse, but they can be implemented differently. Application options mainly include relational databases (warehouses or other SQL databases) with OLAP and multidimensional cubes. So 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 different goals. Data marts can be implemented as part of a data warehouse for security, data collection, or accessibility. Or you can use data marts as a representation of multiple dimensions of an OLAP cube. Note, however, that both data marts and OLAP cubes require separate database setup.
Now that we understand what BI infrastructure is, let’s talk about how to implement it in your organization. Business intelligence applications
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The BI adoption process can be divided into the implementation of business intelligence as a concept for your company’s employees and the actual integration of tools and applications. Let’s explore the main stage.
Step 1: Present business intelligence to 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 will depend on the size of your organization. Mutual understanding is very important here, because employees of 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 goal of this phase is to communicate BI concepts to key people involved in data management. You need to identify the actual problem you want to work on and organize the experts needed to launch your business intelligence initiative.
It is important to note that at this stage you will, technically, make assumptions about data sources and standards are defined to control the flow of data. You will be able to test your assumptions and refine your data workflow in the next stage. Therefore, you should be prepared to change your data source channel and command line. Step 2: Define goals, KPIs and requirements After aligning the vision, defining what the problem is is a big step
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