Business Intelligence Analyst Instances

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Business Intelligence Analyst Instances – All businesses operate on data – data generated from your company’s many internal and external sources. And these information channels act as a companion for executives. It provides analytical information about what is happening with the business and the market. Therefore, misunderstandings inaccuracy Or the lack of information may lead to a distorted view of the market situation as well as internal operations. Followed by bad decisions

Data-driven decision making requires a 360° view of every aspect of your business. Even areas that you couldn’t imagine. But how do you turn a chunk of unstructured data into something useful? The answer is business intelligence.

Business Intelligence Analyst Instances

In this article, we will discuss the actual steps to bringing business intelligence to your existing enterprise infrastructure. You will learn how to set up a business intelligence strategy and integrate the tools into your company’s workflow.

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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. By compiling data into reports or dashboards that are easy to understand. The main purpose of BI is to support data-driven decision making.

Business intelligence is a technology-driven process that relies heavily on data inputs. The technology used in BI to transform unstructured or semi-structured data can also be used for data mining. As well as being a front-end tool for working with big data.

With the help of descriptive and diagnostic analysis or BI, businesses can study the market conditions of their industry as well as their internal processes. A snapshot of historical data helps find bugs and opportunities for improvement.

It depends on the processing of past and present event information. Instead it presents a snapshot of past events. Predictive analytics makes predictions about future business trends. It also allows simulation and comparison of situations. to make possible Complex data architectures involving advanced ML techniques must be created by a professional data science team.

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Therefore, we can say that predictive analytics can be considered as the next step of Business Intelligence. Prescriptive analysis is considered the fourth and most advanced type that aims to find solutions to business problems and recommend actions to solve those problems.

It’s a broad concept. That may include organizational aspects. (data governance, policy, standards, etc.) but in this article We will focus mainly on technological infrastructure. Most often include

Now we will examine all the basic building blocks one by one. But if you want to expand your knowledge about data engineering. Please read our article or watch the video below.

First, the core component of a BI architecture is the data warehouse. A warehouse is a database that stores your data in a predefined format. This usually structures, classifies, and eliminates errors.

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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. As a result, you won’t be able to connect your data warehouse directly to your data sources. Instead, you need to use an ETL tool.

ETL (Extract, Transform, Load) or data integration tools pre-process raw data from the original source. and delivered to the warehouse in three consecutive steps.

ETL tools are usually provided along with BI tools from vendors (we’ll cover the most popular tools more next time).

Once you have configured sending data from the selected source. You will need to set up a warehouse. in business intelligence A data warehouse is a specific type of database that usually stores historical data in tabular form. The warehouse connects to data sources and ETL systems on one end and reporting tools or dashboard interfaces on the other. Makes it possible to present information from various systems through a single interface

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But warehouses often contain large amounts of data (100GB+), which can make response to queries noticeably slow. In some cases, data can be stored unstructured or semi-structured. This leads to a high error rate when parsing data to create reports. Analysis may require certain types of data grouped in a single repository for ease of use. That is why businesses Use additional technology to provide faster access to smaller, more topical information.

Hint: If you don’t have a lot of data. Using a simple SQL data warehouse is enough. Additional structural elements, such as data marts, will cost you a lot without providing any value.

The data stored in the warehouse has two dimensions. This is because they are usually presented in spreadsheet format. (tables and rows) The way a warehouse stores data is called a

There may be thousands of records in a single database. Therefore, searching the data warehouse takes a considerable amount of time. To meet the needs of analysts to quickly access information. Analyze from different dimensions and group them whenever they want, OLAP cubes are used.

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OLAP or online analytical processing is a technology that analyzes and displays data from multiple dimensions simultaneously. Structuring your data in OLAP cubes helps in overcoming the limitations of a data warehouse.

OLAP cubes are data structures optimized for quickly analyzing data from SQL databases (warehouses). Cubes’ data sources from a data warehouse are simply smaller representations of data. However, the data structure assumes more than two dimensions. (a spreadsheet’s row and column layout) Dimensions are key elements that create a report. For example, for a sales department, it might be

Cubes create a multidimensional database that can be adjusted to be grouped in various ways. and create reports more quickly Warehousing and OLAP are used together. This is because cubes store relatively small amounts of data and serve them for ease of processing.

Recommendation: Data warehouse + OLAP cube architecture can be used by companies of all sizes that need complex multidimensional data analysis. If you don’t want to bombard your warehouse with questions. Consider the OLAP architecture approach.

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The warehouse is the first and largest component of a business intelligence architecture. A smaller representation of a data warehouse is a data center that collects information specific to a specific subject area. With the help of data mart Separate departments have access to the required information.

Hint: Data warehouse + data mart is the second most popular architecture style. This allows for continuous reporting or easy access to data. without granting permissions to the end user

Enterprise businesses may need many options for managing data. Data marts and cubes are different technologies. But both are used to display small pieces of information. from warehouse A data mart represents a problem-specific subset of a data warehouse. But it can be used differently. Application options include relational databases. (warehouse or other SQL database) and multidimensional, which is basically an OLAP cube, so you can use both technologies to manage your data and distribute it across different departments of your organization.

Hint: You can use both technologies as they support the same concept. But they have different objectives. Data marts can be used as part of a data warehouse for security, data integration, or accessibility. Or you can use a data mart instead of the multiple dimensions of an OLAP cube, but keep in mind that both the data mart and the OLAP cube require separate database settings.

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Now we have discussed what a BI infrastructure consists of. Finally, let’s talk about how to implement it in your organization.

The BI adoption process can be divided into introducing business intelligence as a concept for employees in your company. and true integration of tools and applications. Let’s explore the main steps.

To start using business intelligence in your organization The first and most important thing is to explain what BI means to all your stakeholders. How you do this depends on the size of your organization. Mutual understanding is important here. Because employees from various departments will be involved in processing the information, 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 concepts of BI to key people involved in data management. You’ll need to define the real problem you want to work on and organize the necessary expertise to launch your business intelligence initiative.

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It is important to mention that at this stage, technically You will make assumptions about the sources of data and the standards set to govern the flow of data. You’ll be able to validate your assumptions and determine your data workflow later. That’s why you need to be ready to change your team’s data acquisition channels and contacts.

An important step after aligning your vision is to define the problem or group of problems that you will solve with the help of business intelligence. Setting objectives allows you to define additional high-level parameters for BI, such as:

In addition to objectives, in this step you will want to think about possible KPIs and evaluation indicators to see how the work is being achieved. Those could be financial constraints. (budget spent on development) or performance indicators such as query speed or report error rates.

At the end of this period You must be able to configure default product specifications in the future. This may be a list of properties in

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