Business Intelligence Software Evaluate

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Business Intelligence Software Evaluate – All business is powered by data – information generated from sources internal and external to your organization. And these data channels become the eyes of executives, giving them analytical information about what is happening with the company and the market. Therefore, misconceptions, inaccuracies, or lack of information lead to a bad perception of the market situation and internal actions – followed by bad decisions.

Data driven decision-making requires a 360° view of all aspects of your business, even those you may not have considered. But how do you turn unstructured data into something useful? The answer is business intelligence.

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In this article, we will discuss the practical steps in bringing business intelligence to your existing business. You’ll learn how to set up a business intelligence plan and integrate tools into your workflow.

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Business intelligence or BI is a set of activities of collecting, organizing, and analyzing raw data to transform it into actionable business information. BI considers methods and tools to transform unstructured data sets, aggregating them into simple reports or informational indexes. The main purpose of BI is to support data-driven decision making.

Business intelligence is a technical process that relies heavily on input. The techniques used in BI can be used to transform unstructured or semi-structured data for data mining, and the first tools to work with big data.

. With the help of descriptive and diagnostic analytics – or BI – companies can study the market conditions of their business, as well as their internal operations. Historical data analysis helps identify pain points and development opportunities.

Based on data processing of past and present events. Instead of making predictions about historical events, forecasting makes predictions about future business trends. It can compare features and comparisons. In order to do so, it must be done by a team of sophisticated data scientists based on advanced ML techniques.

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So we can say that predictive analytics can be considered the next level of business intelligence. However, prescriptive analytics is the fourth, most advanced method that aims to find solutions to business problems and suggest corrective actions.

It is a broad concept that can include organizational structure (data management, policies, standards, etc.), but in this article, we will focus on the technical aspect. Often, inclusive

For now, we’ll look at all the technical stuff, but if you want to expand your knowledge about data engineering, check out our article or watch the video below.

At the outset, the core of any BI strategy is a data warehouse. A store is a repository that stores your information in a predetermined format, usually structured, sorted, and cleaned of errors.

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However, if your data isn’t pre-processed, your BI tool or IT department won’t be able to query it. For this reason, you cannot directly link your database with your data sources. Instead, you should use ETL tools.

ETL (Extract, Transform, Load) or data integration tools first process raw data from primary sources and send it to a warehouse in three steps.

Typically, ETL tools are provided out-of-the-box with BI tools from vendors (we’ll cover the most popular ones first).

When you set up the data transfer from the selected sources, you need to set up a store. In business terms, databases are specific types of data that store historical information in a variety of formats. Stores are connected with data sources and ETL systems on the one hand and reporting tools or dashboard interfaces on the other. This allows the presentation of data from different systems through a single interface.

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But a typical store has a lot of information (100GB+), which makes it slow to respond to queries. In some cases, the data can be stored unstructured or semi-structured, which leads to a high error rate when sorting the data to create a report. Databases may require some type of data to be consolidated into a single storage location for ease of use. That’s why companies are using more and more technologies to provide faster access to smaller data sets.

Tip: If you don’t have a lot of data, using a simple SQL store is sufficient. New tools like data marts will pay you a lot without providing any value.

There are two dimensions of data stored in a store, as it is usually presented in a flat format (tables and rows). The method of data storage is called a

. It can contain thousands of data types in one database, so querying a database takes a lot of time. To meet the needs of analysts to quickly access data, to record it from different parts, and combine it whenever they need, OLAP cubes are used.

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OLAP or online analytical processing is a technique that analyzes and presents data from multiple sources at the same time. Building your data into OLAP cubes helps overcome the limitations of a database.

OLAP cube is a structured data structure for fast analysis of data from SQL databases (warehouse). Data source cubes from a database are a small representation of that. However, the data format assumes that there are more than 2 dimensions (row and column of spreadsheets). The components are the most important elements to create the report, for example, for the marketing department.

Cubes create a multidimensional database of information that can be manipulated to group in different ways and create reports quickly. A warehouse and OLAP are also used, because the cubes store a small amount of data and serve for easy processing.

Recommendation: The data warehouse + OLAP cubes architecture can be used by organizations of all sizes that require complex multidimensional analysis of information. If you don’t want to bombard your warehouse with queries, consider an OLAP design approach.

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A store is the first and most important part of a business plan. A small example of a data warehouse is a data warehouse that collects information dedicated to a specific topic. With the help of data marts, separate departments can access the required data.

Concept: Data warehouse + data marts is the second most popular architecture. It allows for the creation of a permanent display or easy access to information, without giving permissions to end users.

Businesses may need multiple options for data management. Data and cubes are different technologies, but both are used to display small pieces of information from the store. Data represents a problem-solving part of a database, but it can be implemented differently. The implementation option based on relational data (store or other SQL data), and multidimensional, is OLAP cubes. So you can use both technologies to manage your data and distribute it across the company’s offices.

Tip: You can use both techniques when they support the same idea, but serve different purposes. Data may be implemented as part of a database for security, data aggregation, or accessibility. Or you can use the data marts as a representation of some dimensions of the OLAP cube. But remember that both databases and OLAP cubes require different database configurations.

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Now that we’ve covered what business BI is, let’s finally talk about how to implement it in your organization.

The process of implementing BI 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 explore the main steps.

To begin applying business intelligence to your organization, first explain the concept of BI to your stakeholders. How you go about it depends on the size of your group. A shared understanding is important here as employees from different departments will be involved in data processing. So, make sure everyone is on the same page and don’t confuse business information with predictive information.

Another purpose of this section is to focus on BI to the key people involved in data management. You need to define the real problem you want to work on and organize the skills required to start your smart business.

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It is important to mention that now, you will, technically, make assumptions about the sources of the data and the conditions set to manage the data flow. You can confirm your ideas and guide your data processing in the following steps. That’s why you should be ready to change your data streams and your company’s line.

The main task after defining the vision is to define the problem or group of problems that you will solve with the help of business knowledge. Setting goals will help you determine high-quality areas for BI such as:

Along with the goals, at this stage, you need to consider possible KPIs and evaluation measures to see how the work will be done. Money can be fixed (money used for development) or performance indicators such as query speed or error rate.

At the end of this stage, you should set the initial requirements of the next product. This is a list of features in a

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