Self-service Business Intelligence Tools For Keeping Track Of Cryptocurrency Portfolios – All businesses run on data, with information from multiple internal and external sources within your company. And these data channels serve as a pair of eyes for executives, providing them with analytical information about what is happening with 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, followed by wrong decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even those you may not have thought about. But how do you turn chunks of unstructured data into something useful? The answer is business intelligence.
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In this article, we’ll discuss the actual steps involved in bringing business intelligence into your existing corporate infrastructure. You will learn how to create 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 transform it into actionable business insights. BI considers methods and tools that transform unstructured data sets into easy-to-understand reports or dashboards. The primary purpose of BI is to support data-driven decision making.
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 and be key tools for working with big data.
. With descriptive and diagnostic analytics, or BI, businesses can examine market conditions in their industry as well as their internal processes. Reviewing historical data helps identify pain points and development opportunities.
Based on data processing of past and current events. Rather than producing overviews of historical events, predictive analytics makes predictions about future business trends. It also enables simulation and comparison of scenarios. To make this possible, a complex data architecture that includes 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. At the same time, prescriptive analysis is the fourth, most advanced type, which aims to find solutions to business problems and propose actions to solve them.
Is a broad concept that can include the organizational aspect (data management, policies, standards, etc.), but in this article we will mainly focus on the technology infrastructure. Most often it includes:
We will now explore each element of the infrastructure individually, 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 repository is a database that stores your information in a predefined format, usually structured, categorized, and cleaned of errors.
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However, if your data is not pre-processed, your BI tool or your IT department will not be able to query it. For this reason, you cannot directly link your data store to your information sources. Instead, you should use ETL tools.
ETL (Extract, Transform, Load) or data integration tools will process the raw data from the original sources and send it to the warehouse in three sequential steps.
Typically, ETL tools are provided with BI tools from vendors (we’ll look at the most common ones below).
After configuring data transfer from selected sources, you need to create a repository. In business intelligence, data warehouses are special types of databases that typically store historical information in tabular formats. 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.
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But storage typically contains large amounts of information (100GB+), which understandably slows down response to requests. In some cases, data may be stored unstructured or semi-structured, leading to a high error rate when parsing the data to generate a report. Analysis may require certain types of data to be grouped into a single storage area for ease of use. That’s why businesses are using additional technologies to provide faster access to smaller, more topical pieces of information.
Recommendation: If you don’t have a large volume of data, using a simple SQL repository is sufficient. Additional structural elements such as data battles will cost you a lot without providing any value.
The data stored in the warehouse has two dimensions because it is usually represented in a tabular format (tables and rows). A storage form of data storage is also called a
. It can contain 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 quickly access data, analyze it from different dimensions and group it when necessary.
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OLAP, or Online Analytical Processing, is a technology that analyzes and presents data from multiple dimensions simultaneously. Structuring your data in OLAP cubes helps overcome data warehouse limitations.
An OLAP cube is a data structure optimized for fast analysis of data from SQL databases (warehouse). Cubes data source is from a data warehouse which is a smaller view of it. However, the data structure assumes that there are more than 2 dimensions (the row and column format of tables). Dimensions are the important elements that make up the report, for example for the sales department it might be:
Cubes form a multidimensional database of information that can be customized to group it in different ways and generate reports faster. Warehouse and OLAP are used together because cubes store relatively small amounts of data and serve for processing convenience.
Recommendation: Data warehouse + OLAP cube architecture can be used by companies of all sizes that require complex multidimensional analysis of information. If you don’t want to bombard your warehouse with queries, consider an OLAP architectural approach.
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The repository is the first and largest element of the business intelligence architecture. A smaller representation of warehouse datasets is a data mart that collects information about a given subject. With the help of data battles, individual departments can access the necessary data.
Recommendation: Data warehouse + data marts is the second most popular architectural style. It allows for permanent report approval or easy access to information without granting permissions to end users.
Enterprise businesses may require multiple data management options. Data marts and cubes are different technologies, but they are both used to represent smaller chunks of information from a repository. Data wars represent a specific subset of the data warehouse problem, but they can be implemented in different ways. The implementation option includes relational databases (warehouse or any other SQL database) and multidimensional, which are basically OLAP cubes. So you can use both technologies to manage your data and distribute it across departments in the organization.
Recommendation: You can use both technologies because they support the same idea but serve different purposes. Data warehousing can be implemented as part of a data warehouse for security, data aggregation, or availability. Or you can use data marts as a multidimensional representation of an OLAP cube. But remember that both data marts and OLAP cubes will require separate database settings.
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Now that we’ve covered what a BI infrastructure consists of, let’s finally talk about how to implement it in your organization.
The BI adoption process can be broken down into implementing business intelligence as a concept for your company’s employees and the actual integration of tools and applications. Let’s study the main stages.
To start using business intelligence in your organization, first explain the meaning of BI to all your stakeholders. How you go about this will depend on the size of your organization. Mutual understanding is vital here as employees from different departments will be involved in data processing. So make sure everyone is on the same page and not confusing business intelligence with predictive analytics.
Another goal 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 work on and organize the necessary professionals to start your business intelligence initiative.
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It is important to note that at this stage you will, technically, make assumptions about the data sources and the parameters set to control the data flow. You will be able to test your assumptions and specify your data workflow in later stages. That’s why you need to be willing to change your data source channels and your team composition.
After aligning the vision, the big step is to define what problem or set of problems you are going to solve with business intelligence. Defining goals will help you determine further high-level BI parameters, such as:
Along with the objectives, at this stage you should think about possible KPIs and evaluation criteria to see how the task is being carried out. These can be financial constraints (budget applied to development) or performance indicators such as query speed or report error rate.
By the end of this phase, you should be able to handle the initial requirements for the future product. This can be a feature list of a
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