Self-service Business Intelligence Tools: Grasping The Fine Craft Of Computer-facilitated Evaluation – All businesses work with data – information generated from many internal and external sources within your business. And these data channels are a pair of eyes for managers, providing them with analytical information about what is happening in the business and the market. Therefore, any misunderstanding, inaccuracy or lack of information can lead to a distorted perception of both the market situation and the internal operations of the company, followed by poor decision-making.
Making data-driven decisions requires a 360° view of all aspects of your business, even the ones you didn’t think about. But how do you turn unstructured chunks of data into something useful? The answer is business intelligence.
Self-service Business Intelligence Tools: Grasping The Fine Craft Of Computer-facilitated Evaluation
In this article, we’ll cover the actual steps to bring business intelligence into your existing enterprise 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 turn it into actionable business insights. BI considers methods and tools that transform unstructured data sets into easy-to-understand reports or dashboards of information. The main purpose of BI is to support data-driven decision making.
Business intelligence is a technology-based 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, as well as front-end tools for working with big data.
. With the help of descriptive and diagnostic analytics, or BI, companies can study the market conditions of their industry as well as internal processes. Historical data overview helps to find pain points and development opportunities.
Based on data processing of past and present events. Rather than reviewing historical events, predictive analytics predicts future business trends. It also allows simulation and comparison of scenarios. To enable this, a professional data science team must create a sophisticated data architecture that incorporates advanced ML techniques.
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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 analytics is the fourth, most advanced type, which aims to find solutions to business problems and recommend actions to solve them.
Is a broad term that can include the organizational aspect (data management, policies, standards, etc.), but in this article we will mainly focus on the technological infrastructure. In most cases, it contains
Now we will explore each element of the infrastructure separately, but if you want to expand your knowledge of data technology, read 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 holds your information in a predefined format, usually structured, classified, and error-free.
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However, if your data is not pre-processed, neither your BI tool nor your IT department will be able to make these queries. For this reason, you cannot directly connect your data warehouse to information sources. Instead, you need to use ETL tools.
ETL (Extract, Transform, Load) or data integration tools preprocess the raw data from the original sources and send it to the warehouse in three sequential steps.
Typically, ETL tools ship out of the box with vendors’ BI tools (we’ll cover the most popular ones below).
Once you have configured the data transfer from the selected sources, you need to set up the warehouse. In business intelligence, data warehouses are a type of database that typically store historical information in tabular format. Warehouses are connected to data sources and ETL systems on one end 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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However, a warehouse typically contains large amounts of information (100GB+), which makes responding to queries understandably slow. In some cases, data may be stored in an unstructured or semi-structured manner, leading to a high error rate when parsing the data to generate a report. Analytics may require certain types of data to be aggregated into a single storage space for ease of use. As a result, companies use additional technologies to provide faster access to smaller and more topical pieces of information.
Recommendation: If you do not have large data volumes, a simple SQL warehouse is sufficient. Additional design elements like data marts will cost you a lot without providing any value.
Data stored in a warehouse has two dimensions because it is usually represented in a tabular format (tables and rows). The way a warehouse stores data is also called a
. It can contain thousands of data types in a single database, so querying the data warehouse takes a lot of time. OLAP cubes are used to satisfy the needs of analysts to quickly access data, analyze it from different dimensions and group it if necessary.
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OLAP, or web-based analytical processing, is a technology that analyzes and presents data from multiple dimensions simultaneously. Structuring data into 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 source data from a data warehouse, which is a smaller representation of it. However, the data structure requires more than 2 dimensions (row and column format for spreadsheets). Dimensions are important elements in the creation of a report, for example, for the sales department it may be
Cubes form a multidimensional database of information that can be customized to group in different ways and generate reports faster. Warehouse and OLAP are used together because cubes store a relatively small amount of data and provide processing convenience.
Recommendation: Data warehouse + OLAP cube architecture 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 architectural approach.
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The warehouse is the first and largest element of the business intelligence architecture. A smaller representation of warehouse data sets is a data park that collects information dedicated to a specific subject area. Separate departments can access the necessary data with the help of data marts.
Recommendation: Data warehouse + data marts is the second most popular architectural style. This allows for continuous reporting or easy access to information without end user permissions.
Enterprise companies may need multiple options for data management. Datastamps and cubes are different technologies, but both are used to represent smaller pieces of information from a warehouse. Data marts represent a problem-specific subset of a data warehouse, but they can be implemented differently. The range of applications includes relational databases (warehouse or some other SQL database) and multidimensional databases, which are basically OLAP cubes. This way, you can use both technologies to manage your data and distribute it across departments in your organization.
Recommendation: You can use both technologies as they support the same idea but serve different purposes. Data marts can be implemented as part of a data warehouse to provide security, data aggregation, or accessibility. Or you can use data marts as a multidimensional representation of an OLAP cube. But remember that both data centers and OLAP cubes require separate database setups.
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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 introducing business intelligence as a concept to your company’s employees and the actual integration of tools and applications. Let’s explore the main stages.
To start using business intelligence in your company, first explain the meaning of BI to all your stakeholders. How you do this depends on the size of your organization. Mutual understanding is crucial here, as employees from different departments are 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 introduce the concept of BI to key people involved in data management. You need to define the real problem you want to address and organize the professionals you need to launch your business intelligence initiative.
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It is important to mention that in this step you are technically making assumptions about the data sources and the standards set to control the data flow. In later steps, you can check your assumptions and refine your data workflow. Therefore, you must be prepared to change your data acquisition channels and team composition.
A big step after aligning your vision is to define what problem or group of problems you intend to solve with business intelligence. Setting goals helps you define additional high-level BI parameters, such as:
In addition to the objectives, at this stage you need to think about possible KPIs and evaluation metrics to see how the task will be completed. 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 stage, you should be able to configure the initial requirements of the future product. This could be the list of functions in section a
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