Self-service Business Intelligence Tools For Keeping Track Of Linux Iot Gadgets – All businesses operate with data – information generated from a variety of sources inside and outside of your company. And these data channels serve as the eyes of executives, providing them with analytical information on what’s going on with the business and the market. Accordingly, any misunderstanding, inaccuracy or lack of information can lead to a distorted view of the market situation as well as internal operations – leading to wrong decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even the ones you might not have thought of. But how do you turn blocks of unstructured data into something useful? The answer is business intelligence.
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In this article, we’ll discuss practical steps in bringing business intelligence into your existing corporate 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 methods for collecting, structuring, and analyzing raw data to turn that data into useful business insights. BI looks at methods and tools that transform unstructured data sets, compiling them into easy-to-grasp 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 inputs. Technologies used in BI to transform unstructured or semi-structured data can also be used for data mining, and as front-end tools for working with big data.
. With the help of descriptive and diagnostic analysis – or BI – businesses can study market conditions in their industry, as well as their internal processes. An overview of historical data helps identify weaknesses and growth opportunities.
Based on data processing of past and present events. Instead of creating overviews of historical events, predictive analytics makes predictions about future business trends. It also allows simulation and scenario comparison. To be possible, 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 that aims to find solutions to business problems and recommend actions to solve them.
Is a broad concept that can include an organizational aspect (data governance, policies, standards, etc.), but in this article we will mainly focus on the technology infrastructure. Usually it includes
We’ll take a look at each infrastructure element for now, but if you want to expand your knowledge of data engineering, check out our article or watch the video below.
For starters, the core element of any BI architecture is the data warehouse. A repository is a database that holds your information in a predefined format, often structured, categorized, and error-free.
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However, if your data is not preprocessed, your BI engine or IT department will not be able to query it. For this reason, you cannot connect your data warehouse directly to your information sources. Instead, you must use ETL tools.
Data integration tools or ETL (Extract, Transform, Load) will either preprocess raw data from the original sources and send it to the warehouse in three consecutive steps.
Typically, ETL tools are shipped with BI tools from vendors (we’ll cover the most popular ones in more depth).
Once you have configured data transfer from selected sources, you must set up the repository. In business intelligence, data warehouses are specific types of databases that typically store historical information in a tabular format. Warehouses are connected to data sources and ETL systems at 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 repository often contains a large amount of information (100GB+), making it understandable that responses to queries are slow. In some cases, data may be stored unstructured or semi-structured, resulting in high error rates when parsing data to generate reports. Analytics may require a certain type of data to be grouped in a single storage space for ease of use. That’s why businesses use complementary technologies to provide faster access to smaller, more thematic blocks of information.
Recommendation: If you don’t have large volumes of data, using a simple SQL warehouse should suffice. Additional structural elements like data warehouses will cost you a lot of money without providing any value.
The data stored in the warehouse is two-dimensional, as it is often described in a spreadsheet (table and row) format. How a data store is also known as
. It can include thousands of data types in a single database, so querying the data warehouse takes a significant amount of time. To meet the needs of analysts to quickly access data, analyze data from different dimensions, and group whenever they need it, OLAP blocks are used.
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OLAP or online analytical processing is a technology that analyzes and represents data from multiple dimensions simultaneously. Structuring your data in OLAP blocks helps overcome the limitations of data warehouses.
OLAP cube is a data structure optimized for fast analysis of data from SQL databases (warehouses). Cubes that source data from a data warehouse are a smaller representation of it. However, the structure of the data assumes that there are more than 2 dimensions (the rows and columns of the spreadsheet). Dimensions are the important factors that form a report, for example, for a sales department it could be
The blocks form a multidimensional database of information that can be adjusted to group it in different ways and generate reports faster. A repository and OLAP are used together, as blocks store a relatively small amount of data and serve to facilitate processing.
Recommendation: The OLAP block + data warehouse architecture can be used by companies of all sizes that require complex multidimensional information analysis. If you don’t want to attack your repository with queries, consider the OLAP architecture approach.
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The warehouse is the first and largest element of business intelligence architecture. A smaller representation of a warehouse dataset is a data mart that collects information specific to a particular subject area. With the help of a data mart, separate departments can access the required data.
Recommendation: Data warehouse + data mart is the second most popular type of architecture. It allows setting up continuous reporting or easy access to information without giving permissions to end users.
Enterprise businesses may require multiple options for data management. Data marts and cubes are different technologies, but both are used to represent smaller chunks of information from the warehouse. Data marts represent a problem-specific subset of data warehouses, but they can be implemented differently. Deployment options include relational (warehouse or any other SQL) and multidimensional databases, essentially OLAP blocks. So you can use both technologies to manage your data and distribute it across departments of your organization.
Recommendation: You can use both technologies because they support the same idea, but serve different purposes. Data marts can be deployed as part of a data warehouse for security, data aggregation, or accessibility. Or you can use a data mart to represent some size of an OLAP block. But keep in mind that both the datastore and the OLAP cube will require separate database setup.
What Is Business Intelligence?
Now that we’ve covered what BI infrastructure consists of, let’s finally talk about how to implement it in your organization.
The process of adopting BI can be divided into introducing business intelligence as a concept to your company employees and actual integration of tools and applications. Let’s explore the main stages.
To start using business intelligence in your organization, first explain what BI means to all your stakeholders. How you go about it will depend on the size of your organization. Mutual understanding is very important here as employees of different departments will be involved in the data processing. 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 concept of BI to the key people involved in data management. You will have to identify the actual problem you want to solve and organize the experts needed to launch your business intelligence initiative.
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It’s important to mention that at this stage, you’re technically making assumptions about the data source and established standards for controlling data flow. You will be able to verify your assumptions and specify data processing at later stages. That’s why you must be willing to change your data sourcing channels and team lineup.
The important step after adjusting the vision is to define the problem or group of problems you will solve with the help of business intelligence. Setting goals will help you define more higher-level parameters for BI, such as:
Along with the goals, at this stage you will have to think about possible KPIs and metrics to see how the task is accomplished. It could be financial constraints (budget applies to development) or performance metrics like query speed or reporting error rate.
At the end of this phase, you should be able to configure the initial requirements of the future product. This could be a list of features in one
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