Business Intelligence Visual Images Tools – All businesses work with data – information generated from your company’s many internal and external sources. And these data channels act as a pair of eyes for executives, providing them with analytical information about what’s going on with the business and the market. Accordingly, any misunderstanding, misunderstanding, or lack of information can lead to a distorted view of the market situation as well as internal operations—subsequently leading to poor decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even those you didn’t think about. But how to turn unstructured data fragments into something useful? The answer is business intelligence.
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We have already discussed the machine learning strategy. In this article, we will discuss the main steps to bring 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.
Making Sense Of Data With Business Intelligence Tools
Let’s start with a definition: Business Intelligence or BI is a set of processes for collecting, structuring, analyzing, and transforming raw data into actionable business insights. BI considers methods and tools that transform unstructured data sets, compiling them into easily identifiable reports or data dashboards. The main purpose of BI is to provide actionable business insights and support data-driven decision making.
A major part of BI implementation is the use of actual tools that perform data processing. Various tools and technologies make up the business intelligence infrastructure. Often, the infrastructure includes the following technologies that cover data storage, processing, and reporting:
Business Intelligence is a technology driven process that is highly input dependent. Technologies used in BI that transform unstructured or semi-structured data can also be used for data mining, as well as front-end tools for working with big data.
. This type of data processing is also called descriptive analysis. With the help of detailed analysis, businesses can study the market conditions of their industry as well as their internal processes. An overview of historical data helps identify business pain points and opportunities.
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Based on processing information from past events. Rather than producing general observations of historical events, predictive analytics makes predictions about future business trends. These predictions are based on analysis of past events. So, both BI and predictive analytics can use similar techniques for data processing. To some extent, predictive analytics can be considered the next phase of business intelligence. Read more about analytical integrity models in our article.
Prescriptive analysis is a third type that aims to find solutions to business problems and suggest actions to solve them. Currently, prescriptive analytics are available through advanced BI tools, but the entire field has yet to develop to a reliable level.
So here is the point, when we start talking about the actual integration of BI tools in your organization. The entire process can be broken down into introducing business intelligence as a concept for your company’s employees and the actual integration of tools and applications. In the next sections, we’ll get to the key points of BI integration in your company and cover some of the pitfalls.
Let’s start with the basics. To start using business intelligence in your organization, first and foremost explain the meaning of BI to all your stakeholders. Depending on the size of your organization, term frames may vary. Mutual 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 intelligence with predictive analytics.
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Another goal of this step is to introduce the concept of BI to key people who will be involved in information management. You must define the real problem you want to work on, set KPIs, and organize the necessary experts to start your business intelligence initiative.
It is important to note that at this stage, you will, technically, make assumptions about the data sources and standards set to control the data flow. You will be able to verify your assumptions and define your data workflow in the final steps. That’s why you should be prepared to change your data source channels and your team lineup.
The first big step after setting the vision is to define what problem or group of problems you are solving with the help of business intelligence. Setting goals will help you set more high-level parameters for BI such as:
Along with the goals, at this stage, you should think about possible KPIs and evaluation metrics to see how things are getting done. These can be financial constraints (the budget applied to development) or performance indicators such as query speed or report error rates.
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At the end of this step, you should be able to define the basic requirements of the next product. This could be a list of features in a product backlog containing user stories, or a more simplified version of the requirements document. The main point here is that based on the requirements, you should be able to understand what kind of architecture, features and capabilities you want from your BI software/hardware.
Compiling the requirements document for your business intelligence system is a key step in knowing what tools you need. For large businesses, building your own custom BI ecosystem can be considered for several reasons:
For small companies, the BI market offers a large number of tools that are available both as embedded versions and cloud-based (software as a service) technologies. It is possible to find offerings that cover almost any industry-specific data analysis with flexible possibilities.
Based on the requirements, your industry type, size, and your business needs, you will know if you are ready to invest in a custom BI tool. Otherwise, you can choose a vendor who will handle the implementation and integration burden for you.
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The next step is to gather a group of people from different departments of your company to work on your business intelligence strategy. Why do you even need to create such a group? The answer is simple. The BI team helps bring together representatives from various departments to streamline communication and gain department-specific insight into needed data and resources. So, your BI team lineup should consist of two main categories of people:
These people will be responsible for providing the team with access to data resources. They will also help their domain knowledge in selecting and interpreting different data types. For example, a marketing specialist can define whether your website traffic, bounce rate, or newsletter subscription numbers are valuable data types. While your sales rep can provide insight into meaningful interactions with customers. In addition, you will be able to access marketing or sales information through a single person.
The second category of people you want on your team are BI-specific members who lead the development process and make architectural, technical, and strategic decisions. So, as a necessary standard you will need to define the following roles:
Head of BI. This person should be equipped with the theoretical, practical and technical knowledge to support the implementation of your strategy and the actual tools. This can be implemented with knowledge of business intelligence and access to data sources. The BI leader is the person who makes decisions for implementation.
What Is Bi Developer: Role, Responsibilities And Skills
A BI engineer is a technical member of your team who specializes in building, implementing, and configuring BI systems. Typically, BI engineers have a background in software development and database configuration. They should also be well versed in data integration methods and techniques. A BI engineer may lead your IT department in implementing your BI toolset. Learn more about data professionals and their roles in our dedicated article.
A data analyst should also become part of the BI team to provide the team with expertise in data validation, processing and data visualization.
Once you have a team and you’ve considered the data sources needed for your specific problem, you can start developing a BI strategy. You can document your strategy using traditional strategic documents such as a product roadmap. A business intelligence strategy may include different components depending on your industry, company size, competition, and business model. However, the recommended ingredients are as follows:
This is the documentation of your selected data source channels. This should include any channels, whether it’s stakeholders, industry analysis in general, or information from your employees and departments. Examples of such channels could be Google Analytics, CRM, ERP, etc.
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Documenting your industry standard KPIs as well as your specific ones can reveal a complete picture of your business growth and losses. Finally, BI tools are developed to track these KPIs supporting them with additional data.
At this stage, determine what type of reporting you need to extract valuable information easily. In the case of a custom BI system, you might consider visual or textual representations. If you have already selected a vendor, you may be limited by the reporting criteria, as the vendor selects itself. This section may also include the types of data you want to deal with.
An end user is someone who views data through the reporting tool’s interface. Depending on the end users, you may also consider reporting
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