Exactly Just What Business Intelligence Tool Performs Financial Institution Of The United States Utilize – All businesses consume data – information generated from many sources internal and external to your company. And these data channels are a pair of eyes for executives, providing 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 a bad decision.
Making data-driven decisions requires a 360 ° view of all aspects of your business, even if you don’t think so. But how do you turn unstructured chunks of data into something useful? The answer is business intelligence.
Exactly Just What Business Intelligence Tool Performs Financial Institution Of The United States Utilize
We have discussed machine learning strategies. In this article, we’ll discuss the actual steps to bring business intelligence to your existing enterprise infrastructure. You will learn how to set up a business intelligence strategy and integrate the tool into your company’s workflow.
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Let’s start with a definition: business intelligence or BI is the set of practices of collecting, organizing, analyzing, and transforming raw data into actionable business insights. BI considers methods and tools that transform unstructured data sets, compiling them into reports or dashboards of information that are easy to understand. The primary purpose of BI is to provide actionable business insights and support data-driven decision making.
The biggest part of BI implementation is using the actual tools that do the data processing. Different tools and technologies make up the business intelligence infrastructure. Most often, the infrastructure includes the following technologies that cover data storage, processing, and reporting:
Business intelligence is a technology-driven process that relies heavily on inputs. The technology used in BI to transform unstructured or semi-structured data can also be used for data mining, as well as a front-end tool for working with big data.
. This type of data processing is also called descriptive analytics. With the help of descriptive analysis, businesses can study the state of the industry market, as well as internal processes. Overview of historical data helps to find business points and opportunities.
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Based on data processing of past events. Rather than producing a summary of historical events, predictive analytics makes predictions about future business trends. These predictions are based on analysis of past events. So, BI and predictive analytics can use the same techniques to process data. For some, predictive analytics can be considered the next stage of business intelligence. Read more in our article on analytics maturity models.
Prescriptive analytics is a third type that aims to find solutions to business problems and recommend actions to solve them. Today, prescriptive analytics are available through sophisticated BI tools, but all of these areas have yet to develop to a reliable level.
So this is the point, when we start talking about the actual integration of BI tools into your organization. The whole process can be divided into the introduction of business intelligence as a concept for company employees and the integration of real tools and applications. In the next section, we’ll go over the key points of BI integration into your company and cover some of the pitfalls.
Let’s start with the basics. To start using business intelligence in your organization, first explain the meaning of BI to all your stakeholders. Depending on the size of your organization, the term frame may vary. Mutual understanding is important here because 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 phase is to apply BI concepts to key people who will be involved in data management. You need to define the real problems you want to work on, set KPIs, and organize the specialists you need to start your business intelligence initiative.
It is important to mention that at this stage, you will, technically, make assumptions about the data source and the standards set to control the data flow. You will be able to verify your assumptions and define your data workflow at a later stage. That’s why you need to be prepared to change your data source channels and your team lineup.
The first big step after aligning the vision is to define the problem or group of problems to be solved with the help of business intelligence. Setting goals will help you define high-level parameters for BI such as:
Along with the objectives, at this stage, you should think about possible KPIs and evaluation metrics to see how well the task is done. This can be a financial limit (budget applied to development) or a performance indicator like query speed or report error rate.
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At the end of this stage, you should be able to manage the initial requirements of the upcoming product. This could be a list of features in the product backlog that includes user stories, or a simpler version of the requirements document. The key point here is that, based on your requirements, you should be able to understand the type of architecture, features, and capabilities you want from your BI software/hardware.
Gathering the requirements document for your business intelligence system is a key point in understanding the tools you need. For large businesses, building their own BI ecosystem can be considered for several reasons:
For smaller companies, the BI market offers many tools available as embedded versions and cloud-based technologies (Software-as-a-Service). You can find offers that cover almost any type of industry-specific data analysis with flexible possibilities.
Based on the requirements, type of industry, size, and needs of your business, you will be able to know whether you are ready to invest in a specialized BI tool. Otherwise, you can choose a vendor that will do the implementation and integration burden for you.
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The next step is to gather a group of people from different departments in your company to work on your business intelligence strategy. Why do you need to create such a group? The answer is simple. The BI team helps bring together representatives from different departments to simplify communication and gain department-specific insight into data needs and sources. So, your BI team lineup should include two main categories of people:
These people will be responsible for providing the team with access to data sources. They will also contribute domain knowledge to select and interpret different types of data. For example, a marketing specialist may determine that website traffic, bounce rates, or newsletter subscription numbers are important types of data. When your sales reps can provide insight into meaningful interactions with customers. On top of that, 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 will lead the development process and make architectural, technical, and strategic decisions. So, as a required standard, you must define the following roles:
Chairman BI. This person should have theoretical, practical, and technical knowledge to support the implementation of your strategy and real tools. This can be an executive with knowledge of business intelligence and access to data sources. The head of BI is the person who will make the decision to implement it.
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A BI engineer is a technical member of your team who specializes in building, implementing, and tuning BI systems. Typically, BI engineers have a software development and database configuration background. They should also understand data integration methods and techniques. A BI engineer may lead the IT department in implementing your BI toolset. Learn more about data professionals and their roles in our dedicated article.
Data analysts should also be 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 you need for a specific problem, you can start developing your BI strategy. You can document your strategy using traditional strategic documents such as product roadmaps. A business intelligence strategy may include multiple components depending on your industry, company size, competition, and business model. However, the recommended components are:
This is the documentation of the selected data source channel. This should include any type of channel, whether it’s stakeholders, industry analytics 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 created to track these KPIs that support additional data.
At this stage, determine what type of report you need to extract useful information easily. In the case of specialized BI systems, you can consider visual or textual representation. If you have chosen a vendor, you may be limited in terms of reporting standards, as vendors set their own. This section may include the type of data you want to handle.
The end user is the person who will view the data through the reporting tool interface. Depending on the end user, you may also consider reporting
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