Totally Complimentary Business Intelligence Software Application Offers Specialist Solutions. – All businesses are powered by data – information from many internal and external sources within your company. And these data channels serve as a pair of eyes for executives, providing 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 view of the market situation as well as internal operations – and subsequently wrong decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even the ones you may not think of. But how do you turn chunks of unstructured data into something useful? The answer is business intelligence.
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We have already discussed the machine learning strategy. In this article, we’ll discuss the practical steps for incorporating business intelligence into your existing corporate infrastructure. You will learn how to create a business intelligence strategy and how to integrate the tools into your company’s workflow.
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Let’s start with a definition: business intelligence, or BI, is a set of practices for collecting, structuring, analyzing, and transforming raw data into actionable business insights. BI looks at techniques and tools that transform unstructured data sets into easy-to-understand reports or dashboards. The primary purpose of BI is to provide actionable business insights and support data-driven decision making.
The biggest part of implementing BI is using real tools that perform data processing. A variety of tools and technologies make up the business intelligence infrastructure. Typically, the infrastructure includes the following technologies for data storage, processing, and reporting:
Business intelligence is a technology-driven process that relies heavily on input. The technologies used to transform unstructured or semi-structured data in BI can be used for data mining as well as being advanced tools for working with big data.
. This type of data processing is also called descriptive analytics. With the help of descriptive analytics, companies can study the market conditions of their industry as well as their internal processes. Reviewing historical data can help identify business challenges and opportunities.
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Based on the processing of data from past events. Rather than providing an overview of historical events, predictive analytics makes predictions about future business trends. These predictions are based on analysis of past events. Thus, both BI and predictive analytics can use the same techniques to process data. In some ways, predictive analytics can be considered the next step in business intelligence. Read more in our article on analytics maturity models.
Prescriptive analytics is a third type of analytics that focuses on finding solutions to business problems and recommending actions to solve them. Currently, prescriptive analytics are available through advanced BI tools, but the entire area has yet to reach a reliable level.
So that’s when we start talking about actually integrating BI tools into your organization. The entire process can be divided into the introduction of business intelligence as a concept for your company’s employees and the actual integration of tools and applications. In the following sections, we’ll go over the basics of integrating BI into your company and address some of the pitfalls.
Let’s start with the basics. To start using business intelligence in your organization, the first step is to communicate the meaning of BI with all your stakeholders. Depending on the size of your organization, the scope of the term may vary. Here, mutual understanding is very important, since employees of 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.
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Another goal of this phase is to promote the concept of BI to key people who will be involved in data management. You’ll need to identify the actual problem you want to work on, set KPIs, and organize the right professionals to start the business intelligence initiative.
At this stage, it is important to note that you will, technically, make assumptions about the sources of data and standards set to control the flow of information. You can check your assumptions and define your data workflow in the next steps. That’s why you need to be prepared to change your data sources and the composition of your team.
The first big step after aligning your vision is to determine what problem or set of problems you’re going to solve with business intelligence. Setting goals helps define further high-level parameters for BI, such as:
Along with the goals, you’ll need to think about possible KPIs and evaluation metrics to see how the task is progressing at that stage. These can be financial constraints (budget used for development) or metrics such as query speed or error rate.
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At the end of this stage, you should be able to configure the initial requirements of the future product. This can be a list of the product backlog consisting of user stories, or it can be a simplified version of the requirements document. The key here is that based on the requirements, you need to be able to understand what kind of architecture, features and capabilities you want from your BI software/hardware.
Creating a requirements document for your business intelligence system is the key to understanding what tools you need. For large businesses, building their own BI ecosystem can be considered for several reasons:
For small companies, the BI market offers many tools available as embedded versions and cloud-based (Software as a Service) technologies. With these flexible capabilities, you can find offerings that cover almost any type of industry-specific data analysis.
Depending on the requirements, your industry type, the size and needs of your business, you may find that you are ready to invest in a custom BI tool. Otherwise, you can choose a vendor to shoulder the burden of implementation and integration.
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The next step is to gather a group of people from different departments of your company to work on a business intelligence strategy. Why create such a group? The answer is simple. The BI team helps bring together representatives from different departments to streamline communication and gain department-specific insights about the required data and its sources. Therefore, your BI team should include two main categories of people:
These people are responsible for getting the team to access data sources. They also contribute their domain knowledge to select and interpret different data types. For example, a marketing professional can determine whether your website traffic, bounce rate, or newsletter subscription numbers are valuable types of data. Your sales representative can provide meaningful interactions with customers. Moreover, you can access marketing or sales information through one person.
The second category of people you want on your team are BI-specific members who can lead the development process and make architectural, technical, and strategic decisions. Thus, the following roles should be defined as required standards:
BI head. This person should be equipped with theoretical, practical and technical knowledge to support the implementation of your strategy and real tools. This may be an executive with knowledge of business intelligence and access to data sources. The head of BI is the person who makes decisions for implementation.
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A BI engineer is a technical member of your team who specializes in building, implementing, and deploying BI systems. Typically, BI engineers have a background in software development and database configuration. They should also be familiar with data integration methods and techniques. A BI engineer can lead your IT department in implementing a BI toolset. Learn more about data professionals and their role in our dedicated article.
A data analyst 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 in place and you’ve considered the data sources required for your specific problem, you can begin developing a BI strategy. You can document your strategy using traditional strategy documents such as a product roadmap. A business intelligence strategy can include different components depending on your industry, company size, competition, and business model. However, the recommended components are:
This is the documentation of the data source channels you have selected. They should cover all types of channels, whether it’s stakeholders, general industry analytics, or information from your employees and departments. Examples of such channels are Google Analytics, CRM, ERP, etc. can be.
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Documenting your industry standard KPIs as well as your specific ones can reveal a more complete picture of your business growth and losses. Finally, BI tools are created to track these KPIs with additional data supporting them.
At this stage, determine what kind of report you need to get valuable information conveniently. In a custom BI system, you can browse visual or textual representations. If you have already selected a vendor, you may be limited in terms of reporting standards, as the vendors themselves set. This section may also include the types of data you want to resolve.
The end user is the person who will observe the data through the interface of the reporting tool. Depending on the end users, you may also consider reporting
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