Evaluate Business – Every business runs on data – information generated from your company’s many internal and external sources. And these data channels act as a pair of eyes for management, providing analytical information about what’s going on with the business and the market. Therefore, any misconception, inaccuracy, or lack of information can lead to a wrong view of the market situation and internal operations – followed by bad decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even those you may not have thought about. But how to turn 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 will discuss practical steps to bring business intelligence to existing business infrastructure. You will learn how to set up a business intelligence strategy and integrate the tools into your company’s operations.
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Let’s start with a definition: business intelligence or BI is a set of methods for collecting, organizing, analyzing, and transforming raw data into actionable business insights. BI considers methods and tools that transform unstructured data sets, assembling them into easy-to-grasp reports or dashboards of information. The primary purpose of BI is to provide actionable business insights and support data-driven decision making.
A big part of BI implementation is the use of real tools that do data processing. Different tools and technologies form the knowledge base of a business. Often times, the infrastructure includes the following technologies including data storage, processing, and reporting:
Business Intelligence is a technology-driven process that relies heavily on input. The techniques used in BI to transform unstructured or unstructured data can be used in data mining, as well as being advanced tools for working with big data.
. This type of data processing is also called descriptive analysis. With the help of descriptive analysis, businesses can study the market conditions of their business, as well as their internal processes. Historical data analysis helps identify business pain points and opportunities.
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It is based on data processing of past events. Instead of generating an overview of historical events, predictive analytics makes predictions about future business trends. Those predictions are based on analysis of past events. Therefore, both BI and predictive analytics can use similar data processing methods. To some extent, predictive analytics can be considered the next level of business intelligence. Read more in our article about analytics maturity models.
Prescriptive analytics is a third type that focuses on finding solutions to business problems and recommends actions to solve them. Currently, structured analytics are available with leading BI tools, but everywhere has not developed to a reliable level yet.
So here is the point, when we start talking about the integration of BI tools in your organization. The whole process can be divided into the introduction of business intelligence as the concept of your company’s employees and the integration of tools and applications. In the following sections, we will walk through the main points of BI integration in your company and cover some pitfalls.
Let’s start with the basics. To start implementing business intelligence in your organization, first and foremost define the meaning of BI with all your stakeholders. Depending on the size of your organization, time frames may vary. A common understanding is important here because employees of different departments will be involved in data processing. So, make sure everyone is on the same page and not confusing business intelligence with predictive analytics.
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Another goal of this phase is to introduce the concept of BI to key people who will be involved in data management. You need to define the problem you want to work on, set KPIs, and organize the experts needed to launch your business intelligence initiative.
It is important to mention that at this stage, you, as a professional, will make assumptions about the sources of data and the standards established to control the flow of data. You will be able to confirm your thinking and explain your data flow in later stages. That’s why you need to be ready to change your data acquisition channels and your team list.
The first big step after aligning the vision will be to define what problem or group of problems you will solve with the help of business intelligence. Setting goals will help you better understand high-level BI parameters such as:
Along with the goals, at that stage, you should think of possible KPIs and evaluation metrics to see how the work is being accomplished. Those may be financial constraints (budget used for development) or performance indicators such as query rate or report error rate.
At the end of this phase, you should be able to consider the initial requirements of the upcoming product. This could be a list of features in the product backlog that includes user stories, or a more simplified version of the necessary documentation. The key point here is that, based on requirements, you should be able to understand what kind of architecture, features, and capabilities you want in your BI software/hardware.
Creating the necessary documentation for your business intelligence program is the key to understanding which tool you need. For large enterprises, building their own BI ecosystem can be considered for several reasons:
For small companies, the BI market offers a large number of tools available both as embedded versions and cloud-based (Software-as-a-Service) technology. It’s possible to find offerings that cover almost any type of business-specific data analysis that’s flexible.
Based on the needs, nature of your business, size, and needs of your business, you will be able to understand whether you are ready to invest in a custom BI tool. Alternatively, you can choose a vendor who will shoulder the burden of implementation and integration.
Business Intelligence Developer
The next step will be to gather a group of people from different departments of your company to work on your business intelligence plan. Why would you even want to form such a group? The answer is simple. The BI team helps gather representatives from different departments to facilitate communication and get department-specific information about the required data and its sources. So, your BI team list should include two main types of people:
These individuals will be responsible for providing the team with access to data sources. They will contribute their domain knowledge to selecting and interpreting different types of data. For example, a marketing professional might explain that your website traffic, bounce rate, or newsletter subscription numbers are important types of data. While your sales representative can provide insight into meaningful interactions with customers. On top of that, you will be able to access sales or marketing information in one person.
The second type of people you want on your team are some BI members who will lead the development process and make design, technical, and strategic decisions. So, as a required standard, you will need to establish the following roles:
Head of BI. This person should be equipped with theoretical, practical, and technical knowledge to support the implementation of your plan with the right tools. This may be an executive with knowledge of business intelligence and access to data sources. The Head of BI is the person who will make the decisions to drive the implementation.
Enterprise Business Intelligence
A BI engineer is a professional member of your team who specializes in building, implementing and configuring BI systems. Typically, BI engineers have a software development and data analysis background. They should also be well versed in data integration techniques and techniques. A BI engineer can guide your IT department in implementing your BI tool. Read more about data scientists and their roles in our dedicated article.
The 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’ve teamed up and determined the data sources needed for your specific problem, you can begin developing a BI strategy. You can write your plan using traditional planning documents such as a product roadmap. A business intelligence strategy can include different elements depending on your business, company size, competition, and business model. However, the recommended sections are:
This is a document of your chosen data source channels. This should include any type of channel, whether it’s a stakeholder, a general business analysis, or information from your employees and departments. Examples of such channels would be Google Analytics, CRM, ERP, etc.
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Writing down the standard KPIs for your business and your specific ones can reveal the most complete picture of your business growth and losses. Finally, BI tools are created to track these supporting KPIs with additional data.
At this point, specify what type of report you need to extract important information effectively. In the case of a traditional BI system, you may consider visual or textual representation. If you have already selected a vendor, you may be limited by the reporting standards, as the vendors set themselves. This section can include the types of data you want to deal with.
The end user is the person who will analyze the data using the interface of the reporting tool. Depending on the end users, you can also configure the report
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