Information Visual Images Business Intelligence Tools – All business runs on data – information generated from many sources internal and external to your company. And these data feeds serve as a pair of eyes for managers, providing them with 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 bad decisions.
Data-driven decision making requires a 360° view of all aspects of your business, even the ones you haven’t thought of. But how do you turn unstructured blocks of 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 the actual steps to implement business intelligence into your existing enterprise infrastructure. You will learn how to set up a business intelligence strategy and integrate tools into your business workflow.
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Let’s start with a definition: business intelligence, or BI, is a set of procedures for collecting, structuring, analyzing and transforming raw data into actionable business statistics. BI considers methods and tools that transform unstructured data sets and compile them into easy-to-grasp reports or dashboards. The main purpose of BI is to provide actionable business insights and support data-driven decision making.
The biggest part of implementing BI is using the actual tools that do the data processing. Various tools and technologies make up the business intelligence infrastructure. The infrastructure most often includes the following technologies that cover data storage, processing and reporting:
Business Intelligence is a technology-driven process that relies heavily on input. The technologies used in BI to 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 analytics. Using descriptive analysis, businesses can study the market conditions of their industry as well as their internal processes. An overview of historical data helps to find weak points and opportunities of the company.
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Based on data processing of past events. Instead of creating overviews of historical events, predictive analytics creates forecasts of future business trends. These predictions are based on the analysis of past events. So both BI and predictive analytics can use the same techniques to process data. To some extent, predictive analytics can be considered the next phase of business intelligence. Read more in our article on analytical maturity models.
Prescriptive analytics is a third type that focuses on finding solutions to business problems and proposing actions to solve them. Currently, prescriptive analytics is available through advanced BI tools, but the entire field has not yet developed to a reliable level.
This is the point where we start talking about actually integrating BI tools into your organization. The whole process can be broken down into the introduction of business intelligence as a concept to your company’s employees and the actual integration of tools and applications. In the next sections, we will go over the key points of integrating BI into your company and discuss some of the pitfalls.
Let’s start with the basics. To start using business intelligence in your organization, first and foremost, explain the importance of BI to all stakeholders. Depending on the size of your organization, term frames may vary. Mutual understanding is essential 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 purpose of this phase is to introduce the concept of BI to the key people who will be involved in data management. You will need to define the real problem you want to work on, set KPIs and organize the required specialists to start your business intelligence initiative.
It is important to mention that at this stage you will technically make assumptions about the data sources and the standards set to control the flow of data. In later stages, you will be able to verify your assumptions and specify your workflow with the data. Therefore, you must be prepared to change your data acquisition channels and team composition.
The first big step after aligning the vision would be to define what problem or set of problems you will solve with business intelligence. Setting goals helps you determine other high-level parameters for BI, such as:
Along with the goals, at this stage you will need to think about possible KPIs and evaluation metrics to see how the task is being accomplished. These can be financial constraints (budget used for development) or performance indicators such as polling speed or reporting error rate.
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At the end of this phase, you must be able to configure the initial requirements of the future product. This can be a list of features in the product backlog consisting of user stories or a simplified version of this requirements document. The main point is that based on the requirements, you should be able to understand what type of architecture, features and capabilities you want from your BI software/hardware.
Building a requirements document for your business intelligence system is a key point in understanding what tool you need. For large enterprises, building their own BI ecosystem can be considered for several reasons:
For smaller companies, the BI market offers a large number of tools that are available as built-in versions as well as cloud technologies (Software-as-a-Service). It is possible to find offerings that cover almost any kind of industry-specific data analysis with flexible options.
Based on the requirements, the type of your industry, the size and needs of your business, you will be able to understand whether you are ready to invest in your own BI tool. Otherwise, you can choose a vendor that will carry the implementation and integration burden for you.
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The next step would be to bring together a group of people from different departments of your company to work on your business intelligence strategy. Why would you even need to create such a group? The answer is simple. The BI team helps bring together representatives from different departments to streamline communication and gain specific insights into the required data and its sources. So your BI team composition should include two main categories of people:
These people will be responsible for giving the team access to data sources. They will also contribute their domain knowledge to the selection and interpretation of different types of data. For example, a marketer can define whether your website traffic, bounce rate or newsletter subscription numbers are valuable data types. While your sales representative can provide insights into meaningful interactions with customers. In addition, you will have access to 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 you will need to specify the following roles as required standard:
Head of BI. This person must be armed with theoretical, practical and technical knowledge to support the implementation of your strategy and real tools. It can be a manager with business intelligence knowledge and access to data sources. The head of BI is the person who will decide on the implementation.
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A BI engineer is a technical member of your team who specializes in building, implementing and setting up BI systems. BI engineers typically have experience in software development and database configuration. They must also be well versed in data integration methods and techniques. A BI engineer can lead your IT department in implementing your BI toolset. Learn more about data professionals and their roles in our dedicated article.
The 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 consider the data sources needed for your specific problem, you can start 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 your selected data source channels. These should include all types of channels, be it stakeholders, industry analytics in general or information from your employees and departments. Examples of such channels can be Google Analytics, CRM, ERP, etc.
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Documenting both your industry standard KPIs and your specific ones can reveal the most complete picture of your business growth and losses. Ultimately, BI tools are built to track these KPIs and support them with additional data.
At this stage, define what kind of reporting you need to conveniently extract valuable information. For your own BI system, you can consider visual or textual representations. If you have already selected a supplier, you may be limited in terms of reporting standards, as suppliers set their own. This section can also contain the data types you want to deal with.
The end user is the person who will monitor the data through the reporting tool interface. Depending on the end users, you may also consider reporting
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