Business Intelligence Totally Complimentary Tools – All businesses run on 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 misconception, inaccuracy or lack of information can lead to a distorted view of the market situation and internal operations – followed by wrong decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even the ones you don’t think about. But how to turn structured data chunks into something useful? The answer is business intelligence.
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We have already discussed the machine learning strategy. In this article, we discuss the actual steps involved in bringing business intelligence into your existing corporate infrastructure. You’ll learn how to set up a business intelligence strategy and 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 that collect, organize, analyze and transform raw data into actionable business insights. BI considers methods and tools for transforming structured data sets, compiling them into easily digestible reports or informative dashboards. The main 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 handle the data processing. Different tools and technologies make up a business intelligence infrastructure. Most often, the infrastructure includes the following technologies covering data storage, processing and reporting:
Business intelligence is a technology-driven process that relies heavily on input. Techniques used in BI to manipulate 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 known as descriptive analytics. With the help of detailed analytics, businesses can study the market conditions of their industry, as well as their internal processes. Historical data overview helps in finding pain points and opportunities of the business.
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Based on data processing of past events. Instead of creating overviews 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 the same methods to process data. To some extent, predictive analytics is considered the next step in business intelligence. Read more in our article about analytics maturity models.
Prescriptive analytics is a third type that aims to find solutions to business problems and suggest actions to solve them. Currently, prescriptive analytics is available through advanced BI tools, but the entire area 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 divided 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 walk through the key aspects of BI integration for 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 with all your stakeholders. Depending on the size of your organization, the term frames may vary. Mutual understanding is very important here because employees from 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 benefit of this phase is to provide BI concept to key people involved in data management. You need to define the real problem you want to work on, set KPIs and organize the experts you need to start your business intelligence initiative.
At this stage, it is important to mention that you will, technically, make assumptions about the sources of data and the standards set to control data flow. You can validate your assumptions and specify your data workflow in the next steps. That’s why you must be prepared to change your data sourcing channels and your team lineup.
The first big step after aligning the vision is to define what problem or group of problems you are going to solve with the help of business intelligence. Setting goals helps you identify more high-level parameters for BI:
Along with the goals, at that stage, you should think about possible KPIs and evaluation metrics to see how the work is being accomplished. They can be financial constraints (budget applied to development) or performance indicators such as querying speed or report error rate.
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By the end of this step, you should be able to configure the basic requirements of the future product. This could be a list of features in the product backlog with user stories, or a more simplified version of this requirements document. The key here is that based on requirements, you can understand the type of architecture, features and capabilities you want from your BI software/hardware.
Compiling the required document for your business intelligence system is a key component to understanding which tool you need. For large businesses, building its own custom BI ecosystem can be considered for several reasons:
For smaller companies, the BI market offers many tools available as embedded versions and cloud-based (software-as-a-service) technologies. With flexible options it is possible to find offerings that cover almost any type of industry-specific data analysis.
Based on the requirements, your industry type, size, and the needs of your business, you can understand whether you are ready to invest in a custom BI tool. Otherwise, you can choose a vendor that carries the burden of implementation and integration 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 would you create such a group? The answer is simple. A BI team helps to gather representatives from various departments to facilitate communication and gain department-specific insights about the required data and its sources. So, your BI team’s lineup should consist of two main categories of people:
These individuals are responsible for providing the team with access to data sources. They also provide their domain knowledge to select and describe different data types. For example, a marketing expert can define whether your website traffic, bounce rate, or newsletter subscription numbers are valuable data types. Your sales rep can provide insights into meaningful interactions with customers. On top of that, you can 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, you should determine the following roles as required criteria:
Head of BI. This person must be armed with theoretical, practical and technical knowledge to support the execution of your strategy and actual tools. This could be an executive with business intelligence knowledge and access to data sources. The head of BI is the person who makes decisions to drive 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. Typically, BI engineers have a background in software development and database configuration. They must 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 exclusive article.
A data analyst should also be a 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 have examined the data sources needed 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 may have 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 any type of channel, whether it’s a stakeholder, typically industry analytics, 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 open up a complete picture of your business growth and risks. Ultimately, BI tools are created to track these KPIs supported with additional data.
In this step, define what kind of reporting you need to conveniently gather valuable information. In the case of a custom BI system, you can consider visual or textual representations. If you have already selected a vendor, you may be limited in terms of reporting standards, as vendors set their own. This section may also include the types of data you want to deal with.
An end user is a person who observes data through the reporting tool’s interface. Depending on the end users, you may also consider reporting
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