Business Intelligence Functionality – Every business runs on data: information generated from many sources inside and outside your organization. These data feeds act as a pair of eyes for executives, providing them with analytical insights into what’s happening in the business and the marketplace. Consequently, any misconception, inaccuracy or lack of information can lead to a distorted view of the market situation and internal operations, followed by poor results.
To make data-driven decisions, you need a 360° view of all aspects of your business, even those you might not think about. But how do you turn unstructured data into something useful? The answer is business intelligence.
Business Intelligence Functionality
We have already discussed the machine learning strategy. In this article, we’ll take a look at the actual steps to bring business intelligence into your existing corporate infrastructure. You will learn how to set up a business intelligence strategy and how to integrate the tools into your organization’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 considers methods and tools that transform unstructured data sets and compile them into easy-to-understand reports or dashboards. The primary goal of BI is to provide actionable business insights and support data-driven decision making.
A large part of BI implementation is the use of actual tools that do the data processing. A variety of tools and technologies make up the business intelligence infrastructure. Often, 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 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.
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. This type of data processing is also called descriptive analysis. With the help of descriptive analysis, companies can study the market conditions of their industry and their internal processes. Historical data overview helps to identify pain points and trading opportunities.
Based on data processing from past events. Instead of producing summaries 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 techniques to process data. To some extent, predictive analytics can be considered the next step in business intelligence. Read more about analytical maturity models in our article.
Prescriptive analytics is a third category that aims to find solutions to business problems and prescribe actions to resolve them. Currently, prescriptive analytics is available through advanced BI tools, but the entire area has yet to be developed to a reliable level.
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So this is the bottom line when we start talking about actually integrating BI tools into your organization. The whole process can be divided into the concept of business intelligence and the actual integration of tools and applications for the employees of your organization. In the following sections, we’ll discuss the key points of integrating BI into your organization and cover some pitfalls.
Let’s start with the basics. To start using business intelligence in your organization, first explain what BI means to your stakeholders. Depending on the size of your company, the time rules 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.
Another goal of this phase is to provide BI feedback to key people involved in data management. You need to define the real problem you want to work on, establish KPIs, and organize the experts you need to start your business intelligence initiative.
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At this point, it’s important to note that, technically, you’ll be making assumptions about data sources and the standards in place to control the flow of data. Check your assumptions and specify your data workflow in the following steps. That’s why you need to be prepared to change your data source channels and 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 will help determine more high-level metrics for BI:
Along with objectives, at that stage you should think about possible KPIs and evaluation metrics to see how the task is being accomplished. They can be financial constraints (budget used for development) or performance indicators, such as query speed or error rate.
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At the end of this phase, you will be able to build the initial requirements of the future product. This could be a list of features in the backend of a product with user stories, or a more simplified version of this requirements document. The key here is that, based on the requirements, you can understand the type of architecture, features, and capabilities you want from your BI software/hardware.
Compiling a requirements document for your business intelligence system is key to understanding which tool you need. For large companies, creating your own custom BI ecosystem may be considered for a number of reasons:
For smaller companies, the BI marketplace offers a wide range of tools available as embedded versions and cloud-based technologies (Software as a Service). Offerings covering almost any type of industry-specific data analysis can be found with flexible possibilities.
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Based on the requirements, industry type, size and needs of your business, you can understand if you are ready to invest in a custom BI tool. Otherwise, you can choose a provider that takes the burden of implementation and integration for you.
The next step is to assemble a team of people from different departments in your organization to work on your business intelligence strategy. Why should you create such a group? The answer is simple. A BI team helps bring together representatives from various departments to facilitate communication and obtain industry-specific information about required data and its sources. Therefore, your BI team should consist of two main types of people:
They are responsible for providing the team with access to data sources. They will contribute their domain knowledge to select and interpret different types of data. For example, a marketer might define whether their website traffic, bounce rate, or newsletter signup numbers are valuable data types. Your sales representative can provide information about meaningful customer interactions. On top of that, you can access marketing or sales information through a person.
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The second type of people you want on your team are BI-specific members, who will lead the development process and make architectural, technical, and strategic decisions. Therefore, you should determine the following roles as required standards:
BI Manager. This person must have the theoretical, practical and technical knowledge to implement your actual strategy and tools. This could be a manager with knowledge of business intelligence and access to data sources. The head of BI is a person who makes decisions for its implementation.
A BI engineer is a technical member of your team who specializes in developing, implementing, and configuring BI systems. BI engineers typically have experience in software development and database architecture. They must be familiar with data integration methods and techniques. A BI engineer can guide your IT department in implementing your BI toolset. Learn more about data professionals and their roles in our featured article.
Data Analytics & Business Intelligence Platform
The data analyst must become part of the BI team to provide the team with expertise in data validation, processing, and visualization.
Once you have a team in place, once you’ve considered the data sources you need for your specific problem, you can start developing a BI strategy. She may document her strategy using traditional strategy documents, such as product roadmaps. A business intelligence strategy can include many different elements depending on the industry, company size, competition, and business model. However, the recommended components are:
This is a document of the selected data source channels. Whether you are a partner, you should include any type of channel, such as general industry analysis 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 and your own specific ones can reveal the full picture of your business growth and loss. Ultimately, BI tools are developed to support these KPIs with additional data.
At this point, define what type of report you need to conveniently extract valuable information. In the case of a custom BI system, you might consider visual or textual representations. If you’ve already selected a provider, you may be limited in terms of reporting standards, as providers set their own. This section can also contain the data types you want to manipulate.
The end user is the person who views the data through the reporting tool interface. Depending on the end users, you may also consider reporting
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