Business Intelligence In Financial Industry – All operations operate with data – information generated from the company’s many internal and external sources. And these data channels act as a pair of eyes for managers, providing them with analytical information about what is happening with the business and the market. Consequently, any misunderstanding, inaccuracy or lack of information can lead to a distorted view of the market situation as well as internal operations – followed by bad decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even those you haven’t thought of. But how to turn unstructured bits of data into something useful? The answer is business intelligence.
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We have already discussed machine learning strategy. In this article, we will discuss the actual steps to bring business intelligence into your existing enterprise infrastructure. You will learn how to set up a business intelligence strategy and integrate tools into your company’s workflow.
What Is Business Intelligence (bi): Complete Implementation Workflow
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 information dashboards. The main purpose of BI is to provide actionable business insight and support data-driven decision-making.
The biggest part of BI implementation is the use of actual tools that perform data processing. Various tools and technologies form 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 is largely dependent on input. Technologies used in BI to transform unstructured or semi-structured data can also be used for data mining, in addition to being front-end tools for working with big data.
. This type of data processing is also called descriptive analysis. Using descriptive analysis, companies can study market conditions in their industry, as well as their internal processes. Historical data overview helps you find your company’s pain points and opportunities.
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Based on data processing of previous events. Rather than producing overviews of historical events, predictive analytics makes forecasts about future business trends. These predictions are based on past event analysis. So 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 in our article on analytical maturity models.
Prescriptive analysis is the third type that aims to find solutions to business problems and suggests actions to solve them. Currently, prescriptive analysis is available via advanced BI tools, but the entire area has not yet developed to a reliable level.
So here’s the point when we start talking about the actual integration of BI tools into your organization. The entire process can be broken down into the introduction of business intelligence as a concept for the company’s employees and the actual integration of tools and applications. In the next paragraphs, we will go through the key points for BI integration in your company and cover some pitfalls.
Let’s start with the basics. To start using business intelligence in your organization, first of all explain the meaning of BI with all your stakeholders. Depending on the size of your organization, the scope of the term may vary. Mutual understanding is important here because employees in 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 pitch the BI concept to the key people who will be involved in data management. You need to define the actual problem you want to work on, set KPIs and organize necessary specialists to launch your business intelligence initiative.
It is important to mention that at this stage, technically speaking, you will make assumptions about the sources of data and standards set to control data flow. You will be able to verify your assumptions and specify the data workflow at the later stages. That’s why you need to be ready to change your data source channels and your lineup.
The first major step after aligning the vision will be to define which problem or group of problems you will solve using business intelligence. Setting the goals will help you determine additional high-level parameters for BI such as:
Along with the goals, at that stage, you need to think about possible KPIs and evaluation metrics to see how the task is being accomplished. These can be financial constraints (budget spent on development) or performance indicators such as query speed or reporting error rate.
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By the end of this stage, you should be able to configure the initial requirements of the future product. This could be a list of features in a product backlog consisting of user stories, or a more simplified version of this requirements document. The main point here is that, based on the requirements, you should be able to understand which type of architecture, functions and capabilities you want from your BI software/hardware.
Compiling a requirements document for your business intelligence system is a key point in understanding which tool you need. For large companies, building their own customized BI ecosystem can be considered for several reasons:
For smaller companies, the BI market offers a large number of tools that are available both as built-in versions and cloud-based (Software-as-a-Service) technologies. It is possible to find offers that cover almost all types of industry-specific data analysis with flexible options.
Based on the requirements, your industry type, the size and needs of your business, you will be able to understand whether you are ready to invest in a customized BI tool. Otherwise, you can choose a supplier who will carry the implementation and integration burden for you.
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The next step will be to gather a group of people from different departments in 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 facilitate communication and gain department-specific insight into required data and sources. So your BI team lineup should include two main categories of people:
These individuals will be responsible for providing the team with access to data sources. They will also contribute their domain knowledge to select and interpret different data types. For example, a marketing specialist can define whether website traffic, bounce rate or newsletter subscription numbers are valuable data types. While your sales rep can provide insight into meaningful interactions with customers. On top of that, you will be able to access marketing or sales information through a single person.
The other 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, as a required standard, you must determine the following roles:
Head of BI. This person must be armed with theoretical, practical and technical knowledge to support the implementation of your strategy and actual tools. This can be a manager with knowledge of business intelligence and access to data sources. The head of BI is a person who will make decisions to drive 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 must also be well versed in data integration methods and techniques. A BI engineer can lead your IT department in the implementation of the 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 you’ve assessed the data sources required for your specific problem, you can begin developing a BI strategy. You can document your strategy using traditional strategic documents such as a product roadmap. Business intelligence strategy can contain different components depending on your industry, company size, competition and business model. However, the recommended components are:
This is documentation of your chosen data source channels. These should include all types of channels, whether it is a stakeholder, analyzes of the industry in general, or the information from your employees and departments. Examples of such channels can be Google Analytics, CRM, ERP, etc.
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Documenting standard KPIs for your industry as well as your specific ones can open up the fullest picture of your business’s growth and losses. Ultimately, BI tools are created to track these KPIs supporting them with additional data.
At this stage, you define the kind of reporting you need to easily extract valuable information. With a customized BI system, you can consider visual or textual representations. If you have already chosen a provider, you may be limited in terms of reporting standards, as the providers set their own. This section can also contain data types you want to deal with.
An end user is a person who will observe data through the interface of the reporting tool. Depending on the end users, you can also consider a report
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