Exactly Just How Business Intelligence Tools Can Easily Add To Efficient Choice Creating – All business is driven by data – information generated from multiple internal and external sources within your company. And these data channels act as a pair of eyes for executives, providing them with analytical information about what’s going on in the business and the market. Accordingly, any misunderstanding, inaccuracy or lack of information can lead to a distorted view of market conditions as well as internal workings – leading to bad decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even those you may not have considered. But how to turn unstructured 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’ll discuss the actual steps involved in bringing business intelligence to 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 methods for collecting, structuring, analyzing and turning raw data into actionable business insights. BI envisages methods and tools that transform unstructured data sets, compiling them into easy-to-understand reports or information dashboards. The main purpose of BI is to provide actionable business insights and support data-driven decision making.
One of the biggest parts of BI implementation is the use of real data processing tools. A variety of tools and technologies make up the business intelligence infrastructure. Often, the infrastructure includes the following technologies for storing, processing and reporting data:
Business intelligence is a technology-based process that relies heavily on inputs. Technologies used in BI to transform unstructured or semi-structured data can 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 analysis. With the help of descriptive analysis, businesses can study the market conditions of their industry as well as their internal processes. Historical data overview helps identify business pain-points and opportunities.
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Based on data processing of past events. Rather than creating 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 techniques to process data. To some extent, predictive analytics can be 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 are available through advanced BI tools, but the field as a whole has not yet developed to a reliable level.
So this is 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 your company’s employees and the actual integration of tools and applications. In the following sections, we will consider the key points of BI integration in your company and cover some of the pitfalls.
Let’s start with the basics. To start using business intelligence in your organization, first clarify the meaning of BI with all your stakeholders. Depending on the size of your organization, post frames may vary. Here mutual understanding is important 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 objective of this phase is to convey the concept of BI to key people involved in data management. To start your business intelligence initiative you need to define a real problem to work on, set KPIs and organize the necessary expertise.
It is important to mention that at this stage, you will, technically, make assumptions about the sources of data and the standards set to govern the data flow. You will be able to verify your assumptions and specify your data workflow at a later stage. That’s why you should 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 will help you determine the next high-level parameters for BI such as:
Along with the objectives, at that point, you need to consider possible KPIs and evaluation metrics to see how the task is getting done. They can be financial constraints (budget applied to development) or performance indicators such as query speed or report error rate.
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At the end of this phase, you should be able to configure the initial requirements of the future product. This could be a list of features from the product backlog with user-stories, or a simpler version of this requirements document. The key point here is that, based on the requirements, you should be able to understand what architecture types, features and capabilities you want from your BI software/hardware.
Compiling the necessary documentation for your business intelligence system is key to understanding which tool you need. For large businesses, building their own custom BI ecosystem can be considered for several reasons:
For small companies, the BI market offers many tools available as both embedded versions and cloud-based (software-as-a-service) technologies. It’s possible to find offerings that include almost any type of industry-specific data analysis with flexible possibilities.
Based on requirements, your industry type, size and your business needs, you will be able to understand whether you are ready to invest in a custom BI tool. Otherwise, you can choose a vendor that will handle the implementation and integration for you.
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The next step is to get a group of people from different departments of your company together to work on your business intelligence strategy. Why do you need to create such a group? The answer is simple. The BI team helps gather representatives from various departments to facilitate communication and gain department-specific insights into required data and its sources. So, your BI team lineup should include two main categories of people:
These people will be responsible for providing the team with access to data sources. They will also contribute their domain knowledge in selecting and interpreting various data types. For example, a marketing specialist can define whether your website traffic, bounce rate or newsletter subscription numbers are valuable data types. Your sales rep can provide insights for meaningful interactions with customers. On top of that, you will be able to access 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. Therefore, you need to define the following roles as required standards:
Head of BI. This person must be equipped with theoretical, practical and technical knowledge to support your strategy and implementation of real tools. This could be an executive with knowledge of business intelligence and access to data sources. The head of BI is the person who will take decisions to implement.
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A BI engineer is a technical member of your team who specializes in building, implementing, and setting up BI systems. Usually, BI engineers have a software development and database configuration background. 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.
A data analyst should also become 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 you’ve considered the data sources you need for your specific problem, you can begin developing a BI strategy. You can document your strategy using traditional strategic documents such as product roadmaps. A business intelligence strategy can include different elements depending on your industry, company size, competition and business model. However, the recommended ingredients are:
This is the documentation of your chosen data source channel. This should include any type of channel, be it stakeholders, 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 as well as your specifics can reveal a complete picture of your business growth and losses. Finally, BI tools are created to track these KPIs that support them with additional data.
At this point, define the type of reporting 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 chosen a vendor, you may be limited in terms of reporting standards, as vendors set their own. This section may also include data types you want to deal with.
The end user is the person who will monitor the data through the reporting tool’s interface. Depending on the end users, you may also consider reporting
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