Business Intelligence For Small Business – All businesses operate using data – information from many internal and external sources within your company. And these data channels become a pair of eyes for executives, providing analytical information about what is happening in the business and the market. Therefore, any misconception, inaccuracy or lack of information can lead to a distorted view of the market situation as well as internal operations – and subsequently wrong decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even if you don’t think so. But how to turn unstructured data sets into something useful? The answer is business intelligence.
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We discussed machine learning strategies. In this article, we’ll discuss the practical steps for incorporating business intelligence into your existing enterprise infrastructure. You will learn how to create a business intelligence strategy and how to integrate the tool into your company’s workflow.
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Let’s start with a definition: business intelligence, or BI, is a set of practices for collecting, organizing, analyzing, and transforming raw data into actionable business insights. BI looks at techniques and tools that transform unstructured data sets into reports or dashboards that are easy to manage. The primary purpose of BI is to provide actionable business insights and support data-driven decisions.
The biggest part of implementing BI is using the actual tools that process the data. 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-based process that is largely dependent on inputs. Technologies used to transform unstructured or semi-structured data in BI can also be used for data mining, as will future tools for working with big data.
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. This type of data processing is also called descriptive analytics. With the help of descriptive analytics, companies can understand the market conditions of their industry, as well as their internal processes. Reviewing historical data can help uncover business challenges and opportunities.
Based on the processing of data from past events. Rather than providing an overview of historical events, predictive analytics makes predictions about future business trends. This is a prediction based on an analysis of past events. Thus, both BI and predictive analytics can use the same techniques to process data. In some ways, predictive analytics can be considered the next step in business intelligence. Read more in our article on analytics maturity models.
Prescriptive analytics is a third type that focuses on finding solutions to business problems and recommending 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.
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So that’s when we start talking about actually integrating BI tools into your organization. The entire process can be divided into the introduction of business intelligence as a concept for your company’s employees and the actual integration of tools and applications. In the next section, we’ll go over the basics of integrating BI into your company and address 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 timeframe may vary. Mutual understanding is very important here, as the data processing involves employees from different departments. So, make sure everyone is on the same page and don’t confuse predictive analytics with business intelligence.
Another goal of this phase is to present BI concepts to key people who will be involved in data management. You need to identify the actual problem you want to work on, set KPIs, and manage the right professionals to run the business intelligence initiative.
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At this stage, it is important to note that you will, technically, make assumptions about the data source and the standards set to control the flow of data. You can check your assumptions and define your data workflow in the next step. That’s why you need to be prepared to change your data source channels and the composition of your team.
The first big step after aligning the vision is to identify what problem or group of problems you want to solve with business intelligence. Setting goals helps define the next high-level parameters for BI, such as:
With that goal in mind, at that stage, you should think about possible KPIs and evaluation metrics to see how well the task is being accomplished. These can be financial constraints (budget used for development) or metrics such as query speed or report error.
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At the end of this stage, you should be able to configure the initial requirements of the future product. This could be a list of product backlogs created from user stories, or it could be a simpler version of the requirements document. The key here is that based on the requirements, you should be able to understand the type of architecture, features and capabilities you want from your BI software / hardware.
Creating a requirements document for your business intelligence system is a key step in understanding what tools you need. For large enterprises, building their own BI ecosystems can be considered for several reasons:
For small companies, the BI market offers many tools available as embedded versions and cloud-based technologies (Software as a Service). With these flexible capabilities, you can find offers that cover almost any type of data analysis relevant to the industry.
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Depending on your requirements, industry type, size, and business needs, you can determine whether you are ready to invest in a particular BI tool. Otherwise, you can choose a vendor to shoulder the burden of implementation and integration.
The next step is to gather a group of people from different departments in your company to develop your business intelligence strategy. Do you need to create a similar group? The answer is simple. The BI team helps bring together representatives from various departments to facilitate communication and gain department-specific insights about the data needed and its sources. So your BI team should cover two main categories:
These people are responsible for getting the team to access data sources. They also contribute their domain knowledge to select and interpret different types of data. For example, a marketing professional can determine whether your website traffic, bounce rate, or newsletter subscription numbers are valuable types of data. When your sales representatives provide meaningful interactions with customers. Moreover, you can access marketing or sales information through one person.
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The second category of people you want on your team is a BI member who can lead the development process and make architectural, technical, and strategic decisions. So, as a required standard, you should define the following roles:
BI leader. This person should be equipped with theoretical, practical and technical knowledge to support the implementation of your strategy. This could be an executive with knowledge of business intelligence and access to information sources. The BI manager is the decision maker for the implementation.
A BI engineer is a technical member of your team who specializes in building, implementing, and configuring BI systems. Typically, BI engineers have a background in software development and database configuration. They should also be familiar with data integration methods and techniques. A BI engineer can guide your IT department in implementing a BI toolset. Learn more about data professionals and their role in our dedicated article.
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Data analysts should also be 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 considered the data sources you need for your specific problem, you can begin developing a BI strategy. You can document your strategy using traditional strategy documents such as product roadmaps. A business intelligence strategy can include several components, depending on your industry, company size, competition, and business model. However, the recommended components are:
This documents the data source channel you selected. This should include any type of channel, whether it’s stakeholders, industry analytics in general, or data from your employees and departments. Examples of such channels are Google Analytics, CRM, ERP, etc.
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Documenting industry standard KPIs as well as specific ones will reveal a more complete picture of your business growth and losses. Finally, BI tools are created to track these KPIs with additional data supporting them.
At this stage, determine the type of report you need to obtain valuable information. For specific BI systems, you can view visual or textual representations. If you choose a vendor, you may be limited by the reporting standards that vendors set themselves. This section may also include the type of data you want to manage.
The end user is the person who will observe the data through the interface of the reporting tool. Depending on the end user, you can also view the report
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