Totally Complimentary Tools For Business Intelligence Create Information Visualizations. – Works with all business data – information generated from your company’s many internal and external sources. And these data channels are a pair of eyes for managers, providing them with analytical information about what’s happening in the business and the market. Accordingly, any misrepresentation, inaccuracy or lack of information may result in a distorted view of market conditions as well as internal operations.
Data-driven decision-making requires a 360° view of all aspects of your business, even those you might not have imagined. But how do we turn unstructured data fragments into something useful? The answer is business sense.
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We have discussed machine learning strategies. In this article, we’ll discuss the practical steps for bringing business intelligence into your existing enterprise infrastructure. You’ll learn how to build 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 the set of operations to transform, structure, analyze and transform raw data into actionable business insights. BI considers methods and tools that transform unstructured data sets and turn them into easy-to-understand reports or dashboards. The primary purpose of BI is to provide actionable business insights and support decision-making through data.
A big part of implementing BI is using practical tools to process data. Different tools and technologies make up the business intelligence infrastructure. In most cases, the infrastructure includes the following technologies, including data storage, processing and reporting:
Business intelligence is a technology-driven process that relies on input. In BI, the techniques used to transform unstructured or semi-structured data are used for data mining and are the primary tools for working with big data.
. This type of data processing is also called descriptive analysis. With the help of descriptive analytics, companies can study the market situation of their industry, as well as their internal processes. Historical data overviews help identify business pain points and opportunities.
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Based on the processing of data from previous events. Forecasting predicts future business trends rather than providing an overview of historical events. These predictions are based on analysis of past events. So BI and predictive analytics can use the same techniques to process data. In a sense, predictive analytics can be considered the next level of business intelligence. Read more in our article on analytics maturity models.
Prescriptive analysis is a third type that aims to solve business problems and recommend actions to solve them. Prescriptive analytics are now available through advanced BI tools, but the entire area has yet to reach a level of reliability.
Here is a point when we start talking about integrating BI tools into your organization. The whole process can be divided into introducing business intelligence as a concept and integrating tools and applications for company employees. In the following sections, we’ll go over the highlights of integrating BI into your company and cover some of the pitfalls.
Let’s start with the basics. To start using business intelligence in your organization, first explain what BI means to all your stakeholders. Depending on the size of your organization, the scope of this term may vary. Mutual understanding is important here, as employees from various departments are involved in data processing. So, make sure everyone is on the same page and don’t confuse business intelligence with forecasting.
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Another goal of this phase is to communicate BI concepts to key people involved in data management. You need to define the actual problem you want to work on, set KPIs and organize the experts you need to drive your business.
It’s important to note that at this stage you can technically make assumptions about the data source and the parameters that control the data flow. You can validate your assumptions and define your data workflow in the next step. That’s why you must be prepared to change your data source channels and team lineup.
The first big step after setting a vision is to determine what problem or group of problems you will solve with the help of business intelligence. Setting goals helps you define higher-level parameters for BI.
Along with the goals, at this stage, you should probably think of KPIs and evaluation criteria to know how the task will be accomplished. These could be financial constraints (budget used for development) or performance indicators such as query speed or reporting error rates.
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At the end of this step, you must be able to set the initial requirements for the future product. This can be a feature list consisting of user stories from the product backend, or a more simplified version of this requirements document. The key point here is that, based on requirements, you need to understand what architecture, features and capabilities you want from your BI software/hardware.
Developing a requirements document for your business information system is key to understanding what tools you need. For large enterprises, building their own BI ecosystem can be considered for several reasons:
For small companies, the BI market offers many tools available as plug-in versions and cloud-based (software as a service) technologies. It is possible to find offers that include data analysis for a variety of industries with flexible options.
Based on your requirements, industry type, size and needs, you can understand whether you are ready to invest in your own BI tool. Otherwise, you can choose a vendor to take on the burden of implementation and integration for you.
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The next step is to bring together a group of people from different departments of your company to work on your business media strategy. Why would you need to create such a group? The answer is simple. A BI team brings together representatives from different departments to streamline communication and gain cross-departmental insights about the data needed and its sources. So, your BI team’s lineup should include two main categories of people:
These individuals are responsible for providing the team with data sources. They can also add domain knowledge to the selection and interpretation of different data types. For example, a marketing expert can determine whether your website traffic, bounce rate, or newsletter subscription numbers are valuable data types. Your sales representative can provide insights into meaningful interactions with customers. Moreover, you can get sales or marketing information through a person.
The second type of people on your team are BI-specific members who guide the development process and make architectural, technical, and strategic decisions. So you should specify the following roles as required criteria:
Head of BI. This person must be equipped with theoretical, practical and technical knowledge to support the implementation of your strategic and practical tools. This can be a manager with access to business intelligence and data sources. The person responsible for BI is the person who decides to promote the implementation.
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A BI engineer is a technical member of your team who is responsible for 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 may direct your IT team to run your BI tools. Read more about data professionals and their roles in our dedicated article.
A data analyst should also be part of the BI team and provide the team with expertise in data validation, processing, and data visualization.
As a team, you can begin developing a BI strategy once you’ve considered the data sources you need for your specific problem. You can document your strategy using traditional strategy documents such as product roadmaps. A business intelligence strategy can include a variety of components depending on your industry, company size, competition, and business model. However, the recommended components are:
This is a file of your selected data source channels. These should include a variety of channels, whether it’s stakeholders, industry analytics, or information from your employees and departments. Examples of such channels could be Google Analytics, CRM, ERP, etc.
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Keeping track of your industry standard KPIs as well as your specifics can reveal a complete picture of your business growth and losses. Finally, BI tools support these KPIs with additional data.
In this step, determine what type of report you require in order to extract useful information. For custom BI systems, you might consider visual or textual representations. If you choose a vendor, you may be limited in terms of reporting standards as specified by the vendors themselves. This section also includes the types of data you want to process.
The end user is the person who observes the data through the reporting tool’s interface. Depending on the end user, you may want to consider reporting
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