Business Intelligence Solutions In Retail

Business Intelligence Solutions In Retail – Every business runs on data, information generated from many sources internal and external to your company. And these data channels serve as a pair of eyes for executives, providing analytical insights into what’s happening with the business and the market. Accordingly, any misconception, inaccuracy or lack of information can lead to a distorted view of market conditions and internal operations, and subsequent poor decisions.

Making data-driven decisions requires a 360° view of all aspects of your business, even those you may not have thought about. But how do you turn chunks of unstructured data into something useful? The answer is business intelligence.

Business Intelligence Solutions In Retail

We have already discussed the machine learning strategy. In this article, we’ll look at the actual steps to embed business intelligence into your corporate infrastructure. You will 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 practices for collecting, structuring, analyzing raw data and turning it into actionable business insights. BI considers methods and tools that transform unstructured data sets into easy-to-understand reports or dashboards. The primary purpose of BI is to provide business insights and support data-driven decision-making.

The biggest part of BI implementation is using the actual tools that process the data. Different tools and technologies form a business intelligence infrastructure. In most cases, the infrastructure includes the following technologies covering data storage, processing and reporting:

Business intelligence is a technology-driven process that relies heavily on input. Technologies used in BI to transform unstructured or semi-structured data can also be used for data mining, as well as frontend tools for working with big data.

. This type of data processing is also called descriptive analytics. With the help of descriptive analytics, companies can analyze the market conditions of their industry, as well as their internal processes. An overview of historical data helps identify business problems and opportunities.

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Based on the processing of data from past events. Rather than providing overviews of historical events, predictive analytics makes predictions about future business trends. These predictions are based on the analysis of past events. So both BI and predictive analytics can use the same techniques to process data. To some extent, predictive analytics can be considered the next phase of business intelligence. Read more in our article on analytics maturity models.

Prescriptive analytics is the third type, which aims to find solutions to business problems and propose actions to solve them. Currently, prescriptive analyzes are available through advanced BI tools, but the entire field has not yet developed to a reliable level.

So here’s the thing, when we start talking about the actual integration of BI tools in your organization. The entire process can be divided into the introduction of business intelligence to your company’s employees as a concept and the actual integration of tools and applications. In the following sections, we’ll walk through the key points of BI integration in your company and cover some of the gaps.

Let’s start with the basics. To start using business intelligence in your organization, first explain the meaning of BI to all your stakeholders. Depending on the size of your organization, term frames may vary. Mutual understanding is essential here, as employees from various departments will be involved in data processing. So make sure everyone is on the same page and doesn’t confuse business intelligence with predictive analytics.

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Another goal of this phase is to introduce the BI concept to key people who will be involved in data management. You’ll need to define the real problem you want to address, set KPIs, and organize the necessary specialists to launch your business intelligence initiative.

It is important to mention that at this stage, technically, you will be making assumptions about the data sources and the standards set to control the data flow. You can check your assumptions and define your data workflow in the following stages. That’s why you have to be ready to change your data supply channels and your team line.

After aligning the vision, it would be defining what problem or set of problems you are going to solve with the help of business intelligence. Setting goals will help you define more high-level parameters for BI, such as:

Along with the objectives, at this stage you should think of possible KPIs and evaluation metrics to see how the task is being accomplished. These can be financial constraints (budget applied to development) or performance indicators such as query speed or reported error rate.

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At the end of this stage, you should be able to configure the initial requirements of the future product. This can be a list of features from a product backlog of user stories, or a more simplified version of this requirements document. The main point here is that, based on the requirements, you need to understand what type of architecture, features and capabilities you want from your BI software/hardware.

Completing the requirements document for your business intelligence system is a key point in understanding what tools you need. For large enterprises, building their own custom BI ecosystem can be considered for several reasons:

For smaller businesses, the BI market offers a wide range of tools, available both as embedded versions and as cloud-based (Software-as-a-Service) technologies. It is possible to find offers covering almost any type of data analysis in the industry, with flexible options.

Based on the requirements, your industry type, size, and your business needs, you’ll be able to understand whether you’re ready to invest in a custom BI tool. Alternatively, you can choose a vendor that will carry the implementation and integration burden for you.

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The next step would be to gather a group of people from different departments of your company to work on your business intelligence strategy. Why should you create such a group? The answer is simple. The BI team helps bring together representatives from different departments to simplify communication and obtain departmental details on the necessary data and sources. Therefore, the composition of your BI team should consist of two main categories of people:

These people will be responsible for giving the team access to data sources. Moreover, their domain knowledge will also help them to select and interpret different types of data. For example, a marketing specialist can define whether your website traffic, bounce rate or newsletter subscription numbers are valuable types of data. While your sales rep can provide insights into meaningful customer interactions. In addition, you will be able to obtain marketing or sales information through a single person.

The second category you want on your team are BI-specific members who will guide the development process and make architectural, technical, and strategic decisions. Therefore, as a required standard, you should define the following roles:

Head of BI This person should be equipped with theoretical, practical and technical knowledge to help implement your strategy and real tools. This can be an executive with knowledge of business intelligence and access to data sources. The head of the BI is the person who will make the decisions to drive the implementation.

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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 well versed in 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 dedicated article.

The Data Analyst should also become a member of the BI team to provide the team with expertise in data validation, processing, and data visualization.

Once you have a team and have considered the data sources needed 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. A business intelligence strategy can have various components depending on your industry, company size, competition and business model. However, the recommended ingredients are:

This is the channel documentation for the selected data source. These should include any type of channel, be it a stakeholder group, industry analytics in general, or information from your employees and departments. Examples of such channels can be Google Analytics, CRM, ERP, etc.

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Documenting your industry standard KPIs and your specific ones can open up a complete picture of your business growth and losses. Ultimately, BI tools are created to track these KPIs that support them with additional data.

At this stage, determine what kind of reports you need to properly extract valuable information. For a custom BI system, you can consider visual or textual representations. If you have already chosen a vendor, you may be limited in terms of reporting standards, as vendors set their own. This section may also include the types of data you wish to process.

The end user is the person who will be viewing the data through the reporting tool’s interface. Depending on the end users, you may also consider a report

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