Simply The Quantity Of Does A Business Intelligence Developer At Amazon.com.com Produce – Every business runs on data – information generated from your company’s many internal and external sources. And these data channels serve as eyes for executives, providing them with the analytical information about what is going on with the business and the market. Accordingly, 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 hadn’t thought of. But how do you turn unstructured data chunks into something useful? The answer is Business Intelligence.
Simply The Quantity Of Does A Business Intelligence Developer At Amazon.com.com Produce
We have already discussed machine learning strategy. In this article, we discuss the actual steps to bring business intelligence into your existing corporate infrastructure. You will learn how to set up a business intelligence strategy and integrate tools into your workflow.
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Let’s start with a definition: business intelligence or BI is a set of practices for collecting, structuring, analyzing and turning raw data into actionable business insights. BI considers 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 the driven decision-making process.
The biggest part of BI implementation is the use of current tools that perform data processing. Various tools and technologies form a business intelligence infrastructure. Most often, the infrastructure includes the following technologies that cover 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 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 to find the pain points and opportunities of a business.
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Based on data processing of past events. Instead of producing overviews of historical events, predictive analytics makes predictions 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 stage of 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 suggest actions to solve them. Currently, prescriptive analysis is available via advanced BI tools, but the entire field is not yet developed to a reliable level.
So here is the point 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 as a concept for your company’s employees and the actual integration of tools and applications. In the next paragraphs, we will go through the key points of BI integration in your company and cover some pitfalls.
Let’s start with the basics. To start 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 terms may vary. Mutual understanding is vital here as employees from different departments are 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 present the concept of BI to the key people involved in data management. You need to define the current problem you want to work on, set KPIs and organize required specialists to launch your business intelligence initiative.
It is important to mention that at this stage you make technical assumptions about the data sources and the standards to control the data flow. You will be able to check your assumptions and specify your data workflow in later stages. Therefore, you must be ready to change your data quercing channels and your team setup.
The first big step after aligning the vision would be to define which problem or group of problems you will solve with the help of business intelligence. Setting the goals will help you determine further 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 accomplished. These can be financial restrictions (budget applied to development) or performance indicators such as query speed or report error rate.
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At the end of this stage you have to configure the initial requirements of the future product. This can 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 what architecture type, features and capabilities you want from your BI software/hardware.
Compiling a requirements document for your business intelligence system is a key point in understanding what tool you need. For large businesses, having their own custom 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 embedded versions and cloud-based (software-as-a-service) technologies. It is possible to find offers that cover almost any type of industry-specific data analysis with flexible options.
Based on the requirements, your industry type, the size and the needs of your business, you can understand whether you are ready to invest in a custom BI tool. Otherwise, you can choose a vendor who 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 do you even need to create such a group? The answer is simple. BI team helps gather representatives from different departments to facilitate communication and get department-specific insights about required data and its sources. So, the composition of your BI team should include two main categories of people:
These people will be responsible for giving the team 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 your website traffic, bounce rate, or newsletter subscription numbers are valuable data types. While providing your sales representatives with insights and meaningful interactions with customers. In addition, you may access marketing or sales information about a single person.
The second category of people you want on your team are BI-specific members who lead the development process and make architectural, technical and strategic decisions. So, as a required standard you have to 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 current tools. This can be an executive with knowledge of business intelligence and access to data sources. The head of the BI is a person who makes decisions to lead the 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 software development and database configuration backgrounds. They must also be well versed in data integration methods and techniques. A BI engineer can lead your IT department in the implementation of your BI toolset. Learn more about dating 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 have considered the data sources required for your specific problem, you can begin to develop a BI strategy. You can document your strategy using traditional strategic documents such as a product roadmap. Business intelligence strategy can include 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, analysis of the industry in general or the information of your employees and departments. Examples of such channels can be Google Analytics, CRM, ERP, etc.
Domain Knowledge (data Science)
Documenting standard KPIs of your industry as well as your specific ones can reveal the full picture of your business growth and losses. Finally, BI tools are created to track these KPIs, supporting them with additional data.
At this stage, define what type of reporting you need to conveniently extract valuable information. In the case of a custom BI system, you can consider visual or textual representations. If you have already chosen the vendor, you may be limited in terms of reporting standards, as the vendors set their own. This section can also include data types you want to deal with.
An end user is a person who observes data through the interface of the reporting tool. Depending on the end users, you may also consider a report
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