Top Self-service Business Intelligence Tools For Building Custom Reports In Visual Studio – All businesses operate on data – information generated from multiple sources internal and external to the business. And these data channels serve as an eye for management, providing them with analytical information about what is happening 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 operations – followed by bad decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even the ones you didn’t think about. But how to turn unstructured chunks of data into something useful? The answer is business intelligence.
Top Self-service Business Intelligence Tools For Building Custom Reports In Visual Studio
We have already discussed machine learning strategy. In this article, we will discuss the actual steps to bring business intelligence into your existing enterprise infrastructure. You’ll learn how to set up a business intelligence strategy and integrate 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, organizing, analyzing and turning raw data into actionable business insights. BI examines methods and tools that transform unstructured data sets, aggregating them into reports or dashboards that are easy to understand. The primary purpose of BI is to provide actionable business insights and support data-driven decision making.
The biggest part of BI implementation is the use of real tools that perform data processing. Different tools and technologies make up 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 the input. Techniques used in BI to transform unstructured or semi-structured data can also be used for data mining, as well as being front-end tools for working with big data.
. This type of data processing is also called descriptive analysis. With the help of descriptive analysis, companies can study the market conditions of the industry, as well as their internal processes. A historical data overview helps identify a company’s pain points and opportunities.
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Based on data processing of previous events. Instead of creating an overview of historical events, predictive analytics makes predictions about future business trends. These forecasts 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 level 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 propose actions to solve them. Currently, prescriptive analysis is available with advanced BI tools, but the whole area has not developed to a reliable level yet.
So here is the point when we start talking about the actual integration of BI tools in your organization. The entire process can be broken down into the introduction of business intelligence as a concept to the company’s employees and the actual integration of tools and applications. In the next few chapters, we’ll walk through the key points of BI integration in your organization and cover some of the pitfalls.
Let’s start with the basics. To start leveraging business intelligence in your organization, first explain the meaning of BI to all your stakeholders. Depending on the size of your company, the scope of the concept may vary. Mutual understanding is important here because employees of various 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 purpose of this phase is to introduce the concept of BI to key people who will be involved in data management. You must define the real problem you want to work on, set KPIs and organize the necessary experts to start your business intelligence initiative.
It is important to mention that at this stage you will, technically speaking, make assumptions about the sources of the data and the standards set to control the data flow. You will be able to validate your assumptions and specify your data workflow at a later stage. Therefore, you must be prepared to change your data collection channels and team composition.
The first big step after aligning your vision would be to define what problem or group of problems you intend to solve with the help of business intelligence. Setting the goals will help you determine additional high-level parameters for BI such as:
Along with the goals, at that stage, you have to think about possible KPIs and evaluation metrics to see how the project is completed. These can be financial constraints (budget used for development) or performance indicators such as query speed or notification error rate.
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At the end of this phase, you must be able to set the initial requirements of the future product. This can be a list of features in a product backlog consisting of user stories or a simpler version of this requirements document. The 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.
Putting together a requirements document for your business intelligence system is key to understanding which tools you need. For large companies, building their own custom BI ecosystem can be considered for several reasons:
For smaller businesses, the BI market offers a large number of tools that are available as both on-premises versions and cloud-based (Software-as-a-Service) technologies. Offerings can be found covering almost any industry-specific data analysis with flexible options.
Based on the requirements, type of industry, size and needs of your company, you will be able to understand if 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 should you start such a group? The answer is simple. A BI team helps bring together representatives from different departments to simplify communication and get department-specific insights about required data and its sources. So, your BI team setup 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 to select and interpret different data types. For example, a marketing analyst can define whether your website traffic, bounce rate, or newsletter subscription numbers are valuable data types. While your sales rep can provide insight into meaningful customer interactions. In addition, you will be able to access marketing or sales information through a single source.
Another 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. So as a necessary standard you need to determine the following roles:
Director of BI. This person must be armed with theoretical, practical and technical knowledge to support the implementation of your strategy and real tools. This can be a manager with knowledge of business intelligence and access to data sources. The head of BI is the person who will make the decisions to drive the implementation.
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A BI engineer is a technician on 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 implementing your BI toolset. Learn more about data analysts and their roles in our dedicated article.
The data scientist 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’ve considered the data sources needed for your specific problem, you can start developing a BI strategy. You can document your strategy using traditional strategic documents such as product roadmaps. A business intelligence strategy can include various elements depending on your industry, company size, competition and business model. However, recommended components are:
This is documentation of your selected data source channels. This should include any type of channel, be it stakeholder, analytics of the industry in general or the information from your employees and departments. Examples of such channels can be Google Analytics, CRM, ERP, etc.
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Documenting the standard KPIs for your industry as well as your specific ones could unlock a full picture of your company’s growth and losses. Finally, BI tools are created to monitor these KPIs supporting them with additional data.
At this stage, define the type of reporting you need to conveniently extract valuable information. For a custom BI system, you might consider a visual or textual representation. If you have already selected the vendor, you may be limited in terms of reporting standards, as vendors set their own. This section may also contain data types that you want to deal with.
An end user is a person who will monitor data through the reporting tool’s interface. Depending on the end users, you may also consider reporting
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