Types Of Business Intelligence Solutions – All businesses operate with data – information generated from many internal and external sources of your company. And these data feeds serve as a pair of eyes for managers, providing them with analytical information about what is happening with the business and the market. Accordingly, any misconception, inaccuracy or lack of information can lead to a distorted view of the market situation as well as internal operations – followed by bad decisions.
Data-driven decision making requires a 360° view of all aspects of your business, even the ones you hadn’t thought of. But how to turn unstructured pieces of data into something useful? The answer is business intelligence.
Types Of Business Intelligence Solutions
We have already discussed the machine learning strategy. In this article, we will discuss the actual steps involved in implementing business intelligence into your existing enterprise infrastructure. You will learn how to set up a business intelligence strategy and integrate the tools into your company’s workflow.
Database Business Intelligence (bi) & Analytics Software
Let’s start with a definition: business intelligence or BI is a set of procedures for collecting, structuring, analyzing and transforming raw data into actionable business insights. BI considers methods and tools that transform unstructured data sets and assemble them into easy-to-understand reports or dashboards. The main purpose of BI is to provide actionable business insights and support data-driven decision making.
The biggest part of implementing BI is using the actual tools that do the data processing. Various tools and technologies make up the business intelligence infrastructure. The infrastructure most often includes the following technologies that cover data storage, processing and reporting:
Business intelligence is a technology-driven process that relies heavily on inputs. The 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 analytics. Using descriptive analysis, businesses can study the market conditions of their industry as well as their internal processes. An overview of historical data helps to find problems and opportunities of the business.
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Based on data processing of past events. Instead of creating overviews of historical events, predictive analytics creates forecasts of future business trends. These predictions are based on the analysis of past events. So both BI and predictive analytics can use the same data processing techniques. To some extent, predictive analytics can be considered the next phase of business intelligence. Read more in our article on analytical maturity models.
Prescriptive analytics is the third type, which focuses on finding solutions to business problems and suggests actions to solve them. Currently, prescriptive analytics is available through advanced BI tools, but the entire field has not yet developed to a reliable level.
So here’s the point where we start talking about actually integrating BI tools into your organization. The whole process can be divided into the introduction of business intelligence as a concept to your company’s employees and the actual integration of tools and applications. In the next sections, we’ll go over the key points of integrating BI into your company and cover some of the pitfalls.
Let’s start with the basics. If you want to start using business intelligence in your organization, first of all explain the importance of BI to all stakeholders. Depending on the size of your organization, term frames may vary. Mutual understanding is essential here, as employees from different 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.
Data Analytics & Business Intelligence Platform
Another purpose of this phase is to introduce the concept of BI to the key people who will be involved in data management. You will need to define the real problem you want to work on, set KPIs and organize the required specialists to launch your business intelligence initiative.
It is important to mention that at this stage you will technically make assumptions about the data sources and the standards set to control the data flow. In later stages, you will be able to verify your assumptions and specify a data workflow. Therefore, you must be prepared to change your data acquisition channels and team composition.
The first big step after aligning the vision would be to define what problem or group of problems you will solve with business intelligence. Setting goals will help you determine other high-level parameters for BI, such as:
Along with the objectives at this stage, you will need to think about possible KPIs and evaluation metrics to see how the task is accomplished. These can 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 phase, you must be able to configure the initial requirements of the future product. This could be a list of features in the product backlog consisting of user stories or a simplified version of this requirements document. The main point is that based on the requirements, you should be able to understand what type of architecture, features and capabilities you want from your BI software/hardware.
Building a requirements document for your business intelligence system is a key point in understanding what tool you need. In the case of large enterprises, building their own BI ecosystem can be considered for several reasons:
For smaller companies, the BI market offers a large number of tools that are available as built-in versions as well as cloud-based technologies (Software-as-a-Service). It is possible to find offerings that cover almost any type of industry-specific data analysis with flexible options.
Based on the requirements, type of industry, size and needs of your business, you will be able to 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.
Who Makes Up An It Team?
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 would you even need to create such a group? The answer is simple. The BI team helps bring together representatives from different departments to facilitate communication and get department-specific information about the 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 to the selection and interpretation of different types of data with their domain knowledge. For example, a marketer can define whether your website traffic, bounce rate, or newsletter subscription numbers are valuable types of data. Your sales representative can provide insight into meaningful customer interactions. In addition, you will have access to marketing or sales information through a single person.
The second 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 you will need to specify the following roles as the required standard:
Head of BI. This person must be armed with theoretical, practical and technical knowledge to support the implementation of your strategy and real tools. This could be a senior executive with knowledge of business intelligence and access to data sources. The head of BI is the person who will decide on the implementation.
Business Intelligence Architecture: Tech Overview & Payoffs
A BI engineer is a technical member of your team who specializes in building, implementing and setting up BI systems. BI engineers typically have experience in software development and database configuration. 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. Find out more about data professionals and their roles in our special article.
A data analyst should also become part of the BI team, who will provide the team with expertise in the field of data validation, processing and visualization.
Once you have a team and consider the data sources needed for your particular problem, you can begin developing a BI strategy. You can document your strategy using traditional strategy documents, such as a product roadmap. A Business Intelligence strategy can include different components depending on your industry, company size, competition and business model. However, the recommended components are:
This is the documentation of the data source channels you have selected. They should include all types of channels, be it stakeholders, industry analytics in general or information from your employees and departments. Examples of such channels can be Google Analytics, CRM, ERP, etc.
Vendor Lowdown: Tableau Business Intelligence Software
Documenting your industry standard KPIs as well as your specific ones can open up a complete picture of your business growth and losses. Ultimately, BI tools are built to track these KPIs, supporting them with additional data.
At this stage, define what kind of report you need to conveniently obtain valuable information. For your own BI system, you can consider visual or textual representation. If you have already chosen a vendor, you may be limited in terms of reporting standards, as vendors set their own. This section can also include the types of data you want to deal with.
The end user is the person who will monitor the data through the reporting tool interface. Depending on your end users, you may also consider reporting
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