5 Aspects of Business Intelligence Development

Aspects of Business Intelligence Development

Business intelligence (BI) development is a relatively new term that describes a lengthy and detailed process that involves developing tools and systems that help users leverage large amounts of data.

It is one of the primary practical applications of data science for commercial-scale use, as it allows companies and organizations to actually apply data to solve problems and identify opportunities. 

Developing these systems and tools can be a complex process that involves feedback and input from team members throughout an organization’s structure. Likewise, it involves a variety of components, which are discussed below.

What is Business Intelligence Development?

What is Business Intelligence Development?

Before we outline the different components of BI development, we must first determine what business intelligence development is.

Going beyond the simple definition provided above, business intelligence development involves collecting data from many different resources (both in-house and externally). That data is then analyzed, interpreted, and presented in data visualizations and reports that help executives, management-level workers, and other stakeholders quickly understand what the data is telling them.

The data is then used for strategic planning and decision-making processes, the goal of which is to serve as the foundation for making more informed business decisions.

Usually, BI development is undertaken to drive decisions that are related to improving the efficiency of a business, increasing revenue, and the like. It’s often used to identify competitive advantages over other businesses or organizations as well.

In order to arrive at a point at which business leaders can use the data to make informed decisions, business intelligence development must be undertaken. This process can be lengthy and is very detailed, but the combination of analytical data that can be derived from business intelligence development can prove extremely useful for mapping out a business’ future with actionable details for success.

Components of Business Intelligence Development

With a basic working knowledge of what BI is, we can now begin to dive deeper into the pieces and parts that make up the business intelligence process.

Types of BI Information

Types of BI Information

In terms of the types of information that BI refers to, we’re talking about two primary types of information. First, BI examines historical information (e.g., sales figures for a particular product that a company sells) which can be used to inform future practices for everything from product development to marketing strategies. In other words, historical data is typically used for making strategic changes.

Second, BI can look at data in real-time. This means that business intelligence analysts can utilize data as it’s being generated to help businesses and organizations make spur-of-the-moment decisions that can help improve their bottom line. For example, BI can be used to track how satisfied customers are with a product or service. Customer feedback can be tracked in real-time to provide a business with important information that they can use to make corrective actions to improve the customer experience.

Pieces of BI Development

In terms of the pieces that are involved in BI development, there are five primary components needed to get the process up and running:

  • Research and planning
  • Design, coding, and testing
  • Database management
  • User interface and accessibility
  • Automation and machine learning

Let’s discuss each of these in more detail.

Research and Planning

The initial stage of any BI development project is to identify the problems facing an organization and the potential solutions that are available to resolve those problems.

Data scientists working on these projects need to have a firm understanding of business practices in addition to the technical expertise demanded by their occupation. As such, the research and planning phase typically involves comprehensive research to assess the organization’s available data and the identification of key objectives to establish parameters for a successful solution.

Design, Coding, and Testing

Intelligence tools typically take the form of software and digital database infrastructure that supports essential processes. This aspect of development is often the most lengthy and difficult part of the cycle because of the detailed nature of software design. Designers often create a mock-up program before coding and testing a complete solution for implementation throughout an organization.

Database Management

Databases are the foundation of all virtual BI practices. This means that managing and integrating databases is pivotal in establishing effective and comprehensive tools.

This part of BI development can be particularly challenging as it can require working with a relational and multifaceted database to provide meaningful final results. Management of individual databases may not be a part of the actual intelligence development process, but it’s essential for accomplishing intelligence objectives.

User Interface and Accessibility

Data scientists may be comfortable interpreting and analyzing data in an unrefined state, but this isn’t the case for most employees and leaders within a company. Accessibility is an important aspect of the process because it allows a broader range of professionals to actually understand and leverage data for the benefit of the company.

All finished intelligence solutions are built with an accessible interface that allows users to interact with the digital tools without the advanced technical skills required for their creation or maintenance. Many modern BI tools are also designed to function seamlessly on a wide range of devices, ranging from on-site desktop computers to mobile technology.

Automation and Machine Learning

Data science is useful to businesses and organizations because it allows them to apply vast amounts of information in an efficient way. The processes of automation and machine learning allow users to capitalize on technological developments to reduce the workload on actual employees.

The sheer quantity of data used by some organizations, particularly large corporations, means that automation is the only practical way to incorporate and assess massive data sets.

Parts of the BI System

Parts of the BI System

In addition to the steps outlined above to develop BI systems, there are also a number of parts that all BI systems must have.

First, BI systems have some kind of storage facility where the data that’s collected is centralized and stored for easy access.

Second, BI systems must incorporate analytics and data management tools to mine the data and analyze it.

Third, tools like business performance management tools must be used to examine the business’ progress towards achieving its goals.

And, finally, BI systems should have a user interface that provides people with easy access to information. This is where BI dashboards and data visualization tools come into play.

Types of BI Development Processes

BI involves many different approaches and processes for achieving positive end goals. Below is a list of just some of the processes that might be used in BI development.

  • Querying – This is the process of asking very specific questions of the data. BI then retrieves the answers from the datasets in the stored data.
  • Visual analysis – The process involves analyzing the data through visual means like charts and graphs, the purpose of which is to identify insights on the fly.
  • Data mining – Data mining uses statistics, databases, and machine learning to find trends in large sets of data.
  • Descriptive analytics – This is the process by which data scientists determine what happened based on the preliminary data analysis.
  • Statistical analysis – Statistical analyses involve using the findings from descriptive analytics and exploring that data even further. Statistics are used to ask what happened and why.

Using Data from BI Development

Of course, the whole purpose of BI development is to provide stakeholders with data that can be used to make more informed and strategic decisions. To do so, the data that’s collected must be prepared.

Preparing data for analysis requires that the data first be collected and stored, as discussed above, and then cleaned. The process of cleaning data is fairly straightforward – remove bad data from the collection that might present problems in the analysis stage.

Data cleaning involves removing all sorts of data, from corrupt files to duplicate entries, or incomplete data in a dataset, just to name a few. 

Cleaned data should not only make sense, but it should also shed light on the questions being asked at present, prove (or disprove) the working theory, and reveal trends in the data that can be used to inform the next steps in the decision-making process.

Of course, the most important pieces of the puzzle are the actual analysis of data and the visualization of that data.

As noted earlier, analyzing the data helps data scientists zero in on relevant conclusions based on what the data is saying. Visualizing the relevant data allows data scientists to disseminate what the data is saying in easy-to-understand terms that laypersons can easily understand. In other words, data analysis looks for the diamonds in the rough, if you will, and data visualization turns that rough data into the diamonds that can help decision-makers forge a path towards improved efficiency and higher profits.

The Benefits of Business Intelligence

The primary benefit of business intelligence and BI development is that it streamlines the process of decision-making. Not only that, it streamlines the process of data-driven decision-making.

While data isn’t everything, in the business world, it’s a much more reliable pillar of decision-making than relying on one’s intuition or gut feelings about how policies or procedures should be changed. Instead, by using hard data, decisions about things like revenue, expansion, and productivity can be made with a higher degree of certainty based on historical and real-time feedback.

In other words, BI allows for faster decision-making, optimization of operational efficiency, improvement upon business processes, and identifying problems that need to be resolved.

Additionally, BI development can lead to:

  • Higher sales
  • Improved business strategies
  • Identifying potential partnerships
  • Identifying market trends
  • Identifying new markets

And, as discussed earlier, BI development can give a business or organization an advantage over its competitors by identifying ways that business operations can be improved, streamlined, or expanded. There is simply a myriad of ways that the aspects of BI can bring about benefits for businesses.

The Future of Business Intelligence

Business intelligence and its development have certainly changed over the years – and it continues to do so.

Trends in BI point to an increasing importance of augmented analytics and natural language querying abilities, which might replace traditional means of writing queries in programming languages like SQL.

Another trend in BI is the increasing use of the cloud. Initially, data warehouses were stored on-site, but with cloud-based data warehouses becoming more popular, the shift is on to make BI projects cloud-based.

BI is also becoming more widely used by non-coders. With many vendors in the BI space offering tools and applications that require little or no coding, BI processes can be undertaken by people that don’t have a strong background in programming. Part of this is the focus on improving data literacy. With self-service business intelligence becoming ever more popular, it’s more important than ever for new users to understand how to work with data.

Of these emerging tools, data visualization and data dashboards are certainly the most popular. These tools offer users the ability to evaluate and digest data quickly and easily. Services like Tableau, which is a self-service analytics platform, give users data visualization tools that can integrate with a wide range of data sources.

As technology advances and businesses’ needs evolve, business intelligence will continue to evolve as well. Today, business intelligence relies on machine learning, artificial intelligence, and many advanced technologies that didn’t exist just a few years ago. In the future, who knows what exciting new technologies and tools will be at the disposal of data scientists.

What is for certain is that businesses and organizations are becoming increasingly data-driven. That means that business intelligence will likely become an even more important component of the decision-making process.

From sales tracking to customer service feedback, product development to marketing practices, business intelligence offers valuable feedback for data-hungry businesses and organizations. In the future, BI will continue to help decision-makers set the right course to bring in higher profits and do so more efficiently than ever.

There is no doubt that a company’s ability to gather, curate, and apply data impacts its potential for long-term success. Understanding the core aspects of business intelligence development is not only essential for data scientists, but also leaders and decision-makers throughout an organization.

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