All businesses operate with data – information generated from various internal and external sources within the company. These data feeds act as the management’s senses, providing vital information about the business and market conditions. Consequently, any misconceptions, inaccuracies, or information gaps can distort their perception of the market and lead to misunderstandings about internal operations, ultimately resulting in erroneous decisions.
To make data-driven decisions, it’s crucial to gain a clear understanding of all aspects of your business, including those often overlooked. But how can you transform unstructured data into something meaningful? Business intelligence comes to the rescue.
Previously, we discussed the strategy of organizing machine learning. In this article, we’ll focus on integrating business intelligence into an existing corporate infrastructure. You will learn how to prepare a business intelligence strategy and seamlessly integrate the tools into the company’s workflows.
Let’s begin with a definition: Business Intelligence (BI) refers to a collection of practices aimed at gathering, organizing, analyzing, and converting raw data into a comprehensive business overview that facilitates decision-making. BI employs various techniques and tools to transform unstructured data sets, consolidating them into accessible reports or information dashboards. The primary objective of BI is to provide a comprehensive view of the business and support data-driven decision-making processes.
An Illustration of an Interactive Sales Team Dashboard. The entire business intelligence process can be categorized into four stages:
The fundamental aspect of BI implementation revolves around the utilization of tools that handle data processing. The BI infrastructure comprises a variety of tools and technologies, commonly including the following components for data storage, processing, and reporting:
The definition of business intelligence can often be confusing due to its overlapping nature with other knowledge areas, particularly predictive analytics. It would be a serious mistake to assume that business intelligence and predictive analytics are synonymous.
In reality, business intelligence refers to a data analysis technique that seeks to answer the questions “what happened?” and “what happens?”. This type of data processing is also known as descriptive analytics. Through descriptive analytics, companies can explore the current state of the market within their industry and assess their own internal processes. Analyzing historical data helps to identify weaknesses and potential opportunities for the business.
On the other hand, predictive analytics focuses on making predictions based on the processing of data from past events. It doesn’t analyze historical events but rather aims to predict future business trends. Both predictive analytics and business intelligence rely on analyzing past events to some extent, and they can employ similar techniques to process data. In a way, predictive analytics can be seen as the next stage of business intelligence, as it delves into forecasting future outcomes. To learn more about this, you can refer to our article on analytics maturity models.
Both analysis techniques address three main types of data management:
Prescriptive analytics represents the third type, with the objective of identifying solutions to business problems and recommending actions to address them. Presently, the implementation of prescriptive analytics relies on rich Business Intelligence (BI) tools, but this field as a whole has not yet reached a fully dependable level of maturity.
The entire process of incorporating BI tools into your organization can be divided into two main aspects: first, acquainting company employees with the concept of business intelligence, and second, integrating the tools and applications themselves. Below, we will discuss the key aspects of BI integration and shed light on some of its complexities.
Let’s begin with the fundamentals. To initiate the implementation of business intelligence within your organization, the first step involves elucidating the value of BI to all levels of management. Achieving mutual understanding is crucial, as employees from various departments will be engaged in data processing. Therefore, it is essential to ensure consistency to prevent any confusion between business intelligence and predictive analytics.
Another objective of this stage is to familiarize key leaders involved in data management with the concept of BI. You must define the tasks to be addressed, establish Key Performance Indicators (KPIs), and assemble the team to launch your own business intelligence project.
It is important to note that during this stage, you will essentially be making assumptions about the data sources and the standards that will govern the data flow. Subsequently, in later steps, you can test these assumptions and shape your data processing accordingly. This necessitates being prepared to make changes to the channels for receiving data and the structure of the team.
After establishing a shared vision of the situation, the first crucial step is to identify the task or group of tasks that will be addressed using business intelligence. Defining targets will enable the establishment of high-level BI parameters, which include:
During this stage, it is also important to consider potential Key Performance Indicators (KPIs) and evaluation metrics to assess the success of the task. These may include physical constraints like development budget or performance metrics such as request rate or bug reporting rate.
By the end of this phase, you will have the groundwork to configure the initial requirements for the future product. This could be a product backlog containing a list of features described in user stories or a simplified version of the requirements document. The key focus here is to understand the necessary architecture, features, and capabilities required for your BI software/hardware based on these requirements.
Drawing up a document outlining the requirements for a business intelligence (BI) system is a crucial step in determining the appropriate tool for your needs. For larger companies, developing their own BI ecosystem can be advantageous due to the following reasons:
Trust Issues: Enterprise-level organizations often deal with sensitive and valuable data, making it difficult to rely on third-party providers to process and handle this information securely.
Industry-Specific Needs: BI tools are tailored to cater to specific industries. If there are no suitable suppliers in the market serving your industry’s needs, developing your own BI system becomes a viable option.
Handling Big Data: Processing large volumes of information or working with big data may necessitate the development of an in-house BI system. This approach provides greater flexibility in choosing a cloud infrastructure provider.
On the other hand, for smaller companies, the BI market offers a wide array of tools that can function as both embedded and cloud-based (Software-as-a-Service) solutions. These options cater to various industry-specific data analytics requirements while offering flexibility.
Ultimately, the decision to invest in your own BI tool or opt for a vendor’s solution depends on your company’s specific requirements, industry type, business size, and needs. Companies can evaluate their unique circumstances and choose whether to bear the responsibility of implementing and integrating their BI system or rely on a vendor to handle these aspects.
Next, you will need to assemble a diverse group of individuals from various departments within the company to collaborate on crafting a business intelligence strategy. But why establish such a group in the first place? The answer is straightforward: the BI team plays a crucial role in uniting representatives from different departments, promoting effective communication, and gathering valuable input concerning the necessary data and its respective sources. In essence, the structure of the BI team should encompass two primary categories of individuals:
Subject Matter Representatives From Different Departments
These individuals will have the responsibility of granting the team access to data sources, while also contributing their expertise in the subject area to assist in the selection and interpretation of various data types. For instance, a marketer can assess the significance of data such as website traffic, bounce rate, or mailing list subscriptions. Similarly, an account manager can offer valuable insights into customer interactions. Furthermore, each person will provide access to either marketing or sales information.
Positions Related To BI
The second category of people are people related to BI, who will lead the development process and make architectural, technical and strategic decisions. That is, you need to assign people to the following positions:
Head of BI- This individual must possess theoretical, practical, and technical knowledge to bolster the execution of the strategy and tools. Ideally, this role would be filled by a proficient leader well-versed in business intelligence and equipped with access to data sources. The BI lead assumes the responsibility of making crucial decisions that propel the implementation forward.
BI Engineer- A BI engineer is a technical team member who specializes in building, implementing, and configuring BI systems. Usually BI engineers have experience in software development and database configuration. They should also be proficient in the methods and techniques of data integration. The BI engineer may lead the IT department in the implementation of the BI toolkit. Learn more about data scientists and their responsibilities in our article.
Also, a data analyst should become part of the BI team, able to apply their knowledge in data validation, processing and visualization.
Once you have assembled your team and chosen the necessary data sources to address your specific problem, you are prepared to initiate the development of your BI strategy. This strategy can be documented using traditional strategic documents, such as a product roadmap. A business intelligence strategy may encompass diverse components, contingent on factors such as the industry, company size, competition, and business model. Nevertheless, the following essential components are recommended:
This document provides documentation for the selected data source feeds, encompassing all types of channels. These channels include the head, industry analytics in general, and information from employees and departments. Some examples of such channels are Google Analytics, CRM, ERP, and others.
Documenting industry-standard KPIs that are unique to your business can provide a comprehensive picture of its growth and losses. Ultimately, BI tools are designed to track these KPIs, supported by additional data.
At this stage, you must determine the type of reporting required to extract valuable information effortlessly. In the case of a native BI system, you have the option to choose between visual or textual representation. If you have already selected a provider, your choice of reporting standards may be restricted as the provider sets its own. Additionally, you can specify the data types you wish to work with in this section.
Reporting Flow Type And End Users
The end user is the individual who will access the data through the reporting tool’s interface. Depending on the end users, various types of reporting streams can be chosen.
Traditionally, Business Intelligence (BI) has primarily served management purposes. As the user base and data types were limited, full automation wasn’t necessary. Consequently, the traditional BI workflow involved technical staff acting as intermediaries between the reporting tool and the end users. If an end user required specific data, they would make a request, and the technical staff would then generate a report using the requested data. In this setup, the IT department functioned as a power user, with access to the data and the ability to influence its transformations.
The traditional approach offered a more secure and manageable data flow. However, the reliance on the IT department could lead to delays, reducing flexibility and speed when dealing with large datasets, especially in the context of big data. If you seek enhanced reporting control and accuracy, forming a dedicated IT team to handle queries and reporting could be beneficial.
Modern companies and solution providers leverage self-service Business Intelligence (BI) to enable business users and management to receive automatically generated reports from the system. With automatic reporting, there is no need for power users (administrators) from the IT department to manually handle each data warehouse request. However, technical staff is still necessary to set up the system.
While automation streamlines the reporting process, it may lead to a reduction in the quality and flexibility of the final reports. Additionally, the design of the reporting system can impose limitations. Nevertheless, the approach offers the advantage of not requiring constant involvement of technical staff to operate the system. Non-technical users gain the ability to create their own reports or access a dedicated section of the data warehouse.
The tool integration step will demand significant time and effort from the IT department. If you opt to build your own solution, it will entail developing various structural components of the BI architecture. On the other hand, you have the option to select a provider from the marketplace who can deliver an implementation and data structuring that aligns with your needs.
A fundamental aspect of any BI architecture is the data warehouse, which serves as a repository for information in a specified format, typically organized, classified, and error-free. Without proper pre-processing of data, the BI tool or IT department won’t be able to query it directly. Consequently, connecting the data warehouse (data warehouse) to information sources isn’t possible directly. Instead, ETL (Extract, Transform, Load) tools or data integration tools must be utilized. These tools will preprocess raw data from the original sources and transfer it to the warehouse in three consecutive steps: [ETL process].
Data Extraction: The ETL tool acquires data from various sources, including ERP, CRM, analytics, and spreadsheets.
Data Conversion: Following the extraction, the ETL tool commences data processing. All extracted data is thoroughly analyzed, de-duplicated, standardized, sorted, filtered, and validated.
Data Loading: During this stage, the converted data is loaded into the storage.
Usually, ETL tools are readily available alongside BI tools provided by the vendor (we’ll explore the most popular ones below). To understand how to clean and prepare data effectively, please refer to our article.
After configuring data transfer from selected sources, the next step involves configuring the storage. In business intelligence, data warehouses serve as special types of databases primarily designed to store historical information in SQL formats. These storage systems are connected to data sources and ETL (Extract, Transform, Load) systems on one end and reporting tools or dashboard interfaces on the other end. This integration enables the presentation of data from various systems within a unified interface.
Nevertheless, the storage typically houses vast amounts of information, ranging from 100 GB and beyond, which can result in slow response times to queries. In some instances, data might be stored in an unstructured or semi-structured manner, leading to a higher error rate when parsing data to generate reports. To facilitate analytics, specific types of data are often grouped together within a storage space for easy retrieval. As a result, companies employ additional technologies to ensure accelerated access to smaller, thematic blocks of information.
Various solutions are used to provide analytics with access to smaller data sets from the data warehouse. Among these, Online Analytical Processing (OLAP) and Data Kiosk stand out as the most popular. These technologies offer faster reporting and streamlined access to the required data.
Recommendation: If you do not possess substantial amounts of data, opting for a simple SQL storage will suffice. Introducing additional structural elements, such as a data mart, would entail considerable extra costs without yielding any significant benefits. This choice is ideal for small businesses or industries dealing with relatively modest data volumes.
Data Warehouse + Data Cubes Online Analytical Processing
The data stored in the warehouse exists in a two-dimensional format, resembling traditional spreadsheet tables with rows. This storage method is also referred to as a relational database. Due to the vast number of data types that a single database can contain, querying the data warehouse often requires a significant amount of time. To address this and meet the needs of analysts who require quick access to data for analysis across different dimensions and grouping, OLAP data cubes are utilized.
OLAP, which stands for Online Analytical Processing (or interactive analytical processing), is a technology that processes and provides simultaneous access to data across multiple dimensions. By structuring data into cubes, the limitations of the data warehouse can be overcome, allowing for more efficient and flexible data analysis.
An OLAP data cube is a highly optimized data structure designed to facilitate rapid data analysis from SQL databases or data warehouses. The source data used in these cubes is a condensed representation of its original description. However, the cube assumes the presence of more than just two dimensions, resembling a spreadsheet with rows and columns. Dimensions play a crucial role in generating reports. In the context of a sales department, these dimensions could include:
These cubes form a multi-dimensional database of information, offering the flexibility to group data in various ways to enhance reporting speed. Diverse OLAP data cubes, tailored to different data topics, come together to create OLAP databases. The integration of storage and OLAP is beneficial, as cubes store a relatively small amount of data, enabling efficient processing.
Recommendation: The architecture of “data warehouse + OLAP data cubes” can be deemed as typical and is suitable for companies of any size seeking data storage and complex multidimensional information analysis. To avoid overloading the store with queries, it is advisable to opt for an architecture incorporating OLAP.
Data Warehouse + Data Mart Technologies
Storage constitutes the foremost and most extensive component of the business intelligence architecture. A more compact form of storing dataset is called a data mart. Essentially, a data mart represents a specialized section within the repository, gathering thematically similar information pertinent to a particular department. By utilizing data marts, departments gain access to the precise data they require, as these marts offer information directly related to their specific business areas. This obviates the need for developers to establish a permission-based request system for end users.
Recommendation: The “data warehouse + data mart” architectural style ranks as the second most popular approach, utilizing data marts to distribute necessary information to departments. This strategy can be employed for establishing continuous reporting or enabling information access without granting permissions to end users.
Enterprise businesses often necessitate a range of data management options. Two such technologies are kiosks and data cubes, each serving to present smaller amounts of information retrieved from storage. Data marts, on the other hand, constitute subsets of the data warehouse specifically tailored to particular tasks, allowing for various implementation approaches. These implementations encompass relational databases (including the warehouse and other SQL databases) and multidimensional structures, which essentially refer to OLAP data cubes. Consequently, both these technologies effectively handle data and facilitate its distribution across different departments within an organization.
Recommendation: Both technologies can be utilized since they support the same concept but serve distinct purposes. Data marts can be integrated as components of a data warehouse for enhancing security, data aggregation, or ensuring data availability. Alternatively, you can employ kiosks to describe multiple dimensions of an OLAP data cube. Nevertheless, bear in mind that both kiosks and OLAP data cubes will necessitate separate database setups.
Data in Online Analytical Processing cubes or data marts is organized into thematically related blocks of information and presented to end users through the user interface of BI tools. This arrangement facilitates descriptive analysis, benefiting the end user.
Modern BI tools offer various ways to present data as per user requirements. In the past, business intelligence was limited to generating static reports based on past and future events. However, today’s BI capabilities allow for the creation of interactive dashboards with customizable data elements. Nonetheless, reporting templates continue to be the preferred method for data presentation.
One of the most valuable ways to present information is through operational reports, also known as ad hoc reports. Live reporting empowers users to explore the details of a standard report using any relevant data for a specific one-time purpose. These reports replace daily or monthly reports with a more comprehensive version, as the user retrieves data directly from the storage (cube or data mart) at the time of viewing the report. This ensures the information’s freshness by querying the databases for each piece of data. Essentially, an operational report serves as a customizable real-time solution to address a particular business question.
To ensure a seamless employee onboarding process, we strongly advise conducting training sessions. These sessions can be diverse in format. If you utilize an analytics tool integrated into your CRM or ERP, you can employ onboarding practices such as how-to videos or interactive tools that lead users through each step methodically.
In case there is no budget for learning automation, the responsibility falls on the manager or members of the BI team to conduct the onboarding process.
In conclusion, as we approach the year 2023, the realm of Business Intelligence (BI) has undergone significant transformations, solidifying its position as an indispensable element in driving informed decision-making and sustainable growth for businesses across various industries. The evolution of BI in this era is characterized by the seamless integration of strategy, milestones, processes, and cutting-edge tools, forming a dynamic ecosystem that empowers organizations to thrive in an increasingly competitive and data-driven landscape.
In terms of strategy, BI has evolved beyond mere data analysis to encompass comprehensive insights that support long-term planning, market expansion, and identification of untapped opportunities. The strategic adoption of BI frameworks allows businesses to anticipate market trends, respond to changes proactively, and gain a competitive advantage. Milestones have become instrumental in shaping BI initiatives, emphasizing the need for measurable objectives and key performance indicators (KPIs) to gauge the success of BI implementations. Achieving these milestones validates the efficacy of BI efforts, helping organizations fine-tune their strategies and optimize processes for improved outcomes. Processes within BI have become streamlined and automated, thanks to advancements in artificial intelligence (AI) and machine learning algorithms. Real-time data processing, predictive analytics, and data visualization are now integral components of BI workflows, enabling faster and more accurate decision-making.
The proliferation of innovative BI tools has significantly influenced the landscape, empowering businesses to handle vast volumes of data and extract actionable insights. Self-service BI platforms, cloud-based solutions, and augmented analytics have democratized data access, enabling employees at all levels to harness data-driven insights without relying solely on data specialists. However, amidst this digital transformation and the promises of BI, challenges related to data privacy, security, and ethics have emerged. The responsible use of data and adherence to data protection regulations have become paramount to maintaining trust with customers and stakeholders.
Ultimately, embracing Business Intelligence as a strategic imperative, setting achievable milestones, optimizing processes, and leveraging the latest tools will enable businesses to thrive in an ever-evolving landscape. Those organizations that embrace BI as a transformative force will be better equipped to navigate complexities, make informed decisions, and remain at the forefront of innovation, driving success in 2023 and beyond.