Understanding the business intelligence cycle is crucial for organizations in order to have an effective implementation of BI to gain insights and equipe their high-level managers with the ability to make more informed business decisions. Coupled with the latest BI software, business leaders can harness its transformative power to gain a competitive edge in today’s dynamic marketplace.
The business intelligence cycle consists of four stages including data collection, data storage, data analysis and data access. This iterative process allows organizations to gather, analyze, and transform data into meaningful insights to enable informed decisions that drive business growth and success.
By following these stages, implementing BI tips, and adopting the latest trends, organizations will be better positioned to unlock the true potential of their data that will help them gain a competitive edge in today’s fast-paced market.
Let’s get started reviewing the 4 steps of business intelligence life cycle.
Stages of Business Intelligence Cycle
Organizations can reap numerous benefits from an effective business intelligence implementation, particularly in areas such as customer understanding, product/service launches, and decision-making. However, implementing the full role of BI is full of challenges that can be bypassed by understanding the key elements of business intelligence and the various stages it encompasses within the BI cycle.
The business intelligence cycle can be broken down into a 4 key steps:
1- Data collection
Data collection is the crucial first stage of the business intelligence cycle. It usually involves collecting and gathering high quality data from different internal and external sources to the organization.
Not all collected data can be utilized and stored for analysis in the future. First, data engineers should identify which data is needed to address specific business objectives and ensure that it is always updated, accurate, and high quality.
There are different methods that businesses use to collect data such as surveys, interviews, data mining, IoT, business processes automation, and automated document processing. The advancement of technology has made it easy to employ automation methods for capturing data.
Due to the high increase in data volume and formats, businesses need to create the most suited data management strategy to enable the proper management of high quantity data with different types (structured, semi structured, and unstructured).
The quality of the collected data greatly impacts the subsequent stages of the BI cycle, making data collection a critical foundation for obtaining meaningful insights and driving informed decision-making.
2- Data storage
Data storage plays also a critical role in BI life cycle as it provides a safe and centralized way to save collected and generated data for later usage and analysis. It is very important to provide the most suitable data storage medium to facilitate ease of access and analysis by different technologies in the future.
There are different storage methods used by organizations including relational databases, data warehouses, data lakes, and cloud based storage solutions. Each method is used to maintain data based on their types and quantity and provide an efficient way to index and retrieve data ensuring that it is consistent, up-to-date, and readily available for analysis.
Implementing strong data governance structures, guaranteeing data security, complying with regulations, and upholding privacy rules are all part of proper data storage methods. Organizations may efficiently handle and store large amounts of data by putting in place a solid data storage infrastructure. This lays the groundwork for the next phases of the business intelligence cycle and makes it possible to quickly and accurately gain insights that support deft decision-making.
3- Data analysis
The main objective of collecting, storing, and maintaining high quality data is to allow your organization to slice and dice collected data to gain insights and make more informed business decisions.
Organizations use different analytical techniques and tools to allow them to get the most out of their data by uncovering similar patterns, correlations, and trends. Data analysis involves cleansing and transforming raw data to ensure accuracy and consistency, followed by applying statistical methods, data mining algorithms, and machine learning models to extract valuable insights.
This stage allows businesses to improve their KPIs, measure operational success, gain a deep understanding of their customers’ needs, and how to bullet proof their business in the future. It also helps not only to analysis past and present data but also to forecast different future scenarios using predictive analysis.
Businesses can improve operations, acquire a competitive advantage, and seize new chances for expansion and innovation by utilizing the power of data analysis.
4- Data access
In today’s data driven world, losing sensitive data can have significant negative impact to your business and the lost of trust from your customers. In 2022, data breaches dominated the headlines. Companies from Twitter to Microsoft to American Airlines were the victims of data breaches and mostly were happening through internal exposure.
That’s why data access is the most crucial element in the business intelligence cycle as it should be only allowed to authorized personnel only. This step involves providing secure and controlled access to the data in your central repositories and different technologies that are being used to extract insights.
There are different data access mechanism organizations can employ including role based access control and data governance policies to safeguard sensitive information and maintain data integrity.
When accessibility is taken seriously within organizations, self-service analytics can be implemented to empower business users to explore and retrieve relevant data without relying on your IT team. This promotes agility and flexibility for concerned individuals to allow access to real time data.
With proper data access mechanisms in place, organizations can strike a balance between data security and accessibility, ensuring that stakeholders have the necessary data at their fingertips to drive strategic actions and achieve their business objectives.
