Business Intelligence Cycle – Understanding the flow of business data is critical for organizations to effectively implement BI and empower senior executives to make more informed business decisions. Integrated with the latest BI software, business leaders can harness the power of change to gain a competitive advantage in today’s dynamic marketplace.
The business intelligence cycle consists of four stages: data collection, data storage, data analysis, and data access. This iterative process enables organizations to collect, analyze, and transform data into meaningful insights for informed decision-making that drives business growth and success.
Business Intelligence Cycle
By taking these steps, implementing BI advice, and adopting the latest trends, organizations are better positioned to unlock the true potential of their data, helping them gain a competitive edge in today’s fast-paced marketplace.
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Organizations can benefit greatly from implementing effective business intelligence, especially in areas such as customer understanding, product/service offering, and decision making. However, understanding the full role of BI is fraught with challenges that can be avoided by understanding the key elements of business intelligence and the various steps of the BI cycle.
Data gathering is an important step in the business intelligence cycle. This often involves gathering and collating high-quality data into the organization from a variety of internal and external sources.
All data collected may not be used or stored for future analysis. First, data engineers must determine what data is needed to meet specific business goals and ensure that it is always up-to-date, accurate, and of high quality.
There are various methods that businesses use to collect data, including surveys, interviews, data collection, IoT, workflow automation, and document automation. Advances in technology have made it easier to use automation techniques to collect data.
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Due to the rapid growth of data volumes and formats, enterprises must create the most suitable data management strategy to ensure proper management of high volume data of various types (structured, semi-structured and unstructured).
The quality of data collected greatly influences the next stages of the BI cycle, making data collection a critical foundation for gaining meaningful insights and making informed decisions.
A data warehouse also plays an important role in the BI lifecycle, as it provides a secure and centralized way to store collected and generated data for later access and analysis. It is important to provide an optimal data storage environment to facilitate future access and analysis to various technologies.
Various storage methods are used by organizations such as relational databases, data warehouses, data lakes, and cloud-based storage solutions. Each method is used to store data by type and quantity, and provides an efficient way to index and retrieve data to ensure it is consistent, up-to-date, and ready for analysis.
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Implementing strong data governance structures, ensuring data security, complying with regulations and complying with privacy regulations are all part of good data retention practices. Organizations can efficiently manage and store large amounts of data by establishing a secure data storage infrastructure. It lays the foundation for the next stages of the business intelligence cycle and enables fast and accurate insights that support smarter decision-making.
The ultimate goal of collecting, storing and maintaining high-quality data is to enable your organization to slice and dice the data it collects to make better business decisions.
Organizations use a variety of analytics techniques and tools to get the most out of their data, uncovering similar patterns, correlations, and trends. Data analysis involves cleaning and transforming raw data to ensure accuracy and consistency, then applying statistical techniques, data mining algorithms, and machine learning models to extract valuable insights.
This phase allows businesses to improve their KPIs, measure operational success, and gain a deeper understanding of customer needs and how to future-proof their business. It also not only analyzes past and present data, but also helps predict various future scenarios using predictive analytics.
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By harnessing the power of data analytics, businesses can improve operations, gain a competitive advantage, and take advantage of new opportunities for expansion and innovation.
In today’s data-driven world, the loss of sensitive data can have a significant impact on your business and lose customer trust. In 2022, data breaches dominated the headlines. Twitter, Microsoft and American Airlines have all been victims of data breaches, mostly through insider influence.
Therefore, access to data is the most important element of the business intelligence cycle, as it must be allowed to authorized employees. This step involves securing and controlling the various technologies used to obtain data and insights from your central repositories.
There are various data protection mechanisms that organizations can use, including role-based access controls and data governance policies to protect sensitive data and maintain data integrity.
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When organizations take accessibility seriously, self-service analytics can be implemented to allow your users to explore and retrieve relevant information without relying on the IT team. It provides speed and flexibility to enable stakeholders to receive information in real time.
With appropriate data access mechanisms in place, organizations can strike a balance between data security and accessibility, ensuring that stakeholders have the information they need to take strategic action and achieve business goals. We must carefully analyze sales and print data in our store. This type of data analysis is a form of business intelligence.
If there’s one thing in the world today, it’s data. At the heart of every information system is a database that collects transactional information. For example, what, when, how much, etc. bought it. It’s useful to know about the architecture of transactional systems so it’s not a complete mystery how data is captured.
However, it is important to know how to clean and analyze the data obtained to make management decisions. For example, after aggregating thousands of posts, we can find a product that sells well with women of a certain age in a certain area. This meaningful information can be implemented in terms of supply chain and marketing initiatives.
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If there’s one thing in the world today, it’s probably too much information. Transforming this data into meaningful information is a key skill. There are several tools for data analysis. These include spreadsheet programs such as Excel and database systems such as Access. Learning to use these tools will increase your marketability.
Most information systems projects are developed through an analysis-to-implementation lifecycle. The diagram below shows the steps we will cover in this chapter:
To demonstrate the power of summary data, we’ll first show how it can be used as a marketing tool for a website. Impressive statistics encourage repeat business. The same marketing principles apply to nonprofit organizations.
Kiva is a website that provides small loans (often less than $500) to entrepreneurs in developing countries. The small loans industry is called microfinance, which provides very small loans (often less than $500) to entrepreneurs in developing countries. Most loans are repaid in six months to a year. . Microfinance institutions are an important resource in helping Third World citizens escape poverty. Surprisingly, the repayment rate for the world’s poor is between 95 and 98%, which is higher than the debt repayment rate in the United States. More than 80% of Kiva loans go to women entrepreneurs. They put their income back into work and improve the lives of their families.
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Kiva works by pooling resources, so 50 people can each lend $10 for a total of $500. As part of its marketing efforts, Kiva maintains up-to-date performance data. For example, they reported nearly half a million lenders who loaned $161 million over the past three years. These quick facts are collected from websites and show job data after reviewing millions of records. The information not only serves a marketing purpose, but is also Kiva’s internal counter to track performance and influence decisions.
Kiva’s facts and history page is a business intelligence report. These statistics are updated every night (from 1 am to 3 am) under the heading “Latest statistics”. This is typical of business intelligence systems. Searching for millions of records taxes the system so much that these operations usually occur during peak hours.
Kiva’s example is a form of business intelligence that delivers accurate, actionable information to decision makers at the right time to support effective decision making. . Business Intelligence (BI) is the provision of timely, accurate, actionable information to relevant decision makers to support effective decision making.
That’s all we’ve done with this definition