Everything You Want to Know About BI
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Over the past few decades, BI has evolved from a tool to a 'decision-making brain,' and the future will undoubtedly shift toward an 'intelligent decision-making brain,' or 'AI+BI.' In the next five years, BI will no longer be limited to multidimensional statistics of historical data.
When many people still don’t know what BI (Business Intelligence) is, they have already performed tasks within the entire BI workflow.
What exactly does BI do?
In simple terms, it involves a series of actions from data integration, data preparation, data analysis, data visualization, to data distribution and application.
These actions are merely processes; the real goal is to identify issues through the final data results to improve business decision-making.
Take an online education platform as an example. Every company employs roles like sales support or operations to analyze and report data on registrations, activity, first-time payments, repeat payments, VIP status, inactivity, and churn for their websites and apps.
The process of exporting and consolidating data from various platforms into an Excel sheet can be understood as data integration, while deduplicating and cleaning this data is a simple form of data preparation. Calculating conversion rates for each funnel using functions is data analysis, presenting the analysis results in visual charts is data visualization, and sharing these charts in a PowerPoint presentation can be seen as data distribution and application.
But here’s the question: Must data integration involve repetitive export and import tasks? How do you merge data from multiple systems with different structures? Beyond PowerPoint, what other ways can leaders view data? Imagine spending a week preparing a sales data report, only for the boss to suddenly ask about an anomaly in the data. Would you then have to create another report analyzing that anomaly after the meeting? How long would that take, and would the boss wait?
As a company’s data volume grows, the demands for deeper, more granular analysis increase, along with higher expectations for real-time and interactive capabilities. At this point, many tasks that manual reporting cannot handle can be addressed by BI. The value of BI isn’t just telling you what the conversion funnel looks like but explaining why the numbers are what they are and where improvements can be made.
The concept of BI (Business Intelligence) was first introduced by Gartner Group in 1996, but IBM researcher Hans Peter Luhn actually used the term as early as 1958. He defined 'intelligence' as 'the ability to apprehend the interrelationships of presented facts in such a way as to guide action toward a desired goal.'
At the application level, BI has gone through four developmental stages:
From a role perspective, the evolution of BI can be understood as progressing from a data analysis tool to a 'decision-making brain' deeply embedded in scenarios. Initially, companies sought to improve the efficiency of data analysis tasks, but later, the focus shifted to enhancing the efficiency and scientific rigor of decision-making, with results as the ultimate goal.
From Excel to cutting-edge intelligent BI, BI’s evolution has always been driven by market demands. Of course, advancements in big data, cloud computing, and artificial intelligence have also created more possibilities for BI’s development. The entire business world is moving forward, but companies in different industries and at different stages of development coexist, which is why various data analysis products can still thrive alongside BI’s progress.
Currently, the most debated choices are reporting systems, traditional BI, and intelligent BI. Selecting data analysis software first requires clarifying the company’s goals for adopting such a system. If a company’s data volume is modest and analysis is only needed to present final reports to departments—without aiding decision-making at various levels—then a reporting system may suffice. However, you must also consider whether to adopt BI when data volumes grow or when competitors’ market responsiveness outpaces yours. Is it necessary to make the leap now?
Naturally, if the goal is to empower decision-making—especially if you’re tired of delayed data access or unanswered questions in meetings—or if the company already has a forward-thinking data culture, BI is the way to go. Further, if you aim to simplify BI integration and development while reducing analysts’ high-code, repetitive workloads, intelligent BI is the recommended choice.
Intelligent BI can also be seen as 'AI+BI,' representing BI’s trend over the next five years and a field explored by many industry leaders and data analysis service providers. To undertake AI projects, companies must first have sufficient data, clear objectives, and medium-to-long-term plans. Only then is implementation advisable. Additionally, it’s crucial to partner with a big data analytics company with strong AI capabilities.
With the advancement of digital transformation, every company has accumulated vast amounts of data, but this is a double-edged sword. More data means greater potential value, but without robust analysis capabilities, it can become a barrier to efficient decision-making. In this context, BI naturally becomes a strategic weapon for companies to enhance competitiveness in the big data era.
A big data leader at Xiaohongshu, a social e-commerce platform, once said: 'Rapid growth can also be a headache. Under a near-exponential growth curve, the big data operations team faces greater challenges. Only with 60 times the data capability can they support 2 times the user growth and 30 times the data volume increase.'
BI has its own data analysis scenarios in every field. In consumer retail, these include business scenarios like products, stores, marketing, channels, supply chains, customer relationships, finance, and HR. Beyond retail, industries like internet+, manufacturing, e-commerce, finance, and healthcare also have their own analysis scenarios.
Companies can identify entry points based on their needs and gradually build an integrated, intelligent data analysis framework.
Over the past few decades, BI has evolved from a tool to a 'decision-making brain,' and the future will undoubtedly shift toward an 'intelligent decision-making brain,' or 'AI+BI.' In the next five years, BI will no longer be limited to multidimensional statistics of historical data.
By integrating with increasingly accessible algorithms and computing power, BI will enable more automated and intelligent data exploration, real-time alerts, future predictions, automatic diagnostics, and action recommendations. The user experience will also become more 'user-friendly,' emphasizing agility, ease of use, and industry-specific scenarios while continuously incorporating richer, finer-grained data sources to expand data-driven decision-making applications.
In the future, every company will need to build a data-driven decision-making brain, starting with BI and progressively upgrading with AI. Looking ahead three years while acting in three-month increments is a rational and actionable roadmap.