We addressed this question to 20 executives from different companies. The result turned out to be quite predictable: the majority of respondents have a clear knowledge of business intelligence and possess completely different interpretations of “big data”.
Therefore, we are looking for the opposition, let’s assume the terminology.
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BI vs Big Data: The essence of ideas
With Business Intelligence, much associate software with an easy and intuitive interface that allows for manageable analysis of structured data – as a rule, they can be downloaded from Excel.
With Big Data, the situation is somewhat more complicated. When asked about big data, some talk about volumes and structure, while others talk about technologies and processes. Be that as it may, it is important to understand that big data is not a ready-made solution that can be bought, implemented, and successfully used without special skills and knowledge.
Expert Opinions
We are not the first to question the distinction. The most original in his vision of the situation was the technology consultant Erik D. Brown:
Less original, but easier to know, is the position of Alistair Croll from O’Reilly Media. He believes that the difference must be distinguished in three aspects.
Big data is designed and capable of working with larger quantities of learning than business intelligence.
Big Data is applied to explore and analyze rapidly changing, dynamic data. It promotes deep learning and interactivity. Sometimes the result can be obtained even before the page you are interested in has loaded.
Big data is intended primarily for processing huge volumes of unstructured information. We only understand how to use this data after we have been able to collect it.
Craig Baty, chief marketing officer, and chief technology officer for Fujitsu Australia, pointed out that market review is a descriptive method of analyzing the results achieved by a business over some time, while the speed of big data processing makes the report predictive, capable of offering business recommendations for the future.
The path to results
A simple addition of known values may bring results.
- For BI, for example, the situation is standard: the result of the addition of data on paid invoices indicates the indicator of the volume of sales for a certain period.
- When working with big data, the result is obtained in the process of cleaning it through sequential modeling: first, a hypothesis is put forward, a statistical, visual, or semantic model is built, based on which the correctness of the put forward hypothesis is checked, and then the next one is put forward. This process requires the researcher to either interpret visual values or compose interactive queries based on knowledge, or develop adaptive machine learning algorithms capable of obtaining the desired result. Moreover, the lifetime of such an algorithm can be quite short.
Application Objectives
BI is more suitable for analyzing the current situation. That is why users of any level may receive the information they require in real-time.
If a company wants to build a forecast, analyze data not only from internal but also from external sources, use various analytical methods and approaches, then Big Data will come to the rescue.
As you notice, Big Data and Business Intelligence cannot be considered identical or even synonymous ideas. These processes and techniques differ, and not superficially, but deeply applied in ml development services. However, similar goals perform technologies “allies” that get along well together and can bring many times greater business benefits when working together.
