In the modern supply chain, data analysis and business intelligence are extremely necessary and interrelated. In this article, you will find their relationship and their main characteristics, among others.
Where it all meets?
The connection between Business Intelligence (BI) and data analysis is especially true when managers have to deal with big data. This concept refers to a vast volume of information or data at a specific point in time and within a specific area. Furthermore, big data has a limited life span with a rapid decline in practical value, making it difficult for managers to follow up. In addition, big data has no limits in terms of type, shape, or scale, and its scope is too broad to be in a single research area.
5 Main Categories of Big Data
To define Big Data, 5 main categories are important, known as the 5 V’s, volume, velocity, variety, veracity, and value.
- Volume: the size and amounts of big data that companies manage and analyze.
- Value: the most important “V” from a business perspective, the value of big data often comes from the discovery of insights and pattern recognition that lead to more efficient operations, stronger customer relationships, and other clear and measurable business benefits.
- Variety: the diversity and range of different data types, including unstructured data, semi-structured data, and raw data.
- Velocity: the speed at which companies receive, store and manage data – e.g., the specific number of social media posts or search queries received within a day, an hour, or other units of time.
- Veracity: the “truth” or accuracy of data and information assets, which often determines executive-level confidence.
The data collection strategy may be more important than the data itself, as this is where the BI function comes in, as many companies have difficulty turning insights into actions.
BI and big data analysis
BI is a decision support system that encompasses the entire process of collecting large amounts of data, extracting valuable data, and providing analytical applications. In general, BI has three technological elements in common:
- A data warehouse that integrates an online transaction processing system.
- A database that addresses specific topics; online analytical processing that is useful to analyze data in multiple dimensions in order to use that data.
- A data mining, which involves a series of technological methods to extract useful knowledge from the collected data.
One of the essential functions of data collection and business intelligence is to situate how well the data collected matches the current reality. At this point, having several disintegrated systems providing data becomes a real nightmare, after all each source has its own methods of analysis. Therefore, it is necessary to integrate information systems within a single digital platform, so that all data speaks the same language.
How BI and data analysis can help you?
The main objective of deploying big data technologies in a company should be strategic decision-making. The ability to select the best models for operational processes is crucial to be more efficient.
In the area of sales planning, inventory management, operations and big data in the supply chain are capable enough to restructure the entire management process by connecting and interpreting internal and external data. While making demand forecasting more accurate and monitored in real-time.
On the supply side, big data goes beyond the observation of purchasing volume and spending, and supplier performance. Supply processes are tracked in real-time, enabling operational adaptation in case of delivery delays.
In addition, big data helps to calculate supplier risks, map the entire supply chain, and get ahead of the competition with strategic decisions.
Manufacturing processes are also enhanced by adopting this technology. They can be programmed to benefit from price fluctuations of items on the market.
Another way to use big data in the supply chain is to optimize space in warehouses, streamlining the movement of employees in their work routines.
In other words, there are countless possibilities to restructure all areas that involve the supply chain thoroughly. Big data has been growing exponentially for decades; in 2011 the market size was $7.6 billion, and now it is as high as $70 billion. The expected growth over the next five years is 47%, representing a $103 billion market by 2027.
Conclusions
Communication is one of the biggest challenges within logistics companies, as well as technological innovation and data collection. If you are wondering how to achieve them, we have here an integrated solution based on BI.
