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Big Data in Procurement

In today's digital age, data has become the lifeblood of industries, organizations, and societies. With the rapid advancement of technology and the exponential growth of information, we find ourselves amidst an era defined by the sheer magnitude and complexity of data. This is where Big Data steps onto the stage, offering unparalleled potential to unlock valuable insights, drive innovation, and revolutionize how we approach challenges across various sectors.

What is Big Data, and why is it relevant for procurement?

Using Big Data in procurement enables companies immense opportunities to gain valuable insights and save costs. In today's world, data plays an increasingly important role. In procurement, in particular, the processing of large volumes of data, also known as Big Data, offers a wide range of potential uses. Using Big Data in procurement can help companies make informed decisions, identify potential savings, and improve the efficiency of their purchasing processes. As a result of digitalization, data is increasingly being collected and is therefore available. This data ranges from social networks to measurement data in production to queries in search engines. Collecting, storing, and processing this data is becoming a crucial competitive advantage for companies. Big Data is characterized by large data sets that are not only characterized by a constantly growing amount of data but also by their diversity of data sources and data structures.

Big Data is often defined in terms of the five V's:

Types of Big Data

Volume refers to an extensive data set in the tera to zettabyte range. Variety describes the processing of unstructured, semi-structured, and structured data. Velocity stands for the speed at which data can be analyzed in real-time. The use of big data analytics is intended to sustainably increase the value of a company. Veracity refers to the quality of the data and the need for special algorithms to evaluate the data quality.

In procurement, data can be divided into structured and unstructured data. Structured data is organized in a specific format and can be easily stored in a database or data warehouse. Examples of structured data in procurement include data on purchase orders, invoices, payments, suppliers, prices, and contracts. Unstructured data, on the other hand, is stored in an irregular format and indeterminate structure. This includes emails, contracts, supplier reviews, social media posts, and market research reports. Processing unstructured data requires specialized technologies such as natural language processing (NLP), text analytics, or machine learning.

It is important to note that the definition of Big Data is not only based on the amount of data but also on other characteristics such as the speed of data processing, the value generated for the business, and the quality of the data.

The characteristic of Big Data lies not only in the quantity but also in the complexity of the data analysis.

Types of Big Data

Overall, the use of Big Data in procurement offers companies the opportunity to optimize their procurement processes, save costs and make informed decisions. By processing and analyzing large amounts of data, valuable insights can be gained that can lead to sustainable competitive advantage.

How to leverage big data in procurement?

Several studies elaborate on the relevant phases in the purchasing process in which Big Data has the greatest application. The result is Strategic Sourcing (elaboration of a procurement strategy, reverse marketing, and cost analysis) and the Sourcing Phase (supplier evaluations, negotiations, and selection).

Furthermore, it can be stated that due to the higher amount of available data, companies can make decisions faster and more efficiently. In addition, a greater amount of information can reduce the error rate, which leads to an increase in the quality of the procurement process. Through data and digital documents, contract management and supplier selection can be made more efficient and better in favor of procurement due to a better negotiating position. Another finding in the literature is that procurement performance can be increased by implementing Big Data. The result is improved internal procurement processes in terms of time and costs and, as a result, quality and flexibility. Supplier performance in terms of time, quality, innovation, flexibility, and sustainability is also improved by Big Data. Due to the larger amount of available data, suppliers can be better compared, putting the procurement in a better negotiating position. These developments are driving suppliers to rethink.

The Book "Big Data und Data Science in der strategischen Beschaffung" described 30 concrete use cases for Big Data in procurement. It is striking that around half of these measures fall into the area of the source-to-contract process. This correlates with other studies, which see the largest use in the first and second phase of the procurement (Sourcing and Strategic Sourcing) and less in the Supply phase. Scholars see the reasons for this in the fact that the source ton of the contract is the actual core process of the procurement and, accordingly, there, the largest chances for an increase in value lie.

What are the organizational and data requirements to leverage big data?

The requirements can be divided into the areas of data sources and integration, data analysis and visualization, data management, technical infrastructure, and human resources.

In the area of data sources and integration, various data sources should be used. These usually consist of internal sources, such as ERP systems, and external sources. The correct data integration is crucial so that a good database is created in a corresponding data warehouse. Using various data analysis and visualization techniques and with the help of data mining, important insights can be gained from this data, which can improve business decisions in the long term. When storing the data, however, it is essential in terms of data management, especially with regard to data security and compliance, to take into account the relevant data protection regulations. A remedy here is the exclusive storage of non-personal data so that, for example, the data protection regulations of the DSGVO are complied with. The technical infrastructure also represents a decisive factor in the implementation of the Big Data solution. Studies showed that outsourcing the technical infrastructure setup can achieve better results than setting it up independently. In terms of personnel requirements, based on the literature research and evaluation of various studies, the conclusion is that competencies such as good negotiation skills will be less relevant in the future, and technical competencies will become significantly more important. It is also essential that digital skills are developed within the team so that data can be interpreted correctly and the results communicated clearly. Here, continuous training of the procurement team can contribute significantly to a competitive advantage.

Examples of how big data can add bottom-line value.

Cost Optimization: Big Data allows procurement professionals to analyze pricing data, market trends, and supplier performance to identify cost-saving opportunities. By leveraging this information, organizations can negotiate better contracts, identify alternative suppliers, and implement cost-effective procurement strategies.

Risk Management: Big Data analytics can help identify and mitigate risks in the procurement process. By monitoring various data sources such as supplier financials, geopolitical factors, and market trends, organizations can proactively manage risks related to supply chain disruptions, compliance issues, and supplier reliability.

Sustainability and Ethical Sourcing: Big Data can play a crucial role in promoting sustainable and ethical procurement practices. By analyzing data related to suppliers' environmental impact, labor practices, and certifications, organizations can make informed decisions to support sustainable sourcing and ethical supply chains.


-      Big Data does not primarily refer to the amount of data, but also, among other things, such as the speed with which the data is collected and the added value for the company.

-      Big Data can be divided into structured (orders, invoices, supplier data) and unstructured data (social media, emails, contracts).

-      There are many values for procurement such as price trend analysis, supplier scouting and inventory optimization.


ivoflow is leveraging big data in procurement to provide its customers with a procurement-specific database that is connected to the ERP landscape and external market intelligence data sources.

Internal transactional and master ERP data, such as supplier master data, parts master data, and purchase order agreements, are imported automatically, cleansed, and centrally stored.

External procurement market intelligence data such as raw material pricing, logistics & duty, exchange rates, and energy costs are streamlined into the database and mapped to individual commodities or parts.

ivoflow enables strategic decision-making through real-time data analytics and advanced recommendations to get ultimate insights from the top of your spend into purchase orders and cost drivers.

intelflow AI-based Procurement Intelligence Platform

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