Data quality as a competitive factor

How AI-based automation of  procurement increases your data quality

AI and Big Data are increasingly being classified as drivers of new digitalised business models. However, there is often still a large discrepancy between what is touted in theory and what is actually possible in practice. Many employees in purchasing still do not have the right tools at hand to benefit from Big Data in their daily work. You can philosophise about the benefits and potential of Big Data, but you first have to get to grips with Small Data. After all, success through Big Data presupposes that the upstream technical processes for collecting and processing master or company data are right.

 

The role of data quality in procurement 

Data quality is increasingly seen as the key to the successful digitisation of processes and workflows in companies. In this context, data quality is understood to be the evaluation of data stocks concerning their suitability for fulfilling a specific purpose. Criteria for the evaluation are the correctness, relevance and reliability of the data as well as its availability in various systems. In most companies, procurement cannot fully exploit its potential due to insufficient data quality. The reasons for poor data quality in procurement can be found, on the one hand, in the fact that suppliers perceive product data quality as a cost factor and therefore give this topic only low priority and, on the other hand, in the fact that the manual input and reconciliation of data in operational procurement often leads to slowed down or faulty processes. Good data quality is crucial for successful operational processes. Also, data quality increases the productivity of the purchasing team. This means that data quality is increasingly becoming a decisive competitive factor.

 

The mass of data in purchasing

In every company – and every procurement department – vast amounts of data are collected every day. But having Big Data at your disposal does not necessarily mean being able to use it profitably. Due to the many heterogeneous systems and rudimentary IT tools, it is often difficult to derive benefits from high-volume and high-availability information resources, namely gaining deep insights and a basis for decision-making and process automation. That’s why it’s necessary to start at the source where data originates.

 

With AI to data quality in procurement

The stream of data can be made controllable and usable by replacing the laborious and error-prone manual interface directly at the data creation process with an automated one. This is where procurement must come in. There are now uncomplicated software solutions for this, such as the Netfira platform, which can capture, correlate, validate and process the usually unstructured data from suppliers with almost one hundred per cent accuracy. At the heart of this is innovative artificial intelligence. Therefore, the automation of operational procurement with the help of Netfira’s smart automation tool also indirectly leads to a cleansing of master data. For purchasing, this means a high level of accuracy, a considerable increase in speed, and thus more time for value-creating activities. These are all prerequisites for purchasing to position itself for the future and competitiveness within the framework of Industry 4.0.

It is essential to reduce complexity and enable agility. Not every software solution on the market does this. Buyers are often better served with special tools at the right time than with a broad compact solution that supposedly covers the entire process chain but proves to be rigid in everyday use. Flexible tools that are implemented as modules when the organization is ready for them can be recommended. It is also important that the solution can be easily and quickly integrated into the existing IT landscape and does not require any effort on the part of users, IT and suppliers. As a first step, the automation of operational processes,  which leads to drastically reduced manual, time-consuming and error-prone work, has to be a priority. As a result,  data quality can automatically be increased significantly. Only then can Big Data be used for Procurement 4.0.

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