The world’s most successful currency is neither the dollar, nor the euro, nor Bitcoins. It’s data. Every business and almost every interpersonal interaction is based on the exchange of data, figures or numbers. But not all data is equal. To create value, data must first be refined.
The Big Data World
According to a recent IDG study, in 2019, 57 percent of companies in Germany use Machine Learning (ML) and Artificial Intelligence (AI). For the automation of processes through IoT applications and AI, data is essential. If “Big Data” is understood as large amounts of data, these applications help us to evaluate the data jungle, recognize patterns and optimize processes on this data basis. With conventional methods an evaluation of the data would not be possible at all, because they are too complex, fast moving or unstructured.
However, the greatest challenge for many companies in the machine learning process is data quality.
Big data has five core characteristics, the so-called 5 V’s:
- Volume quantifies the scope / quantity,
- Velocity is the speed at which the data volumes can be generated and transferred
- Variety specifies the bandwidth of the data types
- Veracity defines the authenticity of the data
- Value determines the entrepreneurial added value that the data represent.
The process of maximum data value creation starts with the conversion from Big Data to Smart Data. In this process the data is cleansed; irrelevant and erroneous data is sorted out. This reduces the volume of data while increasing the value and veracity of the data set. Smart Data provides the AI with insights that enable it to automate and optimize processes.
Increased Benefit through Collaborative Machine Learning
Every retailer collects data – for instance, transaction data, article master data and inventory data. It doesn’t matter whether the retailer is a large clothing chain, an online shop for cuddly toys or the petrol station around the corner. But when it comes to collecting and analyzing data, traditional, smaller or very young companies are usually inferior to the big online players such as Amazon & Co. Since their data sets are smaller, they struggle to draw any meaningful conclusions from them. In addition, they often lack access to AI applications.
aifora offers these retailers a cloud-based platform for retail automation. On the AI platform, retailers can anonymously and securely share their data with each other, while complying with all applicable legal and data protection guidelines. With a larger data pool, the algorithms can more accurately predict customer demand and thus calculate the optimal prices and merchandise distribution for each retailer. aifora calls this process “collaborative learning”.
aifora’s Intelligent Price Automation (IPA) solutions optimize retailers’ pricing – for all channels and all products. The solutions take all relevant price influencing factors and individual price strategies into account and guarantee the highest possible level of efficiency. The application is complemented by the solutions for Intelligent Stock Automation (ISA). Factors such as the weather, recent purchasing behavior, special events such as Christmas and demand in other stores are taken into account. The benefit for the client: They can optimally distribute merchandise to the respective branches in line with consumer demand. aifora attaches great importance to maximum transparency and thus indicates the quality level of the Smart Data behind each decision made by the machine.
The size of the learning group is significant for success
The three steps from collecting Big Data to filtering Smart Data and combining to Shared Data are the key to success – across all industries. For collaborative machine learning in general, the more companies that feed their data into aifora’s digital ecosystem, the more comprehensive and precise the algorithm can adjust clients’ prices and inventory levels. This is why all aifora customers benefit from new members in the digital “learning group”.
