Qlever Tech, the service provider in the field of corporate data processing, announces the extension of the Retail specialization from Qlik, a world leader in the development of platforms for business analytics.

The industry specialization is given by Qlik for one year and confirms that Qlever Tech has developed a number of analytical solutions for enterprises engaged in retail sales to end customers. The practice of developing solutions for retail has been advancing in Qlever Tech since 2014 and since the company was founded it is the key one.

"By the example of our customers we can see how the Russian retail industry is developing - today the industry enterprises continue to receive investments, and competition is as high as nowhere else," Dmitry Kiselev, CEO of Qlever Tech, "in addition, the consumer behavior of customers is changing, they are using discounts and promotions more frequently, they are shopping online more often, their expectations from the level of service are rapidly growing. This makes it a very dynamic segment, retailers are forced to look for new ways to benefit from their data".

1. Business objectives: "quick wins".

The key feature of getting benefit from corporate data for retailers today is that market leaders no longer have analytical solutions covering not individual, but almost all key business functions. With the help of business analytics retail companies solve a variety of tasks, which are aimed at reducing operating costs and expansion.

Thus, over the past year, among the tasks addressed by Qlever Tech specialists for the customers from the retail segment, there has been an increase in the number of projects related to the consolidation of data from the key information systems, such as cash and ERP systems, with data generated by new solutions. Among the new solutions being implemented are systems for monitoring equipment operation, biometric identification of staff, video analytics for registering consumer behavior, and various mobile applications.

Thus, the IT development focus of retail companies is rapidly shifting towards the implementation of applications designed to address specific business tasks. One of the main criteria for launching such projects is usually a quick payback. The choice of initiatives and applications is typically very pragmatic and based on the assumption that tangible results will be achieved in a short time (the so-called "quick wins" approach). The key success factor here is the quality of analytical data. On the one hand, the use of analytical information allows you to embed a new application into existing business processes, and on the other hand, immediately assess the results of implementation.

"As soon as a company deploys a new business application, its data should immediately appear 'on the radar' of the analytical system," explains Dmitry Savvin, Qlever Tech data architect,"without this implementation is difficult to consider successful. The same is true for new partners and new sales channels. For example, after the retailer has agreed on delivery through Delivery Club, this sales channel should be immediately connected to the reporting".

Today the growing number of outsourcing or partner companies helping retailers to solve some specific tasks is becoming a noticeable trend in the retail segment. These may be tasks related to logistics, delivery of goods to end consumers, outstaffing, creating new sales channels or attracting customers. Retailers exchange data with such partners and they increasingly need analytical information - indicators that reflect the results of interaction with one or another outsourcer or help evaluate a new sales channel.

2. Technology challenges: from targeted analytics to self-service.

Among the technical challenges that customers in the retail segment expect from developers of analytical systems, there is an increasing tendency to create solutions capable of combining internal corporate data with external data, which commonly requires processing Big Data. This may be geodata, Internet of Things (IoT) - relevant data comes from connected devices, data from external information systems.

For example, previously a company needed to obtain some measure using a predefined algorithm. For this purpose, the analytical system turned to the data sources and received only the information required to calculate a specific indicator. This approach allowed to significantly reduce the number of necessary calculations and references to data sources, which made it possible to lower requirements to the data warehouse. At present, however, companies from the retail segment want to receive from the data such dependencies about which nothing is known. To identify them, the whole available data set has to be examined, because context analysis is required. The tool for such research is increasingly needed by business analysts or managers, not only specialists in Data Science.

The way analytical information is obtained from data is changing. Whereas previously retailers were limited to guided analytics, when business analysts and managers only had pre-defined indicators and dashboards available, today retailers increasingly want to add self-service capabilities to their analytics systems. Self-service implies that business analysts and managers have access to data for their own research, i.e. they can create metrics and visualizations themselves.

"Self-service is not a substitute for targeted analytics," Dmitry Savvin, "it rather helps accelerate digital transformation. While previously the metrics and indicators of business processes were proposed by external consultants or managers, today we are more likely to implement ideas proposed by local managers. Some of them turn out to be useful, others have to be abandoned. This is a continuous process, where development is based on the Agile model and new metrics are introduced very quickly. This way facilitates the involvement of people in the business and helps the company to maintain its leadership in terms of costs at any given time".

On the one hand, the transition from targeted analytics to self-service leads to an increase in retailers' requirements to the technical skills of project teams responsible for the development of analytical systems. On the other hand, it leads to a greater demand for data management training services.

"From the technological point of view, the main challenge is the exponential growth of analyzed data," says Dmitry Savvin, even the leaders will soon have to solve infrastructure issues and revise the principles of working with data. We share the common view of retail customers that in the near future their companies will start to profit from information obtained from external digital services".