Ana-maria's picture
Ana Maria Morales
tpariente's picture
Tomas Pariente Lobo

The survey is part of the analysis done by the project to bridge the gap between existing benchmarks by providing certifiable benchmarks of Big Data Technologies of high business impact and industrial significance

Madrid, Spain – DataBench project conducted a survey on 700 European Businesses in 11 EU Member States to evaluate the level of adoption of Big Data and Analytics solutions in different sized companies and identify the relevance of business KPIs for BDA users with the aim to ensure that the benchmarking metrics developed by DataBench are relevant for the European economy and Industry.

The industries participating in the survey were: Agriculture (9%); Banking (5%), Insurance (3%); Other Financial Services (3%); Business or Professional Services (3%); IT Services (8%); Healthcare (12%); Process Manufacturing (8%); Discrete Manufacturing (4%); Retail Trade (8%); Wholesale Trade (4%); Telecommunications (9%); Media (4%); Transport and Logistics (9%); Utilities (7%); and Oil & Gas (3%).

The level of adoption of Big Data and Analytics solutions was measured by adoption on business area, where Marketing, Customer Service and Support, Product Management, Finance, and IT and Data Operations were listed as the Top 5; and by adoption on industry, where Financial Services (43%), Business/IT Services (37%), Telecom and Media (37%), and Retail & Wholesale (35%) were identified as the main adopters of Big Data. 

Even though Healthcare (25%) and Agriculture (23%) presented lower levels of adoption, both demonstrate a high interest in using Big Data and Analytics in the future with 40.5% and 55.4% respectively.

In addition, the survey assessed the importance of business KPIs with the selection of 7 main categories measuring the most relevant business impacts. These were rated by surveyed companies according to the level of relevance for their business as Extremely Relevant (Product or Service Quality (55%), and Customer Satisfaction (53%)), Moderately Relevant (Time Efficiency (42%), and Revenue Growth (41%)); and Less Relevant (Increase of Number of New Products/Services Launched (34%), Business Model Innovation (30%), and Cost Reduction (25%)).

Regarding Business Goals that drive the adoption of Big Data and Analytics technologies, the survey demonstrated that the improvement on business process (41%), improvement of market understanding (43%), and product improvement (42%) are listed as the Top 3 motivations, while the improvement of compliance and control (38%), and improvement of fraud and risk management (38%), are considered as less important for companies.

The correlation between the business goals and KPIs is at the heart of the DataBench project, and with the survey, it was showed that KPIs such as improvement of product-service quality, revenues growth, and improvement of customer satisfaction, have a strong impact on most of the business goals. This leads to the conclusion that companies investing in BDA are reaping benefits contributing to the achievement of the business goals which led to the investment.

The report D2.2 of DataBench project is now available and presents the results of the economic and market analysis carried out in the context of WP2, with a preliminary assessment of the performance measurement metrics that companies are benchmarking to assess their use of Big Data and Analytics tools. This information will be also validated through the case studies carried out by WP4 of the project which will be presented in June 2019, including a list of 12 cross-industry use cases and 23 industry-specific use cases, representing the most frequent and potentially impactful typologies identified so far within the project.

For more information, go to the report available on the DataBench Website.

About DataBench

At the heart of DataBench is the goal to design a benchmarking process helping European organizations developing BDT to reach for excellence and constantly improve their performance, by measuring their technology development activity against parameters of high business relevance.

DataBench will investigate existing Big Data benchmarking tools and projects, identify the main gaps and provide a robust set of metrics to compare technical results coming from those tools.

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