The contextual complexity around big data is increasing exponentially. Governance and regulation, data volume and data variety, the velocity of transmission and computation, and the potential for adoption of open source technologies are a few of the many challenges that companies are facing in the Big Data environment. In addition, digital transformation (DX) in Europe is starting to play a significant role as a basis to enhance the European market position and for a future gain in competitiveness.
According to a study carried out by IDC as part of the DataBench project, the European Big Data and analytics (BDA) environment is diverse and lacks a single and coordinated view.
Key factors driving big data and analytics in Europe
A primary driver that influences the differences in the approach to BDA is company size. The fabric of the European economy is mostly characterized by small and medium enterprises, which are lagging in effective BDA strategic adoption and selection of modern key performance indicators (KPIs) for Big Data benchmarking activities.
Within the SME sector’s cautious context, expectations and results are not clear and are mostly focused on cost reduction (which is regarded by SMEs as a relevant KPI to evaluate BDA efforts significantly more than among larger companies), rather than customer satisfaction and product service quality, which are the most selected in the broader European context
From the study it appears that the latter two KPIs are statistically less significantly regarded by SMEs to assess BDA impact.
A second factor driving a lack of a single approach to BDA investments among SMEs is the effort to achieve multiple business benefits.
Many European companies are seeking to achieve multiple sets of business benefits, rather than focusing efforts and investments on improving one benefit at a time. In doing so, SMEs risk losing sight of what is relevant and needs to be implemented first. The consequence is waste of time, effort, and investment on different projects which reduce the possibility of achieving the objectives targeted, especially considering the limited resources and skill set available within small organizations.
In addition, more than 60% of SMEs are simply storing data in data warehouses or data lakes and are not able to effectively obtain real-time insights. This situation can only intensify the laggard position of European companies.
For instance, only 16% of the European organizations are adopting real-time streaming of data which is used and coupled with other contextual data. Among the 16% of organizations adopting real-time solutions, less than one-third are SMEs (10–499 employees), which is 4% of the whole sample (700 responses).
From a less granular perspective, for most organizations real-time data processing is only an emerging need that will be tackled in the future.
To prove the point, survey data reveals that the most popular types of analytics are descriptive and diagnostic, less modern and advanced types of analysis that don’t generally require real-time streaming and analysis. Predictive and prescriptive analytics are currently considered as less important solutions by European organizations and in many cases not even planned for future deployment.
However, it is not clear why European organizations seek real-time analytics if they are not really interested in adopting more advanced types of analytics that enable deeper and more precise insights. This mismatch is primarily driven by:
- the common perception that not enough data is available and promptly accessible;
- the lack of clarity about the concrete chance to gain relevant insights from data and the real power of data.
Main key takeaways about BDA implementation
The first concluding remark is about SMEs. As the fabric of the European economy is characterized and sustained by SMEs, there is a need to help these companies tackle the Big Data challenge despite traditional thinking that has bound many of these organizations to traditional decision methods, as mostly gut feelings. Along these lines, some BDA solutions specifically tailored for SMEs’ needs are starting to appear. For the most part, these are packaged solutions with an accessible licensing fee but often limited in capability.
As a second remark, it is important to start BDA implementation with a clear view of the priorities among the possible benefits achievable with BDA usage. Once the objective to be targeted is clarified, it is possible to create a precise and definitive road map to achieve it. So what is needed is an initial strategic choice of a simple benefit, to scale to more complex benefits and potentially include more technology driven thinking in a second stage. With this in mind, European companies will be able scale not only objectives, but also projects and technologies, becoming truly data-driven organizations.
The DataBench project addresses the significant gap in the current benchmarking community’s activities by providing certifiable benchmarks and evaluation schemes of BDT performance of high business impact and industrial significance.
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