Executive Summary: ISG Provider Lens™ Analytics - Services - Germany 2021
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- ISG Provider Lens™ Analytics - Services - Data Lifecycle Management Services - Germany 2021
- ISG Provider Lens™ Analytics - Services - Data Engineering Services - Germany 2021
- ISG Provider Lens™ Analytics - Services - Data Science Services - Germany 2021
User attention towards analytics remains high. Organizations are beginning to realize the added value of advanced analytics over traditional reports and spreadsheets. Large IT consultancies and IT technology firms form the largest user groups owing to their strong marketing power. At the same time, most organizations face intense competition from newly established ones and the specialists already mentioned. Also, when there are historical or personal references, the main people responsible for special topics such as analytics tend to disengage quickly. In this way, enterprises can apply their strengths to large-scale projects. One of the main priorities of users is the availability of data across all departments.
Those involved in a task should be able to perform data analysis according to their role, from quick reviews of business process data to scenario analysis to analysis of enterprise-wide metrics. From a technological perspective, there are important trends for future projects. One of these include the move to modern development environments that can be developed by users themselves by using a self-help tool. Analytics environments should be able to deliver this capability. Some vendors already offer platforms with low-code and no-code development environments.
Technical innovations for building intelligent analytic environments include DataOps, machine learning operations (MLOps) and DevOps. Providers in this space should be able to help clints deploy IT environments on-premises and as multi-cloud environments and cross-enterprise data structures. There are also innovations in the implementation – end users in business departments should be able to collect data and present it for analysis through drag-and-drop, point-and-click, self-service, voice-driven AI and natural language queries. These are requirements that service providers should particularly address by providing a broad partner network for infrastructure, implementation and enforcement issues. Well-staffed IT service providers can partially offset the associated expense by deploying many employees.
When it comes to analytic applications, users are becoming familiar with terms such as data monetization, data democratization, and data orchestration. Enterprises want to know the worth of their data in purely pecuniary terms and which ones contain economic information. Although sometimes reluctant, enterprises are moving away from providing company figures to workers for gaining full control. The value of data is becoming increasingly important, both for a company's business processes and results as well as others in the partner network.
Enterprises leverage the expertise and on-site knowledge of their employees to significantly improve the value of analyses and classify them to develop solutions, giving them a competitive edge. Service providers should support this not just from a management aspect but for all levels through a strong presentation of key performance indicators. They should also master concepts such as RPA, hyper-converged infrastructure (HCI), cloud, AI and machine learning around the analytics solution.
Due to the COVID-19 crisis, several IT projects were postponed. Many enterprise clients were urged to virtualize workstations and set up home offices, leading to various ramifications. Some vendors have succeeded in helping their clients manage the impact of the pandemic by offering analytical solutions. Such services helped users to better analyze and take countermeasures against the risks, such as those posed by supply chain disruptions.
Compared to other countries in the European Union, Germany lags in digitization but is gaining speed. The market has been largely stable at the time this study was prepared. Large enterprises are ahead in their digital transformation journey, but many face challenges due to infrastructure issues. For example, real-time analytics can only be applied in logistics if vehicles are constantly on the grid. This also affects the market for analytical platforms. Companies have a considerable investment backlog, making innovation through analysis time consuming.
However, the market does not completely lack momentum. Startups in the enterprise and solution provider ecosystems are creating movement. The latter may overtake established large full-service providers with individual solution approaches rather than a comprehensive portfolio of solutions and services.
One of the main reasons for the rather stagnant market is the shortage of skilled labor, which is being met by the nearshore and offshore labor of companies with high staffing levels. Part of the service providers' income comes solely from the provision of staff. For example, one service provider stated that 4,000 employees were working on an (analytical) project for a German global player.
In addition, investors face various challenges that are being addressed through innovation support programs, though these are applicable to IT innovations to a certain extent. Analytical solutions can be partially funded under the Research Promotion Act.
In the analytics space, Indian vendors have a sizeable market share. Apart from them, other global suppliers have grown significantly and reached a high level in the application of standard technologies. They should now adapt to the specific needs of German users. The challenges for international suppliers are local staff, German as a negotiating language, and a better knowledge of the sector or the needs of German customers. Providers based in Germany took part in the study less often by actively providing information. The market analysis shows that providers generally offer timely solutions to clients and therefore have a strong market position.
Two key challenges still exist – the lack of skilled resources and the need to reduce workloads. In addition, company staff should be able to use data and receive information. Technical innovations include highly intelligent platforms and the convergence of DataOps, MLOps and DevOps, multi-cloud data integration and powerful data fabrics. From an operations perspective, the main issue is about involving users in specialty departments. This can be achieved by leveraging platforms that allow users to create analytical dashboards and more complex applications without programming skills.
The transition from a "data-driven" enterprise towards a "data-enterprise" is a new paradigm for analytic IT in the wake of digital transformation. An important prerequisite for this is a solid strategy. Without a well thought out roadmap, many analytical projects are likely to fail. Some users already have a strategy, but still face unanswered questions, such as where to start, what data to consider, or how the analysis will impact business processes and outcomes.
Organizations that choose to deploy cloud and on-premises platforms and software are often ahead in the data-driven enterprise culture than others that are still testing different service approaches. At the same time, many are often not focused on data strategy and consulting, which can be better addressed by technology independent service providers rather than focused technology, cloud and IT service providers. Service providers that have a strong consulting department or offer consulting for follow-up projects are also a part of this study.
As mentioned earlier, the pandemic has not only accelerated the pace of change in analytics but has also given companies and service providers the opportunity to prioritize investments and innovations to support this process.
Analytics used to play a role primarily in banks, insurance companies and financial service providers, and large corporations with complex issues around what-if analysis and risk analysis, New industries are now investing in analytics. Companies in the healthcare and life sciences, retail, consumer packaged goods and manufacturing industries have increased their investments in analytics solutions and services. Several service providers reported an increase in projects in these sectors, especially for data science and data engineering services. While there were some major projects such as pandemic genome sequencing, most of the projects were related to supply chain or distribution, commodity forecasting, commodity pricing, predictive maintenance, maximizing production yields, sales recommendation engine, segmentation engine and a few others. These investments help improve both revenue and the bottom line, making these companies more efficient through data-driven insights.
Long-time analytics users leverage large data warehouses for their tasks often in parallel. In the course of cloudification of their IT landscape, these users want to use the services of IT service providers for the operation of analytical applications. Users that have only been exposed to analytics in recent years are looking for practical and functional solutions that do not require them to manage their own data warehouses.
At the same time, providers should focus on staff management and employee motivation. The Young Professional Attractiveness Index 2021 (YPAI), in cooperation with the market research institute Kantar, has listed down the most popular employers in Germany. Of the top 10, only Google, SAP and Microsoft are represented in this study. This shows that most providers should take various initiatives such as providing clearly defined career paths, in-house training, salary levels, fringe benefits and others. However, international providers with a strong presence in Germany face a major challenge in these aspects unlike smaller local suppliers that are often further ahead. Smaller players often have long-term employees who can identify with the company culture and apply their strong industry knowledge to projects. Such companies focus on employee empowerment by eliminating micromanagement and micro-reporting and providing many benefits along with a pleasant work environment.
The top vendors in the data lifecycle management services market are Accenture, Alexander Thamm, Atos, Capgemini, Deloitte, DXC, IBM, Infomotion, Infosys, NTT DATA and PwC. Wipro was positioned as a Rising Star.
The leading data science service providers are *um (Orange Business Services), Accenture, Alexander Thamm, Capgemini, Cognizant, Deloitte, DXC, IBM, Infosys, STATWORX and Tech Mahindra. Blue Yonder is a Rising Star.
The following companies have been positioned as market leaders for data engineering services: *um (Orange Business Services), Accenture, Alexander Thamm, Atos, Capgemini, Cognizant, Deloitte, DXC, GFT, IBM, Infosys, PwC and STATWORX. Data Insights is a Rising Star.
Technical innovations for building intelligent analytic environments include DataOps, machine learning operations (MLOps) and DevOps. Providers in this space should be
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