Market Analysis
published on Dec 13, 2016
Report Overview:
NelsonHall's market analysis of the big data and analytics services market consists of 74 pages. It provides an in-depth understanding of the dynamics at play in the big data and analytics market.
Who is this Report for:
NelsonHall’s “Big Data and Analytics Services: Consulting, Workshops, and Platforms” report is a comprehensive market assessment report designed for:
- Sourcing managers investigating sourcing developments within digital and in particular within big data and analytics services
- Vendor marketing, sales and business managers developing strategies to target big data and analytics opportunities
- Financial analysts and investors specializing in the IT services sector.
Scope of this Report:
The report analyzes the worldwide market for big data and analytics services. It addresses the following questions:
- What is the current and future market for big data and analytics?
- What are the client segments for big data and analytics, and their characteristics? What are the drivers, benefits, and inhibitors for each segment?
- What is the size and growth of the big data and analytics markets by client segment, geography, service line, activity and sector?
- How did spending grow in 2016 and how will it increase in 2017 and onwards?
- How is the market organized? Who are the main vendors? How can they be assessed and compared? What are vendor challenges and critical success factors by market segment?
- What are the offerings in the market?
- How are big data and analytics platforms shaping?
- What are the delivery capabilities of vendors providing big data and analytics services?
Key Findings & Highlights:
The big data and analytics services market is a dynamic market with solid market growth in the years ahead (2016-2020 CAGR of 7%). Client demand is articulated around two main events:
- A technology shift from traditional EDW technologies towards big data software services and the activities related to data preparation (including MDM, ILM, and data quality)
- A continued demand for analytics and insights, through the wider usage of data science.
Spending growth will be relatively homogeneous across geographies. Looking in detail, North America is fast adopting big data technologies, therefore phasing out previous investment in EDW and BI software and appliances. It is rebalancing its budgets towards insights.
The U.K. follows the same trend as North America. The uncertainly caused by Brexit will lead to slow spending growth during the 2017-2019 period, then rebounding to ~7.0%-9.0% from 2020 onwards. Growth in Continental Europe and Japan will be relatively muted, reflecting continued momentum in Germany and more mixed growth across France, Italy, and the Netherlands.
Cost savings and organizational improvements are important elements of the enterprise agenda. Approximately 30% of client spending is primarily aiming to achieve cost savings and is awarding mid- to long-term contracts, with delivery out of low cost areas, predominantly in India.
Another 35% of big data and analytics spending is related to enterprise operational efficiencies: clients are looking to improve the efficiency of their activities, either technology-related (e.g. decommissioning EDW systems and deploying big data), or business-led (e.g. improve the efficiency of digital marketing operations).
Regulated clients represent ~20% of big data and analytics spending. Regulated clients typically have a background in banking, insurance, life science, and U.S. healthcare. They award contracts initially based on the domain knowledge of their big data and analytics. Over time, they will transfer delivery from onshore to offshore, from a project based approach to a mid-term contract.
Finally, approximately 15% of big data and analytics spending is related to achieving a new business model reinvention. Contract dynamics are very different from clients with a cost saving priority. Contracts are initially short-term, onshore based, and consulting-led. They are highly strategic and sponsored at the CxO level.
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Big Data & Analytics
published 2017-01-27 | Project by Dominique Raviart