Global Market Outlook on Big Data Solutions

Market Trends – Big Data

Partnerships and acquisitions 

  • To drive innovation, business growth and orchestrate new service experiences within the big data and analytics product portfolio, many a companies are following strategies of partnerships and acquisitions
  • IBM acquired Vivisimo Inc. who is a leader in big data discovery and navigation software


Data archiving

  • Analytics of structured data can help enterprises to detect fraud, maintain security, provide personalized product to improve customer satisfaction and retention

Big data Analytics on the cloud

  • Big data cloud computing offers faster computing and affordable big data analytics solution
  • Most of the big data vendors are providing cloud big data solutions to serve their customers

More data scientists to get hired

  • Big data is booming and so is the data analytics solution. Hence demand for more scientists who can develop such data technologies are in demand

Predictive analytics

  • Predictive analytics will be adopted by most of the companies to identify the likelihood of future, based on historical data
  • Organizations can gain more insights and serve customer better by predicting their behavior

Big Data Drivers and Constraints


Increased data volumes

  • Everyday the volume of big data creation amounts to 2.4 quintillion bytes. By 2020, the amount of data generated is expected to reach 40ZB (zettabyte i.e. 270 bytes)
  • With the exponential growth in the volume, variety and velocity of data, there is a need for advanced data blending and analytics to derive actionable insights

Data warehouse optimization

  • Determine what data should be moved to the warehouse and offload obsolete data or less frequently used data from warehouse and databases using different big data software and tools, which results in data warehouse optimization

Demand for integration

  • Dealing with different types of data (both structured and unstructured) and integrating them requires Big data technologies
  • It is observed that real time integration leads to significant increase in ROI, particularly in a business where there is a large end consumer base


Data Security

  • Ensure Big Data Solution vendors comply with General IT Governance Standards (ITIL), standards specific to country and industry (HIPAA) and ISO/IEC 27000-series

Multiple products

  • Multiple vendors offering multiple products in the market. It is often confusing what to choose, due to lack of best practices

Increasing costs

  • Rising demand and growing operation costs results in price increase which acts as a constraint in this market

Porter’s Five Forces Analysis – Big data Industry

Supplier Power

  • The big data market is highly fragmented. Hence the supplier power is low
  • Big data has a high supplier base, ranging from global to regional vendors, who can cater to specific business needs

Buyer Power

  • The bargaining power of buyer is high since plethora of global and local suppliers operate along with start-ups to choose from

Barriers to New Entrants

  • Big data market has low barriers to entry due to low CAPEX and high availability of skilled workforce (especially the young data scientist engineers)

Intensity of Rivalry

  • Intense competitive rivalry exists as the vendors are competing to establish product and service differentiation and competitive advantage in the marketplace by providing data scheduling, warehousing, cleansing, analytics, consulting, managed services, support and training etc.

Threat of Substitutes

  • Threat for substitution is low in Big data market as it is cost-prohibitive for businesses to create their own infrastructure
  • It is much cheaper to outsource the analytical and visualization software for big data

Pricing Models – Big Data Implementation

  • To start with, buyers can go with fixed price contract, and can gradually move towards outcome based or transaction-based contracts, based on buyer’s maturity. Almost 80 percent of the contracts signed are based on fixed price
  • Cost of application maintenance is not based on the application size, rather it is based on the level of support required, incident volume, volume of defects, number of users, number of interfaces and number of transactions
  • Enterprises should be flexible in choosing an appropriate pricing model from phase to phase
  • E.g. contract can be structured as Time & Material for blueprinting phase and fixed fee for realisation, pilot and deployment phase