CATEGORY

Big Data Solutions

Beroe LiVE.Ai™

AI-powered self-service platform for all your sourcing decision needs across 1,600+ categories llike Big Data Solutions.

Market Data, Sourcing & Supplier Intelligence, and Price & Cost Benchmarking.

Schedule a Demo

Big Data Solutions Market Monitoring Dashboard


Supply Demand

Understand the correlation between costs, margins, and prices impacting your category on a real time basis on Beroe LiVE.Ai™

Big Data Solutions Industry Benchmarks


Savings Achieved

(in %)

The average annual savings achieved in Big Data Solutions category is 7.10%

Payment Terms

(in days)

The industry average payment terms in Big Data Solutions category for the current quarter is 75.0 days

Compare your category performance against peers and industry benchmarks across 20+ parameters on Beroe LiVE.Ai™

Category Strategy and Flexibility

Engagement Model

Supply Assurance

Sourcing Process

Supplier Type

Pricing Model

Contract Length

SLAs/KPIs

Lead Time

Supplier Diversity

Targeted Savings

Risk Mitigation

Financial Risk

Sanctions

AMEs

Geopolitical Risk

Cost Optimization

Price per Unit Competitiveness

Specification Leanness

Minimum Order Quality

Payment Terms

Inventory Control

Meet Abi

The World’s first Digital Market Analyst

    Schedule a Demo
    Meet Abi

    The World’s first Digital Market Analyst

    Abi, the AI-powered digital assistant brings together data, insights, and intelligence for faster answers to sourcing questions

    Big Data Solutions Suppliers


    24,860
    Total Suppliers
    1,520
    Diverse Suppliers
    48
    Normalized Supplier Rating
    Big Data Solutions Supplier

    Find the right-fit big data solutions supplier for your specific business needs and filter by location, industry, category, revenue, certifications, and more on Beroe LiVE.Ai™.

    Sample Supplier
    Company
    ORACLE CORPORATION
    Location
    Jackson, Mississipi
    Duns number
    3862211

    D&B SER Rating

    dnb logo

    Up to 3 months

    1 9
    4
    Low Risk High Risk

    The Supplier Evaluation Risk (SER) Rating is Dun & Bradstreet’s proprietary scoring system used to assess the probability that a business will seek relief from creditors or cease operations within the next 12 months. SER ratings range from 1 to 9, with 9 indicating the highest risk of failure. We’ve prepared an infographic to help business owners better understand what influences their SER Rating.

    Moody`s ESG Solution
    ESG Profile

    Company and Sector Performance
    44

    100
    Limited (1)
    ESG Perfomance (/100)
    Environment
    62
    Social
    37
    Governance
    46
    6 Domains Performance (/100)
    Business behaviour
    46
    Human rights
    47
    Community Environment
    87
    Corporate governance
    46
    Human resources
    27
    Security Scorecard
    55

    Threat indicators
    F
    50
    Network Security
    Detecting insecure network settings
    A
    100
    Hacker Chatter
    Monitoring hacker sites for chatter about your company
    F
    53
    DNS Health
    Detecting DNS insecure configuration and vulnerabilities
    C
    75
    Application Security
    Detecting common website application vulnerbilities
    F
    58
    Endpoint Security
    Detecting unprotected enpoints or entry points of user tools, such as desktops, laptops mobile devices, and virtual desktops
    A
    90
    Cubic Score
    Proprietary algorithms checking for implementation of common security best practices
    F
    48
    Patching Cadence
    Out of date company assets which may contain vulnerabilities of risk
    A
    100
    Social Engineering
    Measuring company awareness to a social engineering or phising attack
    F
    32
    IP Reputation
    Detecting suspecious activity, such as malware or spam, within your company network
    A
    100
    Information Leak
    Potentially confidential company information which may have been inadvertently leaked

    Industry Comparison
    oracle.com
    Industry average
    Adverse Media Appearances
    Environmental Issues
    0
    Workforce Health Safety Issues
    0
    Product Service Issues
    32
    Human Rights Issues
    0
    Production Supply Chain Issues
    1
    Environmental Non Compliance Flags
    33
    Corruption Issues
    3
    Regulatory Non Compliance Flags
    20
    Fraud Issues
    2
    Labor Health Safety Flags
    16
    Regulatory Issues
    15
    Workforce Disputes
    0
    Sanctions
    0
    esg energy transition
    56
    Discrimination Workforce Rights Issues
    16
    esg controversies critical severity
    No

    Big Data Solutions market report transcript


    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

    Drivers

    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

    Constraints

    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