Big Data A Broad Level M&A Strategy
Upcoming SlideShare
Loading in...5
×

Like this? Share it with your network

Share

Big Data A Broad Level M&A Strategy

  • 925 views
Uploaded on

With big data being a challenge for CIOs and CEOs in manufacturing, retail, healthcare, energy and finance industries, there is increasing demand for data-driven decision-making technologies that......

With big data being a challenge for CIOs and CEOs in manufacturing, retail, healthcare, energy and finance industries, there is increasing demand for data-driven decision-making technologies that enable companies to deliver value to both their customers and for themselves. This demand creates both opportunities and challenges for big IT vendors such as IBM, Oracle, HP, EMC and Microsoft to create value to their customers and investors. The following presentation are the efforts to explain CIOs, CEOs of Big IT vendors and other strategic investors to leverage opportunity in big data market from an technology investment stand-point. This presentation should support big IT vendors not only to enable their customer transform from traditional business intelligence (BI) platforms to operational business intelligence (BI) platforms, but also help them retain existing market share (BI) and gain competitive advantage in the big data market through strategically investing in pure-play big data vendors with innovative solutions.
Target Audience: CIOs and CEOs of Big IT Vendors like Oracle, IBM, HP, EMC etc. Additional audience include VC (Venture Capitalists) and other strategic investors in big data markets

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
925
On Slideshare
924
From Embeds
1
Number of Embeds
1

Actions

Shares
Downloads
28
Comments
0
Likes
1

Embeds 1

http://www.linkedin.com 1

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide
  • Descriptive notes for each slide is given in this area.
  • Slide Description: The slide describes the demand for technologies that can manage big data in a better way, which was not addressed by earlier technologies. Generation and availability of abundant data created from sales channels, R&D trials and customer care centers have left CIO’s with a demand to not only manage the data, but also to generate value (analyze data I a meaningful and efficient manner) from that data. Top concerns and challenges faced by CIOs in large organizations are highlighted in subsequent figures of the slide. The slide summarizes the need of the hour for a big data technology that can enable value from data by handling huge volumes of data, handle vaiety of data and process data at high speeds to derive meaningful insights.
  • Slide Description: This slides explains the CIO technology adoption roadmap indicating various technologies that are poised to impact organizations in a positive manner thereby generating competitive advantage in the industry. It explains key points about the evolution of analytics from department level to enterprise level to big data. The slide also brings out the depth of importance in implementing Big Data Analytics in organizations from a technology standpoint.
  • Slide Description: Since big data technologies are a emerging market, early investment in these booming markets not only addresses companies gain competitive advantage, but also creates a promising platform for the emergence of numerous pure-play vendors with innovative big data technologies. Each of the data captured are insightful and provide a broad level scenario for investors (large IT vendors and other strategic investors). Meaningful insights can be generated from the above investment data, so that opportunities in this market can be tapped early before the technology matures.
  • Slide Description: The big data market can be divided into 5 segments namely, next generation datawarehousing, non-hadoop big data platforms, big data-as-a-service, big data analytic platforms and applications, and hadoop distributions. An opportunity evaluation (can share the evaluation document, if required) was performed from an investment angle to provide investors a view on the attractiveness level and success each segment can offer. Meaningful insights are provided next to the grid highlighting the opportunities (immediate, long-term and short-term) associated with investing in each segments.
  • Slide Description: This slide is a bit complicated which can bring up numerous thought leadership when presented. The above chart highlights the location pure-play vendors (portfolios) of each big data market segment in the VC funding series along the Y-axis. Along the X-axis we place each market segment based on the bid data revenues generated by a big data vendor as a % of total revenues. The slide presents strategic investment options for top IT vendors that are interested in making a transition from traditional business intelligence tools to big data intelligence tools to both secure market share and also to create value for its existing customer base. NOTE: Data to create the above chart was generated from multiple sources, including the reliable sources such as IDC, McKinsey and Economic Intelligence Unit
  • Slide Description: The slide gives broad level data to implement M&A strategy in the near-term as a prospective investment option for Big IT vendors and other strategic investors like venture capitalists and business angels. Since 2007, the business intelligence market has witnessed huge consolidations driven by top IT vendors such as IBM, Oracle, HP, Micosoft, SAP, EMC and other recent heavyweights such as Teradata. However, with greater demand from BI customers and their solution providers to integrate big data platforms into their existing database, a consolidation trend similar to business intelligence market in foreseen. The slide also explains prospective investment targets in the big data segments from a M&A standpoint. NOTE: This slide is a broad level strategy targeting some big IT solutions providers such as Oracle, IBM, Microsoft, EMC, Teradata etc. It is also useful for venture capitalists interested in investing into emerging applications in the big data market segments. With more insights on client-specific problems and research, we will be able to support with more information and focus on strategy.

Transcript

  • 1. Big Data: Strategic Investment Opportunities for IT Heavyweights & Other Investors Arjunvasan AmbigapathyCase Topic Description:With big data being a challenge for CIOs and CEOs in manufacturing, retail, healthcare, energy and finance industries, there is increasing demand for data-driven decision-making technologies that enable companies to deliver value to both their customers and for themselves. This demand creates bothopportunities and challenges for big IT vendors such as IBM, Oracle, HP, EMC and Microsoft to create value to their customers and investors. Thefollowing presentation are the efforts to explain CIOs, CEOs of Big IT vendors and other strategic investors to leverage opportunity in big data market froman technology investment stand-point. This presentation should support big IT vendors not only to enable their customer transform from traditional businessintelligence (BI) platforms to operational business intelligence (BI) platforms, but also help them retain existing market share (BI) and gain competitiveadvantage in the big data market through strategically investing in pure-play big data vendors with innovative solutions.Target Audience: CIOs and CEOs of Big IT Vendors like Oracle, IBM, HP, EMC etc. Additional audience include VCs and other strategic investors in bigdata markets
  • 2. Technology for Big Data?: The Need of the Hour fradulant claims We have too much Clinical datasets claims data data, but too few There is lack of laboratory reports resources adverse drug reactions analytical skills to online create value from extensive Healthcare interactions data An Unmet Demand for CIOs electronic imaging instrumented Manufacturing production machinery medical records catalogs, stores Energy sales channels Big Data demand forecast Retail Explosion data retail purchase Financial labor history agencies We can’t get data product Media & Services information into right people tax filing Communication activities in the computer-aided design Our organization organization sales forecasts lack right skills to blogs multimedia content identified R&D and product effectively manage inconsistencies design databases data regulatory filings Top Challenges for CIO’s CIO Challenge Thermometer Demand for technology that can Reconcile Disparate Data process large volume of data Sources (Terabytes, Records, Data Lack of Organizational View Transactions, Tables/files) Quality/Accuracy of Data Accessing Risk of Data Volume Big Data Right Data Leaks Technology in Demand Data Timeliness of Value Security Data Variety Velocity High Data Management Costs StorageDemand for technology that Demand for technology that Capacitycan process various types of can quickly process data data (batch neartime, (Structured, unstructured, realtime, streams) semistructured, etc) 0
  • 3. How Enterprises Create Value from Data through Analytics? The Big Data Era! Departmental Analytics Enterprise Analytics Big Data Analytics • Initial data warehouse model and • Standardized data models • Clear data management strategy architecture • Database mining, high • Business analytics competency • Limited use analytical data due to performance computing and centers are established with data fewer business analysts analytical appliances scientists • CIO (Chief Information Officer) • Tech savvy analytical modelers and • Solve complex problems through level of engagement in data statisticians were used competency centers management is limited • CIO involves in data management • CIO plays a transformative role in • Few KPI (Key Performance strategies decision taken by the organization Indicators) in Revenue Generation • Significant impact in revenues • Frame new business strategy and were found were monitored and measured competitive differentiation based regularly on analytics In-memory Analytics Mobile Business Comprehensive Intelligence Applications: Analytics Cloud Computing Data Visualization Predictive Analytics across Industry Verticals Map Reduce Text Mining Advanced Analytics SQL-based Business Storage Solutions Intelligence OLAP Framework CIOs used Traditional Business CIOs focused Towards Data Storage CIO will focus towards Big Data Analytics Intelligence tools2006 2008 2010 CIO Technology Adoption Roadmap Source: Arjunvasan, Cisco Systems
  • 4. Global Big Data Investment Scenario (2009-12) E Total funding in Big Data The total investment in Analytics have improved Big Data technologies from $76.5 Million in have improved from 6 2009 to $700 Million in deals in 2009 to 25 2011 deals in 2011 Investor Inclinations Vs.N Top Big Data Technology S Top Beneficiaries Segments Hadoop Applications Big Data Analytics Platforms Big Data-as-a-service Investors are actively investing in technologies Non-Hadoop Platformsdeveloped by new market players Top Investors W
  • 5. Investment Opportunity Analysis for IT Solution Developers & other Investors Opportunistic Big Data Technology Segments Analyst Insights Opportunity Strategy Evaluation (OSE) Grid Big Data Analytics Platforms and Applications 10 With increasing demand among organizations to generate value from their existing abundant data, investing in Big Data Analytics Platform Developers is poised for success Non-Hadoop Big Hadoop Distributions Data Platforms Big Data VC funding has increased phenomenally in this market sub-segment withLevel of Attarctivess Analytic Next Generation Platforms & 266% increase in funding from beginning of 2008 to 2011. Cloudera, Data Big Data-as-a- Applications HortonWorks, MapR, Opera Solutions are few major beneficiaries in VC Warehousing Service funding with few portfolios in Series D. Companies focus on certification & Hadoop 5 Distributions training programs in big data Non-Hadoop Big Data Platforms Non-Hadoop Big Data Platforms have long-term (2-3 years) success assured, as far as the penetration of this technology is concerned. Companies in this segment have been attracting VC funding and from other investor sources. The recent IPO of Splunk has created huge waves in this market segment. 0 Next Generation Data Warehousing 0 5 10 The importance of next-generation data warehousing solutions is evident Probability of Success from the recent acquisitions of vendors (Vertica by HP in 2011; Greenplum Source: Arjunvasan by EMC in 2010 and AsterData by Teradata in 2011). This segment is more matured, unless new innovations emerge in future NOTE Investment opportunity analysis was performed based on analysis of each big data technology segments under the following factors: Big Data-as-a-Service • Level of Attractiveness: Sunk Cost, Demand from Industry Verticals, This market segment is poised to grow tremendously in future, as its Favorable Government/Regulatory Initiatives and Barriers to Market Entry implementation saves cost in the form of recruiting ‘data scientists’ and big • Probability of Success: Research Efforts, Challenges to Tackle, Criticality of data infrastructure costs. R&D investment and solving implementation barriers will increase the probability of success for investors Challenges, Funding
  • 6. Strategic Investment Options for IT Heavyweights & Venture Capitalists Current Funding Status Vs. Financial Performance of Key Portfolios Top Strategic Investors Strategic Investment Options Each Segment shows Big Data Revenues of pure-play vendors in big IPO With evaluated high probability of success, Hadoop Distributions Series DFunding Series (in Segments) vendors (such as Cloudera) can make best strategic investment partners in the near term (1-2 years). Big Data-as-a-Service vendors have potential to make big Series C wave in the Enterprise Software market, but funding is needed to improve few technical barriers Big Data Analytics Platforms vendors are strategic partners within the big data Series B data segment $0 - $50 million industry. They drive industry growth by partnering with vendors from other big data technology segments Big Data Market Segments Hadoop Distributions Series A Very few seed investments indicate that it is time to start investing Non-Hadoop Big Data Platforms in these technologies Big Data Analytic Platforms & Applications 0% 25% 50% 75% 100% Big Data-as-a-Service Revenue from Big Data as a % of Total Revenue Next Generation Data Warehousing Source: Arjunvasan, Wikibon NOTE: • Suggestions for strategic investments quoted in the above chart is based on performance of innovative, pure-play big data solution developers, level of funding and revenues. It is vital to ensure the suggested strategic investment fits well with your business model and customer demands • Big data innovations have been primarily from pure-play companies, which have lured investment in the form of venture funding and through IPO (Initial Public Offering). In addition to connecting with venture capitalists, it is also important to evaluate IP (Intellectual Portfolio) of each segment to make an informed decision
  • 7. Technology Transition from Business Intelligence to Big Data Intelligence M&A Strategy for Big IT Vendors 2007 Big Data market (shows consolidation rs Cranes 2008 trend similar to sto In ve Business Intelligence p Infrastructure Data Integration Business To Enterprise Resource Planning Intelligence Database Text Mining 2009 Content market (from Management Predictive Demands Transform Technologies! 2007 to 2008) Analysis •Predictive Analysis to help companies differentiate, compete and Data Qualty es succeed ogi R&D Data nol ech •BI solutions that address business specific and industry vertical issues Management To pT Risk •Independent performance layer that fits enterprise infrastructure Reporting Leveraging the Big Data Opportunity Data Mining Mergers & Acquisitions Traditional Business Operational Business2010 Intelligence Intelligence • There is a greater demand Technology Transformation Web for IT organizations to Analytics integrate Hadoop into existing Storage database to gain competitive advantage in the industry. Planning 2017 Analysis • With mature sales channels and support services, Cloudera and MapR Technologies could Charts be prospective candidates for Data Analysis strategic investment 2011 • Market consolidation is expected by 2017 and will be worth $50 billion 2012
  • 8. For more details:Arjunvasan Ambigapathya.arjunvasan@gmail.comTel: +91-9962361689