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Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 1
Google Strategy Analysis in Big Data
An analysis by Ovidiu Ursachi
This paper is a personal educational assignment for my MBA study. It is based only on
public information and personal researches. This paper hasn’t been supported with internal
information nor reviewed by any Google representative. All ideas and comments in this
paper belong solely to the author.
This material is owned by Ovidiu Ursachi and protected by copyright law. It may not be
reproduced or redistributed without the prior written permission of the author.
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 2
The managerial question
“Big Data is the new definitive source of competitive advantage across all industries”
says an industry report from Wikibon and quoted by Gartner. If we look at the big
data market forecast from the same professional community, we observe a
predicted exponential increase of revenues reaching $53.4 billion by the end of
2016, 10 times more than two years ago.
Sector: Information Technology
Arena: Database Technology
Industry: Big Data
Market: Software Framework
Market Segment: Cloud Services
Table 1 - Industry
Definition for Google BigQuery
Figure 1 - Big Data Forecast 2011-2016
Google is active in this area, with its service BigQuery, part of the Google
Enterprise. However, with a turnover from Big Data of only $27 million, Google is
placed on the 32nd place on the Big Data Services ranking (Benzinga.com, 2013).
So how should be adapted the Google business strategy and respectively the
product strategy to acquire more market share in its battle with the other IT
giants like IBM, Microsoft, Oracle, Amazon, SAP, HP or even the big data pure-play
vendors like Vertica, Mu Sigma, Splunk and others who sale several times more
than Google does in the same industry? What are the product opportunities and
what are the synergies with the current Google’s current product panel,
resources and capabilities?
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 3
The Big Data background
Almost all current players in Big Data use the Hadoop technology which has its
origins “on some whitepapers that Google released in 2004, which introduced
MapReduce and the Google File System (GFS). These white papers showed the
world a form of technology that Google had been working on for a number of years
and that was quite established technology within Google” (Badcock, 2013).
However, it is Apache who adopted it and released it to the world two years later
while Google decided to improve it and created Dremel who stays at the base of the
current service, BigQuery. Meanwhile, Hadoop got traction and became a viable
platform supported by more than 80% of the Big Data players and their customers.
Adapted from Gartner, 2013 Figure 2 - The Gartner Magic Quadrant for Big Data (adapted)
The Gartner Magic Quadrant shown above displays the positioning of the main
market players, whereas their products in Data are seen as a single entity,
regardless to the delivery model: software, hardware or specific services like cloud,
data warehouse appliances and certified configurations. Although Hadoop is the
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 4
main product in the industry, the Apache Software Foundation – its provider – is
purposely not present in the quadrant while all Google's competitors compete on the
basis of this product.
The strategic vision framework
According to Schoemaker (1992) the strategic vision framework for the development
of the core capabilities, and implicitly of the business and product strategy, should
contain 4 main steps, listed below and detailed further:
1. Scenario analysis – generate broad scenarios of possible futures that
Google may encounter.
2. Competitive analysis – conduct a competitive analysis of the Big Data
industry and its strategic segments.
3. Capability analysis – analyze Google’s and competition’s core capabilities.
4. Management agenda – develop a strategic vision and identify the strategic
options within the management agenda.
Scenario Analysis
According to Brauers and Weber (1988) there are primarily two types of scenarios:
‘corporate scenario’ and those tailored for a specific organizational concern.
At the corporate level, “Google benefits from vertical integration in the Big Data
value chain, where it occupies all three positions at once” (Schönberger and Cukier,
2013) According to the same authors, the three potent ways to unleash data's
option value are basic reuse, merging datasets and finding “twofers”. They suggest
that Google BigQuery might have high potential in the opportunity evaluation with
strong strategic fit and portfolio synergies with the other Google products.
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 5
Figure 3 – Google BigQuery organizational fit
For the organizational concern, the time frame chosen for analysis is for the next 5
years, due to factors like: rapid development of the industry, services changes,
competitor time frames, and Google incentive to invest and use the resources.
The current Big Data trends (adapted from Lundquist 2013) and uncertainties for
major enterprise projects are highlighted further:
Big Data Trends Big Data Uncertainties the hybrid data cloud is used by 76% of CTOs for their development strategies
what will be the product demand for Google BigQuery and how this will evolve over Hadoop?
mobility is driving big data investment – what will be the prevalent Big Data
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 6
Google reached 79.3% of market in the third quarter of 2013 (BusinessWire)
initiatives? – Analytics, IT infrastructure or Enterprise Information Management
big data can surround and enhance existing applications like social networking
how trustful will be the Big Data – currently estimated that by 2015, 80% of data will be uncertain
the internet of things with 6 distinct types of applications (Chui et al, 2010) – see detail below
what will be the strength of economy and the rate of adoption for big data at enterprise level?
crowd sourcing as a content marketing tactic with revenues of $375 million in 2011 and almost 50 to 75% forecasted increase rate for the next years (crowdsourcing.org)
Table 2 – Big Data Trends and Uncertainties
Figure 4 - The Internet of Things with 6 distinct types of applications (McKinsey – Chui et al, 2010)
Based on these trends and uncertainties, 3 scenarios are developed, the list being
not exhaustive.
Scenario 1: Big Data Cornucopia with Hadoop
Hadoop has already become synonymous with Big Data and it is very unlikely that
this position will change in the next 5 years. Although Hadoop is already nested with
many tools and applications, BigQuery has the advantage of being much faster,
winning clearly in the benchmarks. Although very important in the decision process,
the high speed must be seconded by other important factors as cost, accessibility,
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 7
privacy, or human resources skills to be decisive in the decision-making process.
Thus, it is very probable that Hadoop will conserve its market share on the mid-
term, especially if the competitive intensity does not grow.
Scenario 2: Big Data Confusion in Intensified Competition
With increasing competition from Big Data products like Google BigQuery, Pregel,
and Percolator, Hadoop’s market share may erode while customers will be more
focused in choosing their data platform. Google can become aggressive in the
industry and may seek to leverage the position of BigQuery, especially among the
small and mid-sized customers. This must be supported by a good market analysis
and agility in the development of the BigQuery application environment.
Scenario 3: Big Data Conversion of Majority to BigQuery
Although the speed is the best competitive advantage, Google BigQuery currently
suffers from having a reduced number of tools as part of the Big Data ecosystem.
The BigQuery weakness of not providing the ability to drill into data, the data privacy
risks for the Google BigQuery customers, or the permanent increase of computing
power at lower costs are factors that help Hadoop keep its current customers.
Hence, it is more likely that Google BigQuery could win the game on a long-term
run, provided that Google will make some compromises either by providing
incentives to the market to contribute to the BigQuery environment or, more
dangerously, by partially disclosing the BigQuery architecture.
Competitive Analysis
Segments market size
The strategic segmentation splits Big Data market providers in three layers –
hardware, software and services – each one with its own clusters. These are
detailed in the table 3 below, including the main players and approximate revenues.
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 8
According to Wikibon, the Services and Software segments will expand further
reaching up to $40 billion by 2018. Therefore, many entries in the market are
expected, seconded probably by an increased number of acquisitions compared
with the past decade.
Customer / application segments
Panasas reveals 4 main categories for the customer/application segments:
engineering collaboration, simulation, data warehouse and analytics. They
correspond to the 4D's of activities using big data – design, discover, deposit, and
decide – and are detailed in the figure below.
Figure 5 - the 4 D’s of activities in Big Data
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 9
Relative to the customer size, the market is currently oriented to the big and mid-
sized customers. However, it is expected that many small sized companies will start
to use Big Data either for marketing strategies or for analyzing specific information
valuable in their niche products development. Thus, the current BigQuery strategy
orientation towards this type of customers should be continued, however through
more marketing and incentives for market to develop the Google Big Data
environment.
Competitor types and strategies encountered
The Big Data market is highly competitive, with many players in each segment, as
discussed above. Therefore, the competitive pressure is on both price and
innovation, whereas the permanent growth makes the industry hardly foreseeable.
Additionally, the big players have different strategies. For example, Amazon is
partnering with both SAP and Oracle, covering all the customer options for Big Data
in the cloud, while Microsoft concentrates its efforts on integrating its current
capabilities on database application with the cloud services and the massive
customer base it has on the enterprise side.
The privacy of data
Currently, the BigQuery users share their data with Google which minimize the
chances that very large sized companies will want to do. Schönberger and Cukier
(2013) come up with a control mechanism using 3 strategies on privacy protection,
human agency and big-data auditors, which, if adopted, “may serve as a foundation
for effective and fair governance of information in the big data era”.
Product pricing
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 10
Depending on the provider, the costs for using Hadoop whether in a Big Data
Appliance or using the “Do It Yourself” model start from at least $600,000 for a three
year model using a 64TB of data and includes software, support, setup and
configuration. For many customers, the problem is the big initial investment which
can be up to half a million dollars whereas in the Google BigQuery case the pricing
is based on query processing and storage. The following table compares Google,
HP and Oracle storage prices in Big Data:
Provider Year 1 Year 2 Year 3 Total 3 years Oracle (BDA costs + annual support + on-site install)
$518,150 $54,000 $54,000 $626,150
HP (Servers + IB Switches + Support costs)
$564,453 $72,000 $72,000 $708,453
Google (storage)
$25,344 $25,344 $25,344 $76,032
Table 3 – Big Data price comparison for Google, Oracle, and HP
However, the Google BigQuery charges an additional cost of $0.035/GB/query in
each column, which complicates the comparison. At an average usage of 4
database columns, the 2 prices form Oracle and Google break-even at about 4000
database queries a year, which are approximately 11 per day, a very low number for
big customers, making BigQuery unattractive from the price point of view.
Capabilities analysis
As Grant (2010) states, “Google's freewheeling informality with low job
specialization, emphasis on horizontal communication and emphasis of principles
over rules reflects its emphasis on innovation, rapid growth, and its fast changing
business environment”. The table below displays some of the most important
resources and capabilities that Google has and should use in the development of a
Big Data strategy:
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 11
Resources Capabilities The IT talent pool is high – Google employs some of the best worldwide software engineers.
High innovation rate – Google is recognised for its balance between creative freedom with discipline and integration
Very good capitalization – The BigQuery product is sustained by the company's management and strong capital may be used
High Big Data skills and availability, but currently mainly internally used
Hardware – high-class infrastructure architecture and readiness, with very good worldwide distribution
Strong Business Intelligence performance
Brand – Strong company brand, currently in the top 10 worldwide according to Interbrand
Very good workload optimization
Table 4 – Resources and capabilities at Google for Big Data
Further on, the following spider chart illustrates a comparative between Google's
BigQuery versus Apache's Hadoop capabilities:
Figure 6 – Big Data capabilities – BigQuery vs. Hadoop
However, BigQuery comes up with a different set of value differentiators at product
level versus Hadoop. They are detailed below, and compared further as key
competitive factors, in the value curve diagram.
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 12
Also, Hadoop became popular for its technology for processing Big Data like log
analysis, user activity analysis for the social apps, recommendation engines,
unstructured data processing, data mining and text mining (Sato 2012). BigQuery
helps in solving parallel disk I/O issues, leverages the disk I/O throughput, and is
highly valuable in appliances for OLAP like analysis of HTTP access logs or ad-hoc
queries.
BigQuery Hadoop
Interactive data analysis Programming framework to batch process large datasets
Speed and better capacity management Processing unstructured data
Cloud-powered massively parallel query service
Distributed computing technology
OLAP (Online Analytical Processing) / BI (Business Intelligence) use cases
Highly scalable
Ease of use Update existing data Table 5 – Value differentiators at BigQuery vs. Hadoop
Figure 7 – Value curve comparison at BigQuery vs. Hadoop
The management agenda
The strategic vision
According to Gratton (1996), the strategic vision begins by focusing on future
business aspirations rather than on current realities. The key success factors have
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 13
to be detailed and quantified for the strategic impact and aligned with the Google's
current capabilities. They may be classified in four main categories (Rockart, 1979)
with the following components:
1. Environmental
Big Data market growth
Big Data players mergers and acquisitions (see also the Google's strategic $700 million acquisition of Farecast, which is a data supplier for Etzioni, a company bought previously by Microsoft)
2. Industry
The product pricing
The privacy of data
Simple, understandable tools for customers (Barton and Court - McKinsey, 2012)
Information – provide access to multiple data sources in the Google own Big Data value chain
Models that balance complexity with ease of use
3. Competitive
BigQuery framework architecture and the product technical capabilities – performance, agility, responsiveness, scalability and data security
A descentralised, international organisational structure with a centralised Google R&D intellectual property and strong innovation capability
Product management supporting the management of change
Marketing knowledge of sales force
4. Time as critical factor in the market as a game (McNutt, 2010)
The strategic position
The strategic position is based on the current company’s very strong financials:
Financial ratio Value Net profit margin 20.46% Return on Assets 12.05% Return on Equity 15.55% Return on Investment 14.15%
Total debt/Total capital 0.0595 Table 6 – Google financial ratios (FT.com – Oct. 2013)
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 14
Therefore, the strategic position and action evaluation graph displays a strong
financial strength value, high data industry attractiveness, stable environment and
good competitive advantage. Thus, Google can move aggressively in the industry
and seek to continuously strengthen its position.
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 15
Figure 8 – SPACE Diagram for Google BigQuery
The strategic options
The technology used by the Big Data providers will likely become less relevant over
time, whereas the business value delivered will be more important for the
customers. Google may differentiate further through the 100% servers availability,
high security of data, and its high-performance computing asset.
However, the main question is – what are the strategic options in the further
development of the platform, as they are the key in the implementation if the
agenda. Some of them are discussed further, and are not exclusive.
Option 1: open architecture – BigQuery will follow the same track as Hadoop
This means Google will monetize BigQuery following the Android business model,
based on the number of installations. Although it might encourage an increase in the
development of compatible tools, and the overall development of the BigQuery
environment, the downsides are at corporate level leaded by the impact on Google's
competitive advantage in search. Thus, this option is rather undesirable unless the
innovation core comes up with an internal better capability, allowing BigQuery
architecture to be made public, as with MapReduce/Hadoop in the past.
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 16
Option 2: competitor / player acquisition – BigQuery gets more exposure to the
market
According to the worldwide Big Data revenues by segment forecast (IDC Report)
table below, two thirds of the revenues are expected to come from software and
services in the BigData arena. Therefore, it may seek to enhance control in the
market via vertical integration of one or maybe more current major players in Big
Data services (like Splunk, DataStax or other) or leverage the international
knowledge of the BigQuery tool using the Google Developers and I/O platforms
following the openSAP model from SAP. However, any acquisition should target the
achievement of complementary resources and capabilities and the increase of
visibility for the BigQuery platform to new potential customers.
Table 7 – Worldwide Big Data Revenues by segment (IDC report)
The acquisition of a current major player in Big Data professional services would
also horizontally sustain the Google Cloud services and improve the Google
BigQuery awareness among the customers of Data and Analytics.
Such a strategy would imply a high capital expenditure. For example, depending on
the evaluation on asset-based, earnings or cash-flow, th acquisition of a player like
Splunk would cost between $700 million and $1.5 billion.
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 17
Option 3: environment development – the BigQuery complementary tools
An important milestone in the product line is the conversion of BigQuery in a Big
Data environment. Google can use the similar experience from Android to attract
companies and developers in the development of complementary Big Data tools
and applications for BigQuery, to enhance the manageability of data and seize
market leadership. The company should create a partner program, which would
provide incentives for new developments and enhance attractiveness to the platform
for new customers and big data consumers.
The business scorecard and the value parallelogram
Since Google uses a policy of “autonomous business units” (Lacy, 2010), the
BigQuery team has the opportunity to run like an independent startup, where the
Business Scorecard (BSC) may be applied at the Level III – SBU, as suggested by
Kaplan and Norton (1996).
The business scorecard details the four perspectives in figure 10 below. The
information included starts from the above mentioned critical success factors and
critical competitive factors. Biazzo and Garnego (2012) suggest the so-called “value
parallelogram” to control its during the strategy implementation. Every factor in the
BSC is evaluated with a value from 1 to 10 depending on the level of goal
achievement. The values are initial, and they must be updated every month during
the supervising of the management agenda over the next 5 years. This way, the
SBU and company management should be capable to interactively control the entire
strategy implementation and the system itself.
The initial evaluation shows that BigQuery needs important efforts to achieve the
targeted objectives for the financial and customer perspective, whereas the internal
processes and the learning and growth perspective have better values due to the
Google Strategy Analysis in Big Data – © Ovidiu Ursachi – http://ursachi.eu – Dec. 2013 – Page 18
corporate influence – same human resources and processes approach company
wide, regardless to the project under development.
Figure 9 – The value parallelogram
Financial perspective
Customer perspective
Learning and growth perspective
Internal process perspective0
50
100
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Google Strategy Analysis in Big Data –ASM/8382322/Jul13/3 – Ovidiu Ursachi – Page 20
Conclusion
Grant (2010) suggests that diversification decisions by firms involve the same
two issues:
how attractive is the industry to be entered
can the firm establish a competitive advantage within the new industry?
In this paper we have seen that with an averaged CAGR of 36%, the Big Data
industry is growing sharply and will be part of almost all businesses in the next
decades, whether they are small, medium or big-sized. With expected worldwide
industry revenues of ~$50 billion, Google has to do more to gain market share
and adapt its product strategy.
We have seen that although Google is present on all the levels of the Big Data
value chain, BigQuery has the important role of linking the following sides:
the high amount of data in the upper side, provided by a high number of
Google applications and tools
the users gate to it through cloud services and (still reduced number) Big
Data specialised tools.
To build this, Google has the necessary resources and capabilities, and also the
similar experience from the development of the Android platform. However, the
customer segments are different and their needs have to be addressed in a
different manner.
Although price is very competitive, we have observed that at Google provides
economies of scale mainly for the small-sized customers, with a relatively low
Google Strategy Analysis in Big Data –ASM/8382322/Jul13/3 – Ovidiu Ursachi – Page 21
number of queries, whereas the BigQuery may become much more expensive
than the competition for the medium and big customers. Additionally, Google
has to tackle the issue of data privacy, which makes the BigQuery platform
unattractive for many potential customers.
However, with strong capabilities, Google can establish a competitive advantage
within the Big Data industry, provided that the strategic agenda will focus on
understanding the critical factors, identify and permanently asses the elements
of the business scorecard, and flexibly react to the changes in the growing
market.
Google Strategy Analysis in Big Data –ASM/8382322/Jul13/3 – Ovidiu Ursachi – Page 22
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