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.

Google

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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