Team Data Analytics submission for Stanford Venture lab - OAP submission 1.

1. nickelring corp.
OAP – Submission 1
Team Data Insights
12 May 2012
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2. Executive Summary
Background
• nickelring is a team dedicated to the common vision of developing a meaningful data analytics solution for SME’s
• We have chosen to adopt a slightly different approach for the OAP, in that:
– We have deployed the survey to understand the current situation at SME’s (their needs and constraints)
– We are conducting a comprehensive competitor analysis to identify features being offered and gaps
– We intend to use the field-interviews to test our specific proposition
Key milestones achieved during the first two weeks
• Based on our research, we are convinced that Data Analytics is a large, growing business and that our target
market (SME’s) is of significant size (this is not a niche play) that is under-served
• The survey feedback (to date) validates our hypotheses that current systems are not “easy to use”, there is only a
average satisfaction with current systems and highlights interesting aspects that will need us to pivot
• We understand that “not listening to customers” is the key reason for most start up failures; we are taking steps
to ensure we listen and built our website (www.nickelring.com) clearly emphasizing this message to target clients
Next steps
• We are working on our value proposition – it is a challenging task to find a middle ground between advanced
data analytics and primitive reporting, which will drive the price point of our product
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3. Our process
Opportunity Analysis Project Opportunity Execution
Phases
Project
Proposition Development Proposition Finalisation
Key tasks  Team formation – Assignment of  Finalise proposition  Develop prototype
roles & responsibilities  Market test proposition with ~25  Develop service proposition
 Agree on communication tools potential clients  Develop marketing strategy
 Develop questionnaire & deploy  Pivot and refine proposition –  Develop distribution strategy
 Develop functional website develop detailed product
 Develop financial model
features
 Educate “diverse” team on  Outline operational risks and
fundamentals of “Data Analytics”  Determine price range
mitigation plan
 Conduct competitor analysis  Outline market level risks &
mitigation plan
 Develop high level market sizing
Outcomes  Initial feedback from potential  Finalised value proposition  Prototype
customers  Results from market testing  Cost benefit analysis
 Market sizing  Execution plan
12 May 19 May
Our approach is slightly different. However, given the complexity of the task,
we believed this to be the optimum approach
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4. Hypothesised market size*
Total number for firms in
Tier 1 the world - ~ 500 million
Total number of SME’s in the world
• ~90% of registered firms are SME’s
Tier 2 • ~450 million SME’s globally
SME’s who are active & material - ~ 90 mil
• There are ~20% are inactive SME’s and
Tier 3 40% are “mom & pop shops”
Target Market Analysis
• ~20% are sizeable firms with no critical need for Data
Analytics (DA) / Business Intelligence (BI) tool
nickelring • ~25% are estimated to have an existing solution
target • Profile of target client
market • Revenue p.a.: USD 1 mil – 500 mil
• Number of employees: 10 – 250
• Target market for nickelring – ~50 million firms
Our proposition is global in nature with a large target market!
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Source: Based on nickelring research and estimates
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5. Industry analysis highlights that this is an attractive market
Barriers to entry
Threat of new entrants
• This is a very lucrative market globally
with strong growth forecast (>15% pa
over the next 5 years) & will attract a
number of new entrants
• However, effective execution and
access to distribution channels is a
challenge
• Strength of threat: High
Supplier power
Supplier power Industry rivalry
Industry rivalry Buyer power
Buyer power
• Technology is continually maturing & • A fragmented market with – large • Typical SME management team will
hence there is a need to constantly adapt global players & niche / boutique firms have limited technology and/or
analytical resources
• Recruiting resources (especially for • Large players dominate big corporates
startup) is a challenge space but are weak in SME’s • SME’s will appreciate simple product
with good service at affordable price
• Funding will be a constraint, but not a • Niche firms lack scale & differentiation
point
show-stopper for unique proposition
• SME market under-served
• Strength of threat: Low
• Strength of threat: Medium
• Strength of threat: Low
Substitutes
• Limited substitutes to BI/DA tools for an
organisation to glean insights
• Excel is perhaps the most popular tool,
especially in the SME space
• Strength of threat: Low
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6. Competitor analysis
Competitive landscape1 Large players that service
big enterprises; Leverage
other products; Not suited
for SME’s (despite promise)
2
Basically reporting /
dashboard systems;
Low level
differentiation; highly
fragmented
• Trad. Players represent the larger firms &
“Open” represents smaller firms
• Analysis will enable nickelring pricing
Source: 1)Source: Lowering the Cost of Business Intelligence With Open Source
2) nickelring analysis based on competitor pricing information
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7. MUNIR & FILIP
Value proposition
Strategy Canvas*
High
Capability
Low
Value Attributes
By providing advanced analytics and choice for efficient deployment,
nickelring wishes to democratize the use of analytics
Concept: Blue Ocean Strategy
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8. Selected Survey results (Venture Lab participants only; N=20)
In which area do you or would you find the most What problems (if any) have you encountered with
benefit from a data analytics tool? your current tool?
Extensive focus
Sales improvement
on sales / Difficult to turn DA
marketing data into a real solution
Marketing Strategy
Usability
Operational
Interpretation of
data
Technology
Lack of
Planning & customisation for
forecasting different data
Ease of use is the
Finance Cost
primary concern –
Customer None costs come in last
relationship
If you are not using a BI/DA tool, it is because of Satisfaction rating of current tool being used
(1 – poor and 5 – excellent)
Reflects complexity
in deploying current
Lack of skill tools A startling response-
set with no scores
Not aware of tools, above 3
which can help me
Cost of tools
A gap in the market
They don’t provide (?)
an useful advantage
for the firm
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9. Risks and mitigations
Based on the analysis* of 32 startups that failed, the following were the top 5 risks that were identified for
new ventures. Here is how we plan to mitigate the risk
Key Risks Mitigating activities
Ignore customers • Deploying our survey to over 300 SME’s globally
• Conduct at least 25 face-to-face interviews with target customers
• Build relationships with 5-10 clients to test proposition on an ongoing basis
No market need • Our research and credible market reports (by IDC, Gartner, IBM) clearly indicate that:
– There is a need for data analytics globally and this is a growing market
– SME market is significantly under-served
– The market is large and profitable
Not the right team • We have a built a balanced team that consists of motivated individuals that are representative
of key organisational functions (Product, Technology, Marketing, Finance, Operations, etc.,)
• During the first two weeks the team members have displayed an affinity to high performance
and those who could not contribute found themselves self-selecting out of the team
Poor marketing • We are working on our marketing strategy in conjunction with product development
• The emphasis of the message will be on key value attributes – Simple, Secure & Easy to use
Need business model • We have a good grasp of our business model (that is being fine tuned) – Both in terms of
solution delivery and the economics of operations
• We will elaborate on this during the OEP presentation
Source: http://www.chubbybrain.com/blog/top-reasons-startups-fail-analyzing-startup-failure-post-mortem/
Note: We excluded “Ran out of cash”, which was the 5th reason, as it is not yet applicable for us.
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10. Next steps
• Finalize document for OAP submission (19 May)
– Consolidate survey feedback from enterprises
– Finalize proposition definition – including product features & points of differentiation
– Market test proposition through face-to-face interviews
– Fine-tune proposition – Pivot as necessary
• Commence work on OEP
– Technical foundation for the prototype is currently underway
– The marketing & distribution team is working on the positioning and brand message
– Financial model is being built
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11. Appendix
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12. SAJITH
Team description
Name Location Work experience Core competence Education
Alessandro Dublin 2 Years Software Engineering (C++, VHDL) BSc, MSc
Anthony Johannesburg 2 Years IT Entrepreneurship, Marketing and Finance Bcom (IT)
South Africa
Arnaud Tahiti, French 13 Years Marketing & Business development BSc, MSc
Polynesia
Bharath Singapore 2 Years Software Engineering, Database BSc Eng.
Administration
Deepak Singapore 8 Years Software Engineering, Machine Learning BSc, MSc, MTec
Techniques
Filip Antwerp Area, 10 Years Business Consultancy BSc Eng.
Belgium
Matej Slovenia, EU 10 Years Software Engineering, System Administrator BSc. Math
Munir NC, USA 15 Years PLM, Business Analytics (Data Mining) BSc. Eng , MBA
Patrick CA, USA 1 Year Software Engineering Reading BSc. Eng
Romil New Delhi, 10 Years Internet Marketing & Branding, E-commerce BSc (HM), MTM
India and Web development
Sajith Singapore 5 years Software Engineering, Data Mining, BSc (Hons) Eng.
Semantics
Sandeep Singapore 14 years Strategy Consulting Engineer & MBA
Business case development
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13. Why SME’s?
• Empirical research has highlighted that SME's currently have 4 critical technology needs
1. An accounting package
2. An email system
3. A website
4. A set of productivity tools (Excel, Word, etc.,)
• It is our hypothesis is that there is a fifth need and that is around the need to better understand their data -
predominantly financial and customer/sales data
• The systems that are currently on offer are
– Too technical to implement
– Reasonably expensive
– Difficult to change and are quite static
– Unable to handle multiple, unstructured data sources in an easy to use manner
– Not intuitive
• However, technology has now advanced such that cloud based solutions delivered in SaaS/PaaS format make
it affordable to develop a meaningful solution for SME's.
• Most software firms aim to service the large enterprise- there is a big gap in the SME space
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14. About nickelring
Why nickelring?
You can visit us at • Nickel is a chemical element with the chemical symbol
www.nickelring.com Ni and atomic number 28. It is a silvery-white lustrous
metal with a slight golden tinge (Source: Wikipedia).
What does that have to do with Data Analytics for SME,
you ask?
• The answer lies in the underlying characteristic of
Nickel. We all know that Nickel is an easily available
metal that was widely used in manufacturing… well a
nickel (US currency). However, did you know that
Nickel is also a key component of superalloys that are
used in the aerospace, industrial gas turbine and
marine turbine industry? Today, over 60% of the
global Nickel production is consumed in making nickel-
steels used as part of high-strength infrastructure.
Moreover, under appropriate treatment Nickel can
obtain the luster of silver.
• One simple metal – multiple benefits!
• Similarly, we are committed to developing a simple
solution that will provide you and your organization
with multiple benefits. We intend to indulge in a
virtuous cycle of listening to you and taking your
feedback onboard to develop insanely great products.
And hence the name – nickelring! 14
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15. Supporting data points – Data Analytics is a booming market (1/2)
More data required to support decisions – McKinsey 2011 Survey*
• Executives say their companies still rely upon a mix of data and experience in decision making, although
they are increasingly looking to analytics tools for support
• Despite the promise of big data to reshape strategy and decision making, more than 75 percent of
respondents to this survey report that their organizations’ greatest benefits from data use flow from clear
and timely reporting of financial and performance metrics
– Only about half say they seek to use data to provide new business insights or develop new
information-based products and services
• Respondents highlight three barriers to more effective use of data and analytics :
– A cultural preference for experience over data;
– A lack of skills in synthesizing and translating the analytics and data for decision makers and
– Concerns that the data quality is poor
Source: McKinsey & Co online survey conducted from October 4 to October 14, 2011, and generated responses from 927 executives.
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16. Supporting data points – Data Analytics is a booming market (2/2)
• Analytics is the application of computer technology, operational research, and statistics to solve problems in
business and industry*
• Analytics is a rapidly growing industry with a lot of existing and emerging players. According to IDC analytics
market will be $33.9 Billion in 2012 – growing at 8+% since 2011.
• Most of the advanced analytics market is currently owned by big players such as IBM, Oracle, SAS,
Microsoft, SAP, Microstrategy, etc.
• Most consumers of advanced analytics capabilities are medium-large size enterprises as these solutions are
resource-intensive in terms of hardware, software and licensing costs.
Excel still the predominant solution in finance – WesierMazars study of Global Insurance related firms 2011
• The WeiserMazars study found that 87% of the CFOs they surveyed relied heavily on Excel spreadsheets in
their financial close and also in their FP&A activities, as well as for budgeting and reporting.
Source: Wikipedia
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