So here's a somewhat flippant post on acronyms in India.  Feel free to add more in the comments section :)

Business

AGM = annual general meeting
ATL = above the line (marketing)
BTL = below the line (marketing)
CII = confederation of indian industries
DD = due diligence
DGM = deputy general mgr
FICCI = federation of indian chambers of commerce and industry
FMCG = fast-moving consumer goods
MD = managing director
MNC = multi-national corporation
PAT = profit after tax
SHA = shareholders agreement
SME = small or medium-size enterprise
TG = target group
TPA = third-party administrator

Education

CBSE = central board of secondary education
IB = international baccalaureate
ICSC = indian certificate of secondary education
IIM = indian institute of management
IIT = indian institute of technology
JEE = joint entrance exam
MCAT = medical college admission test
PG = post-grad
PGDM = post-graduate diploma in management
UG = under-grad

Financial Services/Payments

BFSI = banking and financial services institution
BSE = bombay stock exchange
CA = chartered accountant
COD = cash on delivery
CR = crores
CTC = cost to company
DD - demand draft
DII = domestic institutional investor
EFT = electronic funds transfer
EMI = equal monthly installments
FCNR = foreign currency non-resident (account)
FDI = foreign direct investment
FI = financial institution
FII = foreign institutional investor
IMPS = inter-bank mobile payment service
INR = indian rupee
MF = mutual fund
MFI = microfinance institution
NBFC = non-banking financial company
NFS = national financial switch
NRE = non-resident external (account)
NRO = non-resident ordinary (account)
NSE = national stock exchange
OD = overdraft
PF = provident fund
SIP =  structured investment plan
TDS = tax deducted at source
UTI = unit trust of india

Government

FM = finance minister
IAS = indian administrative service
MLA = member of legislative assembly
MP = member of parliament
NREGA = mahatma gandhi national rural employment guarantee act
NPCI = national payments corporation of india
PAN = personal account number
PIN = postal index number
PM = prime minister
PMO = prime minister's office
PSU = public sector unit
RBI = reserve bank of india
RTO = regional transport office
SC = supreme court
SEBI = securities and exchange board of india
SEZ = special economic zone
TRAI = telecom regulatory authority of india
UIDAI = unique id authority of india

Technology/Media/Telecom

AIR = all india radio
DD = doordarshan
BCCI = board of cricket control in india
IPL = indian premier league
CRBT = caller ring-back tones
DOT = department of telecommunications
GPRS = general packet radio service
IAMAI = internet and mobile assocation of india
LTE = long-term evolution (in telecom)
MVAS = mobile value-added services
NASSCOM = national association of software and services companies
OTT = over-the-top
PCO = public call office
SIM = subsciber identity module (card)
SRK = shah rukh khan
STD = subscribers trunk dialing
VAS = value-added service

Other

BHK = bedroom hall kitchen
BOP = bottom of the pyramid
CCD = cafe coffee day
IRCTC = indian railway catering and tourism corporation
MMT = makemytrip
NRI = non-resident indian
OPD = out-patient department
OT = overtime
OTA = online travel agency
PFA = please find attached (in email)
PP = please print
USP = unique selling proposition
VVIP = very very important person

Categories not covered here (for lack of space!)
Company names (eg ICICI, SAIL)
Movies (eg DDLJ, ZNMD)
Political parties (eg CPI, AIADMK)
Social/political segmentation (eg SCST) 
States (eg AP, MP, UP) 

A huge thanks goes out to the Jedis of Data: Russ Glass of Bizo; Jason Cavnar of Singly; Gil Elbaz, Eva Ho, Tyler Bell and Vikas Gupta at Factual; Miten Sampat of Quova; and Ivan Mitrovic of Where.com/Paypal; as well as my colleagues David Pakman and Brian Ascher for critiquing and suggesting content for this article. 

This decade will be the decade of Data.  Why? Because software development has become commoditized and super-cheap - the main dimension of competition has shifted to data and the intelligence that comes with it.  Secondary reasons for this are that we've never had so much data collected in the past; and the tools to analyze this ginormous amount of data have now become widely available.

I think large companies will be built around data that is dynamic or difficult-to-aggregate as well as have algorithms that have network effects, feedback loops and machine learning built in.  I call these closed-loop data businesses, adjacent to Big Data.  Adding closed-loop data to your product and business model can help your startup break through the software arms-race and margin pressure of a standard data business.

More on closed-loop data businesses further down in this post. Let's first start with a general overview of data businesses.  

DATA BUSINESSES

Typically, data businesses arise when:

1. companies realize that data is critical to their business but their own efforts at building a usable database will be too costly or time-consuming (e.g. governments/businesses needing to do geospatial planning but not having real detailed geographic maps) OR 
2. multiple competitors need to pool data in a neutral company to fix something fundamentally broken in their business models (e.g. credit bureaus to fix/address the problem of high delinquency rates) OR
3. new methods of data collection (e.g. networks of sensors like GPS devices or smart meters) and processing arise that are 10x faster/cheaper/easier than existing methods OR
4. new pools of disparate data can be exclusively or de-facto exclusively accessed for the first time

Strategically, data businesses have several advantages.  If one can build a network-effect-based data business, then it is hard for competitors to catch up because of all the data that has been processed that has in turn improved proprietary algorithms through machine learning.  Also, at scale, margins tend to be quite high (85%+) as data can be produced once and consumed multiple times with almost zero marginal cost.  This low marginal cost also allows data companies to give away free access to certain slices of data to drive initial customer value, drive paid acquisition and generate leads for paying customers.  And finally, once customers have incorporated this data into their own businesses, switching costs are usually high because of semi-proprietary formats (sometimes evolving into de-facto industry standards such as FICO scores)

There are challenges, though.  It usually takes a long time (sometimes years) to build out a big enough and accurate enough database - there is a problem of acquiring enough data to build out tuned algorithms and an accurate value-added database.  Ingested data changes (sometimes quite dynamically) and the value-added database needs to get updated quite often.  In the early days, labor costs are usually high in manually normalizing, processing and cleaning this data.  And sometimes data collection is not possible from third-parties and has to be done manually (Navteq famously spends upwards of $400 million per year on its van network to collect and process map data).  Because third-party data sources are many times paid sources, companies have to figure out how to scale their revenue without scaling data costs in line with revenue.  And finally, data businesses are usually further down the stack and therefore at the mercy of competitive or governmental forces threatening their customers. 

Some more recent challenges that have emerged: First, data businesses with direct revenue from their customers are now being threatened by data businesses that monetize through reach-driven advertising, not direct revenue.  Second, think carefully before trying to exactly replicate a US or Europe data business in an emerging market - in many cases, the sophistication of the specific vertical eco-system may be in earlier stages and markets may not have evolved enough to understand the value of nor respect data.  And finally, many consumer businesses have swapped or augmented advertising with data sales as a business model - having user data doesn't mean someone will pay a lot for this data nor that the data is at scale or is differentiated. (thanks, Brian).

Here are some examples of data businesses:

• financial Services (Equifax, CIBIL India, CCIS China, FICO, Bloomberg, Venrock-backed BillFloat)
• retail (Catalina Marketing, Venrock-backed RSi)
• location (Skyhook, Navteq, Factual, OpenStreetMap, MapMyIndia, Venrock-backed INRIX)
• search/advertising (Google search, Dataxu, Rapleaf, BlueKai, Venrock-based Bizo and Media6Degrees)
• healthcare (Venrock-backed Castlight and Kyru.us) 
• travel (ITA Software, Galileo, Sabre)
• social (LinkedIn, Bit.ly, Klout, Singly, Gnip)

Let's turn now to a special subset of data businesses:

CLOSED-LOOP DATA BUSINESSES

These businesses:

• collect data from third-party sources (and potentially their own proprietary application/sources)
• process that data with their own 'special sauce' i.e. proprietary rules and algorithms to produce value-added data
• (In some cases,) monetize by directly selling data to third parties. (In some other cases,) monetize by using the data to improve core product (like Google and LinkedIn do) or provide other value-added services such as an ad network or an application.
• receive data back from consumers of their data to aid in machine learning and data quality verification. 

Here's a rough diagram of the data flow in a closed-loop data business:

Feedback loops are the crux of the issue here: depending on the vertical you're in, you can spot these in the form of click-through rates, ground-truth testing, fraud rates, acceptance rates, user complaint rates, redemption rates, multi-source cross-checking etc.

In starting a data business, think about whether you can turn it into a closed-loop data business.  Here are some questions to ask yourself in doing so:

Data sourcing and normalization:

• Can you source some of your data from businesses that collect this data as a byproduct of their business?
• Can you source data from free and easily-accessible databases e.g. Tiger maps, info.gov, etc.
• Can you get exclusive access to some of this data?
• Can your find or buy orphan data businesses from other companies in your eco-system?  As a corollary, can you get other players in the eco-system to outsource their data function to you.
• Can you barter with data suppliers so you reduce your COGS and increase gross margins?
• Can you involve the community to explicitly or implicitly input more data and verify/correct the data?

Data processing & machine learning:

• Can you introduce feedback loops from consumers of your data? Can it be made automatic, rather than manual?  Can your algorithms learn from this feedback loop?
• How will you verify data quality and prove this to customers?  Different customer segments may require different levels of quality.
• Do you have data scientists (and perhaps algorithms) to model and produce value-added data?  Are you giving your data scientists the mandate to come up with new products? They are the only ones that can discover new products in data that consumer product managers typically don't see and can't plan for. (thanks, Ivan)
• Be ready to innovate on data pipeline and data storage technologies. Off-the-shelf "Big Data" technologies may not cut it. Example: LinkedIn's Voldemort project. (thanks, Ivan)
• What scale do you need to reach to have minimum viable database size?

Pricing:

• Does your pricing model reflect the business model already accepted by buyers in the industry? It should.
• Can you give away any lower value slice of the data to drive adoption and lower customer acquisition costs/barriers? (thanks, Russ)
• Are you pricing below other higher-cost data providers in your sector? You may or may not need to engage in price-based competition.
• Is the value in the data itself or in the application of the data?  Should you keep the data and build out the application instead to capture full value? (thanks, Vikas)

Packaging and Standards:

• Your value-added data is not one big monolithic product you sell to customers. Think about how you would slice this up to provide different levels of value to different customers (e.g. historical slices, real-time feeds, segmented data, different data delivery types).  
• Can you provide applications or value-added algorithms for specific segments of customers?
• Flexible APIs and tools will drive faster adoption e.g. provide sample code in all popular languages, write plug-ins for popular CMS programs, make the data available in popular formats etc.  Don't just provide a read API. Think about mandating a write in exchange for certain types of data that your customers read from you.
• Are you adopting industry standards for how your data is stored and transferred? In other words, are you on the side of open or on the side of proprietary? (thanks, Gil).
• Invest early on in visualization capabilities - your customers will need to see and manipulate the data before they understand the true value it may bring them. Your data scientists and data visualization should be able to produce not just charts but customer insights. (thanks, Miten).

[Data visualization from LinkedIn blog post on InMaps]

Legal:

• How will you protect your data from being copied?  Dynamic, fast-changing data is hard to copy; static/slowly-changing data is easier to copy or commoditize.  Map companies insert phantom geographic features/streets into their maps to track copying by other map database companies.
• Construct the terms of service for easy adoption and maximum feedback.  Also take into account residual rights on data produced by your customer's algorithms that has your data as an input.  OpenStreetMap was in limbo for a while with the change to an Open Database License (ODbL) from a CC-BY-SA 2.0 license. (thanks, Miten)
• Be especially careful on storing Personally Identifiable Information (PII) - preferably, don't do this. Also take into account different legal definitions and standards-of-use for PII in different countries.  Consult a lawyer on this one.

TO FINISH UP...

Congratulations to those of you that have made it through this dense prose!  You'll need some of this same fortitude to build your own data business!  

As you think about your new startup idea or how to put extra legs on your existing venture, really think about whether you can introduce closed-loop data into your product and business model.  In my opinion, pure data processing businesses have a limited shelf life and ever-decreasing margins whereas closed-loop data businesses have increased odds of breaking through.

Come talk to me if you have a world-changing idea in this area and need a partner-in-crime!  And go hire a mad data scientist!

[Photo from Cayusa]

(Photo by Stormwarning)

I read with interest Fred Wilson's article on "Mobile First, Web Second." from last August. I had written about this subject prior to that and like the direction in which our collective minds are going. However, I think we've all gone a little far in taking this concept and elevating it to: Mobile-Only is the Future.

Despite my enthusiasm about mobile, allow me to rant a bit on why the above may not be so:

---

By BS-sensor gets activated whenever I hear a 'Mobile is completely different. Don't try to stuff the Web onto a mobile device' pitch.  Or even a 'The future is mobile' pitch.

Ten years ago, I was on the other side of this argument, pitching how every website needs to be modified and optimized to be more usable on a cell phone (as these things were called at that time!).  And I was adamant that mobile experts were the only people who knew how to succeed with mobile technologies/startups.

But it's different now:  The Web has invaded mobile.  

• Gone are the days when a specific company's application could be the sole mobile-operator-nominated solution in a specific area e.g. mobile road navigation, mobile browser, mobile payment, mobile advertising, mobile sales force automation, mobile games, mobile commerce, mobile marketing, etc.

• Mobile and Web technologies have converged.  Think browser standards (HTML5, CSS, Javascript), advertising (IAB-standard banner ads, mediation, audience buying), standardized payments (card-not-present credit card payments), cloud computing (public APIs, storage), webcams (FaceTime, anyone?), social (Facebook Connect everywhere, Facebook/Twitter/LinkedIn sharing everywhere).

• And it's not just Web technologies making their way into mobile but mobile concepts moving onto the Web- think two-factor authentication on ecommerce sites, notifications/ SMS alerts from your bank, app stores for websites (Chrome web store), location-based personalization on your favorite web apps.

To be fair, there are unique features on  both sides like all the sensors on mobile devices and the massive real-estate on PC browsers.  And there is a must-do-it-now/can-do-it-now quality to anything available/usable on a mobile device as well as potentially some different usage scenarios because you are sometimes interacting with the 'real world' when on your mobile device.  And there are also some big gravity-wells in the mobile eco-system - namely Google, Apple, mobile operators aka ISPs, etc. - that don't behave quite the same way they do in the Web eco-system.

However, I think technologies and usage scenarios will continue to converge.  A page on mobile browser will be a page on a PC browser. A payment from a mobile device will be a payment from a PC.  Increasinly, one integrated backend system will aggregate content, access third-party systems, provide analytics, deliver advertising, manage security across Web & mobile. 

And this means that mobile-specific technologies may have a shorter window of opportunity that you would otherwise think, some shorter than others.

So pause, for a second, next time you hear 'Mobile is completely different' or 'Think mobile-first' and ask: 'Really? How so? And for how long?'

To put things in perspective, I thought I would do a little walk down memory lane:

Way back in early 2000, I thought that mobile had arrived. Little did I know that we were too early, too early by a decade.  

I co-founded a mobile app development platform company (Phonespan at that time, later renamed to Covigo) in early 2000. It was so early, in fact, that our demos were literally delivered on green-screen Motorola StarTAC phones (remember those!).   Our revolutionary (at least to us, at that time) demos included the ability to transfer money between checking and savings accounts on Bank of America and the ability to check eBay auctions, both from a WAP 1.0 phone.  The only problem was that in our pitches to VCs, our demos would never work because there never was (and still isn't) good cell coverage on Sand Hill Road.

Since then, the a lot has changed (except for cell phone coverage on Sand Hill Road).  A lot of the players in mobile are different, the apps are different, the business models are different, the go-to-markets are different.  However, a lot remains the same as well - app development in a heterogenous environment remains tough, mobile enterprise is still in its early stages (although picking up steam), carriers/OEMs still have some power to exert in distributing services (although far less than before), featurephones still dominate the installed base around the world and texting/SMS still dominates mobile communications.

To summarize this change, I've tried to list below some of the memorable differences and similarities between mobile in 2000 and mobile in 2011.  

I can only imagine how the mobile industry will change in the next ten years. Bring it on!

It will be an exciting year for us mobile enthusiasts who have seen each year be proclaimed as the year of mobile for the past decade.  Two mobile companies (Motricity and Velti) have gone public in the past few months.   I'm excited to see the next two go public: Epocrates - mobile healthcare solutions; and Boingo Wireless - global WiFi access network.  

Motricity, Velti and Epocrates all point to the strength of mobile software, content & applications, as opposed to what we've been used to seeing so far in the public markets i.e. access, device and semiconductor companies.  I think Motricity and Velti are precursors to many other mobile distribution/advertising companies that will grow big over time - perhaps including companies like Getjar, Pocketgear, Where (full disclosure: a Venrock investment), Millenial Media, Jumptap, Transpera etc.

All this activity just makes me more excited about the prospects for new mobile/tablet-based software startups in all areas - consumer apps, payments, ad-tech, security, enterprise, communications etc.  Dare I say that perhaps the '10s will be the decade of mobile and by the end of this decade, we will look back and wonder (i) why laptops/desktops did not disappear from our lives even sooner and (ii) how mobile has leveled the playing field across the globe for access to the Web & communications.

Anyway, back to IPOs.  The last mobile company to go public was Motricity (NASDAQ: MOTR).  Motricity provides a white-label content store for mobile operators to tell ringtones, wallpaper etc. MOTR has more than doubled in valuation since its IPO in June 2010, now trading at $650M enterprise value (or ~$800M equity market cap) at a TEV/2011 revenue multiple of 3.7x (TEV/2010 revenue multiple is 4.9x) and Price/2011 earnings multiple of 25x (Price/2010 earnings multiple is 51x).  2011 revenues are estimated at ~$175M, up from $133M in 2010.  Here's the Motricity prospectus.

Velti (NASDAQ: VELT) went public last week.  It provides mobile marketing solutions (ad creation, ad buying, ad serving).  The company is valued at $700M enterprise value (or ~$770M equity market cap) at a TEV/TTM revenue multiple of 5.6x and Price/TTM earnings multiple of 63x.  Revenues have grown nicely, from $90M in 2009 to $125M for trailing twelve months ended Sept 30, 2010.  TTM EBITDA is $39M.  Here's the Velti prospectus.

Velti is clearly more of an international play - only 2-5% of Velti's revenue comes from the US, with the bulk 75-80% coming from Europe and the remainder from Asia.  In addition, Velti has created JVs in China (Cellphone Ads Service E-Exchange) and India (HT Mobile Solutions).

Despite not having a large revenue base in the US, the company turned to the US to cheaply acquire key technologies (each for at or below $10M):

• Mobclix (mobile ad exchange) in September 2010
• Media Cannon (mobile ad creation tools) in June 2010
• Ad Infuse (mobile ad network) in May 2009