Why open source companies dont dance?

I have been pondering on this seemingly logical paradox for some time now-

1) Why are open source solutions considered technically better but not customer friendly.

2) Why do startups and app creators in social media or mobile get much more press coverage than

profitable startups in enterprise software.

3) How does tech journalism differ in covering open source projects in enterprise versus retail software.

4) What are the hidden rules of the game of enterprise software.

Some observations-

1) Open source companies often focus much more on technical community management and crowd sourcing code. Traditional software companies focus much more on managing the marketing community of customers and influencers. Accordingly the balance of power is skewed in favor of techies and R and D in open source companies, and in favor of marketing and analyst relations in traditional software companies.

Traditional companies also spend much more on hiring top notch press release/public relationship agencies, while open source companies are both financially and sometimes ideologically opposed to older methods of marketing software. The reverse of this is you are much more likely to see Videos and Tutorials by an open source company than a traditional company. You can compare the websites of ClouderaDataStax, Hadapt ,Appistry and Mapr and contrast that with Teradata or Oracle (which has a much bigger and much more different marketing strategy.

Social media for marketing is also more efficiently utilized by smaller companies (open source) while bigger companies continue to pay influential analysts for expensive white papers that help present the brand.

Lack of budgets is a major factor that limits access to influential marketing for open source companies particularly in enterprise software.

2 and 3) Retail software is priced at 2-100$ and sells by volume. Accordingly technology coverage of these software is based on volume.

Enterprise software is much more expensively priced and has much more discreet volume or sales points. Accordingly the technology coverage of enterprise software is more discreet, in terms of a white paper coming every quarter, a webinar every month and a press release every week. Retail software is covered non stop , but these journalists typically do not charge for “briefings”.

Journalists covering retail software generally earn money by ads or hosting conferences. So they have an interest in covering new stuff or interesting disruptive stuff. Journalists or analysts covering enterprise software generally earn money by white papers, webinars, attending than hosting conferences, writing books. They thus have a much stronger economic incentive to cover existing landscape and technologies than smaller startups.

4) What are the hidden rules of the game of enterprise software.

  • It is mostly a white man’s world. this can be proved by statistical demographic analysis
  • There is incestuous intermingling between influencers, marketers, and PR people. This can be proved by simple social network analysis of who talks to who and how much. A simple time series between sponsorship and analysts coverage also will prove this (I am working on quantifying this ).
  • There are much larger switching costs to enterprise software than retail software. This leads to legacy shoddy software getting much chances than would have been allowed in an efficient marketplace.
  • Enterprise software is a less efficient marketplace than retail software in all definitions of the term “efficient markets”
  • Cloud computing, and SaaS and Open source threatens to disrupt the jobs and careers of a large number of people. In the long term, they will create many more jobs, but in the short term, people used to comfortable living of enterprise software (making,selling,or writing) will actively and passively resist these changes to the  paradigms in the current software status quo.
  • Open source companies dont dance and dont play ball. They prefer to hire 4 more college grads than commission 2 more white papers.

and the following with slight changes from a comment I made on a fellow blog-

  • While the paradigm on how to create new software has evolved from primarily silo-driven R and D departments to a broader collaborative effort, the biggest drawback is software marketing has not evolved.
  • If you want your own version of the open source community editions to be more popular, some standardization is necessary for the corporate decision makers, and we need better marketing paradigms.
  • While code creation is crowdsourced, solution implementation cannot be crowdsourced. Customers want solutions to a problem not code.
  • Just as open source as a production and licensing paradigm threatens to disrupt enterprise software, it will lead to newer ways to marketing software given the hostility of existing status quo.

 

 

Analyzing Conversations on Twitter

If you are a marketing , analyst relationship, public relationship or a product manager who uses or abuses social media, you sometimes need to track what influencers and analysts are saying. A tool called Bettween allows you to capture public conversations between two influential (or interesting) tweeps.

See conversations between Neil Raden http://www.beyeblogs.com/raden/ and Curt Monash http://www.dbms2.com/ two noted BI gurus

http://bettween.com/neilraden/curtmonash

  • @NEILRADEN66
  • @CURTMONASH61
  • TOTAL MESSAGES127
  • SHARE CONVERSATION


unless Google decides to license its Wave technology to Twitter for separate encrypted , or public tweets. 🙂 They do share some history and employees (cough cough) or Twitter waits to create or better its public /protected tweet mode to be more granular

http://bettween.com/neilraden/curtmonash#statistics

tools to analyze Twitter conversations in SAS

Clustering Business Analysts and Industry Analysts

In my interactions with the world at large (mostly online) in the ways of data, statistics and analytics- I come across people who like to call themselves analysts.

As per me, there are 4 kinds of analysts principally,

1) Corporate Analysts- They work for a particular software company. As per them their product is great and infallible, their code has no bugs, and last zillion customer case studies all got a big benefit by buying their software.

They are very good at writing software code themselves, unfortunately this expertise is restricted to Microsoft Outlook (emails) and MS Powerpoint ( presentations). No they are more like salesmen than analysts, but as Arthur Miller said ” All salesmen (person) are dreamers. When the dream dies, the salesman (person) dies (read transfers to bigger job at a rival company)

2) Third -Party Independent Analsyst- The main reason they are third party is they can not be tolerated in a normal corporate culture, their spouse can barely stand them for more than 2 hours a day, and their Intelligence is not matched by their emotional maturity. Alas, after turning independent analysts, they realize they are actually more dependent to people than before, and they quickly polish their behaviour to praise who ever is sponsoring their webinar,  white paper , newsletter, or flying them to junkets. They are more of boutique consultants, but they used to be quite nifty at writing code, when younger, so they call themselves independent and “Noted Industry Analyst”

3) Researcher Analysts- They mostly scrape info from press releases which are mostly written by a hapless overworked communications team thrown at a task at last moment. They get into one hour call with who ever is the press or industry/analyst  relations honcho is- turn the press release into bullet points, and publish on the blog. They call this as research Analysts and give it away for free (but actually couldnt get anyone to pay for it for last 4 years). Couldnt write code if their life depended on it, but usually will find transformation and expert somehwere in their resume/about me web page. May have co -authored a book, which would have gotten them a F for plagiarism had they submitted it as a thesis.

4) Analytical Analysts- They are mostly buried deep within organizational bureaucracies if corporate, or within partnerships if they are independent. Understand coding, innovation (or creativity). Not very aggressive at networking unless provoked by an absolute idiot belonging to first three classes of industry analyst. Prefer to read Atlas Shrugged than argue on business semantics.

Next time you see an industry expert- you know which cluster to classify them 😉

Image Citation-

http://gapingvoidgallery.com/

Business Analytics Analyst Relations /Ethics/White Papers

Curt Monash, whom I respect and have tried to interview (unsuccessfully) points out suitable ethical dilemmas and gray areas in Analyst Relations in Business Intelligence here at http://www.dbms2.com/2010/07/30/advice-for-some-non-clients/

If you dont know what Analyst Relations are, well it’s like credit rating agencies for BI software. Read Curt and his landscaping of the field here ( I am quoting a summary) at http://www.strategicmessaging.com/the-ethics-of-white-papers/2010/08/01/

Vendors typically pay for

  1. They want to connect with sales prospects.
  2. They want general endorsement from the analyst.
  3. They specifically want endorsement from the analyst for their marketing claims.
  4. They want the analyst to do a better job of explaining something than they think they could do themselves.
  5. They want to give the analyst some money to enhance the relationship,

Merv Adrian (I interviewed Merv here at http://www.dudeofdata.com/?p=2505) has responded well here at http://www.enterpriseirregulars.com/23040/white-paper-sponsorship-and-labeling/

None of the sites I checked clearly identify the work as having been sponsored in any way I found obvious in my (admittefly) quick scan. So this is an issue, but it’s not confined to Oracle.

My 2 cents (not being so well paid 😉 are-

I think Curt was calling out Oracle (which didnt respond) and not Merv ( whose subsequent blog post does much to clarify).

As a comparative new /younger blogger in this field,
I applaud both Curt to try and bell the cat ( or point out what everyone in AR winks at) and for Merv for standing by him.

In the long run, it would strengthen analyst relations as a channel if they separate financial payment of content from bias. An example is credit rating agencies who forgot to do so in BFSI and see what happened.

Customers invest millions of dollars in BI systems trusting marketing collateral/white papers/webinars/tests etc. Perhaps it’s time for an industry association for analysts so that individual analysts don’t knuckle down under vendor pressure.

It is easier for someone of Curt, Merv’s stature to declare editing policy and disclosures before they write a white paper.It is much harder for everyone else who is not so well established.

White papers can take as much as 25,000$ to produce- and I know people who in Business Analytics (as opposed to Business Intelligence) slog on cents per hour cranking books on R, SAS , webinars, trainings but there are almost no white papers in BA. Are there any analytics independent analysts who are not biased by R or SAS or SPSS or etc etc. I am not sure but this looks like a good line to  pursue 😉 – provided ethical checks and balances are established.

Personally I know of many so called analytics communities go all out to please their sponsors so bias in writing does exist (you cant praise SAS on a R Blogging Forum or R USers Meet and you cant write on WPS at SAS Community.org )

– at the same time someone once told me- It is tough to make a living as a writer, and that choice between easy money and credible writing needs to be respected.

Most sponsored white papers I read are pure advertisements, directed at CEOs rather than the techie community at large.

Almost every BI vendor claims to have the fastest database with 5X speed- and benchmarking in technical terms could be something they could do too.

Just like Gadget sites benchmark products, you can not benchmark BI or even BA products as it is written not to do so  in many licensing terms.

Probably that is the reason Billions are spent in BI and the positive claims are doubtful ( except by the sellers). Similarly in Analytics, many vendors would have difficulty justifying their claims or prices if they are subjected to a side by side comparison. Unfortunately the resulting confusion results in shoddy technology coming stronger due to more aggressive marketing.