Instead of explaining a how to buid a product roadmap in generic terms, I thought I would explain how I would use consumer data to start to generate some hypotheses that could be tested, using banking as an example. Remember this is only the start of the process. …


Every day, informed readers evaluate the veracity of an online news article in several ways: checking the source site (is it reputable), examining the quotes in the article (is the quoted person real and, for public figures, does this person’s statement exist in the public record) and checking if the headline matches the story. A “yes” to each of these attributes give the reader reasonable confidence that an article is legitimate.

This “common sense” approach could be systematized as follows: Let’s start with the source site — does the content come from a site of high reputation/authority based on internal…


I recently read a great article on agile transformation in the Journal of Systems and Software, entitled — An empirically developed framework for Agile transition and adoption: A Grounded Theory approach. It was written by a group of computer science researchers out of Islamic Azad University, in Iran.

It details a data-centric development of a framework to create successful agile transformations. As you may or may not know, many agile transformations fail. The reasons are multiple:

  • the evaluation tools to determine if an organization is “ready” for agile transformation are too labor intensive and too unweildly to quickly show results

I found a great article on Quora about product management. One thing to add, you should learn to code, but accept you will never be anywhere close to the guys who build all day every day. Some basic know of how stuff works will help you better understanding what your engineering teams go through, and in doing so will foster goodwill that you can use when you really need them to pull through for you.

With that said here is the article from https://www.quora.com/profile/Phil-Wolff

Product management greatness and technical prowess are weakly linked. The minimum necessary:

  • Ability to speak with…

The original article can be found on TechinAsia, but here is a summary.

  1. There are lots of entrepreneurs with good ideas chasing big markets, but most can’t execute. Remember you are not investing in what you think you could do with a company, but what the team can achieve.
  2. There is a paucity of information on how to grow a business. So many founders don’t really know how to create sustainable organizations. There is lots of information on idea generation (Lean Startup), as well as how to run an established company (MBA or Corporate America), but not much in between.


Sometimes it is the people who disagree with us who can teach us the most.

A great and humbling article from someone who acted before they did their homework. Take home message: Always ask “why?” before passing judgement, and then fall on your sword when you screw up. Also learn from the experience.

I’ve always told my team making a mistake is not a sin, it is making the same mistake over and over that starts to rub people the wrong way.

http://www.inc.com/justin-bariso/why-i-was-completely-wrong-about-amazons-cto.html


Bernstein’s central argument is that manufacturing jobs are effectively disappearing globally, replaced by robots. China is leading the way but the trend is global and it means promises made by politicians like Donald Trump to bring overseas industries back to America are unlikely to benefit working people generally.

http://www.businessinsider.com/bernstein-china-robots-and-the-end-of-adam-smiths-wealth-of-nations-2016-12


From Venturebeat. Here is a summary, but you should check out the full article:

Bots currently have three significant barriers to break through to be able to solve customer issues:

  1. Natural language understanding (NLU)
  2. Integration with self-service APIs
  3. Decisioning in a wide variety of situations

The key is understanding context.

Context is itself very complex. It is made up of multiple facets:

  1. Close context — All the messaging around the current interaction, the customer profile and the companies offerings and policies.
  2. Near context — historical customer data
  3. Far context — Industry knowledge, and world knowledge.

The infant bots of 2016…


Fred Wilson from Union Square Ventures makes several great points in his posts on the Venture Capital math problem.

But instead of rehashing how the venture capital model needs fixing, I thought it would be interesting to use the data to shed light into how an venture funds are set up and the financial metrics that drive equity investments.

First lets talk about how a venture fund works:

Venture funds make investments into illiquid and risky investments with expectation of high returns over the long term. …

Jeevan Padiyar

Hi, I'm Jeevan Padiyar. Passionate about great products, skiing, paintball, cats and tech. CPO@NDR, but am a startup, venture and private equity expat.

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