For those of you who don’t know yet, I have shifted tracks to heading a tech team in a start-up. This firm focuses on helping first time home buyers with the largest hurdle in home buying, the down payment.

At Homeville, one of the immediate challenges that I had to face was to understand a myriad of requirements from speaking to the operations team, to the business analysts, to the developers, to some of the customers and even to some of our investors.

Since, the approach is that of a technology platform, it also means that the team had to start worrying about multiple systems all at once. Deciding to move away from one huge monolithic system to a micro-services based architecture was natural.

How does one manage loads of Micro-services?

A major challenge with a spread of micro-services was that the management overhead of systems went up. Different services were in different repos, in different languages and hosted in different methods. Yes, there was an API gateway on top to present a uniform access method for all, but the code management and documentation was a challenge.

Thankfully most popular versioning systems have solved the code management issue. One of the first steps I initiated with this was using the README.md to quickly jot down what the service is supposed to do, and how it functions. This was created more from the point of a new team member who wants to get started with the respective service. You need to be comfortable with Markdown for this. I’ll get to markdown in a minute, but this was a great starting point for me to understand what a developer really needs in the documentation.

As a person overseeing multiple services, it was essential for my team members to quickly pick up the bare essentials and use the documentation available. Having a small entry point in the repo is a perfect way to give access without creating too formal a structure. My choice of working with markdown was made.

What is Markdown?

In case if you do not know what this is, then you mostly haven’t edited a wiki. Markdown language is a super lightweight language that allows one to quickly convert the text into a rich formatted document (such as HTML, PDF, etc). To read more about this, head on to the Wiki on Markdown.

Try practicing using Markdown for some time and you will realize its almost as simple as using notepad or gedit to take down your notes. It also helps you to create a more complex structure and is super flexible for future use-cases.

Generating a usable README.md

For those of you who want to try this out, hop on to Make a README and see the basic placeholder sections needed to make a developer friendly file.

I had by this time quickly written these files and was happy that at least I had some formal documentation available in a system that was fast growing. A side note here – In most rapidly evolving systems, people often take decisions that they regret later on. This technical debt although is meant to be avoided, but often it just can’t be avoided. As long as you are willing to come back and clear the debt, it’s fine. You could re-think your approach and do it faster in a correct fashion – but then you need to be a lot more mature and I just don’t see that developer maturity yet. This side note will need to be expanded into a separate post of it’s own

What to do with a cart load of README.md files?

Quickly, I had many individual standalone files sparsely connected to each other. While this was sufficient for a developer to get started, this did not fully cover the breadth and width of the system.

This is where my past experience of working with the WordPress India community helped. The community is building an independent document made of such .md files using gitbook. Gitbook used to be a CLI based command that you could install on your machine and use to build a developer website. This using the very .md files that I now had.

At the time of writing this post, the gitbook CLI is available on npm, however, do note that the site now talks about a version 2, which is not a CLI based offer but is more of a SaaS product with a freemium offering. You could also look at some other alternatives to do this, but the ease of use of the gitbook CLI is to be applauded.

How to get started with gitbook?

1. Head on to the npm page for gitbook-cli and install this first.
2. Create a new folder and in the console hit gitbook init
3. Answer the questions and create your first markdown file
4. In the console hit gitbook serve and in your browser go to http://localhost:4000

That’s it

Core concepts

Keep in mind the following things –

• The SUMMARY.md maps to the sidebar on the left hand side. This can be styled and the content of this file pretty much decides the navigation of your gitbook
• gitbook is extendable through the config file – book.json, not just in look and feel, but also using plugins. My must plugins are – [“collapsible-chapters”,“insert-logo”,“image-captions”,“tbfed-pagefooter”,“copy-code-button”,“ga”,“sitemap”,“mermaid-gb3”]
• Create sub-folders for different modules/services
• Have a list of all entry points in SUMMARY.md
• Maintain a CHANGELOG.md to have a history of major changes made
• When a particular module becomes more complex, divide that into more parts and put those parts into nested folders. Do not forget to update the links in the respective .md files
• Make the respective indents in the SUMMARY.md file as well

Building your gitbook

You can even host this somewhere (such as an S3 bucket or a static hosting). Simply execute the following command –

gitbook build

This will create a new _book folder in your gitbook folder. Host this as the static site.

That’s all there is to it. A simple and easy way to manage an evolving set of markdown files using gitbook.

Data is being used to showcase that value has been generated. In order to do this, the most beautiful reports have to be eked out. Now if you are a follower of Avinash Kaushik and don’t like data pukes, then you would be aghast at some of the reports that agencies in India tend to dish out.

I was, and 13 Llama Interactive was born out of that need to do better at both data driven marketing and reporting with transparency.

The road to hell is paved with good intentions

If you’ve been providing paid marketing services to clients for any extended period of time, you know that every person you work with has a different level of online marketing knowledge. Some people might be experienced account managers, others might know basics, while others still might not know the industry at all.

Apparently “agency reporting” is a thing. This is where every week or every month, the agency that is handling the brand account (or the performance account if you may) sends across reams of PDFs (or excel sheets) that’s meant to prove that whatever hair brained plan that they had cooked up the last period has worked.

The most common method to justify existence is to keep throwing boatloads of data reports from all tools and then talk about worthless metrics. Each of these tools mentioned in the article that I have shared helps agencies do this at scale, effortlessly.

Is too much data a bad thing?

It can be. If all that data is leading to Analysis Paralysis … or if it leads to falling in love with data analysis itself and forgetting real business outcomes (the reason why you got money for funding the collection of all that data).

If no one is using this mountain of data for solving problems, then it’s better that the data not be collected at all.

Yes, you are letting go of possibilities, but so be it. The damage to the business by wasting resources on gathering more liabilities instead of assets is much worse.

That’s what creates a paradox. Should we or shouldn’t we collect data?

Here’s a great video from Superweek that makes the case pretty well.

Any analysis team would work day and night to justify the reason for their being. There are enough articles being shared on the internet on arriving at a Return on Investment for Analytics (RoIA). However, the main service that any of these teams did was to crunch business data into A-has. This hasn’t changed over the years, and a lot of analysts derive job satisfaction through this very hunt for the A-ha! from their audiences.

The switch to being a core business

Data and business analysis was until now a support function, which needed business data in order to thrive and be effective. Aside from very few models (those that sold business critical data such as ratings, organizational data, etc), the data was never used as the primary product.

There was always a pre-activity and an analysis activity for that data to be useful. However, over the years I am seeing that has changed. Data is now being presented and sold as the main product.

Data as the product

Those of you who know Bloomberg, Hoovers, S&P or CRISIL, would know that data as a product business model works. Now that you know the pattern, let’s take a look at how this business model works.

Data collection as a ancillary service

There is one function of the business which works with the entire industry it is catering to, to collect data. This more often than not is made available as a freemium or free service.

Some examples of this would be – Alexa Certified metrics, Google Analytics, Walnut app, Swaggerhub, etc.

You get the general idea here. If a good product or service is offering you a free plan, more often than not the data you are entering on that platform would be mused for multiple usecases. Not just for your primary use case.

Data aggregation and visualization

This is akin to the marketing function, and most probably gets a lot of early adopters talking good things about the product.

E.g a blogger singing paeans about Google Analytics, an industry benchmark visualization being shared, data report about a competitor, etc.

This way, the inherent value in the data is presented.

Data access and pricing plans

This is how the business is monetizing the data. By selling access to it. Often on a pay per use basis, or a per data point basis. Note, there might be multiple reports given to the user, however the user has to do the analysis on their own.

E.g SEMRush, SimilarWeb, Alexa, etc.

Wait, these are all old products

Yes. They have been around for quite some time. However, I am seeing that other industry are also copying this model. I recently spoke to someone in the pharma industry who was selling aggregated prescription data to pharma companies.

The credit industry has already been doing this for so many years. TransUnion is a perfect example. In India, most working professionals are familiary with their CIBIL scores. What few people realize that CIBIL is a TransUnion company. Similarily, CRIF score (which is an alternative bureau) belongs to Experian.

What gets my goat in this scenario, is that the firm which is collecting data is based out of another country! This firm now claims to own and know the data of citizens belonging to another country.
Shut up and take my data

Let’s go back 300 years or so. The British killed the Indian textile industry by mutilating the weavers who used to make cloth. Then they bought the cotton and other crops at throwaway prices, that cotton is similar to the data that is being collected. The industry grade cotton which was then imported back in India is similar to the data aggregation and reports that are being sold.

The only difference is that 300 years back, we were scared of the East India Company. This time around, we are welcoming the data traders with open arms. Should we not be a bit more aware of who and how our data is being used?

One of the things that going digital does to any brand, is that it suddenly gives access to a lot of data. Data, that opens up a world of possibilities.

Possibilities which had not earlier been anticipated or even thought of. Somehow, it propels teams to start thinking in terms of achieving certain data metrics … and that seems to justify the sheer obsession with data.

However, in data lies a certain trap. Let me walk you through how a typical marketing team approaches data.

Measure & Monitor

At the start, the team starts collecting data and reporting this on a daily basis. The fact that data is being collected and monitored allows for these teams to make pretty graphs and bar charts which get discussed in weekly or monthly meetings.

Don’t get me wrong, I am all for #measurement. It’s just that what do you wish to measure is much more important than the fact that you are collecting data. I have already written about this in the past, having defined a simple Success-Failure framework for measurements.

However, when I see the work being done by most teams in India, it drives me up the wall. The sheer volume of data, and sometimes the lack of understanding the context can confuse the data analyst. What happens next is just Analysis Paralysis.

Analysis Paralysis

This is a state of over analysing data to such an extent that no action is taken and its being discussed to no end. The data ends up getting regurgitated from excel sheets to powerpoint to emails, but no real action is taken.

It’s not that everyone keeps an eye on the more important metrics. If you are looking at a list of smart and helpful series of insights, then do go through this list of analytics insights published on the SEMRush Blog. Keep these aside for a minute, and try and see how tracking each of them would really impact your business.

Most teams, don’t necessarily do that, and that’s what bugs me. Here are some of the metrics that really get my goat.

Bounce Rate

Yes. It’s something that you would look at and obsess over. Wouldn’t you? Think again. It’s a subjective metric. What would you say to a high bounce rate for a landing page that’s giving you a high conversion rate?

The metric if considered purely on a standalone basis tends to bias the analyst, and that’s why its something you need to avoid.

Traffic

This is my most hated metric. What is the point in running after loads of traffic or buying traffic that has zero business outcomes? However a lot of teams are made to run after traffic, and then instead of focusing on getting the right customers, teams start focusing on getting as many footfalls as possible.

Impressions

Yes, your brand got the most number of impressions and that’s a high number of eyeballs. However, if you are getting only one person to act out of every 100, then don’t you think there is a problem?

Brand building is always a good idea, but does brand building translate into driving higher number of impressions? The mental model associated with this approach is scary – because then as marketers and businessmen, we start focusing on running after the wrong metric.

Imagine showing a wrong message to a vast audience. Scary right?

Clicks

This is a metric for those of you who are are running paid promotions on digital. Since a lot of the work that is done on paid promotions is customer acquisition, there is an unhealthy focus on looking at clicks.

Clicks are good, but when it comes to paid promotions, who knows perhaps the person clicking is being incentivized to click your banners and has zero interest to engage with your business.

Social Likes

This again is a personal favourite. For a lot of content oriented brands, the number of social likes is somehow extremely paramount.

If we step back and examine why people likes things on social platforms, then we will see that most of the times it is to express an opinion about something (yes, we are opinionated, and we love it!). Some times it is to open a dialogue and have a conversation about a topic.

Yes, having more likes typically does translate into a greater reach and engagement. However, this number is also a very subjective metric. Perhaps the link being liked is so bad that it’s good (think Gunda, or the Friday-Friday song). Would you as the content creator be happy or sad that people are (dis)liking your content?

The next time you sit down to define which metrics to track, definitely think about how the metric is going to help your business. If you can generate a straight line visibility between that metric and revenues for instance, then that should be something you need to track.

I am in the middle of reading Shashi Tharoor’s An Era of Darkness: The British Empire in India. If you have been living under a rock like I was, then you may not have heard about his Oxford debate where he smashed his contemporaries on why Britain should do reparations to India.

At this time, I chanced on the movie Gold in Amazon Prime. What perfect timing! A movie about India’s first Olympic gold medal – as a free country. The movie stars Akshay Kumar as a sports manager of the British India National Hockey team, and their ability to keep winning the Olympic gold for British India. History buffs and hockey buffs (preferably both) would be quick to point out that during that time the team was led by the Wizard of Hockey, Dhyan Chand.

For the sake of preserving identities of the negative roles, the names have been changed, and Dhyan Chand is portrayed by Kunal Kapoor as the legendary captain Pritam. If you do not know who is Dhyan Chand, please stop reading and head on to the wiki link. India was well known in the history of hockey largely due to this chap. We owned the international circuit from 1928 (pre-independence) up till as late as 1980. Pretty much the time cricket took over as the national craze and the national sport lost its crowds. Ironically, in 2014 when Dhyan Chand’s name was being considered for Bharat Ratna (the highest civilian award in India), it was never nominated and the award winner was none other than apna Sachin!

But I digress, this is about the movie and not a diatribe about hockey losing out to cricket!

The movie is about getting India’s first Olympic gold, and how the main character in the story (a Bengali team manager played by Akshay Kumar) helps the team get its gold. This under the backdrop of the partition and post-independence struggles that the new country faces make for a riveting story.

Bollywood has oft taken an anti-Pakistan stance in the past, and it’s very easy to take this stance. However, you should see how this movie has spun the entire India-Pakistan tale. It’s heart-rending and one might wonder … a magnificent what-if … our national leaders back then were brave and foolhardy to take such a decision then. What stops from doing something equally foolish now?

History tells us the outcome of this story … India dominated the hockey scene for a long time. However, the story also talks about the role of administration in ensuring that the sport has enough backing. In the chaos of IPL and slogans like fan banna padega … I ask you this … what about our national sport? I wish this movie had done much better on the box office, it deserves to be seen, not only for the acting – but also for the narrative.