On a new team, start with meeting people. This includes your team, stakeholders and cross-functional partners. Ask them about the company, product, team, help they need and seek advice. Understand the career growth plans for every member of your team.
2. Understand product/company:
Read docs. Ask questions (lots of them). Attend cross-functional meetings. Try out the product yourself. Dig deeper to understand goals and success metrics of the products and company. Recommend creating an shared live doc where you invite other folks to add their comments & suggestions.
3. Build out team vision and roadmap:
Document customer pain points. Map that against the projects that your team is executing. Learn about the top successes and misses. Articulate team vision. Build a roadmap. Iterate with partners and get alignment with leadership.
4. Focus on Impact:
Identify projects in the first 90 days that will deliver impact early. Stay focused on long term vision and impact. Keep learning. Get alignment with the leadership on how success will be measured. Roll up your sleeves and start delivering what the team & customers needs most.
I like frameworks — it helps structure your thoughts. One of the most basic questions that I have asked looking at a company/org is to figure out how to evaluate the whether it’s good or great? And more importantly, how to help drive it to greatness? There’s a list of things that I could rattle off but it was not complete and also, I didn’t really have a structure. That is where the book “Good to Great” by Jim Collins comes into picture. It’s a great book that shares a “framework of ideas” for steering a company from good to great by sharing six key learning’s wrapped in a continual process he calls “flywheel”:
I encourage you to read the book if you can. But if you don’t have time, here’s a good overview:
As a data scientist, I am not dissatisfied. I love what I do!
But I might have gotten lucky since I got into this for the right reasons. I was looking for a role that had a little bit of both tech & business and so few years back, Business Intelligence and Data Analysis seemed like a great place to start. So I did that for a while. Then industry evolved and the analytics maturity of the companies that I worked also evolved and so worked on building predictive models and became what they now call “Data scientist”.
It doesn’t mean that data science is the right role for everyone.
One of my friends feels that it’s not that “technical” and doesn’t like this role. He is more than happy with data engineer role where he gets to build stuff and dive deeper into technologies.
One of my other friends doesn’t like that you don’t own business/product outcomes and prefers a product manager role (even though he has worked as a data analyst for a while now and is working on transitioning away).
So, just based on the empirical data that I have, data science might not be an ideal path for everyone.
It depends on your target industry & where they are in their life-cycle.
It has four stages: Startup, Growth, Maturity, Decline.
Generalization is great in earlier stages. If you are targeting jobs at startups; generalize. You should know enough about lot of things.
T-shaped professionals are great for Growth stage. They specialize in something but still know enough about lot of things. E.g. Sr Growth/Marketing Analyst. Know enough about analytics & data science to be dangerous but specializes in marketing.
Specialization is great for mature industries. They know a lot about few things. E.g. Statisticians in an Insurance industry. They have made careers out of building risk models.
Someone asked on Quora: What analytics data gives you the most actionable advice to improve your blog? so here’s my answer:
I have been blogging about Analytics for past few years and this question is at the intersection of both so let me give it a shot:
It depends on two things: 1) Your goal for running the blog 2) Age of the blog
#1: Your goal
First let’s talk about your goal for running the blog. It’s important to define this as this would help set the metrics that you will monitor and take actions to improve it.
Let’s say that the goal of your blog is to earn is to monetize using ads. So your key performance indicator (KPI) will be monthly ad revenue. In that case you can improve by one of the three things: Number of People visiting the blog x % of visitors clicking on ads x average revenue per ad click. You can work on marketing your blog to increase number of people visiting the blog. Then you can work on ad placement on your blog to increase % of visitors clicking the ad and then you can work on trying different ad networks to see which one pays you the most per click.
let’s take one more example. Like me if your goal is to use your blog for “exposure” which helps me build credibility in the field that I work in. In this case, the KPI i look at is Monthly New Visitors. I drill down further to see which marketing channels are driving that change. That helps me identify channels that I can double down on and reduce investments in other areas. For example: I found that Social is not performing that great but Search has been working great — I started investing time in following SEO principles and spent less time on posting on social.
So first step: Define your goal and your KPI needs to align with that.
#2: Age of your blog:
Early: Now at this stage, you will need to explore whether you can achieve what you set out to using blogging. So let’s say you wanted to earn money online. In first few weeks/months, you need to figure out if it’s possible. Can you get enough traffic to earn what you wanted? yes? Great! If not, blogging might not be the answer and eventually all your energy is being wasted. Figure this out sooner rather than later — and take first few weeks/months to make sure blogging helps you achieve your goal.
Mid: By this stage, you should know how blogging is helping you achieve your goal. So it’s time to pick one metric that matters! So if your goal was to earn money using ads then go for Monthly ad revenue and set up systems to track this. Google Analytics will be a great starting point. Also, at this stage, you should be asking for qualitative feedback. Ask your friends, ask on social, get comments, do guest blogging on popular platforms and see if you get engagement — basically focus on qualitative feedback since you won’t have enough visitors that you can analyze quantitative data.
Late: In this stage, you have the data and the blog is starting to get momentum. Don’t stop qualitative feedback loops but now start looking at quantitative data too. Figure out the underlying driving forces that move the needle on your KPI. Focus on improving those!
TL;DR: Define your “why” and then pick a metric— then use combination of qualitative and quantitative data to improve the underlying driving factors to improve the metric.
I just successfully completed the Marketing Analytics course from coursera. The certificate was issued by coursera and university of virginia — it was great to brush up some of my existing skills and then build upon it by learning some new techniques/frameworks.
The course covered:
Marketing Resource Allocation
Metrics for Measuring Brand Assets
Customer Lifetime Value
If you haven’t checked out courses on coursera yet then I would recommend to check those out! There’s a ton out there for data professionals!
A new milestone for this blog — 500+ posts! To commemorate this, I decided to change the domain name from ParasDoshi.com to InsightExtractor.com — my goal was two-fold:1) provide an easy to remember name 2) representative of the work that we all do: we help extract insights from data.
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I saw this ad on a highway earlier today and my reaction: why would I switch to a network that has just “96%” coverage.
T mobile ad — example of data puking
…instead of converting a potential buyer, this ad actually made me more nervous. You know why? Its a case of what I like to call “data puking” where you throw bunch of numbers/stats/data at someone hoping that they will take action based off of it. So what would have helped in this ad? It would have been great to see it compared against someone else. Something like: we have the largest coverage compared to xyz. My ATT connection is spotty in downtown areas so if it said something like we have 96% coverage compared to ATT’s 80% then I would have been much more likely to make the switch.
I wrote about this adding benchmark in your analysis here
Takeaway from this blog: don’t throw data points at your customers. Give them the context and guide them through the actions that you want them to take.
There are two type of things I learned in Graduate school:
2. Not Useful! (useless)
This post is NOT about discussing useless learning’s! So let me share one of most useful things that I learned in my two years at a School of management: How to solve business problems? Sounds cliched but that was, I think, one of the most important skills I picked up there. In particular, I learned about Frameworks used to solve business problems, one of them was called “MECE” which is what I want to share with you in this post.
(Side-note: Most folks learn this at some strategy consulting firm like McKinsey but unlike them, I learned about it in school)
Before we begin, I want to share about why you should care and then I’ll talk about what is it.
No matter which team you work for, you are solving problems. You wouldn’t have a job if you’re not doing that — so why not get better at it?
If you want to find a root cause of a business problem (& find the solution faster!) then you need to break it down…to break it down, you need to structure it. Now, they are many ways (or Frameworks) to structure a problem — MECE is one of the most effective frameworks out there. So lets learn about that:
(side-note: MECE framework may sound like a simple idea BUT it’s NOT easy to apply!)
What is MECE?
It’s an acronym and it stands for “Mutually Exclusive and Collectively Exhaustive” which means that when you break a problem into sub-items then they should be:
1. Not overlap with each other (mutually exclusive)
2. If you add up all sub-items then it should represent all possible solutions (collectively exhaustive)
Let’s take an example:
Say that you are asked to analyze “why is Profitability declining?”
Here’s a non-MECE way:
Find Top 10% profitable products [does NOT pass the collectively exhaustive test]
Out of them find products that are have declining profits
Try to find reasons why those products would have declining profits
Here’s a MECE way:
Visual for MECE principle
Break it down to Revenue & Cost
let’s start with cost, let’s say it’s constant = revenue must be going down for declining profits
further break down revenue into 1) Revenue from all non-usa locations 2) USA locations (Note the use of MECE principle here)
let’s say that revenue for non-usa locations is increasing, then it must be USA locations that’s the problem! (Note how effectively are able to narrow down and find the root cause faster!)
Let’s further break down to product categories for USA locations…Continue breaking down the sub-items in a MECE way till you find the root cause
I hope that gives you a good overview of MECE principle.
MECE is one of the few effective frameworks that you can use to solve a business problem. If you want to get better at structuring your ideas (to solve business problems), consider practicing MECE as there are ample resources available online that would help you master this!