Microsoft Business Intelligence

All things Data Newsletter #15 (#dataengineering #datascience #data #analytics)

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(if this newsletter was forwarded to you then you can subscribe here: https://insightextractor.com/)

The goal of this newsletter is to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll be sharing articles across various sources that I found interesting. The following 5 articles made the cut for today’s newsletter.

(1) Scaling data

Fantastic article by Crystal Widjaja on scaling data. It shares a really good framework for building analytics maturity and how to think about building capabilities to navigate each stage. Must read! Here

three stages.png
Image Source: reforge

(2) Building startup’s data infrastructure in 1-Hour

Good video that touches multiple tools. Watch here: https://www.youtube.com/watch?v=WOSrRTaNIm0 (it’s a little outdated since it was shared in 2019 which is 2 years ago but the architecture is still helpful)

(3) Analytics lesson learned

If you haven’t read lean analytics, I recommed it! After that, you should read this free companion which covers 12 good analytics case studies. Read here

(4) Organizing data teams

How do you organize data teams? completely centralized under a data leader? or do you structure it de-centralized reporting into leaders of business functions? some good thoughts here

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(5) Metrics layer is a missing piece in modern data stack

This is a good article that encourages you to think about adding metrics layer in your data stack. In the last newseltter, I also shared an Article that talks about Airbbn’s Minerva metrics layer and this article does a good job of providing additional reasons to build something simiar. Read here

Thanks for reading! Now it’s your turn: Which article did you love the most and why?

All things data newsletter #10 (#dataengineer #datascience)

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(if this newsletter was forwarded to you then you can subscribe here: https://insightextractor.com/)

The goal of this newsletter is to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll be sharing articles across various sources that I found interesting. The following 5 articles made the cut for today’s newsletter.

1. Architecture for Telemetry data

A good reminder that the software development architecture can be significantly simplified for capturing telemetry data here

2. 5 popular job titles for data engineers

This post here lists 5 popular job titles: data engineer, data architect, data warehouse engineer — I think Analytics engineer is missing in that list but a good post nonetheless. I hope that we get some consolidation and standardization of these job titles over the next few cycles.

3. [Podcast] startup growth strategy and building Gojek data team – Crystal Widjaja

Really good podcast, highly recommended! here

4. Tenets for data cleaning

A must-read technical whitepaper from legendary Hadley Wickham. These principles form the foundation on top of which R software gained a lot of momentum for adoption. Python community uses similar tenets. Must read! here and here

5. Magic metrics that startup probably as product/market fit from Andrew Chen

A must-follow Growth leader!

  1. Cohort Retention curves flatten (stickiness)
  2. Actives/Reg > 25% (validates TAM)
  3. power user curve showing a smile

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TelemetryTiers
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Thanks for reading! Now it’s your turn: Which article did you love the most and why?

Business Analytics Continuum: Descriptive, Diagnostic, Predictive, Prescriptive

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Think of “continuum” as something you start and you never stop improving upon. In my mind, Business Analytics Continuum is continuous investment of resources to take business analytics capabilities to next level. So what are these levels? 

Here are the visual representation of the concept:

business analytics continuum

Data Culture Mental Model.

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What is Data Culture?

First, let’s define what is culture: “The set of shared values, goals, and practices that characterizes a group of people” Source

Now building on top of that for defining data culture, What are set of shared values? Decisions will be made based on insights generated through data. And also, group of people represent all decision makers in the organization. So in other words:

An org that has a great data culture will have a group of decision makers that uses data & insights to make decisions.

Why is building data culture important?

There are two ways to make decisions: one that uses data and one that doesn’t. My hypothesis is that decisions made through data are less wrong. To make this happen in your org, you need to have a plan. In the sections below, i’ll share key ingredients and mental model to build a data culture.

What are the ingredients for a successful data culture?

It’s 3 P’s: Platform, Process and People and continuously iterating and improving each of the P’s to improve data culture.

How to build data culture?

Here’s a mental model for a leader within an org:

  1. Understand data needs and prioritize
  2. Hire the right people
  3. Set team goals and define success
  4. Build something that people use
  5. Iterate on the data product and make it better
  6. Launch and communicate broadly
  7. Provide Training & Support
  8. Celebrate wins and communicate progress against goals
  9. Continue to build and identify next set of data needs

Disclaimer: The opinions are my own and don’t represent my employer’s view.

How Analytics changed Scouting in Soccer

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An interesting video that’s a great reminder on how Analytics is a game-changer when applied correctly. The video shared above how small clubs uses analytics to compete with big clubs and continue to not only stay relevant but grow in the process.

Similar analogy can be drawn for startups (or early-mid stage products inside big companies) where they can use Analytics to compete with incumbents in the market.

Let me know what you think. What’s your favorite analogy to help explain why analytics is useful to your org?

Great example of storytelling through data:

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End of the beginning by Benedict Evans.

Two great posts on DAU/MAU and Measuring Power Users

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Two great posts from Andrew Chen. Links below:

These posts were perfectly timed for me as we started thinking about Annual Planning for Alexa Voice Shopping org (Amazon) this week. As a part of my research of which metrics to use to measure things that our business cares most about and then setting the right benchmarks/goals for the org, the posts below were super helpful. So if you are in tech and if you care about 1) measuring frequency of usage 2) measuring the most engaged cohort then you should take some time to read these posts.

Power user curve 

DAU/MAU is an important metric to measure engagement, but here’s where it fails

Cheers!

Excel 2013: Display hidden rows columns and data on an Excel Chart

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If you hide rows, columns and data on excel, the chart that’s uses this data also hides it — while this is the default behavior, you can override this by following the steps below.

Let’s reproduce the behavior first.

I have a simple excel chart like shown below:

Excel Chart Hide Data 1Now, if I hide the data that is selected for this chart then the chart stops showing this as well:

Excel Chart Hide Data 2To fix this and if you want the cells (rows, columns and data) to be still hidden but still have the chart show up, then follow the steps:

  1. Select the chart
  2. Under Chart Tools > Design > Select Data Excel Chart Hide Data 3
  3. Click on Hidden and Empty Cells Excel Chart Hide Data 4
  4. Check the Show data in hidden rows and columns check-boxExcel Chart Hide Data 5
  5. Go back to excel and you should see the data on the chart now even though the data is hidden Excel Chart Hide Data 6

Hope that helps!

Springboard Data Analytics for Business Office Hours

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I was invited to lead the office hours for the Springboard’s Data Analytics for Business course and I wanted to share the recording with you all:

CLICK HERE

I answer following questions during the office hours:

  • What tools have I used in my career for Data Analytics & Data Science?
  • What are the different analysis/modeling that you do?
  • What are the biggest challenges that I found when I got in this Industry?
  • Being data-driven is not binary but it’s a scale — how do you do analyze what is their current level and how do you make a company more data-driven?
  • What is the challenge for newcomers in this industry? And what are the changes coming in next few years?
  • Which tools are widely used today? Which industry uses which tools heavily?
  • How do you verify “what’s next”? How do you verify that your forecast is good enough?

Related Post: $100 Discount Code For Springboard

News: PASS outstanding Volunteer award & stepping down as Business Analytics Virtual Group Co-leader

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I am honored to get the PASS outstanding volunteer award again for June 2017! It’s been so much fun helping grow the chapter from 1K to 10K members within last 4 years — the PASS HQ Team & Dan English (Group Lead) were great to work with and there’s so much more growth left for the next few years! The Group was recently classified as a “tier-1” group and got new sponsors which mean that group has some funding to pursue paid growth opportunities that weren’t accessible before.

Outstanding Volunteer Award PASS

URL: http://www.pass.org/Community/GetInvolved/Volunteers/OutstandingVolunteers.aspx

So since the group has the perfect platform to continue growing and we have a really good process in place to keep our growth flywheel running, I figured it’s a great time to step down. Over the past few years, my career moved me from Business Intelligence -> Analytics -> Data Science and along with that, I have slowly moved away from Microsoft-centric architectures too. I started out working for a Microsoft Gold Partner and then worked for an Open-source heavy shop at a startup-mode organization in silicon valley and now I work in an organization that uses a little bit of everything. Something like best of both worlds — and so there’s a much bigger gap now between where my career is taking me and the mission of the business analytics virtual group. They don’t perfectly align anymore and even though it’s a very rewarding experience, after some reflection, I figured the group deserves a leader whose mission aligns better than mine does.

Thank you PASS for the opportunity!

And there’s an open position for new volunteers on the Virtual group and so if you like to be involved, reach out to Dan English through the group’s website: http://bavc.pass.org/