Time and energy are finite resources and it’s important to use them effectively and efficiently. This requires having a good prioritization framework. In this post, I’ll share 3 frameworks that I have frequently used to prioritize.
A similar 2×2 matrix that is equally relevant and helps you identify if you delete (low benefit, high cost), defer (low benefit, low priority), plan (high priority, high cost) and do (high benefit, low cost).
3 Scoring Models or Weighted prioritization matrix:
If you want to make a decision to prioritize, you can list all factors and their weights to come back with a List. E.g. Choosing a restaurant:
Depending on the focus, you might also have a specific scoring model or framework. For e.g. In Product, RICE framework is pretty common that scores based on Reach, Impact, Confidence and Effort:
As a data professional, you would invariably end up spending a lot of time on data cleaning & transformation and a lot of times, you might be doing your work in Excel — if so, then check out Power Query if you haven’t already! It will save you a LOT of time and unlock Jedi powers that you didn’t know you had!
BUT…
if you are using a Mac — and there’s a lot of data scientist and data analyst who are on this platform then you are unfortunately out of luck! So for Mac users out there, I had shared this feedback which has 50 comments & 337 votes (as of 6/16/17) on the official Power BI ideas site; If you are one of the Mac users, then I encourage you to check it out and vote! Microsoft does take it seriously and their roadmap is heavily influenced by ideas site.
If you are a data science professional and haven’t heard about bots, you will soon! Most of the big vendors (Microsoft, Qlik, etc) have started adding capabilities and have shown some signs of serious product investments for this category. So, let’s step back and reflect how will bot impact the adoption of data platforms? and why you should care?
So, let’s start with this question: What do you need to drive a data-driven culture in an organization? You need to focus on three areas to be successful:
Data (you need to access from multiple sources, merge/join it, clean it and store it in cental location)
Modeling Layer/Algorithm layer (you need to add business logic, transform data and/or add machine learning algorithm to add business value to your data)
Workflow (you need to embed data & insights in business user’s workflow OR help provide data/insights when they in their decision-making process)
Over the past few years, there was a really strong push for “self-service” which was good for the data professionals. A data team builds a platform for analysts and business users to self-serve whenever they needed data and so instead of focusing on one-off requests, the team could focus on continuously growing the central data platform and help satisfy a lot of requests. This is all great. Any business with more than 50-ish employees should have a self-service platform and if they don’t then consider building something like that. All the jazz comes after this! Data Science, Machine learning, Predictive modeling etc would be much easier if you have a solid data platform (aka data warehouse, operational data store) in place! Of course, I am talking at a pretty high-level and there are nuances and details that we could go into but self-service were meant for business users and power users to “self-serve” their data needs which is great!
Now, there is one problem with that! Self-service platforms don’t do a great job at the third piece which is “workflow” — they are not embedded in every business user’s workflow and management team doesn’t always get the insights when they need to make the decision. Think of it this way, since it’s self-serving platform, users will think of it to react to business problems and might not have the chance to be pro-active.Ok, That may seem vague but let me give you an example.
Let’s a take a simple business workflow of a sales professional.
She has a call coming up with one of her key customers since their account is about to expire. So she logs into the CRM (customer relationship management) software to learn about the customer. She looks at some information in the CRM system and then wants to learn about the product usage by that customer over last 12 months.
She opens a new browser tab and logs into the data platform. Takes about 10 minutes to navigate to data model/app that has that information. Filters the data to the customer of interest and a chart comes up.
Goes back to the CRM system. Needs something else so goes back to the data platform. That searching takes another 10 minutes!
Wasn’t that painful? Having to switch between multiple applications and wasting 10 minutes each time just to answer a simple question. So business users do this if this is critical but they will ignore your platform if it’s not business-critical.
So to improve data-driven culture you need to think about your business users workflow and think of ways to integrate data/insights. This is probably one of the most under-rated things that has exponential pay-off’s!
So how do bots fit into all of this? So we talked about how workflows are important, right? To address this, tools had data alerts and embedded reports feature which works too but now we have a new thing called “bots” which enables deeper integration and helps you embed data/insights to a business user’s workflow.
Imagine this: In the previous example, instead of logging into data platform, the business user could just ask a question on one of the chat applications: show me the product usage of customer x. And a chart shows up. Boom! Saved 10 minutes but more importantly, by removing friction and adding delight, we gained a loyal user who is going to be more data-driven than ever before!
This is not fiction! Here’s a slack bot that a vendor built that does what I just talked about:
So to wrap up, I think bots could have a tremendous impact on the adoption of the data platforms as it enables data professionals to work on the third pillar called “workflow” to further empower the business users.
And the increase in data consumption is great for both data engineers and data scientists. it’s great for data engineers because people might ask more questions and you might have to integrate more data sources. It’s great for data scientists because if more people ask questions then over time, they will get to asking bigger and bolder questions and you will be looped into those projects to help solve those.
What do you think? Do you think bot will impact the adoption of data platforms? If so, how? if not, why not? I am looking forward to hearing about what you have to say! please add your comments below.
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.
Question (on Quora) Is the R data science course from datacamp worth the money?
Answer:
It depends on your learning style.
If you like watching videos then coursera/udacity might be better.
If you like reading then a book/e-book might be better.
If you like hands-on then something like Data Camp is a great choice. I think they have monthly plans so it’s much cheaper to try them out. When I subscribed to it, it was like 30$/Month or so. I found it was worth it. Also, if you want to see if “hands-on” is how you learn best. Try this: swirl: Learn R, in R. — it’s free! Also, Data Camp has a free course on R too so you could try that as well.
Why is “Doing Data Science at Twitter” a great read?
This is an insider’s perspective from someone who is working at a company that I classify as having the highest level of analytics maturity — In other words, Twitter is known to apply knowledge gained from data science into their products and business processes.
It’s also important to recognize that every company is different and the analytics/data-science tools/techniques/processes that would be implemented would also vary based on the analytics maturity — I love that this was one of the key insights shared in this article.
Also, the article talks about two types of data scientists…I thought it was great way to classify them because there’s a lot of confusion in the industry around what a Data scientist does. With that, Here’s the URL:
Introduction to Data Science course taught by Bill Howe just started on coursera platform. Having studied the Data Intensive Computing in Cloudcourse at UW taught by Prof Bill Howe, I can say that this course would be great resource too!
