Ask the leadership team of any organization and they will tell you that making difficult decisions is part of the job. One word they often love and hate is “strategy”. My co-founder, Jian, wrote a blog about how the paralysis of analysis can kill strategy and how to avoid that. Jian referenced an oft quoted phrase that resonated with readers and myself personally. That was: “culture eats strategy for lunch”.
As a co-founder and the “human side” of our data science startup, I talk and consult daily with many of our users from both the corporate and nonprofit worlds. I have seen a similar phenomena that can make a big impact on workplace culture—groupthink. Let me explain.
So, there is a lot of data at our disposal these days. Lots of it. And along with all this data often comes the feelings of “I am not getting what I need from the data” or “it’s just a lot of wasted time and effort”.
Well, maybe it’s a matter of not looking at it right. Maybe you need a better means of visualizing the data. Plotting curves, bar charts, pie charts are the expected forms of visualization. They are used everywhere. However, simplistic visualizations such as these don’t always tell a compelling story…
Good feedback, a willingness to understand, a desire to improve … These altogether lead to meaningful actions and positive changes.
Getting good feedback is obviously the starting point; and business leaders run tons of surveys for that purpose. Even with so much technological progress made in the past 100 years, getting good feedback through an online survey remains elusive, however. Let’s peek underneath the hood a bit and take a look at how advancements in data science and applied psychology are changing the game.
Data jobs are “the sexiest jobs this century”. Data jobs are also among the hardest to positions to fill. The qualifications are so high that candidates have the leverage to “name their own price.”
Recently I was contacted by several recruiters seeking to fill a few mid-career to senior data related positions. The positions typically fell under the broad theme of innovation, change management, or capacity building to make an organization more data-enabled. The titles range from Director to VP or C-suite levels; some in business units, some in functional departments.
I myself am extremely thankful for where I am in my career. I am not looking. :-) The job descriptions and phone calls I received got me thinking though and raised some questions in my mind that I’d like to share. In particular, what does a “data person” look like? And, what should a data-enabled business look for? Does the common stereotype for data scientists hold up in a business environment?
Deep learning was prominent in the venture capital world of 2016 and rightfully so. This wave of excitement about AI and computing grew strong, because of a new-found comfort on letting unprecedented rich data guide progress. Interestingly, the term “deep learning” draws another contrast, that is, previous generations of machine learning lacked support of real data — in other words they were “shallow”.
Similarly, data analytics is transforming business decision making from “shallow” to “deep”. This leap from shallow to deep is embodied by a change in mindset — what used to be “data-supported” has to become “data-enabled”.
Making heads or tails from all the available data can be like drinking from a firehose. Simply overwhelming. Fortunately, there is a way to deal with this dilemma. As simple as it sounds – you need to narrow your focus on the things that truly matter. Doing so may only require as few as 4 data points. There are plenty of interesting examples to look at.
Hard to believe the holiday season is already upon us … Time once again to race towards a strong Q4 finish and prepare for a new year …
Here at Survature we run this race but are also uniquely positioned to watch others do the same. As a founder of a software startup, part of my job (and joy) entails talking to users and helping them to envision and manage their feedback data cycle. Through that process, many users have become friends of ours. A recent conversation really resonated with me. The reason – my friend leads innovation efforts at a multi-billion dollar company, his work makes a big impact on their global business, he is respected throughout the company, just architected a $100M partnership through a business model innovation, … yet he feels “innovation” has become a bad word.
Strategy is a big word. Every decision maker faces the task of developing and deploying strategic initiatives many times throughout the year. Whether it is the C-suite, VPs with profit and loss responsibilities, Directors in charge of initiatives, and Managers leading front-line operations, everyone is looking for better information that will help drive strategic decisions.
Ahead of the 2016 U.S. Presidential election, polling and the voter projection industry predicted Hillary Clinton as the clear favorite to win over Donald Trump. They failed spectacularly. Trying to determine why the pollsters missed the mark so badly has been a common theme among the post-mortem election reports (Trump’s Win Isn’t the Death of Data—It Was Flawed All Along, Wired).
Clearly the methods and models used to predict the voting outcome failed to deliver an accurate forecast. Another article from The Hill stated it’s an industry-shattering embarrassment, which indicates that the polling industry is facing an inevitable disruption in order to regain trust.
So what went wrong and what should the polling industry do about it?
Much of the modern economy runs on fuel. Much of the future economy will run on data. In this analogy, data is the oil and analytics is the gasoline. Given this data economy, how do you build a strong data analytics pipeline of your own? Should you build your own refinery or should you buy gasoline direct? Simply put, does it make sense to outsource or not?