As data science processes continue to become operationalized and embedded within business processes, the importance of governing those processes continues to rise. While governance has been a major focus for many years when it comes to managing data, governance focused on data science processes is still far less mature. That needs to change. This blog will discuss a couple of distinct areas of governance that organizations should consider.

Image for post
Image for post
Scott Webb @ Unsplash

Governance and Ethics Are Inextricably Linked

When defining governance procedures and guidelines, it is necessary to account for ethical considerations up front. The reason is that once governance policies are put in place, they will incentivize and disincentivize various behaviors. …


Less than two years ago, data literacy was not something I heard many people in the business world talking about. Recently, it is something that comes up in more conversations than not. In this post, I’ll address a few misconceptions about data literacy and then make the case that while it is a challenge, data literacy is actually a great problem to have.

Image for post
Image for post
Image from Pexels

Data Literacy Is About More Than Data

I will start by making clear that the term “data literacy” is being used in the context of a much broader span than just data. Data is certainly part of the equation, but I argue that “data” is not really the key issue organizations are struggling with when they raise the concept of data literacy.


A few weeks back, I was in a discussion with some analytics executives when one gentleman made a point that sounded odd at first. He suggested that in many cases we actually want the predictions we make with our models to be wrong, not right. After hearing his explanation, however, I totally agreed with him. This post will explain and illustrate with some examples.

Image for post
Image for post

Many Events We Model Target Desired Outcomes

We often think about models in context of trying to achieve something positive and helping do that with accurate predictions. For example, we build a pricing model to set a more profitable price. Or, we build a sales forecast to plan a budget and logistics for the coming quarter. …


Getting too fancy by using complex and layered data science approaches can magnify the issues in data instead of controlling them. This blog will explain why and illustrate with a real-world example that I also discussed in The Analytics Revolution to show that the old rule of keeping it simple fully applies to complex areas like data science.

Image for post
Image for post

A Surprising, But Recurring, Pattern

One pattern surprised me when I was first confronted with it. Namely, when building analytical processes that must be operationalized to an enterprise scale, simpler solutions can actually perform better than fancy solutions . . . not just from a systems and processing perspective, but also from an analytical perspective! This can be true even when, theoretically, a more sophisticated method should work better. I’m convinced that this is because data always has some uncertainty, is often sparsely populated, and is never fully complete. …


The rise of analytics and data science executives has received a lot of attention in recent years. Similarly, there has been substantial focus on the analysts and data scientists who get the work done. Both types of roles are required if success is to be achieved. However, is an important layer missing? I think so and will discuss why and what that layer is in this blog.

Image for post
Image for post

Defining the Gap That Exists

In large companies, much focus has been placed at the executive end of the organization. While a CAO or VP of Data Science may be able to define and prioritize important analytics, that person won’t realistically be able to directly manage the execution of those requests on a daily basis. For example, a CAO or VP will, by necessity, be constantly drawn into the politics and ever-changing priorities that exist at the corporate level. There are many distractions, that while important and valid, take away from the ability to focus on day to day tactical execution.


That title probably got your attention and now you think I have some explaining to do! The key word in the title is the word “A”. Self-service analytics isn’t a thing if “a thing” means a single, distinct corporate initiative or set of requirements. Many companies make the mistake of assuming self-service is “a” thing. In reality, it is many things to many people, depending on their skill level and role. In other words, self-service is actually many things. Let’s discuss why!

Image for post
Image for post

The Typical Narrow Definition Of Self-Service Analytics

Many organizations think of self-service analytics as being the enablement of business users to handle more analytics on their own. Even within this narrow view, self-service really isn’t “a” thing. It could be argued that companies are pursuing the single, consistent concept of “enabling business users to handle more analytics” . . . and at a high level that is true. However, different business users have different levels of skill and different needs, which leads to different self-service functionality for different people. Enabling business users to have self-service capabilities will require multiple different deployments with varying levels of functionality and complexity. …


One way to explore what trends may be emerging is to talk to people about what has them most excited or worried about the future. In the analytics and data science space, a recurring theme among experienced leaders is the concern of not being able to keep up with all of the rapid change taking place — both individually and as a team. New algorithms, platforms, data, business partners, and more are constantly challenging analytics leaders’ ability to stay current on everything they oversee.

Image for post
Image for post
Alexandra Gorn on Unsplash

The Rise of Complexity and Disruption

Until well into the 2000’s, the number of tools and platforms for performing analytics was relatively small. Virtually all analytic logic was coded using SAS, SQL, or (sometimes) SPSS. Most data use for analysis was stored in a relational database or (sometimes) a mainframe. The majority of analytics being pursued at major corporations involved classic statistical and forecasting models. Nothing was easy, but skill needs were concentrated in a few core areas. Analytics generalists ruled the day, and generalists filled roles from the bottom to the top of the analytics organization. …


There are many factors that go into making an enterprise analytics and data science program a success. At IIA, the application of our Analytics Maturity Assessment methodology to hundreds of companies over the past several years has allowed us to identify some important and intriguing patterns. Here, I’ll walk through a few of the patterns IIA has identified that can appear counter-intuitive at first but make perfect sense upon reflection.

Image for post
Image for post

Capability Without Awareness & Adoption = Failure!

Many analytics and data science organizations have made the mistake of focusing purely on the technical aspects of progress. What is often neglected is the ongoing communication and internal marketing of that progress to stakeholders throughout the enterprise. Those involved in a major project will be acutely aware of its potential and progress, but without expanding that awareness to the eventual users and beneficiaries, progress will not be subjectively perceived and recognized. …


The general population is not used to interacting with data and analytics and interpreting results. With the COVID-19 crisis, millions of people are now becoming armchair analysts and they have plenty of time on their hands to practice their newfound analytical “skills”. Add to this a media industry that loves to put forth shocking headlines to grab clicks and a social media complex that similarly rewards attention, however achieved. We are left with an environment ripe for widespread confusion about what’s really happening with COVID-19. …


As I write this blog, we are deep into the ongoing COVID-19 crisis and lockdown. At IIA, we’ve been tracking the impacts that analytics and data science organizations have felt thus far. You can track our regular COVID-19 impact survey updates, executed jointly with Burtch Works, at this link.

This whole mess has had me thinking about what type of analytics leadership is required to weather the storm. …

About

Bill Franks

Bill is an internationally recognized analytics, data science, AI, & big data thought leader, speaker, executive, and author. http://www.bill-franks.com

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store