Is There Value in Puking All Over Your Data?

data puking

puking data
That’s essentially the question asked by Avinash Kaushik in his newsletter today. But, maybe puking data seems a little unfocused, so let’s get down to what that means.

Avinash, a Google Analytics Guru, introduced the terms “Reporting Squirrels” and “Analysis Ninjas” with the image below to reflect the difference between simply puking data all over the place, versus providing valuable insights based on data. In his post, Avinash recommends we report LESS data and more insights — he even provides 7 dashboards you can use to evaluate the performance of your own websites using Google Analytics.

data puking
Image courtesy of Occam’s Razor

Puking data versus insights

So, what do we mean when we talk about puking data?

Data puking involves sharing massive amounts of data and producing reports of that data without providing valuable insights on what the data means for business processes.

The skills needed for Reporting Squirrels and Analysis Ninjas are totally different. While Squirrels need to know Java, SQL, Excel, Python, and other data manipulation tools, Ninjas need higher level skills including (according to Avinash):

~ Understanding relationships between numbers

~ Interpreting mathematical information

~ Visual perception of information

~ Ability to organize information

~ Argumentation and logical thinking

~ Understanding trends

~ Ability to create insightful data visualizations

~ Strategic thinking skills

And, I absolutely agree with him. But, I don’t think his skill set goes far enough, especially in marketing. That’s probably because he focuses on website analytics (after all, he does work for Google Analytics), while my concern is much broader — including all analytics focusing on marketing processes.

To add my 2 cents to this, you need the following to be a great marketing Ninja:

  • Theoretical understanding of consumer behavior, which is the root of all good marketing
  • Practical ability to apply marketing concepts such as segmentation, CLV, etc.
  • Ability to query data to answer specific, strategic questions (knowing the right question to ask)
  • Mastery of tool to analyze unstructured data (images, words, etc)
  • Understanding of how to merge structured and unstructured data to improve understanding

For me, the difference between Squirrels and Ninjas is that Ninjas provide actionable insights, while Squirrels just summarize numbers, leaving the insight building to others. This supports my contention that we need to train business majors in analysis, rather than simply hiring more folks who can puke data at management.

Do you need Squirrels or Ninjas?

You need both.

Squirrels help the organization by cleaning data, merging data sets, and creating reporting interfaces used by the organization.

These are important contributions, but they stop short of providing the information (as opposed to data) necessary to guide decision-making. Unfortunately, a recent study by Econsultancy and LinchPin shows firms mainly focus on hiring Squirrels, and almost totally ignore Ninja-like skills. Here’s a graph showing those data skills in highest demand by organizations:

puking data
Image courtesy of Econsultancy and Linchpin

The only skill in this set that looks like Ninjas is statistical modeling. The rest all have to do with puking data all over the place, without providing actionable insights. And, statistical modeling without a theoretical basis is likely garbage because it uncovers spurious correlations not just real ones. And, that’s dangerous!

Even a Google search for the term “Data Ninja” turns up websites advising tools and training that aren’t really analytical, but more reporting based.

Reporting versus Analyzing

When you tabulate data, create graphs of relationships within your data, or use analytic tools to create queries, you’re reporting, not analyzing data.

reporting versus analysis
Image courtesy of Adobe

Reporting is a necessary first step in analysis, but it isn’t analysis. Reporting alone assumes whoever consumes your report can make the right analysis from the data presented. And, often, that’s just plain inaccurate. But, add a little visualization, like the one below from our friend Avinash, and viola, insights come to the forefront.

generating insights
Image courtesy of Occam’s Razor

Now, those responsible for actions can clearly see that Yandex produces the highest value/session, while Yahoo and Ask both underperform. As a manager, this suggests how to allocate resources to optimize ROI.

Here’s a great explanation from Adobe:

Reporting translates raw data into information. Analysis transforms data and information into insights.  Reporting helps companies to monitor their online business and be alerted to when data falls outside of expected ranges. Good reporting should raise questions about the business from its end users. The goal of analysis is to answer questions by interpreting the data at a deeper level and providing actionable recommendations. Through the process of performing analysis you may raise additional questions, but the goal is to identify answers, or at least potential answers that can be tested. In summary, reporting shows you what is happening while analysis focuses on explaining why it is happening and what you can do about it.

Going beyond Analysis

As a consultant for many years, I find that regardless of whether you’re doing reporting or analysis, the entire value chain falls apart if you don’t take appropriate action based on them.

Perhaps an example will help:

I did a project for a major scientific equipment manufacturer who developed a new product. But, marketing insisted they do market research to explore potential user acceptance of the new product. So, they hired us.

We conducted several one-on-one interviews and a couple of focus groups with existing customers who were likely purchasers for the new product. Using this, we constructed a survey and did phone interviews with 300 current customers for the existing product.

Data reporting showed that few customers wanted the new product. Analysis showed the major reason for the poor attitudes was the complexity of the product. Instead of seeing the new bells and whistles as advantages, potential buyers saw them as just more pieces to break, causing the workhorse machine to go down, delaying critical operations.

Instead of using our insights, the company chose to continue with plans to introduce the new product.

Result: If failed to reach breakeven or produce the desired results. In fact, return rates were well above the average for the company in that product class.


Take away message for businesses

Returning to Avinash for a moment, he talks about IABI — which stands for Insights, Actions, and Business Impact. For business success, all three have to be aligned and supported by data.

As a true analyst, however, you need to go beyond creating cool visualizations of your data to aid insights. You need to do statistical modeling based on theory to create insights directly from the data.

For instance, I worked with a company wanting to improve the ROI of its email marketing. I build a complex algorithm based on an econometric model that assigned a value to each subscriber on their list based on what articles they read from the email (assessed by viewing the links clicked) as a function of how long they’d been on the list (to disattenuate for length of subscriber) to develop a lead scoring mechanism. Subscribers scoring above a threshold were sent to sales as leads for immediate follow-up.

This is where it becomes important to master the theoretical concepts of a discipline, not just analytic principles.

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