How Analytics Has Changed (and Not Changed)

Creativity alters the meaning of an event -- even dark ones. For example, take the rock group REM's usage of a phrase from a weird incident involving CBS news anchor Dan Rather.

That phrase became the popular song What's the Frequency, Kenneth? (Rather was mugged by a disturbed man who, thinking CBS was sending radio messages to his mind, referred to Rather as "Kenneth" while asking what "the frequency" was.) Some people even consider the phrase as exclamatory slang for something insane that happens.



Creativity has certainly been applied to data, at least from what I have seen during my analytics career. Data is information. That part has not changed.

But the quality of its information has changed -- due to creative observations that digitally represents the activity of people, products, and services.

When analytics was first launched, professionals were spending their time not only gathering information but also requesting that management invest in analytics. During my career transition to analytics, marketers focused on encouraging businesses to add tags to websites so that customer behavior at a website could be interpreted.

Those encouraging endeavors proved very difficult at first. Information doesn't always reveal a strategy-forward path to profit models, and data analysis can sometimes limit insights to past behaviors and not tie directly into traditional business metrics. Thus many managers did not invest in analytics quickly at the beginning.

Data and Decisions

Today I find it amusing how so many businesses are appreciating the value of data, even if they still struggle to implement decisions. They now consider data a gold mine because they can see the information associated with the data. But we must remember finding "gold strikes" in the form of business results took time and, yes, creativity. The business world celebrated Amazon's milestones in tech and retail this year, but easily forgot how Jeff Bezos had to convince investors of a business model where investment in scale came years before profitability.

Over time the nascency of analytics evolved into an ubiquity, driving business managers to reimagine how to achieve objectives digitally. Website analytics were a diagnostics tool that extended utility to include marketing campaigns through URL tags. That utility extended further as JavaScript was applied beyond client-side applications, establishing a true programming language for app development, for lightweight data-interchange as JSON, for data visualization, and for various full-stack developer frameworks.

Technology for 2018

With 2018 on the horizon the business community is seeing machine learning techniques transforming the skills needed for taking business strategy further. Data mining assures better metrics quality, data visualization adds programming dynamics and considerations to dashboards, and developers provide new coding frameworks to make analytic solutions more useful.

That developer attention has brought forth new tools meant for varying professional skills, from graphic offerings such as Neo4j for analysts with minimal database experience to scientific-based solutions such as R programming to incorporate data. Analytics solutions choices can seem dizzying, but all those choices mean we have many ways to implement our technical and statistical prowess to understand and translate data-driven results.

Modeling for Good

The attention has spread to non-commercial institutions, as many societal causes have become digitally represented. It has revolutionized how we interpret our world. We can model all sorts of conditions using R programming or Python to understand how the coral reef is impacted from global warming, how voter sentiment has developed over the course of an election, how cancer patients can receive more accurate treatment through classifying patients by cluster techniques, and how city service planning can be better through identifying housing and urban activity.

What has changed in 10 years is that our capabilities must disseminate what we know and do so in a creative manner. Creativity means "the use of the imagination or original ideas, especially in the production of an artistic work." While data can sometimes feel less interesting than a REM song -- or any song, for that matter -- we must be inventive with its application, especially with so many global issues that now can be scrutinized though data.

Be a Data Ethics Steward

Analytics practitioners must also imagine themselves as being stewards for data ethics. That also means being imaginative about applying data to solve real world problems and preventing malevolent uses where possible. Years ago, the Digital Analytics Association rallied the community to sign The Code of Ethics, a pledge to provide ethical measurement and data usage. But opportunities highlighted how public behavior has yet to incorporate risk. ZDnet, for example, reported a pop up shop in London that emulates a shop but demonstrates to shoppers where they risk their personal identities. Analytics practitioners must find ways to educate the public at large.

How analytics has changed is allowing us to be more visionary about how our world exists. That means we can't sing the same old song about how data is used. The analytics narrative is now a part of business due diligence: A business that manages its business intelligence assets will outlast its competition. A good manager knows this. Analytics practitioners should not have to repeat it.

In a way akin to what Michael Stipe and company did with Dan Rather's story, we have to continue inspiring the ways analytics enhances life and steward the data risks that arise with it.

Pierre DeBois, Founder, Zimana

Pierre DeBois is the founder of Zimana, a small business analytics consultancy that reviews data from Web analytics and social media dashboard solutions, then provides recommendations and Web development action that improves marketing strategy and business profitability. He has conducted analysis for various small businesses and has also provided his business and engineering acumen at various corporations such as Ford Motor Co. He writes analytics articles for and Pitney Bowes Smart Essentials and contributes business book reviews for Small Business Trends. Pierre looks forward to providing All Analytics readers tips and insights tailored to small businesses as well as new insights from Web analytics practitioners around the world.

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Re: Subjective?
  • 12/4/2017 12:04:09 PM

Yes, it is going to be an interesting journey to watch as analysts try "being stewards for data ethics," while those at the top, looking for higher profits and sales, may not be so eager to follow a more correct way of doing business.

Re: Subjective?
  • 11/30/2017 2:05:16 AM

@ Louis,  I think there are and will be a lot of non-commericial uses of analytics, though other nations may lead the U.S. on that.  The reason is that in the U.S. the ideology is that people are pretty much motivated only by money.   Which is something that is false.  You have beta testers who test products for free or write programs and others that do things just to be a part of something greater.  

Re: Subjective?
  • 11/29/2017 10:44:47 PM

I agree Tom, I think the profit motive really detracts from Analytical Integrity.  This is the elephant in the room that none appears to want to acknowledge or at least not very often.

Re: Subjective?
  • 11/29/2017 10:34:56 PM

More unbiased than non-commercial. If you are really trying to follow the information, then bias only gets in the way and sends you down the worng path.

Re: Subjective?
  • 11/29/2017 10:32:36 PM

@Seth @tomq    While I appreciate and support your position of noncommercial Anayltical initiatives, I would be surprised to see this happening anytime soon.  I would love to be wrong here though.

Re: Subjective?
  • 11/29/2017 10:27:54 PM

Thanks Seth for the information from the Analytics Guild.  The issues raised here are very concerning.  Companies trying to find the "sweet spot" for individual production can tread into unwelcoming waters and I don't see the necessity to do so quite frankly.

Re: Subjective?
  • 11/29/2017 5:34:32 PM

Tom, bias is definitely intertwined within the process, so recognize that it will always be and take steps to manage it as best possible. As long as human intuition remains a bar in measuring quality of what the data is showing, manipulation of the data becomes enticing.

Re: Subjective?
  • 11/29/2017 9:31:51 AM

It sounds like directions for the race to the bottom. Studying the enivironment would be far more useful.

Re: Subjective?
  • 11/29/2017 9:19:08 AM

I would love to see more funding for analytics for non-commercial purposes such as studying the environment.  

While I believe that analysts being moral shepherds, unfortunately, that is not always the case.  Here is an example that was written by the Analytics Guild.  

"With the rise of data technology, we have entered a new era of performance management and employee engagement. We can now regularly and easily measure employee engagement and track engagement over time. These engagement metrics can be used to predict when an employee becomes disengaged at work, ultimately leading to churn or poor work quality. Ideally, these metrics would be used by employers to improve the quality of the work environment, build up team cohesion, and disrupt the spiral toward disengagement. 

But better work environments are expensive. What if the environment was just good enough to keep an employee from churning, but not good enough to make them feel engaged? How costly is that? Instead, it might be cheaper to use employee engagement metrics, coupled with employee retention metrics to determine where the average minimum employee engagement mark rests. Instead of producing a better outcome, companies could use data technology to produce a work environment that is "just good enough" to stay and work, but not good enough to hit satisfaction marks. The worries over employee metrics at companies like Amazon are just beginning of the race to the bottom."

  • 11/29/2017 9:17:35 AM

I agree with the idea that the quality, types and modeling of data today is a far cry from just a few years ago. I also agree that it is easier to make logical decisions- the question is whether that is happeing. I run into cases all the time where people tryo to manipulate data to prove their own points and bias is still fetured in many decsions. Hopefully we are moving in the right direction and will continue to do so.