What are research AOR and ‘Big Data’ analysis?
Q: We’re a group of graduating marketing students from your alma mater. We’d like to ask you some questions and you don’t have to suspect that we’re asking so we can use your answers for our Monday marketing class where we discuss your Friday MRx column.
We’d like to be clear about two items in your column last Friday regarding market research trends.
The first is about the research Agency of Record (AOR) trend. How does that really work? Why would a research-using company want his research budget bundled with another company’s research budget and the combined budget managed by one AOR manager?
Our other question is about “Big Data.”
When you ended last Friday’s column by saying, “It’s now clear that the Shah position is the more realistic perspective,” what exactly did that mean? So, we should listen to Shah and not to Anderson?
A: You raised two good questions. I apologize if I was in such a hurry in that portion of the column.
Here are my more detailed answers.
Under the research AOR agreement, it’s your AOR contract where you will and should find the assurance of fairness that you are looking for. So get a good commercial lawyer.
But after the contract has been signed, consider how you should manage the relationships among the three actors in an AOR.
There’s the research AOR manager, the two of you who are research agencies, and your research clients as research users.
Your concern over fairness is in your relationship with the AOR manager. Think of an analogy to understand the point I wish to drive at.
Think of this relationship as the relationship between a hospital administrator and his doctors.
The AOR manager is the hospital administrator while the doctors are the research agencies.
As long as the hospital administrator keeps to his administrative responsibilities and does not intervene in what the doctors are about, are doing and are responsible for, the hospital does well and peace prevails.
At the same time, as long as the doctors keep to their health care and patient responsibilities and do not intervene in what the hospital administrator is about, is doing and is responsible for, the hospital will do well and peace will prevail.
In a similar way, we can say that for as long as the research AOR manager keeps to his research management responsibilities and does not intervene in what the research agencies are about, are doing and are responsible for, the research will do will and peace and quiet will prevail.
But the moment the AOR manager plays the researcher’s role and intervenes in the design and research analysis, then trouble will start and can multiply.
Those troubles will only worsen until the AOR manager and the research agencies sit down and agree to go back to their proper role definitions.
The role extension danger lies more with the AOR manager.
He is typically a former researcher and became known as good in this or that research category. When he succeeds in organizing a research AOR, eventually or after 2 or 3 years, the temptation to assert his research expertise often becomes too overwhelming.
He then colors his AOR decisions with what his former research experience dictated as “better.”
The offended research agency or agencies grumble and soon organize themselves and present a united front.
The beginning of the end has come.
Now, to your second question. Let’s recall both positions. Chris Anderson says: “Big Data will spell the end of theory.” In contrast, Shventank Shah takes the opposite position and said: “Big Data, no matter how comprehensive or well analyzed, needs to be complemented by ‘big judgment.’”
Let’s understand Anderson first.
From a historical standpoint, Chris Anderson is taking us back to the late ’60s and early ’70s when the first “Big Data” idea came along.
Sociologists, political scientists, and social psychologists got into a whole lot of surveys. The accumulated database became so big, a new movement by the name of “Grounded Theory” came about.
Two sociologists, Barney Glaser and Anselm Strauss, developed the research method in their best-seller 1967 book, The Discovery of Grounded Theory. The research method of Grounded Theory reversed the traditional social science research process. It started with the collection of masses of data through a variety of methods and then analyzed the mass of data to create and arrive at a “theory.”
Chris Anderson’s version says more and assumes that Big Data will in effect speak for themselves.
In the view of Shventank Shah and his colleagues, the mountain of Big Data cannot make sense by themselves.
It is theory, and a big one, that can make sense out of them or segments of them. Big data can make sense only by big judgment which in turn can made bigger sense only by big analysis and analysts.
The emerging winner in this debate is Shah and company. That was what I meant when I said that: “It’s now clear that the Shah position is the more realistic perspective.”
In my own work with Big Data, this is from where I organize my data analysis and reanalysis engagements.
Keep your questions coming. Send them to me at [email protected].
