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DATA SCIENCE AND ANALYTICS
DEMYSTIFIED
Kevin GrayCannon Gray LLC
http://www.cannongray.com/[email protected]
Cannon Gray LLC
Now
Cannon Gray is a marketing science and analytics company based in Tokyo
Partners with clients, marketing research agencies, consultants and ad agencies located in many regions of the world
Founded in 2008 One-man consultancy Work from home office Company name honors both sides of the
family
Cannon Gray LLC
Now
Provide Advanced Analytics and Consultation for a broad range of quantitative marketing research, including: Consumer Segmentation Data Mining and Predictive Analytics Market Response Modeling Demand Forecasting Key Driver Analysis Pricing Research Coaching and Mentoring
Cannon Gray LLC
Now
Company Philosophy: Marketing science is social science and
technical proficiency by itself will not make one a competent marketing scientist
It requires diverse skills, such as being able to partner with marketers to help them see the big picture and anticipate key decisions they'll have to make
Cannon Gray LLC
Now
Company Philosophy: Marketing scientists must deliver knowledge
and insights that facilitate cost-effective and profitable decisions - this is Cannon Gray's focus, not number crunching
Each project is tailored to address specific marketing issues and to the country or countries being researched
Cannon Gray LLC
Once upon a time…
Began my marketing research (MR) career in the 1980s in Financial Services in Manhattan
In some ways, a different world: No World Wide Web No social media PCs had limited capacity and were mainly
regarded as curiosities
Cannon Gray LLC
Once upon a time…
But plenty for a researcher to do: Survey research by postal mail or telephone Some postal surveys were entirely DIY, from
design through analysis and reporting. Also spent many days behind one-way mirrors
observing focus groups
Cannon Gray LLC
Once upon a time…
Because research was harder and more expensive: Thought more about our objectives More careful in planning, execution and
analysis Today, IT makes it easy to fix mistakes...so we
make more of them!
Cannon Gray LLC
Once upon a time…
For legal, operational and marketing purposes my company maintained substantial amounts of customer data
Computers were mainframes networked together into "virtual machines" and processing was quite speedy
Some data were still stored on magnetic tape but much of it had been migrated to servers
Cannon Gray LLC
Once upon a time…
Used SAS for building data files and for analysis Did not have a “data warehouse”
With support of very capable MIS colleagues, created data files ad hoc for specific purposes
Bigger challenge was finding out which data were kept in which parts of the organization and what the assorted data codes meant
Cannon Gray LLC
Once upon a time…
Able to integrate "hard" customer data with "soft" data from surveys or with exogenous data such as economic trends
Performed fairly advanced statistical analyses of merged data files: Segmentations Key Driver Analysis Time Series Analysis
Cannon Gray LLC
Was I a “data scientist”?
By some definitions, YES: “Data science is, in general terms, the
extraction of knowledge from data.” - Wikipedia
“I think data-scientist is a sexed up term for a statistician....Statistics is a branch of science. Data scientist is slightly redundant in some way and people shouldn’t berate the term statistician.” – Nate Silver
"Sometimes things don’t change as much as all the terminology changes!" - veteran US marketing research recruiter
Cannon Gray LLC
“Data science” not just for marketing research
We should note that much “data science” has no connection with marketing
Used in many other fields, for example: Medical and pharmaceutical research Fraud detection Credit scoring Human Resource Management Oil and gas exploration Military and security Seismology
Cannon Gray LLC
Much has changed
Computer hardware and software has greatly advanced
Big data as many would characterize it today was scarce and Hadoop didn't exist
Bayesian methods were very limited and techniques such as LARS, Stochastic Gradient Boosting and Random Forests had not yet been developed
Cannon Gray LLC
But much hasn’t changed
I cannot accept claims that data science is entirely new
That it has caught many marketing researchers by surprise is disturbing
That many veteran researchers have become confused about the meaning of analytics is even more worrying Analysis is a core component of marketing
research!!
Cannon Gray LLC
But much hasn’t changed
For many years in many countries, there have been specialist agencies and consultancies working in this advanced analytics space
Also, many clients have been doing these sorts of things internally for a long time
Cannon Gray LLC
But much hasn’t changed
Data base marketing, data mining and predictive analytics describe much of is now called analytics or data science These terms have been in use since the 80's
and 90‘s Aren't "trad" marketing science business
practice areas such as driver analysis and segmentation analytics and data science?
Cannon Gray LLC
What is “analytics”?
There are many ways analytics can be defined
One way is as a research procedure for decision making
Another is as statistical procedures These are just two ways!
Cannon Gray LLC
Analytics as a research procedure
Analytics is the discovery and communication of meaningful patterns in data. It makes use of information technology, statistics and mathematical algorithms to develop knowledge, to quantify performance or to make predictions. It uses the insights gained from this process to recommend action or to guide decision making. Analytics is best thought of as a research procedure for decision making, not simply as isolated tools or steps in a process.
My own definition, heavily inspired by Wikipedia and other knowledgeable sources
Cannon Gray LLC
Basic components of procedure
1. Defining Objectives2. Data Collection3. Data Preparation and Cleaning4. Model Building5. Model Evaluation6. Interpretation7. Scoring New Data or Simulations Using the
Model8. Communication of Results and Implications to
Decision Makers
Cannon Gray LLC
Some basic kinds of statistics
Descriptive and Exploratory Analysis - frequencies, means, bar charts
Models that Predict - predicting consumption frequency of new customers
Models that Explain - identifying brand choice drivers
Analysis of Cross Sectional Data - data collected at one period in time
Analysis of Longitudinal or Time Series Data - data collected at several periods in time
Text Mining - analysis of social media conversations
Cannon Gray LLC
Some basic kinds of statistics
Models with Quantitative Dependent Variables - monthly spend
Models with Categorical Dependent Variables - product user/non user
Time to Event Models - customer churn analysis Methods that Group Variables - factor analysis
of attribute ratings Methods that Group Cases - cluster analysis of
consumers Simulations and Forecasts - sales forecasts
under various marketing mix scenarios
Cannon Gray LLC
Some textbooks on research methods and statistics
- Handbook of Statistical Distributions (Krishnamoorthy)- Practical Tools for Designing and Weighting Survey Samples
(Valliant et al.)- Design and Analysis of Experiments (Montgomery)- Experimental and Quasi-Experimental Designs (Shadish et al.)- Propensity Score Analysis (Guo and Fraser)- Methods of Meta-Analysis (Schmidt and Hunter)- Applied Multivariate Statistical Analysis (Johnson and Wichern)- The R Book (Crawley)- Regression Modelling Strategies (Harrell) - Categorical Data Analysis (Agresti)- Multilevel and Longitudinal Modeling (Rabe-Hesketh and
Skrondal)
Cannon Gray LLC
Some textbooks on research methods and statistics
- Time Series Analysis (Wei)- Multiple Time-Series Analysis (Lütkepohl)- Bayesian Data Analysis (Gelman et al.)- Applied Bayesian Hierarchical Methods (Congdon)- Risk Assessment and Decision Analysis (Fenton and Neil)- The Data Warehouse Toolkit (Kimball and Ross)- Data Mining Techniques (Linoff and Berry)- Data Mining (Whitten et al.)- Applied Predictive Modeling (Kuhn and Johnson)- An Introduction to Statistical Learning (James et al.)- Elements of Statistical Learning (Hastie et al.)
Cannon Gray LLC
Wars of Words
"The exact meaning of [data science] is a matter of some debate; it seems like a hybrid of a computer scientist and a statistician." Statistics and Science: A Report of the
London Workshop on the Future of the Statistical Science
Product of a meeting in London in November, 2013 attended by more than 100 prominent statisticians from around the world
Cannon Gray LLC
Wars of Words
Moreover, lack of agreement about the meaning of “big data”
For example see:http://datascience.berkeley.edu/what-is-big-data/
43 experts asked…43 definitions offered!
Cannon Gray LLC
Computer Science vs. Statistics
Many data scientists are computer scientists mainly concerned with IT matters Confuse data management with data analysis Confuse mathematics with statistics
Often are not well-versed in statistics and some actually distrustful of statistical models
Conjoint, structural equation modeling, time series analysis and many other statistical tools are a foreign world for some
Cannon Gray LLC
Computer Science vs. Statistics
Statisticians (and marketers) often criticize current data science practice as: Mechanical and algorithm driven Focusing too much on the What and not
enough on the Why
Cannon Gray LLC
Computer Science vs. Statistics
Marketing is also about changing behavior, not just predicting it! Two people can do the same things for the
same reasons Two people can do the same things for
different reasons Two people can do different things for the
same reasons Two people can do different things for
different reasons
Cannon Gray LLC
Computer Science vs. Statistics
There is also the “multiple me”. On different occasions: I can do the same things for the same reasons I can do the same things for different reasons I can do different things for the same reasons I can do different things for different reasons
Cannon Gray LLC
A Declaration of Peace
To be fair, many statisticians (like me) should learn more about computer science
Data science teams can include computer scientists, statisticians, economists, psychologists and specialists from many other backgrounds
No need for such teams to be comprised of only one type of data scientist!!
Cannon Gray LLC
Hype vs. Reality
"Corporations are not as sophisticated or as successful as we might grasp from the sound bytes appearing in conferences, books, and journals. Instead opinion-based decision making, statistical malfeasance, and counterfeit analysis are pandemic. We are swimming in make-believe analytics.“
Randy Bartlett in A Practitioner's Guide to Business Analytics
The author is an analytics veteran of more than 20 years with degrees in both computer science and statistics
Cannon Gray LLC
Hype vs. Reality
We humans do not appear to be hard-wired to use data to make decisions
For years, managers have complained of information overload!
Our schooling has not prepared us fully exploit new data sources and advanced information technology
Cannon Gray LLC
Hype vs. Reality
As long as there are human managers and human consumers, data and analytics will never entirely replace gut feel in decisions
Many important decisions cannot simply be calculated Even thermostats are regularly overruled by
humans!
Cannon Gray LLC
Hype vs. Reality
More data - particularly when the numbers aren't trending in the same direction - may be more fuel for organizational politics and make decision-making more unwieldy
Also, humans naturally resist change and many companies very bureaucratic
Abrupt and radical transformation in the way we make decisions is unlikely
Cannon Gray LLC
The future?
However, decision-making will gradually evolve and become more, if never wholly, evidence-based This fits in neatly with the essential purpose of
marketing research! Over the next few years decreasing emphasis on
data infrastructure and more emphasis on what data tell us and how they can be leveraged
With bigger and messier data, understanding people will become more critical, not less Demand will rise for marketing scientists able to see
beyond math and programming who truly understand marketing and consumers
Cannon Gray LLC
The future?
More analytic options also mean more risk and more need for well-trained and experienced researchers
The resurgence of Bayesian statistics is further evidence that human judgment cannot be purged from analytics "Science cannot be done by the numbers." -
Noel Cressie and Chris Wikle in Statistics for Spatio-Temporal Data
Cannon Gray LLC
Some things Marketing Research can do
A downside of rapid technological change is increasing specialization and even more silos
In marketing research, the well-rounded generalist becoming hard to find and over-specialization is increasing MR educational and training programs will
need to provide more cross-training to counteract this flip side of progress
“Be a jack of all trades and a master of at least two” – David McCallum (former Global MD Nielsen Customized)
Cannon Gray LLC
Some things Marketing Research can do
Embrace new technologies and methodologies but don’t neglect less exotic activities Educating clients about how to use marketing
research to make better decisions Changing habits of thinking and improving
our own decision-making skills
Cannon Gray LLC
Some things marketing research can do
We need to be better at marketing marketing research
We must compellingly respond to contentions that data science has made marketing research irrelevant We need to show that "data science" has
actually been part of marketing research for a long time!
Cannon Gray LLC
In summary
Data science is not entirely new and not entirely old
It can do amazing things but cannot work miracles
Despite the hype and hogwash, I see it much more as friend than foe
Cannon Gray LLC
In summary
Change is a threat to those who stick too closely to the tried and true but an opportunity for those able to blend new skills with knowledge that has stood the test of time
Cannon Gray LLC
Some more history…
The foregoing shows you what presentations looked like in
the 1990’s