Analytics Strategist

March 14, 2009

reading notes : 2009 Digital Outlook

Filed under: Advertising, business strategy, misc, reading — Tags: , — Huayin Wang @ 6:04 pm

With six hundreds (in 5 days) tweets from readers of the 180 pages 2009 Digital Outlook from Razorfish, this report is certainly captured the attention of many working in marketing/advertising. It is an exciting read and I will share a couple of my notes here.

Clark Kokich’s introduction sets up the story line really well.  

The opening paragraphs went to the key point directly.

 “I spent the first 30 years of my advertising career focused on saying things. What do we need to say to persuade people to buy our product or service? How do we say it in a unique and memorable way? Where do we say it? How much will it cost to say it? How do we measure consumer reactions to the things we say to them?”

Now, after 10 years in the digital space, I find myself spending my time talking to clients about building things. What do customers need to make smart decisions? What applications do we need to build to satisfy that need? Where are our customers when they make a decision?”

He then described the new role agency need to play: ” .. it’s about the actual role they should be playing in setting business strategy, designing product and service offerings, delivering service after the sale, creating innovative distribution channels and developing new revenue models.”

These are great insights.  Ad agencies are expert of creative messaging – “saying things”; the new challenge is about shifting the focus away from that and go beyond. This is a tremendous challenge indeed, one that would require new skills and “deep collaboration between creative, technology, media, user experience and analytics”.

July 29, 2008

Google’s achilles’ heel

Filed under: Advertising, business strategy, Datarology, Technology, Uncategorized — Tags: — Huayin Wang @ 5:22 pm

just a thought 🙂
There have been three search engine ranking principles at works: 1) by content match with search query, 2) by user feedback (or social search) data to query or similar query, and 3) by bidding price. The logic that used by Google Adwords is a complex combination of all three (relevancy, CTR and bid price).

For example, Amazon and Netflex represent the pure form of 2).

All three principles have their own merit and, here’s why it is important, many times one pure logic may match users’ intent better than a complicated mix.

Google’s ranking logic for Adwords evolved overtime, keeping a careful balance so far. But how far can it goes? Will a dynamic logic that mixes the three in significantly different way be a disruptive technology one day?

Your thoughts?

July 28, 2008

The sad reality of today’s business

Filed under: Advertising, business strategy, Datarology, Uncategorized — Tags: , — Huayin Wang @ 4:16 pm

The sad reality of today’s business has something to do with analytics, and technology in general for that matter, in a bit of twisted way.

September 25, 2007

the skill levels in data analytics

Filed under: business strategy, Datarology — Tags: , , — Huayin Wang @ 10:43 pm

Data analytics is a collection of disparate techniques and applications covering practically every fields and every industries. What holds it together as a coherent discipline is the skill set of the data analyst: the intrinsic structure, levels and connection logics of the skill components that ultimately define and delineate what data analytics currently is and will be in the future.

Data analytics is not a mature discipline. Naturally, there are widely shared confusions about what data analytics is, and particularly what are the skills and level of skills. The lack of common understanding in this has negative consequences on talent search, training and education, project management etc.

One such misunderstanding originated from mixing-up of data analytics skills with subject domain knowledge. Subject domain knowledge are things accumulated through experiences, memorized information and practices related to the subject matter. Information and insights are stored in the brain and can be readily queried without relying on external data, although originally may come from working with data. As a contrast, data analytics skills are skills of extracting intelligent information from data fresh on the spot. Without the explicit provision of data, data analytics will results in no knowledge! Taking out data is like taking out the fuel for data analysts. Comparing this Data-Driven process, the former can be called Grey-Cell-Driven process. Data are useful food for data analytics. Without data analytic skills, data are useless, just like gasoline are useless for a bicycle.

There are varying skill levels in data analytics. For starter, there are roughly 4 levels of data analytic skills:

  1. basic
  2. reporting
  3. professional
  4. expert

At level 1, basic analytic skills are mostly obtained from education and experience. It consists of comparing numbers (big/small, high/low, bigger/smaller), calculating percentage/fraction/ratio/index, reading pie-chart, bar-chart, and understanding two-way tables without relying others to translate into words. Use of excel is optional, but in general, most are able to put data into spreadsheet and do some arithmetic calculations. It does not require any programming skill.

At level 2, reporting analytic skills are generally acquired through working experience. This level includes primarily data analysis skills using excel, or analytic tools that can dump data into excel. It includes the use of formula, the use of numeric and text functions, excel macro, selection of some of the more advanced skills including pivot table, VlookUp, Regression, VBA, Solver etc. The data analytical process of breaking down and aggregating up, trending and graphing are also belong to this level. They understand the concepts of data table or dataset, where records as row and fields as columns, records subseting and filtering, some ways to measure the strength of the relationship between fields …

At level 3, the hallmark of professional data analytics skills is the ability to not only extract information but also evaluate the reliability of the extracted information. In other words, it consists of skills to extracting intelligent information, rather than just information. It also includes a much expanded set of knowledge extraction skills. At the core of it: sampling theory and experimental design, regressions and decision tree models, model development process and common validation principles, basic types of statistical distributions, significant level and p-value, distribution models of 3 basic types of fields (numeric, ordered, categorical) and proper estimation of relationship between fields of different types. Modeling and algorithm knowledge, the use of software/tools and programming languages are intrinsic to professional data analysts.

At level 4, expert data analytics are generally hard to define. Like tree branches, the higher they are the more split they are, both in directions and in varying levels. The one thing that I noticed is their sensitivity and awareness of all explicit and implicit assumptions behind the algorithms used and the general conclusions. Of course, there are many narrower data analytic fields and niches, one could be an expert in one and not in others.

It is also worth to mention that there are a few skills that related to but not part of the data analytics; among other things, it includes making an analogy, generating pretty charts or animating graphs, and last but not the least of all: the skills of selling and promoting data analytics.

September 18, 2007

Data Driven Intelligence

Filed under: business strategy, Datarology — Tags: , , , — Huayin Wang @ 6:01 pm

Abundance of intelligent people and intelligence is one major characteristic of our time!

Data Driven Intelligence, at least under its current moniker, is a modern invention. In the broadest sense, it refers to intelligence derived solely from data. It takes data, including meta-data, as the only input while outputting intelligent information.

The professionals in this trade are ones with the knowledge and skills for the extraction of intelligent information from data. This profession is still young and diversified. It has been called many names, including statistics, data analytics, machine learning, data mining, artificial intelligence, knowledge discovery, pattern recognition etc. I call it Datarology. Feel free to use your own favorite substitute.

But what about the similarly-named Numerology? Isn’t it also taking in data and generating “insightful” information?

It is true that both derive interesting and intelligent information from numbers, or claim to do so. It is amazing to see how much numerologists can derive out of as small a piece of data as a birthdate! Another profession marked by such an ability to derive much from little data or few words, is theology.

What distinguishes datarology from these two is how very careful it is about what information can be reliably drawn from the available data. I can’t imagine a datarologist being excited about working with a single data point—a birthday! This is not an indictment of numerology, or even a challenge of the validity of its intelligence. This is mainly to illustrate the difference between the two. In all fairness, numerologists do not really work with one data point, they work with huge amount of data going through intricate processes. The key difference lies in the fact that these data and processes are implicit and hidden in the dark (brain cells).

In contrast, Datarology is characterized in large part by its explicitness. It requires that every data and meta-data (including assumptions about the data characteristics) be made explicit; it also promises to make deductive process explicit. The intelligence-generating process can be so transparent that it could be understood and carried out by machine!

This is one force that is radically transforming business today and every day. It advantages businesses that have a lot of data, it improves efficiency of business operation, it pushes the digitalization of every aspect of business.

Most of all, it creates an evolutionary threat to the traditional forms of intelligence and intelligent people. The intelligence based on remembering facts, folklores, and rules that are readily derivable from data, the type that simply comes with age and experience is becoming endangered. If this is unfamiliar, read the book Moneyball by Michael Lewis.

It pays to learn these new knowledge and skills – the capability of extracting intelligence from data, all kinds of data.

The abundance of intelligence is greater now, with the addition of intelligent machines.

 

September 14, 2007

business ecology

Filed under: business strategy, Random Thoughts — Tags: — Huayin Wang @ 3:40 pm

On Aug 16 2007, Q Interactive reinvested in Didit for SEM. Both are excellent companies.

In thinking about business ecology, there are many dimensions, layers, and spaces to consider: companies in markets (both horizontal and vertical) are competing and cooperating, selling and buying, and otherwise connected in numerous ways, forming a complex and dynamic picture.

Within this metaphor, there is an “ecological divide”, a separation that often escapes our attention: good companies compete, cooperate, transact, and connect with good companies while the hopeless ones live amongst themselves.

Why is this?

In truth, it is not all that puzzling. Business are social animals just like human beings are social animals. They survive better and are happier when they are connected. The desire to connect, relate, share and transact with each other is there by nature. The order of separation comes not from lack of intention, but from the barriers that prevent the actuation of it. In this case, there is the technological barrier, the capability barrier, the communication barrier, even barrier in business cultures! All of these together contribute to a much higher transaction cost for the relationship.

It makes sense.

What about the vendor-client relationships?  Why would a vendor care if the client is competent or not in their relative marketplace? There seems to be no problem for sup-bar vendors to take on sub-par clients, rights?

My experience tells me that it is in fact very expensive to serve sub-par clients; some costs are explicit, while others are hidden/latent/opportunity costs. It would certainly take another post to full elaborate this point.  Suffice it to say that I am now very sensitive to how my clients stand in their respective markets during project/relationship evaluation.

What’s been your experience?

May 19, 2006

No free lunch theorem

Filed under: business strategy, Datarology, Random Thoughts, Uncategorized — Tags: , — Huayin Wang @ 2:06 pm

There are many forms of NFL theorem. I particularly like the one when applied to optimization/search algorithm. In one version, it can be stated as ” all algorithms that search for an extremum of a cost function perform exactly the same, when averaged over all possible cost functions. In particular, if algorithm A outperforms algorithm B on some cost functions, then loosely speaking there must exist exactly as many other functions where B outperforms A.” [Wolpert and Macready (1995)], see also No Free Lunch Theorem

It is a humbling experience when meditating on it, to be reminded of the importance of contextual knowledge of the problem.

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