Analytics Strategist

June 6, 2012

The professed love of data science

Filed under: Business — Tags: , — Huayin Wang @ 8:01 pm

It seems everyone fall in love with Big Data, Data Science and Data Scientists lately;  not a lot good stories outside the few poster-child start-ups.  It reminds me of an ancient Chinese story and a well known idiom:  Professed Love of What One Really Fears

In the spring and autumn period (770-476bc), there lived in Chu a person named Ye Zhuliang, who addressed himself as “lord Ye”.

It’s said that this lord ye was very fond of dragons – the walls had dragons painted on them, the beams, pillars, doors and the windows were all carved with them. As a result, his love for dragons was spread out.

When the real dragon in heaven heard of lord Ye, he was deeply moved. He decided to visit lord Ye to thank him. You might think lord Ye was very happy to see a real dragon. but, actually, at very the sight of the creature, he was scared out of his wits and ran away as fast as he could. From then on, people knew that lord Ye only loved pictures or carvings which look like dragons, not the real thing.

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”.

February 24, 2009

get out of group think

Filed under: Business, Random Thoughts — Tags: , — Huayin Wang @ 5:24 pm

Sometimes our over-confidence of our own expertise may prevent us from finding the right solution for a problem.  After all, we do not know what we do not know.  The recent web 2.0 movement that arms us with all the capabilities to listen, publish, and connect to our peers and people “like me” may have actually exasperated the situation.

The new challenge now is: how to get out of groupthink.  Popular opinion is not necessarily the right thing to spread around, and popular support is not a confirmation of getting something right.

Sometime it helps to step back from our narrow vision and awareness and immediate interest and local network of friends, to realize that the world is bigger than we thought and all the smart people are not in our profession and solutions to our problem may already be there in the open. 

February 20, 2009

The desire to last forever …

Filed under: Business, Random Thoughts, spirituality, Technology — Tags: , , , — Huayin Wang @ 5:29 pm

and to do things that could last forever .. the desire that used to be a synonym for ego is perhaps one of the most important, and subtle, force for why we do not see reality as it is when it is right in your face.  It is perhaps the one force that comes so natrually for us in preventing us from going with the flow of nature.

I used to see this when it comes to spiritual matters, not knowing that this is so applicable to business as well.

December 30, 2008

The annoying misuse of web analytics

Filed under: Business, Web Analytics — Tags: — Huayin Wang @ 10:04 pm

I really dislike people using Web Analytics as a synonym for web analytic tools and softwares working with web traffic data.  Software is not discipline; wake up people!

The confusing usage comes from the fact, based on my wild guess, that there are some people for whom there is no analytics other than Web Analytics.  Well, get educated!

For those who have trouble with how I felt, please read the following paragraphes:

“Analytics solutions typically fall short in understanding post-impression data …”

“the common limitation of analytics is that it lacks the data insights of offline behavior …”

The misuse of the word “Web Analytics” is corrupting the commnication – it may make sense in the little corner of some people and only within their little corner.

Web Analytics is not refering to a set of software/tools (which includes Google Analytics), it refers to a subdiscipline of data analytics.  Please, stop labeling “Best Web Analytics” when just you really mean just a comparison of web analytics tools.

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.

July 27, 2008

When data floods, analytics is Noah’s Ark.

Filed under: Business, Datarology, Uncategorized — Tags: , — Huayin Wang @ 3:48 am

get on it fast!

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.

 

Older Posts »

Create a free website or blog at WordPress.com.