Big Data & Digital Marketing

Data is certainly growing at an exponential rate, and the Internet Of Things is just making the process stronger. However, the buzz has created some false myths that need to be busted.

Five biggest myths about big Data:

1. The Entire Data is Accessible

2. Entire Data is Needed

3. Offers Certainty

4. Big Data isn't Here to Stay

5. Granular Data is Better

Big Data can be extremely dangerous without a Big Brain to analyse them properly.

Huge data sets ALWAYS contain some relations: some of them are right (causation), others are simply wrong. It pertains to data analysis to uncover the truth.

"As we move into an era in which personal devices are seen as proxies for public needs, we run the risk that already existing inequities will be further entrenched.

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This goes beyond merely conducting focus groups to confirm what you already want to see in a big data set. It means complementing data sources with rigorous qualitative research."

Most organizations have realized both the potentials and the difficulties of Big Data.

Here is a TDWI checklist report that can help to get organized before beginning a new project:

1. Identify key business drivers

2. Create an effective team

3. Emphasize communication skills

4. Embrace visualization and storytelling

5. Access all the data

6. Operationalize Analytics

7. Improve governance

The 3 elements that I particularly consider crucial are:

A. Set clear goals

B. Invest on the team, not on individuals

C. Communicate and operationalize your project findings 

Interview to IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley.

1. Deep Learning has nothing to do with Neuroscience

People continue to infer that something involving neuroscience is behind deep learning, and that deep learning is taking advantage of an understanding of how the brain processes information, learns, makes decisions, or copes with large amounts of data. And that is just patently false.

For issues of higher cognition—how we perceive, how we remember, how we act—we have no idea how neurons are storing information, how they are computing, what the rules are, what the algorithms are, what the representations are, and the like.

So we are not yet in an era in which we can be using an understanding of the brain to guide us in the construction of intelligent systems.

3. A Big Data Winter is waiting ahead

When you have large amounts of data many of your inferences are likely to be false. It’s like having billions of monkeys typing. One of them will write Shakespeare.

A lot of people are building things hoping that they work, and sometimes they will. And in some sense, there’s nothing wrong with that; it’s exploratory. But society as a whole can’t tolerate that. Eventually, we have to give real guarantees. Civil engineers eventually learned to build bridges that were guaranteed to stand up.

So with big data, it will take decades, I suspect, to get a real engineering approach, so that you can say with some assurance that you are giving out reasonable answers and are quantifying the likelihood of errors.

If nothing changes, there will be a “big-data winter.” After a bubble, when people invested and a lot of companies overpromised without providing serious analysis, it will bust. And soon, in a two- to five-year span, people will say, “The whole big-data thing came and went. It died. It was wrong.”

In Data Science "talent" means to be "relentless in the face of failure"

Insights (aka Big Data Value) builds on Big Brains:

No machine can be a miracle cure. Humans have to find the patterns, ask the right questions and make the connections in the data.

Insights come from Big Brains, not Big Data.

Big Data doesn't necessarily have to be terabytes or petabytes in size to be considered “Big.” Any amount of data that a marketer can't easily convert into actionable insights with internal data management resources can be considered “Big Data”, at least as far as the owner of the data is concerned.

Therefore, when it comes to developing insights, I would say that the size of the data is fairly irrelevant. Ultimately, success via insight depends not on the size of the data set, but on the effectiveness of the analytics used to generate results. 

Here are 4 key components for developing actionable insight:

1. Develop a data-centric approach to marketing;

2. Create an appropriate data management infrastructure;

3. Collect relevant data;

4. Use effective analysis tools.