Big Data & Digital Marketing

Here are 6 suggestions by Sarvesh Bagla on how to become "the person that everyone wants" in digital marketing. Keep in mind that he is mainly targeting the Indian market.

The 6 points are good suggestions, but definitively not enough to excel in the industry. In particular I believe that a strong focus on data and analytics is needed.

1. Be different

2. Play a lot on Social Networks

3. Develop interests outside of your immediate curricular pursuits

4. Get great at language

5. Develop your Listening skills

6. Take the lead

The Internet of Things will include many strange and wondrous devices. That's why analysts at ABI Research predict more than 30 billion devices will be wirelessly connected by 2020. Health-related data collection will play a large role in the IoT, of course.

Here are 6 possible trends in Big Data in 2015. I mainly the following 3:

1. Big Data will go mainstream -

so much that we might start dropping the term "Big" and just talk about "Data"

2. Everything with go on the cloud -

this will simplify the usage, cloud is simple, cheap and, above all, flexible

3. Analyses will become faster -

thanks to improvements in big data tools and technologies

Big Data help to make better decisions also when it comes to choose a "person".

1. Tune hiring policies

2. Focused recruitment marketing

3. Evaluation based on "public" work

4. Proactive hiring

5. Recruiters still matter (and they need to update their own skills)

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.”

This is one of the best article I have ever read on Big Data.

Big Data is not just about having more data, or at a higher rate, or in different shapes. It is a profound shift in the way we deal with data analysis. Actually 3 shifts:

1. from "sample" to "population"

2. from "clean" to "messy"

3. from "causation" to "correlation"

Data collection is not a news (cookies exist since the beginning of the internet). Now it has expanded into our life in the "real world", and it is bringing incredible benefits to:
1. Cities
2. Healthcare
3. Environment

Nevertheless Big Data is a double-edged sword, and cuts both ways. Although the potential of Big Data to do good is great; it is just as easy to manipulate and abuse.

Big Data: hero or villain?

1. Better Gaming Experience.

Our real life produces tons of digital data, imagine how much data your virtual life can generate ...

2. higher Gamer Engagement

Data can help to fine tune the difficulties players can find during the game.

3. Increased In-Game Revenue

Upselling is becoming the standard in monetizing games. Data on customer usage should be mined to increase the efficiency of this revenue stream.

Ubisoft is already leveraging Big Data power, as Halo 4. A revolution is on the horizon.

Increase profit or improve customer experience?

Big Data is a great tool. If used wisely it can deliver great value and long-lasting success.

In 1910, Scottish writer and poet Andrew Lang said, "He uses statistics as a drunken man uses lampposts—for support rather than illumination." Decades later, many modern businesses still do just that, using data to support rather than drive their decisions.

Here you can find some simple suggestions on how to create a method that can help you to make sense out of your data (whatever their size):

1. Defining the data - easy and simple: do not neglect

2. Building the framework - the most difficult part: sketch, prepare and visualise

3. From data to action

Sometimes it's good to go back to the basics:

What is Big Data?

Gartner in 2011 gave the 3V definitions, today we have a better understanding ad we find more appropriate to add two more concepts.

1. Volume: how much data?

2. Velocity: how data grows or moves?

3. Variety: about the shape

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4. Veracity: about the reliability

5. Value: about the ROI

The first 4 points describe what Big Data is, the fifth one reminds us that a Big Data project is relevant if it adds value.

I repeat it every time I can: always state your goal *before* embracing a Big Data project.

What is the biggest problem you have? Why do you want to collect all this data? What kind of insight are you looking for? Just saying 'insight' and 'innovation' is a wonderful thing, but first and foremost you need to focus.

And one more thing: a successful Big Data project is not a matter of having a super-hero data scientist, but a talented TEAM.

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.

In the past, emerging technologies might have taken years to mature. Now people iterate and drive solutions in a matter of months, or weeks.

The article presents the top emerging technologies and trends that should be on your watch list. Here are my best 4 pick:

1. Big Data analytics in the cloud

2. SQL on Hadoop: Faster, better

3. More, better NoSQL

4. Deep learning

Wearable tech is the integration of digital tools in lifestyles to improve health, they carry not just data mining capability but also power lifestyles.

Big data analytics, data science, and wearable computing is predicted to help better analyze patient data collected objectively with potential life-changing implications for drug development, diagnosis, and treatment.

Cloud plays an integral role here: storing, computing and eventually beaming the relevant information from the smallest of devices. In fact, cloud can also aid in analysis of this data.

I cannot think of any other place for Big Data but the cloud!

Amazon and Microsoft are clear leaders at the moment, but there is a lot of movements and Oracle might well catch up.

My Hadoop clusters have always been running on the cloud and will always be (I am currently using HDInsight on Azure).