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

Data can be useful in a number of different ways. I like to say that Small Data gives you answers, while Big Data gives you questions.

In this article we read the case study of Ovolo Hotels. it's a small company that is leveraging data to improve their business in many ways.

Don't collect data just because Big Data is cool: if you don't use your data efficiently it's all a big waste.

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.

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.

At the moment, companies are nowhere near reaching their individual data frontiers. They only use 12% of the data they already have, because data are siloed and they have a portfolio of hundreds of applications!

Data science is very different from traditional analytics.

Traditional analytics are based on managers' theories and is a human-driven approach. On the data science side, it's very different. We don't need a big meeting. We don't need your hypotheses. We don't need your ideas. What we need is all the data you've got.

Customers expect a lot. They want the right product or service to fit their needs, they want it quickly, and want it to work well.

Data plays into satisfying all of those goals. At its core, data for the small business means streamlining processes and finding what is most relevant to customers.

Here are the main two guidelines:

1. Decisions should be informed by data;

2. Departments within the company need to measure everything.