Big Data & Digital Marketing

A key topic when trying to leverage Big Data is data integration.

Data integration can take long time and is crucial to really benefit from big data.

Silo breaking, made possible by data integration, is what can let a company move from applying short-term tactics to creating a long-term strategy.

It goes without saying that without some good questions (i.e. business objectives) even good data integration is of little use.

One good suggestion for defining the goal is to put the customer in the center, for real.

8 basic question to help you get started on the right track:

1. Who is your customer?

2. What do they need?

3. What data should you be looking for to see if you are delivering?

4. Where is the data coming from?

5. How is it stored/organized?

6. Who looks at it and how often?

7. Who is analyzing it?

8. Who is presenting it?

Five mistakes to avoid to fail a big data project:

1. Going at Big Data alone

2. Using outdated data management practices

3. Ignoring Big Data best practices

4. Failing to understand the importance of Big Data governance

5. Understanding the power of Big Data, aka concentrating at the finger and missing the moon.

I was not aware of the existence of economic theories that disagree with the positive effect of the digital revolutions. Their main points are:

1. The impact of the current ICT revolution is not as radical as the previous one (steam engine, railways, telephone ...)

2. Too many traditional businesses are disappearing, and the average required level of skills to enter the market is going up

3. The peak of the revolution has passed, and the progress is slowing down.

Mark Cliffe, ING's chief economist, replies like this:

1. Part of the current growth slowdown is due to the financial crisis

2. One needs more time to evaluate the impact of new technologies (even electricity took decades to have its full effect)

3. A key aspect of the "Anytime Anywhere" technology is its network effects, i.e. benefits spread faster with adoption

4. Hundreds of millions of people in the emerging world are being lifted out of poverty

5. The digital revolution is actually increasing, becoming the Internet Of Things, or "Anytime, Anywhere, Anything" economy

In such a scenario machine learning and predictive anlytics will be essential to not drown in Big Data. But keep this in mind: "Machines are for answers, Humans are for questions".

As organizations become more and more data-driven, it becomes progressively more important to set up a healthy and productive way to manage data.

Here are 4 major steps to follow to help you improve on this:

1. Data Management

2. Data Security

3. Data Quality

4. The Team

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1.

Data management is the “administrative process by which the required data is acquired, validated, stored, protected, and processed, and by which its accessibility, reliability, and timeliness is ensured to satisfy the needs of the data users.”Basic pillars are: provisioning, protection, replication and recovery.Evaluate data before engaging in big data analytics.Have a maintenance plan.
2.

Data security must be prioritized by any organization to enable it to function properly and for operations to flow efficiently. It also provides stockholders and executive teams peace of mind of knowing that the information they have stored in their servers will not be easily exploited by hackers or cyber-criminals.
3.

A study conducted by Experian Data Quality shows that outstanding data quality has a direct correlation to an increase in company profits. 4 steps to reduce incidence of human error (cited by 65 percent of organizations to be the main cause of data problems): Identify data entry points, train staff, Automated verification, clean data over time.
4.

Hire a competent team of professionals who know their roles very well: data management supervisor, data entry staff, data analyst, quality and training staff

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 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

Introducing an organization to BI is a learning process from the start.

Many enterprises see where they want their Business Intelligence platform to be, without having a baseline understanding of the roadmap to achieve that end product.

McDonald's recipe for Big Data:

1. Discover - rapidly come up with ideas and incubate them

2. Develop - get the right perspective and develop these new projects

3. Deploy - more diverse departments become involved, such as marketing or design

McDonald’s has became an information-centric organization that makes data-driven decisions.

In particular McDonald’s focuses on providing the best possible experience to customers. That's why, although all McDonald’s around the world look the same, each restaurant is slightly different as they are optimized using all that data for the local market.

I particularly like the first point:"Big Data is not a technology initiative, but a business one."
Here is the list of 4:

1. Ownership

2. Data

3. People

4. Technology

The beauty of Big Data is that it makes it possible to answer "simple" questions like “How can I get a better deal?" automatically.

Here is an example of this beauty in the travel industry, where Big Data is needed because one need to search through a lot of data (fares different in time, space, conditions, reliability, personalisation, prediction).

Personal suggestion for a possible improvement: include social media data.

Mistake #1 – Not getting business stakeholders involved
Mistake #2 – Not having a clear goal
Mistake #3 – The tool to answer all requests
Mistake #4 – Understand BI is not about reporting
Mistake #5 – A project team with missing skills

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 

Top Performers consistently apply analytics in almost every activity across their organization. They prefer Analytics over Intuition 5 times more than Low Performers.

This Survey by MIT Sloan, in collaboration with IBM, draws a clear picture on how organizations can approach big data, what the major challenges are and how a successful analytics culture can be established.

Organizations are usually found in one of these 3 stages:

1. Aspirational: just started with analytics. The main target is to improve cost efficiency. Are not very rigorous.

2. Experienced: use analytics to guide actions, target at growing revenues, some use of rigorous approaches, applications are limited for future strategies.

3. Transformed: use analytics to prescribe actions, use analytics at all levels also in day-by-day activities, use rigorous approaches.

Sometimes organizations transition from state 1 to 2 to 3.

The Survey suggests a 5-step methodology for successfully implementing analytics-driven management:

1. Focus on the biggest and highest value opportunities

2. Within each opportunity, start with questions, not data

3. Embed insights to drive actions and deliver value

4. Keep existing capabilities while adding new ones

5. Use an information agenda to plan the future

When a successful Data Science project is well presented, it usually looks very simple. It reminds me of some of the proofs in Calculus or Physics that I studied when an undergrad.

In fact, they just *look* simple, and they do so because someone has done an incredibly hard and difficult job before hand.

In Data Science projects, the hidden job is usually related to data: looking for data, cleaning the data, joining the data, realising you are missing some data and iterate.

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"

If you find yourself lost on creating a successful roadmap for your Big Data strategy, go back to the basic:

1. Acquire the resources
2. Build analytics across business verticals
3. Utilize analytics for decision-making
4. Coordinate and align analytics
5. Create a long term strategy

My first scoop of the year is an interactive tool to know more about Big Data. This is a web app that allows you to explore lots of resources such as articles, videos and infographics.

Here is a summary of the topics that you can find:

1. What is Big Data?
Volume: the Big Data explosion
Variety: structured and unstructured data
Velocity: fast, streaming, real-time data
Value: Big Data in action

2. Why is Big Data so important?
The challenges of Big Data
Opportunities for Big Data leaders

3. Who are your Big Data key players?
The data scientist or data analyst
The executive team
The line-of-business manager
The IT leader
The end-user, customer or supplier

4. How do you lead with Big Data?
Getting to Big Data
Identifying business goals for Big Data
Define data strategy
Deploy Big Data technologies
Build analytics models
Operationalize insights

5. Which Big Data solutions do you need?
Choose a Big Data partner
Manage data growth
Use Big Data analytics
Build Big Data applications
Big Data consulting and education
Big Data, cloud, and security
Success stories in Big Data

6. When do you start with Big Data?
Next steps checklist for Big Data
Big Data resources and information

A rush to deploy predictive analytics tools without proper planning sets the stage for unmet expectations.

The initial challenge for an analytics team is diligently thrashing out and defining both short- and long-term objectives with corporate and business executives.

Obtaining correct, but irrelevant, information is a waste of time, effort and resources. Close interactions between an analytics team and business managers can help you address the right questions

What's also needed from the top ranks is the endorsement of a corporate culture that values creative thinking, fresh ideas and data-based decision making.

MetLife, British Airways and Tivo Research Analytics give their best suggestions:

1. The Plan
Set a clear plan with reliable goals, keep it simple and short term.

2. Use The Right Data
There 's a lot of data out there, select the right mix for your needs

3. Don't Create A Black Box
At the end of the day a Big Data project needs to give understandable insights. There are still CEOs and CMOs who want to know where the customers are and the CFO wants to know whether there's a return on the investment.