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?

Based on Big Data analysis, one has the ability to capture and uncover meaningful insights that can be translated into improved ROI on marketing activity.

Here are a few lessons learned in the performance marketing world that small businesses can apply to their own efforts to exploit the potential of Big Data:

1. Use Active Tracking Links Where Possible

2. Get Granular

3. Analyse Holistically, aka avoid silos!

4. Analyse in Real-time

5. Visualise

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)

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.

[Just for this time, in Italian]

Il mondo del marketing è uno dei settori prediletti dei Big Data.

Non a caso, grandi aziende come Google, Facebook e Twitter sono sempre più interessate ai migliori talenti di Big Data con uno spiccato senso del marketing.

Integrazione tra dipartimenti e rottura dei silos consente di portare i Big Data al livello successivo.

E tu sai quanto la tua azienda sta effettivamente beneficando dei Big Data?

Ecco dei suggerimenti su come calcolare il ROI.

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

Data Lakes as the Big Data alternative to Data Warehouses.

Their main features are:

1. Contain ALL of the data, both structured and unstructured, internal and external;

2. Store all data in RAW format;

3. No SCHEMA is imposed on the data.

I like Data Lakes because they:

1. Are flexible

2. Break silos

This increases the success rate of Big Data projects.

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 

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

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.

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.

We're embarking on the era of big data in the cloud, and we're already seeing the emergence of cognitive systems: systems able to cope with ambiguity, make sense of enormous data sets and reason their way into probabilistic determinations.

This shift should prompt communications and marketing leaders to re-evaluate and restructure their departments. Here are three principles to consider:

1. Build independent, entrepreneurial teams around issues. 

2. Create systems of engagement that generate enormous sets of data. 

3. Compete for engagement with constituencies, not attention from audiences.

Traditionally digital marketing agencies would have separate departments for different functions. The converged agency will challenge this model because of increasing interdependence of variables marketing channels. Silos are simply treason to good ideas because they limit scope of thinking from agencies.

The converged agency sounds like a very hard type of agency to pull off in the context of many agencies today but it has to happen because it is able to fulfil needs that the traditional agencies did not fully understand.