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Big Data & Digital Marketing
Data analytics as the key to know your customers and offer them what they really want.
Curated by Luca Naso
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Big Data: The 4 Layers Everyone Must Know

"The different stages the data has to pass through on its journey from raw statistics to actionable insight."


Via Ana Cristina Pratas
Luca Naso's insight:

The main purpose of Big Data is to use data to create actionable insights.

 

In order to achieve such a goal, the data itself has to pass through a series of 4 layers:

 

1. Data Source
2. Data Storage
3. Data Processing/Analysis
4. Data Output

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kral2's curator insight, September 21, 10:53 AM

Here is a clean "Big Data 101", in only 12 slides. 5 minutes to get at least an overview and understand if you have something to do with this huge buzz word or not :-)

 

For System Integrators, the challenge is cleary to be involved building what's need for layer 2 & 3 : say scale-out storage and massive parallel compute nodes!

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The CIO of 2020: not only Big Data

What will the role of the CIO look like in 2020? Paul Muller, VP of strategic marketing at HP Software, explains how Big Data, Security and IT Management will shape the way Chief Information Officers make decisions in the future.

Luca Naso's insight:

A wonderful presentation which covers many important aspects.

1. Customers (age, language, location, connection)

2. apps, services, management, delivery
3. Silos, marketing, sales, finance
4. Big Data: important information is hard to find, data need to be protected
5. Automation
6. Staff and skills

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Admin's curator insight, February 16, 3:49 AM

Nice presentation what CIO will need to handle in 2020. Interesting overview not only for CIOs. What is the IT future? At least data, globalization, large scale analysis ...

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Marketing data analytics: 3 effective ways to generate insights

Leverage all the customer data you have collected over the years and use these simple data analytic techniques to align your marketing expense better and identify your best customers.

Luca Naso's insight:

Simple steps to get started with Data Analytics.

Collect the data, clean them up and then start generting insights.

Here are 3 simple and effective techniques:

1. RFM (Recency, Frequency, Monetary)

2. LTVC (Life Time Value of a Customer)

3. Segmentation & Clustering

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Big Data in Retail: Examples in Action

Big Data in Retail: Examples in Action | Big Data & Digital Marketing | Scoop.it
Savvy retailers use big data analytics from web browsing, social media, industry forecasts, existing customer records to predict trends, prepare for demand, pinpoint customers, optimize pricing and promotions.
Luca Naso's insight:

Top retailers are using Big Data to gain a competitive advantage:
predicting trends and preparing for future demand.

 

Synchronize prices hourly with demand, inventory and the competition.

 

Pinpoint customers who will likely buy your product and contact them as they wish to be reached, when they are in the right location, and engaging them with personalized real-time offers.

 

Retailers using big data technology are able to provide a smarter shopping experience to influence purchase decisions and edge out the competition.

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Big Data Handbook

The use of new forms of data is not an evolution. Instead, powering big data supply chains, and innovating through new forms of analytics, is a step change.

Luca Naso's insight:

Break trough the hype and understand where companies are in their Big Data journeys (in Supply Chain).

 

3 most likely systems to benefit from Big Data project:

89% Demand planning

83% Order management

81% Price Management

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