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3 principles for marketing in the age of cognitive computing

3 principles for marketing in the age of cognitive computing | Big Data & Digital Marketing | Scoop.it

It’s my impression that communications and marketing teams are undergoing a significant transformation, and should draw inspiration from transitions taking place in the realm of computer science. Specifically, the shift to cognitive computing.

Luca Naso's insight:

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.

more...
Fàtima Galan's curator insight, October 2, 2013 9:39 AM
Luca Naso's insight:

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.

Big Data & Digital Marketing
Data analytics as the key to know your customers and offer them what they really want.
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Analytics and Big Data: A 5-Step Path to Value

Analytics and Big Data: A 5-Step Path to Value | Big Data & Digital Marketing | Scoop.it

 

How the Smartest Organizations Are Embedding Analytics to Transform Insight Into Action

Luca Naso's insight:

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

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Glenn Wallace's curator insight, June 24, 1:17 PM

Let's keep growing in knowledge

Pavel Škerlj's curator insight, June 26, 7:30 AM

Analytics in action.

Anthony Ricketts's curator insight, August 29, 9:16 AM

Surely big data has more value to marketers than ever before

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Top50 BigData accounts on Twitter

Top50 BigData accounts on Twitter | Big Data & Digital Marketing | Scoop.it

Which accounts should I follow on Twitter to stay up to date with Big Data?
Here is the answer from a new algorithm.

Luca Naso's insight:

After screening the whole Twitter looking for #BigData posts, counting tweets, retweets, favourites and followers, the article reports the top50 accounts that best performed in the month of July 2015.

 

Here are the top5:

1. KPMG  (Score = 100.0%)

2. World Economic Forum (83.8%)

3. CloudEXPO ® (57.6%)

4. UN Development (45.4%)

5. Popular Science (41.4%)

 

Following them could be a wise idea for those who want to know more about the field!

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Clotilde Pasquet's curator insight, August 9, 5:33 AM

After screening the whole Twitter looking for #BigData posts, counting tweets, retweets, favourites and followers, the article reports the top50 accounts that best performed in the month of July 2015.

 

Here are the top5:

1. KPMG  (Score = 100.0%)

2. World Economic Forum (83.8%)

3. CloudEXPO ® (57.6%)

4. UN Development (45.4%)

5. Popular Science (41.4%)

 

Following them could be a wise idea for those who want to know more about the field!

Laura Rathe's curator insight, August 9, 11:36 AM

After screening the whole Twitter looking for #BigData posts, counting tweets, retweets, favourites and followers, the article reports the top50 accounts that best performed in the month of July 2015.

 

Here are the top5:

1. KPMG  (Score = 100.0%)

2. World Economic Forum (83.8%)

3. CloudEXPO ® (57.6%)

4. UN Development (45.4%)

5. Popular Science (41.4%)

 

Following them could be a wise idea for those who want to know more about the field!

Glenn Wallace's curator insight, August 12, 8:35 AM

Stay up .......

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Mechanize Your Hiring Process to Make Better Decisions

Mechanize Your Hiring Process to Make Better Decisions | Big Data & Digital Marketing | Scoop.it
Hiring the best is hard because there's so much human variation. Amazon's Anurag Gupta shares a system that always raises the bar.
Luca Naso's insight:

"Good intentions don't work, but a repeatable, measurable and improvable process can" by Anurag Gupta, at Amazon.

 

Nobody wants to hire the wrong person and nobody wants to work for the wrong company. By following processes, it is possible to reduce the probabilities for things like these to happen.

 

1. Raise the bar, steadily and honestly;

2. Build a hiring funnel. By consistently repeating a process, you improve it. Do yourself a favour: filter candidates at each step;

3. Don't go easy on culture. This is the hardest to standardize, and yet it's one of the main reason why employee will not stay.

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Glenn Wallace's curator insight, July 23, 10:14 AM

Stay updated & relevant 

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Actionable Ways Big Data Analytics Can Improve Innovation

Actionable Ways Big Data Analytics Can Improve Innovation | Big Data & Digital Marketing | Scoop.it

Signals assembled some of the big thinkers in big data. The discussion centered on the challenges faced by innovation teams at leading corporates, the potential (and hurdle) of “big data”, and fresh ideas on how to bridge that gap and tapping big data for innovation work.

Luca Naso's insight:

Here are 4 highlights from the conversation:

 

1. Product launchers need something to hold onto

2. Connecting the dots for the right context (aka find the right question)

3. Seeing is believing: the power of visualization

4. Caution: more data does not equal smarter data

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AndyDrooker's curator insight, July 1, 8:46 AM

Two (2) very important topics...

promptcloud's curator insight, August 7, 6:31 AM

Here are 4 highlights from the conversation:

 

1. Product launchers need something to hold onto

2. Connecting the dots for the right context (aka find the right question)

3. Seeing is believing: the power of visualization

4. Caution: more data does not equal smarter data

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Palantir Valued At $20 Billion In New Funding Round

Palantir Valued At $20 Billion In New Funding Round | Big Data & Digital Marketing | Scoop.it
The secretive data-processing company is raising up to $500 million in a previously undisclosed round of funding. The round makes it the third most valuable startup in the United States.
Luca Naso's insight:

There are a lot of secrets around Palantir, and probably you would not expect anything different from a company that has received funding from CIA (actually Q-Tel, CIA's venture capital arm).

What is known is that its data-processing software is being used to fight terror and catch financial criminals.

 

Palantir is now raising $500 million at a valuation of $20 billion, surpassed only by Uber ($50 billion) and Airbnb ($24 billion).

 

I say it's time to merge sharing economy and data science!

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Glenn Wallace's curator insight, June 28, 11:18 AM

I need 10%

UCD CCI's curator insight, July 2, 7:04 AM

big data... big business...

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SAP HANA expands its reach into Big Data analytics and the IoT

SAP HANA expands its reach into Big Data analytics and the IoT | Big Data & Digital Marketing | Scoop.it

SAP SE has rolled out an updated version of its flagship HANA in-memory database and application platform this week, with the emphasis on Big Data analytics and the Internet of Things (IoT).

Luca Naso's insight:

According to Steve Lucas, SAP’s president of platform solutions, service pack 10 is one of the biggest-ever updates to the platform since it was launched in 2010.

 

Main changes:
1. A new remote data-synchronization feature that allows organizations to synchronize data between remote locations and the enterprise;
2. A new Web-based development workbench to help enterprises cleanse data and manage duplicates (this could dramatically reduce the size of data centres);
3. Expanded data-integration capabilities that include support for the latest Cloudera Inc., Hortonworks Inc. and MapR Technologies Inc. Hadoop distributions.

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Gartner Says Advanced Analytics Is a Top Business Priority

Gartner Says Advanced Analytics Is a Top Business Priority | Big Data & Digital Marketing | Scoop.it

Gartner, Inc. said that advanced analytics is the fastest-growing segment of the business intelligence (BI) and analytics software market and surpassed $1 billion in 2013.

Luca Naso's insight:

A picture is worth a thousand words. The reason I am sharing this article is exactly for the figure it contains.

 

Data can be analysed with 4 different targets:

1. Descriptive - knowing what happened

2. Diagnostic - knowing what it happened

3. Predictive - knowing whether/when it will happen again

4. Prescriptive - knowing what to do

 

Usually, organizations approach data science aiming at goals from 1 (at the beginning) to 4 (after developing experience).

 

There is, however, an implicit flaw the figure. It seems to suggest that human intervention goes from large to almost absent. In fact, it is just shifting from "after the analysis" to "prior the analysis".

 

The Human intervention now lies in the data science team that develop and deliver the advanced analytics project.

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Jim Thompson's curator insight, June 16, 11:48 AM

Analytics and decisioning engines will help markets surf a wave of exploding data!

Christophe Acher's curator insight, June 18, 4:00 AM

TIBCO SPOTFIRE est depuis longtemps précurseur sur le segment de l'advanced analytics à la portée de tous, notamment avec l'intégration native de R et de fonctions statistiques avancées (segmentation, prévision, corrélations, ...).

 

A l'heure où de nombreuses entreprises se posent la question du remplacement de SAS, essentiellement pour des questions de coûts et de complexité, et SPOTFIRE s'impose comme solution de substitution partielle ou complète dans bien des cas.

 

N'hésitez pas à me contacter pour en savoir plus. christophe.acher@tibco.com

Robert McKenzie's curator insight, June 21, 3:50 AM

Sounds simple ..just add data and insight

 

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Gartner Predicts 3 Big Data Trends for Business Intelligence

Gartner Predicts 3 Big Data Trends for Business Intelligence | Big Data & Digital Marketing | Scoop.it

 

Three trends Gartner has identified describe information’s ability to transform business processes over the next few years.

Luca Naso's insight:

After giving us the definition of Big Data, Gartner is now giving us 3 predictions on what will happen with Big Data in Business Intelligence:

 

1. By 2020,

information will be used to reinvent, digitalize or eliminate 80% of business processes and products from a decade earlier.


2. By 2017,

more than 30% of enterprise access to broadly based big data will be via intermediary data broker services, serving context to business decisions.


3. By 2017,

more than 20% of customer-facing analytic deployments will provide product tracking information leveraging the IoT.

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5 Biggest Myths About Big Data

5 Biggest Myths About Big Data | Big Data & Digital Marketing | Scoop.it

Big Data myths or the noise that surrounds this term has created a lot of confusion in the tech world. For this reason, it is essential to highlight the myths surrounding Big Data technology.

Luca Naso's insight:

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

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Democratising finance: Banking on big data

Democratising finance: Banking on big data | Big Data & Digital Marketing | Scoop.it
Imagine if your bank sent you a financial health-check every morning, predicting how likely you were to go overdrawn that month. Or tracked the stores, cafés and restaurants where you spend money and sent discount offers. Or if your investment
Luca Naso's insight:

 

Big Data is a big opportunity in banking, but there are a lot of things to sort out along the way.
In the UK, banks are teaming up with retailers to offer personalised discounts to customers through their mobile applications based on an individual’s spending patterns.Banks are starting to think of their small business customers and using their data to see in real time what is happening to their businesses and where banks can step in to help.
More than a third of banks still buy data on clients from third parties, because it is too hard to extract the same data from their own systems.Most banks are held back by concerns over privacy and the technical challenges stemming from their ageing and complex systems.

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Understanding big data leads to insights, efficiencies, and saved lives | Harvard Magazine

Understanding big data leads to insights, efficiencies, and saved lives | Harvard Magazine | Big Data & Digital Marketing | Scoop.it
Luca Naso's insight:

There is a lot of content in this (long) article published by the Harvard Magazine.

 

Here are my main 3 takeaways:

1. "The Big Data revolution lies in improved statistical and computational methods, not in the exponential growth of storage or even computational capacity" by Gary King

2. Big Data isn't everything: "We had petabytes of data and yet we were building models that were fundamentally flawed, because we didn't have insights about what was happening" by Nathan Eagle

3. "No matter how much data exists, researchers still need to ask the right questions to create a hypothesis, design a test, and use the data to determine whether the hypothesis is true." by Nathan Eagle

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Build your first IoT device with IBM and ARM kit

Build your first IoT device with IBM and ARM kit | Big Data & Digital Marketing | Scoop.it
The mBed IoT Starter Kit launches today - providing a kit for building IoT prototypes that can be sending data to the cloud for analysis within minutes.
Luca Naso's insight:

A common challenge when dealing with the Internet Of Things is the lack of standardization. This, in turns, makes it difficult for all of the sensors to communicate, for all of the data to be gathered, analysed and eventually leveraged.


IBM and ARM have teamed up to lower the barrier for developers in IoT: the mBed IoT Starter Kit costs just around 100eur, and can be directly connected to the IBM IoT Foundation (to channel data into IBM Bluemix serivces).

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Abdulmalik Ofemile's curator insight, May 10, 3:23 AM

Big Data DIY

Leonard Bremner's curator insight, May 25, 5:15 AM

Looks a fun project

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Business intelligence and analytics trend towards self-service at the Gartner Summit

Business intelligence and analytics trend towards self-service at the Gartner Summit | Big Data & Digital Marketing | Scoop.it
Self-service analytics, business intelligence on Big Data, and the changing role of the IT buyer were the belles of the annual Gartner Business Intelligence and Analytics Summit.
Luca Naso's insight:

Today's buyers are increasingly coming from the business side of the house and not from corporate IT and self-service analytics is growing while traditional dashboard BI is in remission.
Self-service analytic tools allow power users to quickly explore, blend and visualize data to produce new business insights and to validate business data requirements to support application development and data management. 
Is the pendulum swinging in the direction of analytics empowerment and reduced time-to-answer and away from cost control and data quality management?
Gartner thought leader, Frank Buytendijk, suggested that we look to the business model that cracked the code on optimizing the centralization versus decentralization trade-off; namely, franchising.
This implies standardization of tools and enterprise licensing to drive down costs, tool-specific skilling to create larger pools of skilled workers to be shared across projects and centralized provisioning of compute infrastructure to save time and money.

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IBM, Cloudera and Amazon Big Data announcements

IBM, Cloudera and Amazon Big Data announcements | Big Data & Digital Marketing | Scoop.it
New products, new technologies and an acquisition announced over the last two weeks may be small items on their own, but they add up to an important shift in the Big Data space.
Luca Naso's insight:

1. IBM is improving its cloud-based elastic data warehouse;

2. Cloudera announces Ibis: a project for letting data-scientists use Python directly on Hadoop;

3. Amazon is making Elastic MapReduce almost plug-and-play.


What's especially interesting about all of these announcements is that they are signs of a maturing market. No one's introducing a brand new data processing engine. Instead, IBM is improving, acquiring and competing; Amazon is streamlining; Cloudera is re-platforming; and Apache NiFi is integrating.

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JẴї❣'s curator insight, August 29, 6:47 AM
Big shift in the Big Data space
Anthony Ricketts's curator insight, August 29, 9:15 AM

I am taking a few courses in Data Analytics. My friend @laurakenyon told me it is a huge deal for marketers now. Indeed ROI and understanding metrics are fundamentals of the marketing profession.

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All about Data Lakes

All about Data Lakes | Big Data & Digital Marketing | Scoop.it

Here is an infographic developed by Aureus Analytics which shows how a Data Lake really works

Luca Naso's insight:

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.

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Chris Balbrick Infographiste's curator insight, August 2, 7:40 AM

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.

iSparkCEO's curator insight, August 4, 2:28 PM

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.

MH's curator insight, August 24, 6:41 AM

Les "lacs de données", une nouvelle façon de stocker les données

 

Infographie listant les informations de base sur les lacs de données et leurs différences avec le stockage classique. Les lacs de données regroupent les données en vrac et sans traitement. L'accès y est plus ouvert que pour les bases de données classiques. De ce point de vue, ils s'apparentent à l'open data.

 

Cette nouveauté semble intimement liée au développement de méthodes de recherche capables de relier entre elles des données hétérogènes.

 

(infographie en anglais)

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Internet of Things Dissected By SmartThings CTO Jeff Hagins

Internet of Things Dissected By SmartThings CTO Jeff Hagins | Big Data & Digital Marketing | Scoop.it

The Internet of Things is the 3rd wave of internet that is transforming the ways in which we live and work. Therefore, it has to involve the physical world.

Luca Naso's insight:

"Automation" is an important part of the equation. Internet of Things is not about moving the switches from the wall into the device (smartphone or tablet), but to remove them all!

 

We are at a stage where software has a concrete opportunity to reinvent the hardware.

 

Security is another important part of the equation. And it only happens if you include it in your design. This requires time, people, resource and money.

 

Even more, security and easy of use are on different part of the balance. There is a trade-off between "plug and play" and security, and it is not yet clear how to achieve the best balance.

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Glenn Wallace's curator insight, July 4, 8:38 AM

What are they speaking about

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7 More Big Data Companies to Watch

7 More Big Data Companies to Watch | Big Data & Digital Marketing | Scoop.it

Like any market in its infancy, the big data industry is bustling with companies jockeying for a comfortable position in a niche that is as surefire as any.

Luca Naso's insight:

Here is a list of 7 "new" companies that might make the difference in the realm of Big Data.

 

1. Palantir Technologies

Founded in 2004 and evaluated at $20B it is probably not a startup anymore

2. Crayon Data 

Big data for decision making

3. Neo Technology

Best known for its graph db Neo4j

4. Couchbase

The company behind the NoSQL db Couchbase server.

5. PromptCloud

Specialised in Data-as-a-Service

6. Snowflake

Big Data technologies for SMBs.

7. Saama Technologies

Is this a startup? Founded 18 years ago, it has clients such as Apple and Cisco

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Glenn Wallace's curator insight, June 30, 11:18 AM

Leave your comment

iSparkCEO's curator insight, August 4, 2:30 PM

Here is a list of 7 "new" companies that might make the difference in the realm of Big Data.

 

1. Palantir Technologies

Founded in 2004 and evaluated at $20B it is probably not a startup anymore

2. Crayon Data 

Big data for decision making

3. Neo Technology

Best known for its graph db Neo4j

4. Couchbase

The company behind the NoSQL db Couchbase server.

5. PromptCloud

Specialised in Data-as-a-Service

6. Snowflake

Big Data technologies for SMBs.

7. Saama Technologies

Is this a startup? Founded 18 years ago, it has clients such as Apple and Cisco

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IBM Serves Wimbledon Tennis With Data Analytics

IBM Serves Wimbledon Tennis With Data Analytics | Big Data & Digital Marketing | Scoop.it

It’s Wimbledon season and that means tennis, strawberries and Pimm’s. It also means big data analytics. Previous Forbes writers have chided tennis for being behind other sports in data analytics, but this suggestion may need some clarification.

Luca Naso's insight:

At the Wimbledon tennis tournament, IBM will use a solution based on InfoSphere + Watson. This will make it possible not only to collect information just about everything that happens to the players and to the balls, but also to relate this large set of data with historical information and to query it using Natural Language.

 

There's more.

With so much data about each shot and player, it will be possible to analyse players' playing style. This will in turn allow the players to study their opponents' weakness and strengths to better prepare next matches.

 

A very interesting note.

IBM is training tennis professionals to use the data entry system, rather than training data scientists on tennis.

Yet another proof of the importance of having field experts (about the specific industry under consideration).

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Glenn Wallace's curator insight, June 24, 1:16 PM

We must stay abreast with current events

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Big Data Integration: Five Biggest Pitfalls to Avoid

Big Data Integration: Five Biggest Pitfalls to Avoid | Big Data & Digital Marketing | Scoop.it

The big data revolution has captivated a global audience. What you rarely hear is that there are some pitfalls that can sink your project to the abyss of no return.

Luca Naso's insight:

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.

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Jabbar Ziadi's curator insight, August 1, 9:08 AM

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.

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7 steps for executing a successful data science strategy

7 steps for executing a successful data science strategy | Big Data & Digital Marketing | Scoop.it

Data Science often points to the need for change - and change can be difficult. Get tips from TDWI for making your foray into data science a success.

Luca Naso's insight:

 

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 

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Five ways small companies can leverage big data

Five ways small companies can leverage big data | Big Data & Digital Marketing | Scoop.it

 

With so much to track, organise and analyse, it can be difficult to know where to begin.Today, leveraging big data is technically and financially viable for smaller companies in a way that it never has been before.

Luca Naso's insight:

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

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The ‘anytime, anywhere, anything’ economy: Defying the economic gloom

The ‘anytime, anywhere, anything’ economy: Defying the economic gloom | Big Data & Digital Marketing | Scoop.it
Lots of economists are not very optimistic about the future. And this has got to stop argues Mark Cliffe. ING’s chief economist shines his - positive - light on the global economy.
Luca Naso's insight:

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".

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Eric Morineau's curator insight, June 2, 3:56 AM

ajouter votre perspicacité ...

attigs's curator insight, June 3, 9:19 AM

There will be a future regardless

Flavio Calonge's curator insight, June 3, 10:07 AM

Change is good and wee need to adapt, and fast!

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5 Signs It’s Time to Outsource Your Data Management

5 Signs It’s Time to Outsource Your Data Management | Big Data & Digital Marketing | Scoop.it

If your company is struggling with big data because it is taking up too much time and resources, consider hiring the services of a data center outsourcing firm.

Luca Naso's insight:

 

Nowadays, companies in virtually any field have the capability to gather, store and take advantage of massive amounts of information.But not all of them have the capability to manage the data in the proper way.
Here are 5 signs that it could be time to outsource your data management:1. Overpriced in-house management costs.2. Lack of in-house Big Data management experts.3. Constant need to re-deploy employees to do other tasks.4. Inability to comply with regulatory requirements.5. Failure to respond quickly to technological changes.
Avoid creating strong dependency on the third party expert and keep in mind these three things that can massively help you:1. Have a clear plan on how you want to use the data2. Embrace cloud technologies3. Develop in-house skills

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Hendrik Feddersen's curator insight, May 26, 4:50 PM

Nowadays, companies in virtually any field have the capability to gather, store and take advantage of massive amounts of information. But not all of them have the capability to manage the data in the proper way. Here are 5 signs that it could be time to outsource your data management: 1. Overpriced in-house management costs. 2. Lack of in-house Big Data management experts. 3. Constant need to re-deploy employees to do other tasks. 4. Inability to comply with regulatory requirements .5. Failure to respond quickly to technological changes.

Biel's curator insight, May 27, 11:54 AM

Com Big Data canviarà la comercialització empresarial

Suggested by Carol Soriano
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A Comprehensive Guide to Data Management for Businesses

A Comprehensive Guide to Data Management for Businesses | Big Data & Digital Marketing | Scoop.it

In order to leverage data for your business effectively, you have to first develop a clear understanding of what data is and how you can efficiently make the most out of it. This ultimate guide to data management will help you out.

Luca Naso's insight:

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

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Hadoop and the Internet of Things: Better together

Hadoop and the Internet of Things: Better together | Big Data & Digital Marketing | Scoop.it

 

The Internet of Things continues to grow more popular, and the network of devices connected to it gets bigger every day. Gartner has estimated that there will be 26 billion devices connected to the IoT within the next six years.

 
Luca Naso's insight:

Up to now the Internet of Things has mainly focused on the data generation part (sensors and devices).

 

It is now time for the Analytics side to take over. Here is where Hadoop can make the difference.

 

However, my expectations are that IoT will boom with Real-Time analytics, and Hadoop can be of little use in this scenario.

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