How Many Websites Use Google Analytics? Estimates Suggest 30-50 Million

How Many Websites Use Google Analytics? Estimates Suggest 30-50 Million

How Data Will Transform B-to-B Marketing in 2016

How Data Will Transform B-to-B Marketing in 2016

5 Tools For The Most Productive Social Media Analytics Monitoring Ever

5 Tools For The Most Productive Social Media Analytics Monitoring Ever

2015 Business Process Management Market Landscape Report: Selecting a BPM Solution for the Modern Business

Para aqueles que trabalham com Excelência Operacional (Melhoria Contínua), sugiro a leitura!!!

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2015 Business Process Management Market Landscape Report: Selecting a BPM Solution for the Modern Business

2016 Predictions: All That Data Will Finally Drive Business Action

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Do you consider yourself “data-driven”? If you’re like most business and technology leaders, you do. But the reality is that most businesses have only scratched the surface when it comes to transforming all of that data into insight that drives real business action.
In our 2016 predictions report, my colleagues Brian Hopkins, Jennifer Belissent, PhD., and I predict what will happen in the hottest areas of big data, analytics, business intelligence, and systems of insight — and tell you what to do about it. Here’s a sneak at just a few highlights:
◾Chief data officers (CDOs) will gain power, prestige, and presence . . . for now. The trend toward appointing a CDO accelerated in 2015, and will continue in 2016. CI pros should take advantage of this. How? Extend customer insights beyond marketing to drive a culture of insights-to-execution across the organization.
◾Firms will try to come to terms with data science scarcity. Two-thirds of firms will have built predictive systems capability by mid-2016, but will struggle to find data science talent. Customer insights teams must increase analytic yield without waiting for hard-to-find data scientists. How? Some analytics platforms from vendors like AgilOne, Custora, and Origami Logic can empower business users without a rigorous statistical background.
◾Demand for real-time customer engagement will test streaming technology. Traditionally, marketers use analytics retrospectively to analyze highly structured, at-rest customer and campaign data from customer databases. But as marketing technologists try to build contextual marketing engines, they will need to apply analytics as customer interactions occur in order to drive deeper engagement. To succeed, CI pros must prioritize in-motion data and time-sequenced analysis.
◾Only a few elite teams will take the leap from BI to systems of insight. Only a few BI teams are taking baby steps toward agile BI, and Forrester expects that in 2016, less than a third of these will be ready to make the leap to systems of insight. Why? Because BI and big data are firmly rooted in the technology while business executives impatiently await results. We see better business outcomes in organizations that merge BI and customer insights teams.
These are just a few of the highlights from Forrester's 2016 predictions. Read the full report to see what 2016 holds for marketing and customer insight professionals. One thing is for sure…it takes more than big data to be an insights-driven business.

(About the author: Carlton Doty is an analyst at Forrester Research)

What every CEO/MD needs to know about Big Data & Data Analytics

Introduction

In order to remain competitive, and viable, businesses now have to deal with a vast and rapidly growing sea of what has been termed ‘Big Data’.  They need to be able to transform this raw data, often in real-time, into more meaningful insights about their markets, customers, competitors, and to measure and manage their performance more accurately using using techniques such as ‘Data Analytics’. In many cases this represents a paradigm shift from their comfort zone of approaches based more on experience, guesswork, or painstakingly constructed models of reality.

It used to be that Big Data and Data Analytics were the preserve of large global corporations but consider this definition for Big Data: When volume, velocity and variety of data exceed an organization's storage or compute capacity for accurate and timely decision making. Big Data is a relative term. Every organization will rapidly reach a point where the volume, variety and velocity of their data will be something that they have to address.

A Brief History

Big Data is far from being a new concept, we just gave it a new name a few years back. Probably the earliest examples of Big Data date back to Mesopotamia 7,000 years ago when accounting practices were introduced to record the growth of crops and herds. The first data-processing machine appeared in 1943 and was developed by the British to decipher Nazi codes during World War II. This device, named Colossus, searched for patterns in intercepted messages at a rate of 5,000 characters per second. Thereby reducing the task from weeks to merely hours.

In 1965 the United Stated Government decided to build the first data centre to store over 742 million tax returns and 175 million sets of fingerprints by transferring all those records onto magnetic computer tape that had to be stored in a single location. The project was later dropped out of fear for ‘Big Brother’, but it is generally accepted that it was the beginning of the electronic data storage era.

In 1989 British computer scientist Tim Berners-Lee invented the World Wide Web. He wanted to facilitate the sharing of information via a ‘hypertext’ system. As of the ‘90s the creation of data is catalyzed as more and more devices are connected to the internet. In 1995 the first super-computer was built, which was able to do as much work in a second than a calculator operated by a single person can do in 30,000 years.

It was only in 2005 that Roger Mougalas from O’Reilly Media first coined the term Big Data, only a year after they created the term Web 2.0. It referred to Big Data as a large set of data that is almost impossible to manage and process using traditional business intelligence tools.

A Few Facts

Data

• The number of bits of information stored in the digital universe is thought to have exceeded the number of stars in the physical universe
• If you burned all the data created in just one day onto DVD’s and stacked them on top of each other it would reach to the moon and back
• Every day we generate as much information as we did from the beginning of time to 2003
• 90% of existing data has been created in the past two years alone meaning more data has been created in the past 24 months than our entire history
• The NSA is thought to analyze 1.6% of all global internet traffic at around 30 petabytes (30 million gigabytes) per day, but quite staggeringly less than 1% of all global data is ever analyzed.

IT Consumer

• Every minute we send 204 million e mails, 1.8 million Facebook likes and we send 278 thousand Tweets
• Facebook users share 30 billion pieces of data every day
• There are 1.2 billion smartphones in the world
• By 2020 we will have over 6.1 billion smartphone users globally and this will be greater than the number of fixed line subscriptions
• The Internet of Things connected devices will rise from 13 billion to 50 billion by 2020
• 570 new websites arrive every minute of every day
• The Internet of Things (IoT) will generate in excess of $300 billion revenue by 2020.

IT Industry

• By the year 2020 about 1.7 megabytes of new information will be produced every second for every human being and we will be dealing with 40 Zettabytes of data
• Data Centres now occupy an area of land equal to 60,000 football fields
• AT&T is believed to hold the worlds largest volume of data in a single DBMS at 312 terabytes with 2 trillion rows
• Estimates show that large companies with at least 10,000 employees store an average of 200 terabytes in data – and that figure climbs daily. That’s more data than all the information that was produced by humanity up to the 21st century
• By 2015 the demand for data and analytics resources will reach 4.4 million jobs globally, but only one-third of those jobs will be filled.

What are Big Data and Data Analytics?

Big Data describes a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques.

• Unstructured data: Information that is not organized or easily interpreted by traditional databases or data models, and typically, it’s text-heavy. Metadata, Twitter tweets, and other social media posts are good examples of unstructured data.
• Multi-structured data: Data formats and types which can be derived from interactions between people and machines, such as web applications or social networks.

Data Analytics is a term used for the techniques used to interrogate Big Data to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The findings from this can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits. It uses techniques such as the simultaneous application of statistics, computer programming, operations research and data visualization to help communicate insight.

Firms may apply analytics to business data, to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, enterprise decision management, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.

Big Data analytics is a rapidly evolving science and now drives nearly every aspect of our modern society, including mobile services, retail, manufacturing, financial services, life sciences, and physical sciences.

Key Uses of Big Data and Data Analytics

There are many applications for Big Data and Data Analytics and the following three highlight just a few of these to help explain their importance.

Market and Customer Understanding


Businesses can employ dynamic pricing to allow them to offer different prices at different times in different places to different consumers. This allows them to optimize revenue by incorporating real-time datasets, including supplier and inventory data, models of consumer likelihood to purchase and financial forecasts.

Businesses can monitor the web and social media for mentions of their brand by consumers. They can review the analytics for their own digital assets (website, microsites, blog, social media, and third-party signals) and use this information to identify potential product extensions.

Marketers can use Big Data to determine the optimal channels to place their products. Big Data can also be harnessed to test and predict likely consumer reaction to various marketing messages

Internet of Things (IoT)

These are everyday objects embedded with electronics, software, sensors, and network connectivity, which enables them objects to collect and exchange data.

There are mainly three types of technologies that enable IOT:

• Radio Frequency Identification (RFID) and Near-Field Communication (NFC): In the 2000s, RFID was the dominant technology. In more recent years NFC has become more prevalent
• Optical tags and Quick Response Codes (QCR): Phone cameras decode QCR using image-processing techniques.
• Bluetooth Low Energy (BLE) - All newly releasing smartphones have BLE hardware in them. Tags based on BLE can signal their presence at a power budget that enables them to operate for up to one year on a lithium coin cell battery.

Each thing is uniquely identifiable through its embedded computing system but is able to interoperate within the existing Internet infrastructure. Experts estimate that the IoT will consist of almost 50 billion objects by 2020 and represents $14.4 trillion in value to companies and industries across the globe.

Operations

Sensors on a commercial aircraft can generate up to 20 terabytes of data an hour. Car manufacturers are incorporating technologies that continually reporting back data collected from onboard car sensors and dealer service systems. In the UK, Thames Water combines data from embedded connected sensors on its pipes and treatment facilities with data from a wide range of business systems. This enables them to track and predict the cost risk and performance of its assets resulting in faster response times, saving money and improving customer service.

IT Operations Analytics (ITOA) (also known as Advanced Operational Analytics or IT Data Analytics) can provide the necessary insight to identify meaningful information buried in piles of complex data and can help IT operations teams to proactively determine risks, impacts, or the potential for outages that may come out of various events that take place in the environment (e.g., application and infrastructure changes). This enables a new way for operations to proactively manage IT system performance, availability, and security in complex and dynamic environments with less resources and greater speed.

ITOA contributes both to the top and bottom line of any organization by reducing operations costs, and increasing business value through greater user experience and reliability of business transactions. In addition, conventional analytics and problem-solving responses generally only respond to events that have occurred. The new generation ITOA solutions can use historical and real-time data to build predictive models of future behavior and help to mitigate risks.  

Business Challenges

Management

Big Data and Data Analytics provides a set of powerful tools to business and should not be seen as a ‘magic bullet’! Leadership teams who can build comprehensive strategies and plans to apply these technologies and techniques effectively, and to ensure that their teams are involved and enabled will be able to leverage the benefits.

People

A recent employers survey by the Tech Partnership shows that Big Data Analytics represents the most significant area of IT skills gaps. While data may be part of the answer to the productivity gap it also appears that barriers to accessing analytical talent are preventing businesses from fully harnessing their potential.

The problem is finding people with the right mix of skills. The data scientists who combine technical skills, mathematical, analytical and industry knowledge, along with the business acumen and soft skills to turn data into value for employers are very hard to find and they are starting to be referred to as ‘unicorns’

Data analysts must be able to ask the right business questions, analyze the resulting data effectively, and understand the appropriate statistical techniques in order to harness the multitudes of unstructured data. Data analysts must also be able to apply a wide range of skills when extracting and analyzing data, and presenting the results to executive management or departmental managers, such as business acumen, presentation skills, database skills, analysis skills, and often coding abilities.

Technology

Big Data needs highly performant technologies to efficiently process large quantities of data in short elapsed times. The tools available to handle the volume, velocity, and variety of big data have improved greatly in recent years and, in general, are not now prohibitively expensive with much of the software available as open source. Key techniques include A/B testing, crowdsourcing, data fusion and integration, genetic algorithms, machine learning, natural language processing, signal processing, simulation, time series analysis and visualization. Hadoop, the most commonly used framework, combines commodity hardware with open-source software. It takes incoming streams of data and distributes them onto cheap disks. Hadoop also provides tools for analyzing the data.

Big data has increased the demand of information management specialists in that Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, HP and Dell have spent more than $15 billion on software firms specializing in data management and analytics.

Conclusion

The availability of Big Data, low-cost commodity hardware, and new information management and analytic software have produced a unique moment in the history of data analysis. The convergence of these trends means that businesses of all sizes now have the capabilities to analyze vast data sets quickly and cost-effectively. This represents a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue, and profitability.

Big Data and Data Analytics is a rapidly developing science and the next phase will be Predictive Analytics. Rather than react to insights gained through data analysis, businesses will use a combination of real-time, historical and third-party data to build progressively more sophisticated forecasts of what might happen in their business.

Research has indicated that predictive maintenance can generate savings of up to 12% over scheduled repairs, leading to a 30% reduction in maintenance costs and a 70% cut in downtime from equipment breakdowns. For a manufacturing plant or a transport company, achieving these results from data-driven decisions can add up to significant operational improvements and savings opportunities.

These new approaches will also enable businesses to capitalize on opportunities to market products to customers, such as targeting prospective customers after key events. Forrester analysts Rowan Curran and Mike Gualtieri in a Forrester Wave research paper entitled Big Data Predictive Analytics Solutions, Q2 2015 stated that predictive analytics have never been more relevant and easier to use, and offer ways for forward-thinking enterprises to succeed in competitive sectors.

The original blog can be seen here: (https://korolit.com/2015/11/what-every-ceomd-needs-to-know-about-bi...).

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5 SEO Tools You Need to Use BEFORE Publishing a YouTube Video

 ANN SMARTY DECEMBER 15, 20145 COMMENTS

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In most cases, the most visible part of the video search listing is the video thumbnail. But it's not the only element that can make your video more prominent in the search results. When I search YouTube, I usually pay more attention to the title, for relevance, and also to the description. The title and description play an important part in both the YouTube search and "related videos" algorithms, and the more relevant and optimized they are, the more chances your videos have of being visible to your target audience.

YouTube videos rank well in Google's Universal Search results as well - especially for "how-to" queries where users are likely to be interested in video instructions. Clear, relevant, optimized video titles and descriptions will stand out to the user, enticing them to click through and watch your content. Take the example below, the key term searched for was 'how to play piano', and the results returned highlighted that phrase in bold, allowing me to quickly skim through results to pick the one with a more eye-catching and relevant description:

Therefore, performing some basic keyword, and also competitor research, may boost your video views and channel visibility. Here are 5 Keyword, Trend Research, and SEO tools and techniques that will come in very handy for video SEO, YouTube marketing, and beyond:

#1 YouTube Search Filters: Understand What's Already Working

Whenever you are creating anything, searching to see what others have done before you should become part of your routine. I search YouTube several times in the video creation process. For example, when I am brainstorming topics, when I want to see how creators design their video thumbnails, when I want to see how competitors name their videos, etc.

There are lots of YouTube search filters to play with. I usually check "Sort by upload date" to see most recent videos. Sorting by rating is another very useful option because it gives me more insight into what people seem to react to more positively. I may also play with searching for channels and playlists to find more competitors. There's also a way to search for longer videos which I try when I am into how-to content:

#2 YouTube Search Auto-Suggest: Long Tail Key Phrases

Using more specific key phrases is a good way to get ranked for less competitive phrases and thus drive natural highly-targeted traffic to your video. That's where looking at YouTube Auto-complete (Auto-Suggest) results can help a lot. Start typing your search term in the search field at YouTube and you'll see most popular search terms people typed for that word. It's an awesome resource of keyword information because it shows you what people tend to search for.

The keyword suggest tool from SEOChat will also give you even more insight: It queries YouTube for your base term and then adds each letter of the alphabet after it to retrieve more results. It also supports Google, Bing and Amazon for you to get even more keyword suggestions:

3. SerpStats: Discover Questions You Can Answer

SerpStats is a keyword tool that is also based on Google Suggest but it works a bit differently. Letting it research a phrase is not as effective as giving it one base word and let it generate more loose suggestions. My favorite tab to go through the multiple key phrases is "Only questions". The tool uses a separate algorithm that let's it find and filter out interrogative questions people tend to type into the search box. This is a very useful insight into how people tend to phrase the question that may be answered in your video. That's also a great inspiration source for your future videos.

ALSO ►  Google and YouTube Turn April Fools’ Day into Tent-Pole Event

There are more keyword tools that can help you find questions people are wondering about in your industry including Quora and MyBlogU.

#4 Google Plus /Explore: Research Related Concepts and Trends

/Explore is one of my favorite Google Plus features, along with Google Hangouts on Air. It's the most up-to-date source of keyword inspiration giving you a better understanding of:

• Your base term related concepts: This allows you to expand your keyword research and make your video information richer
• Slang and user-generated hashtags that usually neighbor your base term.
• Relevant hot trends: Time-sensitive phrases that tend to currently come in close proximity with your base term

The third point is priceless! If there's a way to target time-sensitive terms in your video title and/or description, you'll have much more chances to be searched for and found.

In many cases, a very specific trending hashtag used in the video title will help it get more exposure in social media, especially Twitter, because YouTube video title makes it to most tweets.

I did a more detailed piece on how to use /Explore section and Google Plus hashtags to become a more efficient content marketer in this post.

#5 Cyfe: Create a Content Monitoring & Archiving Dashboard

Cyfe is my ultimate productivity and content marketing dashboard. I am using it to monitor and archive multiple search results from multiple sources on one page. My YouTube keyword research dashboard contains:

• Twitter search results referencing YouTube videos with my key term included
• Google Plus search results referencing Youtube videos with my key term included
• Different variations of the above using most relevant hashtags

I check the Cyfe dashboard every time I am thinking of future videos to create, as well as before publishing a video on YouTube to get some related context inspiration.

SEO Basics: How to Optimize Your YouTube Video

Here are some resources to better understand how to implement keyword data from the above tools:

• Listen to the podcast: How to Optimize YouTube Tags and Descriptions as well as this guide on optimizing video titles
• First Site Guide provides some basic, yet solid info on understanding and using keywords
• Apart from optimizing videos, don't forget that you can optimize your channel and playlist

Which tools are you using for YouTube keyword research? Please share them in the comments!

Source: 5 SEO Tools to Use BEFORE Publishing a YouTube Video http://www.reelseo.com/5-seo-tools-youtube-video/#ixzz3rfkaWCZj 
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