Videos: Newest Form of Big Data for Companies?

Written by Rory Welch

Data comes in all shapes and forms. In most cases, individuals initially think of data as statistics, numbers, and text. At this particular stage of big data management, video is on the back burner. But should we begin focusing on utilizing big data for videos?

YouTube – Endless Hours of User Uploaded Videos


Why should businesses or companies be interested in video? YouTube, one of the most popular websites on the Internet, allows users to upload personal videos, movie clips, TV clips, music clips, blog clips, and short videos. YouTube has released statistics about video usage that you may find interesting. 

  • Each month, YouTube receives over 800 million unique visits.
  • Each month, there are over 3 billion hours of video being watched.
  • Every minute, 72 hours of videos are uploaded.
  • YouTube received over 1 trillion views in 2011.

What about the social media aspect?

  • Every day, over 500 years of YouTube videos are watched on Facebook.
  • Every minute, 700 videos get shared on Facebook.
  • Every week, over 100 million people like, share, or comment on a YouTube video.

Don’t Forget about Video!

Big data for video is still in the early stages. Most companies are overlooking what could be a substantial asset to their company. Rebecca Jacoby, CIO and SVP of Cisco, is telling individuals not to forget about video when it comes to big data. Jacoby says they “most often use video data when they’re looking to bring new information to an argument that outside experts can provide…if you rely on video, you have a totally different level of trust established around that relationship,” she said.

“A Picture is Worth a Thousand Words”

Who knew that a small phrase could have such a powerful meaning? “A picture is worth a thousand words” simply means “a picture that tells a story as well as a large amount of descriptive text.” From the big data standpoint, we should take this meaning to heart. A video is but a picture in motion. If there is so much information that can be gathered from a picture, why couldn’t we do the same thing with video? So, what exactly can be gathered from a video? The great thing about videos is that every video is done by a real individual. An individual with ideas, desires, interests, wants and needs, and it is being publicly shared for anyone to see. Depending upon the video, companies use big data to test certain factors and they can also see where the user is located, videos posted, date and time of posting, what type of clothing they are wearing, what technology they use, what social sites were used to share the video and other beneficial information. Videos are becoming more popular with users and social networks. A user can watch a video for demonstrations, fashion advice, news, product reviews and other topics. With the ease and simplicity of video usage, statistics show that video popularity is rising at an accelerating rate. As more users are beginning to use videos, companies would be wise to start noticing this trend and the possibilities that it brings.

Big Data for Videos: Can it be done?

Right now, there are currently no applications to analyze videos because data management tools are currently working with text files or other file formats. Can analysts find a way to analyze videos to collect and understand data in the same way that it is done with big data management systems today? If so, do you think video data will be in the near future?

Customized Consumer Experience – Compliments of Big Data

Written by Rory Welch

In the business world, ridiculous amounts of customer data is created every hour, minute, and second of each day. Businesses are monitoring where you clicked, what you bought, where you bought it, the location of the store the item was bought, and other information that allows them to understand their customer’s interests. All of this is done in order to provide the customer with more customized service, or in other words – to show them more products they will like so they will buy more. Some companies such as Sears, Wal-Mart, and Amazon have really been pioneers in studying their data to identify and anticipate customer behaviors. 

How do businesses collect data? 

There are a number of standard methods:

  • Questionnaires
  • In-home product testing
  • Surveys
  • Focus Groups
  • Interviews
  • Mystery Shopping
  • and many more!

Then you have the electronic data collection processes:

  • Radio Frequency Identification Technology
  • Software Logs
  • Credit Card Purchases
  • Mouse Click Logs
  • Web Forms
  • Website Logs
  • Computer Cookies
  • and many more!

Success in the Analysis of Customer Data

Sears – Improving Loyalty Program Performance

So what does all of this mean? Essentially, a business is able to track current or potential consumers’ interests and purchases. For example, Sears is currently using a big data management system that is constantly monitoring and analyzing customer activity which will allow Sears to provide a more personalized experience for customers in their loyalty program. As a result, Sears is able to make more educated and accurate decisions on future communications and offers for members. Sears created a solution that analyzed “customer activity at an individual level.” Dr. Phil Shelley, CTO at Sears Holdings Corporations says “We [as people] want personalized experiences. They mean more to us and are more relevant. I haven’t thought about it much until we were getting into that level of personalization because it wasn’t possible before these tools became available. Now you can get very personalized.” As a result, Sears’s loyalty program exceeds 80 million members and is showing no signs of slowing down in the future.

Wal-Mart – Learning Customers’ Habits

Wal-Mart, the largest retailer in the world, is currently using big data for a number of marketing strategies. For example, Wal-Mart acquired Kosmix to form @WalmartLabs which is being used to gather information from social networks such as Facebook, Twitter and YouTube to strengthen Wal-Mart’s online sales. They have created what is called a “Social Genome” which is used to analyze social media feeds. This means that when a customer buys an item on the Wal-Mart website, Wal-Mart can combine th
is data with information from social networks with actual transaction history. Anand Rajaraman says “It’s a race to see who can use all this data the best. This will change the retail industry, as well as most other industries.”

Amazon – Building Better Customer Relationships

Amazon, one of the largest e-businesses in the industry, has been utilizing big data for years. For example, Amazon analyzes customer purchases to make future recommendations. Amazon also logs what products are viewed, how often, and what was ultimately purchased. By doing this, Amazon can use this information to make product recommendations and display these statistics to others that view that product as well. Amazon’s marketing strategy aims to know what interests the customer and uses this information to make educated suggestions. In 2010, it was estimated that Amazon had over 59 million active customers which resulted in over 42 terabytes of data.

Will Big Data Change how Businesses Market to the Public?

Big and small businesses are starting to implement big data management systems to better customize the customer experience. Sears, Wal-Mart, and Amazon are but a few that have been using big data to revamp their marketing strategies. So what do you think? Can big data improve the consumer’s shopping experience? Or do you think they are encroaching on your privacy?

Government Requests for Big Data on the Rise

Written by Rory Welch

“Big Brother.”  The term was introduced into the American pop culture lexicon by George Orwell in his novel Nineteen Eighty-Four and has since become synonymous with the abuse of government power as it relates to mass surveillance.  In the novel, Big Brother is the dictator of a fictional state that has its citizenry under complete surveillance at all times.  Big Brother relies on the use of telescreens to aid in its surveillance operations, but in today’s present our governments would find telescreens outdated to say the least. 

Today, telescreens have been replaced by their real world counterpart, the cell phone, and nearly every single American has one in his or her pocket or purse.  Based on data collected by the U.S. Census Bureau, there are currently 327.6 million active wireless subscriptions in the United States.  Cell phones now effectively outnumber our nation’s population.  And those cell phones represent one of the fastest growing sources of big data on the planet- one that “the man” can easily get information from. 

Most would agree that the government has a vested interest in collecting data, but for the first time ever an audit of cell phone carriers disclosed that U.S. law enforcement has made over 1.3 million requests for customer information in the past year, and that number is expected to grow in 2012.  Pursuant to a subpoena, the only customer data that can be disclosed to law enforcement is limited to basic subscriber information, which is is strictly limited to six specific categories of information (name, address, local/long distance records, length/type of service, telephone/subscriber number and method of payment).  However, the data that law enforcement agencies collected from cell phone carriers went well beyond basic customer information to include text messages, location information, as well as both incoming and outgoing calls.

Many in Congress are currently expressing concern over the amount and types of information that law enforcement are seeking from cell phone carriers.  Representative Edward Markey (D-MA), co-chair of the Congressional Bi-Partisan Privacy Caucus, has proposed placing limits on the types of subscriber data that law enforcement may access.  In a statement to the New York Times Rep. Markey stated, “We cannot allow privacy protections to be swept aside the the sweeping nature of these information requests especially for innocent consumers.  Law enforcement agencies are looking for a needle, but what are they doing with the haystack?”

Representative Markey’s concern over what law enforcement agencies are doing with the data they collect is more than justified.  According to the U.S. Census Bureau, approximately 1.2 trillion voice minutes were used and an additional 1.2 trillion text messages were sent in 2011. Wireless data accounted for a staggering 341.2 petabytes over the past year, which required some carriers, including Verizon Wireless, to increase their staff in order to comply with the requests for data they have received.  Verizon Wireless has had to dedicated nearly 70 employees working 24-hours a day, seven days a week and

Cell phones may be in the hands of nearly every American, but few of us stop to think about the data our devices generate on a minute-to-minute basis.  We make call our friends and families, make and receive text messages, play Words with Friends, and check our email.  As our mobile devices become less of a convenience and more of a necessity, we will continue to generate more data and that data is valuable to both corporations and governments.  It is important to know the user agreement and policies of your cell phone carrier and to be familiar with the laws and regulations imposed by government.  Thankfully all of that information can be found on the Internet, and most likely you can find the Internet on your cell phone.

Big Data Means Big Improvements in Weather Forecasting

Written by Rory Welch

We have already discussed how companies in various industries as well as educational institutions are leveraging their big data to improve in virtually all aspects of their operations. So, now its time to look at a use of big data that can affect everyone, every day, and not encroach on your privacy or be used to more effectively market a corporation to you.  Most people don’t realize that big data analysis is now being used to monitor and predict the weather, and companies such as The Climate Corporation and Earth Risk are using it in conjunction with climatology and agronomics to vastly improve weather predictions and other climatological information.  

How Big Data Anticipates Climate

The Climate Corporation processes large weather data sets in excess of 50 terabytes of data. On a daily basis, the corporation utilizes:

  • 2.5 million weather measurements
  • 150 billion soil observation
  • 10 trillion scenario data points

These measurements and observations generate over 10,000 weather scenarios that are processing decades of historical data, which they then use to produce over 10 trillion scenario data points. The data points are entered into complex learning algorithms which are then used to predict the weather. Scientists estimate that this data set is will grow 10x this amount every year!

Climate Corporation: Providing Farmers with “Total Weather” Insurance

            Recently, Climate Corporation was given 50 million to hire 50 data scientists and various other positions to thoroughly analyze the large amounts of data being imported each day and to use this data in complex algorithms in order to predict the weather. The Climate Corporation provides insurance plans to farmers for a fee each month and will pay each customer if any extreme weather occurs that affects the profit from their harvest.  By using its big data management system, the corporation can provide farmers with custom and personalized insurance plans to suit their needs.

EarthRisk: Billions of Calculations

            EarthRisk is also using big data to predict the weather… 40 days in advance! EarthRisk aims to provide long-range forecasts for energy and other various companies that rely on accurate extreme temperature forecasts. Last year, EarthRisk went against the national consensus and performed over 82 billion calculations using data dating back to the 1940s to determine that a warming trend would continue through the winter months. Not only were they correct, last winter proved to be the second-highest heat event since 1948.

EarthRisk: Complex Data Patterns

            EarthRisk uses a database that contains over 60 years of weather data to make predictions on future weather patterns. EarthRisk use these patterns to determine extreme events. Stephen Bennett said, “Extreme events are the ones that have the highest impact. The places where the opportunities are to be seized, and the risks managed, are at the extremes… The data patterns have become so complex that it’s too much for a meteorologist—for one brain—to digest.”

Why are Weather Predictions Important?

            Let’s be honest, Mother Nature can be very temperamental at times. The weather can be dangerous and life-threatening to an individual or business, and it is one of the few things that we have absolutely no control over. According to Merriam-Webster , weather means “The state of the atmosphere with respect to heat or cold, wetness or dryness, calm or storm, clearness or cloudiness.” Weather is everything that affects our lives on a daily basis and has the power to make permanent changes to a person’s life or business.  So why are weather predictions important to all of us? For individuals or businesses that require outside activity, weather predictions are essential. Big data management systems are providing weather companies the ability to analyze data taken from previous decades to analyze weather trends and patterns. By analyzing these large data sets, weather predictions are becoming more accurate and are being provided quicker now than in the past giving individuals and businesses adequate time to prepare for the weather’s impact on outside operations.

Big Data Weather Predictions: Saving Lives and Businesses?

            Are big data weather predictions going to be the standard for all weather companies in the future? The advantages of being able to analyze years of past weather patterns are endless. Are there any disadvantages of using big data to predict the weather? Please leave your comments below.

 

Academic Institutions are Warming to the Idea of Big Data Possibilities

Written by Rory Welch

Everyone has heard about how companies are using and exploring analysis of big data to improve myriad operating functions. Now, academic institutions are joining the movement to understand the advantages of using big data applications to sort and analyze data for both educational and operational benefits. In the past, academic researchers relied on individuals with a technical background to analyze the large quantities of data generated within an institution. However, applications are now entering the market that offer user friendly interfaces, simple and complex search queries, and increased functionality. With an academic institution, data sets can have thousands or even millions of records to process that are generated from many sources such as classroom records, student records, and administrative records. What is one primary advantage of using big data applications? These applications provide the opportunity to quickly retrieve and analyze large amounts of data in real time across all departments, with increased efficiency from both a time and reporting standpoint. 

So Who is Joining the Big Data Movement?

Several academic institutions such as Stanford University, MIT, and Harvard University are showing increased interest in the advantages and possibilities associated with the analysis of big data to improve student learning. Campus Technology, who is consistently searching for the newest technology to improve higher education student learning, has already begun using big data applications to improve or implement numerous concepts:

  • Ability to recognize students’ preferences and needs that will allow analysts to create a custom reminder service.
  • Ability to provide a student an up-to-date service that will send “academic health records” to their mobile device.
  • Create an alert system that notifies faculty and advisers when a student needs intervention.
  • Ability to improve student learning by implementing personalized automated learning objects to increase student interaction with their studies.

The goal for institutions using big data is to identify students’ preferences and learning styles to help them learn quicker and better. Ideas about automated teaching strategies to improve student learning are also being discussed at length.

MIT was recently selected to be the newest Intel Science and Technology Center for big data and they will be in charge of analyzing large amounts of data generated across various industries. In turn, MIT will be able to train and produce individuals who will be able to analyze and offer greater insight on how companies and institutions can fully leverage their big data.

What can Big Data do for Your Institution?

There are endless possibilities when big data comes into play. Big data is changing how individuals utilize their data. What was a mass cluster of unorganized, unstructured data and information can now be organized, sorted and reported on to improve institutional statistics and student learning. Institutions will be able to mold and tweak their results to their preference and then they will be able to make sense of all the data they have acquired. Some institutions are using big data to quickly seek out which students are in danger of dropping out and for what reason. Further analysis can then display which degrees these endangered students are associated with as well as which actions could be used to improve institutional retention rates and thus, student success.

Is Big Data the Future for Academic Institutions?

Do you think that all academic institutions will incorporate a big data application in the near future? How do you think it will be used? Leave your comments and opinions below.

Big Data May Already Be in a Stadium near You

Written by Rory Welch

How Big Data is Changing the Way Sports are Watched and Sponsored.

As our world of technology continues to expand at an exponential pace, our applications of those expanded technologies continue to touch and change every aspect of our lives. The use of big data analytics is without a doubt the next big frontier: touching most every industry from health and science to retail and technologies. Now, we can add sports to the list.

Olympic Playing Fields Monitored by Olympic Sized Data Centers.

To service a record audience, London, the 2012 host of the Olympics, has promised to provide the most technologically advanced Olympic games to date. A Technology Operations Centre (TOC) has been erected to provide stats and standings on the different events continually taking place at the games. The TOC boasts 450 employees working 24/7 on 10,000 computers, servers, and network and security devices. This vast IT system will undergo a total of 200,000 hours of testing to ensure systems function properly preceding the games.

The TOC will generate a staggering 30% more data than the 2008 Beijing Olympics. One concentration of this data will be for the use of mobile devices to access results as well as standings.  Computer Weekly reports that the IT team is working on mobile apps for delivering event results and spectator information for fans and venues. This data center will grant unparallel access to the games, the results, and the standings of each country and its athletes.

Big Data in the Press Box.

Said to be golf’s “toughest test,” the 2012 U.S. Open had an incredible amount of attention surrounding it due to the participation of Tiger Woods, Phil Mickelson, Rory McIlroy and 17 year-old amateur, Beau Hossler.

A team led by data scientist Mike Foley analyzed up-to-the second, dynamic reports including a live conversation stream and geographical distribution of social media contributors posting comments. Using this information allows the U.S. Open to predict emerging trends and “hotspots.” The data also allows them to proactively take advantage of opportunities to gain new customers and to reach key influencers.

For example, the top Twitter mention was, to no astonishment, “Tiger Woods” despite his having finished in twenty-first place. Insight into who garners the most attention allows agents and media companies to select and appraise sponsorship opportunities.

The End of the “Home Town” Team.

Allan Swan of CBR Online recently reported that, David Beckham’s goal against Greece which qualified England for the 2002 World Cup has gained “some 6 million hits on YouTube… If this video was exclusively hosted on FA’s own website, allowing ads and sponsorship to be attached, it would pay for its own infrastructure costs.” Some American sports franchises are already utilizing cloud storage to create vast digital video libraries. Both the MLB and the NBA use shared storage to the tune of $600 million a year.

Having content hosted by individual franchises or professional associations gives unprecedented access to the fans. It allows them to connect with their favorite team no matter where they live. Apps derived from this library of athletic data can easily segment the stored information and categorize it into statistics and reports of games as well as provide video clips rather than full games. This could change the way fans consume sports. The information captured by such apps could also generate invaluable insights into fan behaviors, helping sports franchises to discover new ways to monetize their existing content.

How Big Data Will Push Us off Our Couches.

With increased usage of big data, the full value of ‘mobile’ viewership is still being discovered. By providing insightful new ways to interact with content, franchises will have more opportunities than ever to keep the action going after the fans have left the stadium.

How do you interact with your favorite sports teams after the game? Please share your thoughts below.

 

Can Big Data Predict the Future?

Written by Rory Welch

Want to know what stocks to invest in, or what country is poised for the next political revolution? Big data can tell you. 

It may be a commonly accepted fact that big data analytics can help you make better business decisions, but have you ever thought key-to-future-big-data-technologyabout the possibility of using big data for your own personal monetary gain or even predicting major world events? Many experts believe that big data will provide the key to do this and more.

Why Big Data is Better than Your Stock Broker.

Everyone wishes they had a surefire way to figure out what the next hot stock will be before the masses. By monitoring several big data sources on the internet (blogs, news stories, Twitter, press releases, etc.) these types of predictions are now entirely possible.  In fact, for $149 a month, companies like Future Research claim that they can tell you the 50 most popular and least popular stocks based on analyzing popular sentiment gathered from these sources, or that you can use their database to find answers to even more specific questions related to the companies/industries you’d like to invest in, like “What companies are releasing new pharmaceutical products this year?”  

Can Big Data Accurately Predict the Next Major World Conflict?

That’s what Kalev H. Leetaru, former Assistant Director for Text and Digital Media Analytics at the University of Illinois, believes.  By analyzing hundreds of millions of news stories related to specific locations, people, organizations (including terror groups), and actions, Leetaru believes that predicting the revolutions in Libya and Egypt would have been possible and that the same type of big data analysis could be used to predict major world conflicts  – or even the location of terrorists like Osama Bin Laden – in the future.  According to Leetaru, the key to this type of big data analysis is a combination of automated mood detection, the ability to interconnect a web of 100 trillion relationships – and, of course, a supercomputer.

How Big Data Predictions Will Help Define the Web 4.0 Era

In a recent presentation by Becky Wang, the VP Director of Research & Analytics for the integrated communications company Saatchi & Saatchi, predicts that an even higher level of online engagement and our ability to use billions of different online big data sources to uncover the “semantics of social connections” will largely define the Web 4.0 era The combination of the two, will create a real-time feedback loop that makes it even easier for big data analysis to take place, help predict the future and further interconnect our lives with the online applications we love so much.    

 

Are you ready to start using big data to help guide the future of your business? Download Big Data Simplified™: The WaLa! Platform™ Overview.

Uncovering the hidden costs of IBM’s large-scale software solutions for enterprise-wide content management

Written by Rory Welch

While many big players in the industry offer comprehensive solutions for enterprise-wide content management and archiving, no one has come up with the magic bullet for a solution that is cost effective, quick to implement and easy to use across an ever-growing set of content platforms.  In fact, the companies that rank the highest in Gartner’s Magic Quadrant for Enterprise Information Archiving, including IBM and  Symantec, can actually be the most cumbersome in terms of time-to-deployment, snowballing costs and complexity.

Big Blue = big costs for consulting and custom integrations

Let’s consider IBM’s  Content Collector (ICC) as an example. It sounds like a simple solution on the surface; one that provides archiving and content management modules for Exchange, Lotus Domino, instant messages, social media content and file systems for Windows, Unix, Linux, etc. But, the real cost of the ICC solution doesn’t lie in the price of the software, it lies in the consulting services and custom programming costs you’ll need to integrate content types not covered by the modules. When this all adds up, the consulting and programming fees can cost a multiple of the software pricing, leaving you with a bill that can reach the six figures. Along with the high price, the project could end up taking several months to a year or more to finalize.

Gartner also cautions that “ICC is best-suited for organizations using IBM’s broader enterprise content management products, or planning to use them in the future.” This is due to the fact it may be difficult to integrate other content management products with ICC offerings, meaning that you need to choose to go with IBM all the way or risk dealing with additional complexity and added costs to update your solution in the future.   

So what is the magic bullet?

I mentioned earlier, there’s no magic bullet for companies looking for a comprehensive content management solution that is fast, affordable and allows you to easily add other content types down the road.  So, what is that magic bullet and how can we get there? I think some of the most important attributes of the ideal solution include:

  • Not requiring a long-term commitment to one platform or company like IBM for all your future software and hardware needs
  • Open APIs that allow you to:
    • Add additional content platforms in days instead of months 
    • Customize your solution using in-house programmers
    • The ability to securely store your content on physical devices, in cloud or both
    • Easy-to-use search functionality that allows you to see data trends across your company and use them as business intelligence

What would be included in your magic bullet solution?

Have you dealt with a frustrating large-scale software integration in the past? How would you define your ideal enterprise information archiving and content management solution? Please add your comments and ideas below.

 

 

 

Bad Advice on Big Data?

Written by Rory Welch

Why CIO Magazine’s recent top five list on big data misses the mark

In his recent article, “Five Things CIOs Should Know About Big Data” CIO Magazine writer Joab Jackson shares his top five pieces of advice about the big data revolution for CIOs. He includes reasons why the hype about Hadoop isn’t all it’s cracked up to be. While I definitely agree with his #5 assessment that “Big data is not just about Hadoop,” I don’t entirely agree with the other four points that made his list. Here are my counter-arguments surrounding the other important issues he raises.

    1. You will need to know about big data.

      I would assume any company big enough to have a CIO inevitably knows about big data and what it can do to help improve business. But, it’s no longer enough to know about big data. If you’re not in the process of developing or implementing big data initiatives – even on a fairly basic level – you are already at risk of falling behind your competitors. It’s not knowing about big data, it’s knowing what to do with it that counts.

    2. Useful data can come from anywhere (and everywhere).

      Actually, I wholly agree with this statement. My counter point comes from the fact that Mr. Jackson focuses on data generated by machines: like log files that show how people interact with your company’s website, or sensors in cars that reveal people’s driving habits. While there is no doubt that machines and sensors are invaluable sources of data, they represent only a fraction of a much bigger data ecosystem.

      Many analysts are already hard at work using social media, blogs and other readily available sources of information to gauge buyer sentiment, anticipate customers’ needs and find valuable clues for what technologies and products will be in demand in the future. Social media, blogs and even company emails are rich sources for insights into your customers and your business. These fountainheads of information should be a crucial part of your businesses big data strategy.

    3. You will need new expertise for big data.

      Jackson raises the argument that organizations will need to “hire statistical modelers, text mining professionals, people who specialize in sentiment analysis” in order to set up a big data analysis system. While having these specialists at your company’s disposal can be a huge benefit, I disagree that you will need them. In fact, the WaLa! Platform™ was specifically designed to help solve this business problem. Not only does it plug into any data source you would like to analyze, its semantic big data search tool allows you to integrate multiple data sources into a single interface so any business user – from CEOs to administrative staff – can analyze insights across the enterprise.

    4. Big data doesn’t require organization beforehand.

      While I understand the idea behind this sentiment (i.e., don’t get hung up on what you’re going to do with it yet since it’s more important to start collecting it), I would definitely recommend taking a more strategic approach to planning your big data initiatives so you don’t spin your wheels on collecting data you won’t use. Most experts agree that the first step in developing a successful big data initiative should start with an evaluation of your company’s biggest challenges and how you can potentially use the big data at your disposal to solve those. The output of your evaluation will lead you to the types of data you should start with and you can build from there.

Is your organization struggling with how to get started with big data?

Download The WaLa! Platform™: Big Data Simplified™ to find out how our plug-in-play solutions can help you quickly and easily use big data to get ahead of the competition.

CXOs Are Choosing Big Data Over Social Media

Written by Rory Welch

Why a lack of social media may actually undermine their big data initiatives

Although the ever-growing excitement surrounding social media recently hit a speed bump due to Facebook’s underwhelming IPO, most companies have come to recognize that social media should be an obligatory – if not key – platform for engaging consumers online. But according to a new study released by McKinsey & Co. in May, surprisingly few companies are using it. Instead, C-level executives are prioritizing company-wide use of big data over social media by a significant percentage in their attempts to analyze consumer behavior today.

While it’s definitely understandable that big data initiatives are taking top billing, given the deep layers of data and information social media can provide about its users, I wonder if these companies are missing the forest through the trees or just need more time to ramp up social media usage so it can be aligned with their big data analytics efforts.

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