We had a full house last week for our spring Open House, which gave us a chance to have friends of the company including current (of course) and future employees, strategic partners and even members of our alumni network.

As with any growth business in a highly dynamic market, occasionally people decide to move on but it was gratifying to have a number of former colleagues join us – some of whom have become customers, and a Read More »]]> It’s boom times at HP Vertica – as the Boston area’s first and biggest Big Data technology provider, we continue to grow and expand our employee base and ecosystem.

We had a full house last week for our spring Open House, which gave us a chance to have friends of the company including current (of course) and future employees, strategic partners and even members of our alumni network.

As with any growth business in a highly dynamic market, occasionally people decide to move on but it was gratifying to have a number of former colleagues join us – some of whom have become customers, and a few of which have even re-joined the company recently!

Over the next few weeks, HP Vertica will be taking things on the road to HP Discover Las Vegas and will be involved in some major strategic announcements regarding HP’s Big Data strategy, so watch this space for more.

We’re also going to be on the road for our Discover Performance series and a number of industry events – check our website for details.

And last but not at all least, we’ve got our HP Vertica Big Data Conference in early August – click here or on the image below to learn more and to register. We’re expecting another full house including our base of current and future customers, strategic partners and of course many of our colleagues from worldwide HP – sign up now, since space is limited and Early Bird Pricing expires June 28!

You can check out the stories by going to the Customer Case Studies page, but I wanted to give you a short description of the company and a quote for each story to entice you to check them out:

Read More »]]> There are two new case studies posted on the site – Guess? and Kansys – and they offer two very different stories about how these world-class companies are using Big Data as a competitive differentiator in the retail and service provider industries.  But there is one common thread between the two stories – the HP Vertica Analytics Platform.

You can check out the stories by going to the Customer Case Studies page, but I wanted to give you a short description of the company and a quote for each story to entice you to check them out:

Guess?, Inc., a leading global retailer, is achieving business and technology benefits with its HP Vertica Analytics Platform that includes a flexible, compelling mobile business analytics iPad application called G-Mobile. The easy-to-use mobile app empowers a range of non-traditional BI users including designers, buyers, planners, and allocators—to analyze sales, inventory and logistical information. The company has improved its insight into customer purchasing behaviors, and can ensure employee resources are allocated appropriately to optimize productivity. With HP Vertica, Guess? has the infrastructure in place to tackle the challenges retailers face—including Big Data and next-generation analytics. The company plans to roll out the analytics platform to Europe and Asia in the near future.

“Vertica gives us the flexibility to tackle Big Data. With HP Vertica, our organization is ready for the challenges retailers are facing—from Big Data to next-generation analytics.”
– Bruce Yen, director, Business Intelligence, Guess?, Inc.

Founded in 1997, Kansys brings deep experience and expertise to the business support systems and operations support systems (BSS/OSS) for Communications Service Providers (CSPs) & Multiple System Operators (MSOs). The company is known for an unparalleled ability to align, manage, optimize, and report on huge volumes of data from disparate sources for its clients.With expertise gathered over years of experience, Kansys is unique in its depth of knowledge across the areas that matter to CSPs — Mediation, Process Optimization, Revenue Assurance, Fraud Detection & Analysis, Billing, Audits, Customer Analytics, and Reporting. HP Vertica powers the CSP & MSO Big Data services provided by Kansys to their customers.

“As a consumer of data, time to information is important, and we don’t want to be limited in the amount of data we process. HP Vertica solves that problem.”
– Tom Wisnasky, CIO, Kansys

To check out these two case studies, and to learn more about Guess?, Kansys, and some of our other customers who are taking full advantage of the HP Vertica Analytics Platform, please visit the Customer page on our web site.

Please join us by clicking this link and RSVP’ing. All friends of HP Vertica are welcome though space is limited and filling fast!

It’s coming together nicely – see below for a few previews – we’ll be taking more photos on Monday and want you in them. Join us and learn more about Boston’s Biggest Data!

Why? Because we believe that the combination of the HP Vertica Analytics Platform running on the HP Moonshot Servers offers a truly game-changing value proposition for a variety of customers, and new segments of the market.

Moonshot is, simply put, a groundbreaking system which offers customers the ability to rapidly deploy, scale and manage with dramatically lower space and energy constraints. While traditional IT services that support business functions will continue to be served by general purpose server infrastructure, a new computing platform is required for specialized workloads that can deliver innovative solutions to market at unprecedented speed and scale.

Read More »]]> HP Vertica is very excited about Monday’s announcement of the HP Moonshot system.

Why? Because we believe that the combination of the HP Vertica Analytics Platform running on the HP Moonshot Servers offers a truly game-changing value proposition for a variety of customers, and new segments of the market.

Moonshot is, simply put, a groundbreaking system which offers customers the ability to rapidly deploy, scale and manage with dramatically lower space and energy constraints. While traditional IT services that support business functions will continue to be served by general purpose server infrastructure, a new computing platform is required for specialized workloads that can deliver innovative solutions to market at unprecedented speed and scale.

We’ve already successfully tested the HP Vertica Analytics Platform on HP Moonshot Servers, and achieved very comparable performance to traditional Big Data Analytics hardware across certain performance ranges, which for a large segment of the market is more than sufficient to handle their Big Data Analytics loads – while offering very significant potential cost, space and energy savings.

Running Vertica on Moonshot offers yet another proof point of the unmatched value provided by HP’s combination of Information Optimization solutions, and a great example of the opportunity created by innovation that makes us so excited to be a part of the greater OneHP.

To learn more about HP Project Moonshot, visit http://www.hp.com/go/moonshot

A pattern mining algorithm

Frequent patterns are items that occur often in a data set. After finding frequent patterns, analysts can use methods Read More »]]> Pattern mining can help analysts discover hidden structures in data. Pattern mining has many applications—from retail and marketing to security management. For example, from a supermarket data set, you may be able to predict whether customers who buy Lay’s potato chips are likely to buy a certain brand of beer. Similarly, from network log data, you may determine groups of Web sites that are visited together or perform event analysis for security enforcement. In this blog post, we will show you how the HP Vertica Analytics Platform can efficiently find frequent patterns in very large data sets.

A pattern mining algorithm

Frequent patterns are items that occur often in a data set. After finding frequent patterns, analysts can use methods such as association rule mining to discover rules in the data. A classic example of an association (from Wikipedia) is that customers who buy diapers also tend to buy beer from a supermarket. While there are many frequent pattern mining algorithms in literature, we will use the FP-growth algorithm. FP-growth is considered efficient as it performs fewer database scans and does not require candidate set generation [1].

Instead of describing FP-growth in detail, we list the main steps from a practitioner’s perspective. We need to perform the following steps to obtain frequent patterns using FP-growth:

1. Create transactions of items
2. Count occurrence of item sets
3. Sort item sets according to their occurrence
4. Remove infrequent items
5. Scan DB and build FP-tree
6. Recursively grow frequent item sets

Let’s use an example to illustrate these steps. We will assume that our data set is a Web proxy log from a corporate network that, among other things, has IP address and Web sites visited as fields. Our goal is to find patterns such as Web sites that are visited together. After step 1, we obtain a set of transaction items shown in Table 1. Each transaction lists the Web sites visited from each IP address. After steps 2 and 3, we get Table 2 that has items sorted by their frequencies. Assuming that an item is considered frequent only if it occurs more than three times, then in step 4 we will discard cnn and yahoo from the table. In step 5 we use the pruned table to create an FP-tree (Figure 1). Finally, in step 6 we grow frequent patterns. The final output is shown in Table 3. The output, for example, shows that many users tend to visit both the Web sites of HP and Amazon.

Table 1: Sites Visited	Table 2: Sorted Items
Figure 1: FP-tree	Table 3: Final output of frequent patterns

Parallel pattern mining on the HP Vertica Analytics Platform

Despite the efficiency of the FP-Growth algorithm, single-threaded sequential version of FP-Growth can take very long on large data sets. Fortunately, we can rewrite the algorithm using SQL and HP Vertica user-defined functions (UDFs), and let the HP Vertica Analytics Platform parallelize the implementation. The main issue to resolve is how to map the algorithm to SQL statements and then remove dependencies between UDFs so that they can run independently and in parallel. Below are the statements that we used in the HP Vertica Analytics Platform. Let’s assume that we are still working with the Web proxy log example introduced earlier.

1. Create transaction of items
   • SELECT DISTINCT srcIP, hostname INTO uniqueSipHn FROM networkLog;
2. Count frequency of occurrence of each host name
   • SELECT count(hostname) INTO hnCnt FROM uniqueSipHn;
3. List host names visited by each IP and also the frequency of each host name.
   • SELECT a.srcIP, b.hostName, b.frequency into sipHnCnt FROM uniqueSipHn a INNER JOIN hnCnt b ON a.hostName=b.hostName;
4. Build conditional transactions. Assume an item is frequent if it occurs more than 20,000 times.
   • SELECT t1.hostName, t1.srcIP, t2.hostName AS condItem INTO condTr FROM sipHnCnt t1 JOIN sipHnCnt t2 ON (t1.srcIP=t2.srcIP) and (t1.count>20000 and t2.count>20000) and ((t2.count>t1.count) or (t2.count=t1.count and t2.hostName>t1.hostName))
5. Generate patterns in parallel using UDF.
   • SELECT FPGrowth(srcIP, condItem, 20000) OVER(PARTITION BY hostName ORDER BY srcIP) INTO frequentItems FROM condTr;

The real test: a billion records, and, of course, Hadoop

Now that we know how to implement parallel frequent pattern mining in the HP Vertica Analytics Platform, let’s see how the implementation performs a large data set. Our input data is a few days’ worth of Web proxy logs. The log file is 330 GB in size, and has a billion records each with 22 fields. For comparison, we use Mahout’s implementation of parallel frequent pattern mining (Cloudera Hadoop 2.0 and mahout-0.7). We wrote a MapReduce program to create transactions from the log (step 1 of the algorithm). Our test bed consists of 12 HP ProLiant servers, each with 12 cores, 96GB RAM, and 128GB SSD.

Figure 2 depicts our results. On 4 servers, the HP Vertica Analytics Platform can complete the end-to-end pattern mining in fewer than 140 seconds. Hadoop takes 1,250 seconds (20 minutes)—approximately 9x more time than the HP Vertica Analytics Platform. As we increase the number of servers to 12, both the HP Vertica Analytics Platform and Hadoop take less time to complete. However, unlike Hadoop, the HP Vertica Analytics Platform has close to linear scaling for this setup.

Are you searching for patterns in your data set? Want a fast and easy-to-use data analytics platform? Evaluate the HP Vertica Community Edition today.

[1] Mining frequent patterns without candidate generation. Jiawei Han, Jian Pei, Yiwen Yin. SIGMOD 2000.

Now, HP Vertica newbies as well as seasoned database/data analytics veterans and everyone in between can post questions, share ideas, report issues, and give praise. To make this information readily available for your convenient access, all questions are cataloged and searchable.

To remove this Big Data complexity, we are pleased to announce the general availability of the HP AppSystem for Vertica. Following through on the initial announcement at HP Discover as part of the HP AppSystems portfolio, the HP AppSystem for Vertica ensures system performance and reduces implementation time from months to a matter of hours.

But what is an AppSystem and is it right for you?

Built on the Read More »]]> New analytics deployments can be complex, taking up to 18 months to implement and optimize. The complexity of maintaining and integrating these environments often results in missed deadlines, incomplete projects, increased costs, and lost opportunities. In fact, only 32 percent* of application deployments are rated as “’successful”’ by organizations.

To remove this Big Data complexity, we are pleased to announce the general availability of the HP AppSystem for Vertica. Following through on the initial announcement at HP Discover as part of the HP AppSystems portfolio, the HP AppSystem for Vertica ensures system performance and reduces implementation time from months to a matter of hours.

But what is an AppSystem and is it right for you?

Built on the HP Converged Infrastructure, the new HP AppSystem for Vertica is a fully pre-integrated technology stack that includes a specifically optimized hardware configuration, factory pre-loaded OS, and the HP Vertica Analytics Platform environment.

HP AppSystem for Vertica is ideal for organizations interested in accelerating time-to-business value with high-performance, massively scalable analytics at each layer of IT infrastructure — server, storage, network, and management. As a result, you can scale seamlessly, while adding capacity as your analytics needs for Big Data evolve.
We encourage you to learn more about the HP AppSystem for Vertica — and get started removing complexity to capitalize on your big data analytics initiatives.

* = CHAOS Summary 2009, Jim Johnson, Standish Group, April 2009

The NCAA 2013 Men’s Basketball March Madness Tournament officially tips off on Thursday, March 21st.  For those of you unfamiliar with the tournament, 64 teams from colleges and universities across the United States compete for the championship, awarded to just one winner in early April. Buzzer-beating upsets are as common as fan face paint and schools from parts unknown, making it challenging to choose the winner in your office tournament bracket.

To give you a sense of the tournament’s popularity and appeal, according to USA Today “Last year’s championship game alone had about 20 million TV viewers. The overall tournament had 52 million visits across March Madness Read More »]]>

The NCAA 2013 Men’s Basketball March Madness Tournament officially tips off on Thursday, March 21^st.  For those of you unfamiliar with the tournament, 64 teams from colleges and universities across the United States compete for the championship, awarded to just one winner in early April. Buzzer-beating upsets are as common as fan face paint and schools from parts unknown, making it challenging to choose the winner in your office tournament bracket.

To give you a sense of the tournament’s popularity and appeal, according to USA Today “Last year’s championship game alone had about 20 million TV viewers. The overall tournament had 52 million visits across March Madness on Demand’s broadband and mobile platforms.”

So, what is the buzz on this year’s tournament on Twitter, and can social sentiment foreshadow ultimate success? A small team of us here — representing Autonomy, HP Vertica, and HP Information Management & Analytics (IM&A) — set out to answer that very question by building a March Madness Sentiment Tracker Demo to track the “sentiment of the crowd.”

The Technology Behind the March Madness Sentiment Tracker

Using HP Labs’ Academy Awards Meter demo as our guide, we created a framework in roughly a week based on Autonomy, HP Vertica, and Tibco Spotfire.

We unveiled the demonstration at the Sloan MIT Sports Analytics Conference. See Chris Selland’s blog post from that event and his participation on the Big Data in Sports panel.

Since the MIT Sports Analytics Conference was held weeks before the tourney had begun, we first collected roughly half a million Tweets using Autonomy’s data aggregator from February 20^th to March 1st. The Tweets included anything related to the Top 25 ranked teams at the time as well as the top scorers. Our colleagues at Autonomy also used Autonomy IDOL to structure and sentiment to the data. For example, a Tweet like “I am excited to watch my Jayhawks win #MarchMadness!” would carry a positive sentiment. However, a Tweet like “I hate #MarchMadness – it interrupts my favorite TV shows!” would carry a negative sentiment.

Our very own Will Cairns, who presented on the main stage of the MIT Sloan Sports Analytics Conference, loaded the data into the HP Vertica Analytics Platform, ran some analytical queries and provided an output file for HP IM&A to create the visualization front-end with Tibco SpotFire. That is where the insight (and conversation with the data) began to happen.

Visualizing the Sentiment and Lessons Learned

HP IM&A created impressive visualizations that helped us (and attendees) to explore:

• Volume of tweets by team
• Volume of tweets by player
• Positive, negative, and neutral sentiment groupings
• Volume of tweets by U.S. city and by worldwide country
• Volume of tweets by language (English, French, Spanish, etc.)

They say that a picture is worth a 1,000 words. Well, the visualizations provided for great conversation – some results were not surprising such as NCAA perennial teams steeped with rich history, such as Kansas and Duke, leading the total volume of tweets. Some players ranked higher than others in volume of tweets, leading attendees to observe, “ Well, Trey Burke had a monster game the other night, so that makes sense.”

But why did Chicago rank as the U.S. city with the highest number of tweets, despite having no college or university from Illinois team ranked in the top 25 at the time? Well, the Big 12 is one of the more competitive conferences in the country this season, and Chicago area schools (such as the University of Illinois) play Wisconsin, Indiana, Michigan, and Michigan State. It’s also one of the top five major media hubs in the country.

Spirited debates and conversations aside, most importantly, this exercise clearly demonstrated the power of sentiment for a range of use cases in nearly every industry with a major product, brand, or service. In the telecommunications industry, network providers are actively tracking social media channels to measure customer satisfaction. If there is an issue with the service, say in a certain region of the country, you better believe that customer service will soon receive calls to that very point. Using sentiment analysis to quickly address issues by, say, adding more network bandwidth and improving service can help reduce service costs, improve customer satisfaction, and minimize churn.

But can sentiment foreshadow success? I guess you will have to tune into the games to find out, while tracking your favorite social media channel. Better yet, why not use HP Vertica’s tight integration with R to develop a statistical model based on data available from ESPN and the likes on hard basketball statistics, such as field goal percentage, points allowed, head-to-head scoring, and more? You could correlate that statistical data with sentiment data trending from Twitter.

Hmm…that sounds like a perfect complement to our March Madness Sentiment Tracker demo. Stay tuned for more details or share your thoughts on how you could marry sentiment data with statistical data to ultimately predict this year’s winner.

But this isn’t just a case of data growth outstripping technology growth. Big Data embodies fundamental differences that necessitate new approaches and new technologies. Big Data takes Read More »]]> Aside from the sheer quantity of digital data created every day—about 2.5 exabytes¹ —there’s more to Big Data than volume. Big Data offers enterprise leaders the opportunity to dramatically change the way their organizations operate to gain competitive advantage and find new revenue opportunities. But realizing the value Big Data promises requires a new approach. Traditional data warehouses and business intelligence tools weren’t built for the scale of Big Data, and can’t provide insight quickly enough to be useful or even keep up.

But this isn’t just a case of data growth outstripping technology growth. Big Data embodies fundamental differences that necessitate new approaches and new technologies. Big Data takes many forms, three in particular we’ll discuss here:

• Transactional data
• Sentiment and perceptual data based on conversations taking place in social media
• Data from networked sensors—the so-called “Internet of Things”

Transactional Data

As businesses have expanded—and expanded onto the Internet—the volume of business transactions has grown. The Economist reported in 2010 that Wal-Mart processes more than 1 million customer transactions every hour and maintains databases exceeding 2.5 petabytes (million gigabytes)². Imagine how those numbers have grown since then.

What’s even more critical is that companies can now capture not just sales transactions, but the detailed histories and clickstreams that lead to the sale. From web-based clickstream analysis to call data records, pre- and post-transaction histories are more robust than ever—and our ability to collect, analyze and act on that data must adjust accordingly.

The social media explosion

Today’s online customer has progressed well beyond accessing information. Today’s consumers are not only interacting and collaborating with each other, but they’re talking about and interacting with your brand. Facebook has more than 1 billion active subscribers³, and it’s estimated they share almost 700,000 individual pieces on content every minute. On Twitter, more than a billion tweets go out every two to three days⁴. (You can watch them mapped geographically in real-time at tweetping.net.)

Product reviews, user communities, forums and blogs allow consumers to generate content that contains critical insight for the business. The proliferation of user-generated content in these social channels has lead to new techniques and tools for “sentiment analysis”—the ability to measure emotion to determine how your company and brand are perceived.

The Internet of Things

The amount of information generated by devices rather than people is also growing explosively.
Mobile devices—and the apps people use on them—regularly broadcast individuals’ location, performance and other factors to the network. Retailers and distributors are using radio frequency identification (RFID), bar and QR codes to track inventory and enhance their supply chain and inventory performance. The healthcare industry seeks to improve care and reduce costs through remote patient monitoring. The automotive industry is embedding sensors in vehicles. And utilities are beginning to rely on smart meters to track usage. McKinsey Global Institute reports that more than 30 million networked sensors are in use in the transportation, automotive, industrial, utilities and retail sectors—and the number is growing by 30 percent every year.⁵

We recently presented a webinar on the Internet of Things and the Power of Sensor Data, which delves into this exciting area in much more detail.

Disrupting conventional analytics – developing a ‘conversational relationship with data’

Using Big Data to make operations more efficient, improve competitiveness and increase revenue is not about generating traditional statistics or producing standard reports.

Just as important as systems to collect and store data are systems to analyze and extract insight from that data. Without insight, you can’t gain new knowledge into your markets, your products and your operations.

When you have this insight at your disposal, you can act faster and with greater probability of success.

Extracting business value from Big Data requires a new approach. We believe that Big Data analytics is an iterative process. We describe it as developing a conversational relationship with your data. Analytics becomes a continuous improvement loop, which uses the results of analyses to frame better, more meaningful analyses, which, in turn, produce more definitive results. When results are available in minutes, analysts can ask, “What if?”

When properly applied, Big Data analytics enables business leaders to:

• Understand market reaction and brand perception
• Identify key buying factors
• Segment populations to customize actions
• Enable experimentation
• Accurately predict outcomes
• Reinvent and enhance inventory and supply chain systems and processes
• Disrupt their industries, gain an edge over competitors and enable new business models

Big Data already proved its game-changing power during the 2012 U.S. presidential election. Obama campaign chairman Jim Messina said: “We were going to demand data on everything, we were going to measure everything…We were going to put an analytics team inside of us to study us the entire time to make sure we were being smart about things.”
And, in fact, Big Data analytics helped the Obama campaign ratchet up the three key levers in any election: voter registration, persuasion and turnout. Rolling Stone magazine singled out Messina and the campaign’s CTO, Harper Reed, as two among a handful of unsung heroes in Obama’s victory.

You can hear more about how HP Vertica contributed to the high-tech strategy behind Obama’s reelection in a recent webinar featuring Chris Wegrzyn, director of data architecture for the Democratic National Committee.

The traditional data warehouse won’t get it done

The concept of the data warehouse evolved in the 1980s. Then, data warehouses were simply databases into which data from multiple sources was consolidated for the purpose of query and reporting. But today, these systems fall short when confronted with the volume, velocity and variety of Big Data. Why? They fail to enable the conversational approach to data required by Big Data analytics.

Traditional databases and data warehouses don’t easily scale to the hundreds of terabytes or even petabytes needed for many Big Data applications. Data is often not compressed, so huge amounts of storage and I/O bandwidth are needed to load, store and retrieve data. Data is still stored in tables by row, so access to a single data element through many rows—a common operation in business analytics—requires retrieving practically all of the data in a dataset to extract the specific element(s) needed. That strains I/O bandwidth and extends processing time. We have seen cases where the velocity of incoming data exceeds the capacity of the system to load it into the database, and queries produce answers in hours rather than the seconds or minutes needed for iterative business analytics. As a result, systems cost too much to maintain, and they fail to deliver the insight business leaders seek.

Take sentiment analysis, for example. The goal is to extract meaningful information from unstructured data so results can be stored in databases and analyzed. But the formats of resulting data are less predictable, more varied and subject to change during iterative analytics. This requires frequent changes to relational database structure and to processes that load data into them. For IT, it means the iterative approach to extracting business insight from Big Data requires new approaches, new tools and new skills.

Challenges for business leaders

Big Data is not just a technical challenge. Gaining and applying business insight compels business leaders to adopt new and disruptive ways of thinking and working.
Successful leaders we have known in data-driven organizations become more familiar with the sources of data available to them. Rather than asking IT what information is available in the database, they view information as a key competitive asset and explore how insights might be extracted from it to offer immediate and sustainable competitive advantage.

A solution for Big Data analytics

HP Vertica Analytics Platform is a new kind of database designed from the ground up for business analytics at the scale of Big Data. Compared to traditional databases and data warehouses, it drives down the cost of capturing, storing and analyzing data. And it produces answers 50 to 1,000 times faster to enable the iterative, conversational analytics approach needed.

• HP Vertica Analytics Platform compresses data to reduce storage costs and speed access by up to 90 percent.
• It stores data by columns rather than rows and caches data in memory to make analytic queries 50 to 1,000 times faster.
• It uses massively parallel processing (MPP) to spread huge data volumes over any hardware, including low-cost commodity servers.
• It uses data replication, failover and recovery to achieve automatic high availability.
• It includes a pre-packaged, in-database analytics library to handle complex analytics and development framework.
• It supports the R statistical programming language so analysts can create user-defined analytics inside the database.
• It dynamically integrates with Hadoop to analyze large sets of structured, semi-structured and unstructured data.

HP Vertica Analytics Platform means better, faster business insight at less cost.

Test drive the HP Vertica Analytics Platform at www.vertica.com/evaluate.

• HP Vertica White Paper – The Disruptive Power of Big Data
• HP Vertica Webinar – The Disruptive Power of Big Data (featuring guest speaker Chris Wegrzyn, Director of Data Architecture for the DNC/Obama campaign)
• HP Vertica Webinar – Unlocking the Massive Potential of Sensor Data and the Internet of Things
• Blog – CIOs: The letter I in your title never meant so much
• Blog – Unlocking the value of Big Data

[1] “Big Data: The Management Revolution,” Andrew McAfee and Erik Brynjolfsson, Harvard Business Review, October, 1012.

[2]“Data, data everywhere,” The Economist, Feb 25, 2010.

[3]Facebook key facts.

[4] http://www.mediabistro.com/alltwitter/tweetping_b35247

[5] “Big data: The next frontier for innovation, competition, and productivity,” The McKinsey Global Institute, June 2011.

Nearly 80,000 attendees (72,000 from 200 countries to be precise — an all-time high) were blanketed with announcements and presentations about the latest gadgets and devices, the wireless enablement of mainly everything, Machine to Machine (M2M), and, largely, the growth and value of managing and analyzing Big Data.

It’s no surprise to us, given that 7 of the top 10 communications and service providers (CSPs) trust the HP Vertica Analytics Platform to manage and analyze terabytes to petabytes of Read More »]]> Barcelona, Spain is known for its tapas, futbol, and Gaudi-inspired architecture. However, as host to the world’s largest annual mobile industry event — Mobile World Congress  — the city has also become synonymous with all things mobile.

Nearly 80,000 attendees (72,000 from 200 countries to be precise — an all-time high) were blanketed with announcements and presentations about the latest gadgets and devices, the wireless enablement of mainly everything, Machine to Machine (M2M), and, largely, the growth and value of managing and analyzing Big Data.

It’s no surprise to us, given that 7 of the top 10 communications and service providers (CSPs) trust the HP Vertica Analytics Platform to manage and analyze terabytes to petabytes of data (i.e., Big Data) in near-real time.

But why are CSPs managing and analyzing all of this Big Data — in other words, where is the value?

Miguel Carrero, GM, Actionable Customer Intelligence, HP and I covered this very question at the show during a short video interview. Miguel also covered this topic in more detail in the day three editorial recap of Mobile World Daily 2013.

At the HP booth (as well as the transformation workshops), CSPs met with HP CMS (Communications and Media Solutions) to learn how the HP Smart Profile Server Solution — powered by the HP Vertica Analytics Platform — helps them realize a range of real-world use cases with real business value:

• Targeted product and marketing offers – Gain complete contextual insight into your customers’ needs then take action to improve customer satisfaction and achieve better retention rates.
• Network optimization – Improve your network engineering and planning and user experience via optimized network utilization and real-time response to traffic congestion situations.
• “Bill shock” mandate – Provide pre-paid and subscriber mobile customers with visibility into their mobile usage, including voice, data, and roaming.
• New business model enablement – Capture the real-time business value of each of your customers and leverage it via new collaborative business models — increasing upsell opportunities and delivering prioritized resolutions.

What is your organization’s most prevalent use case for managing and analyzing Big Data?