Statistica - Dell Community

Blog - Post List
  • Information Management

    Advanced Analytics - A #ThinkChat Recap

    Predictive, advanced, statistical analysis all these terms seem to be inter-changeable.  Do they mean the same thing?  How can they be distinguished from one another and does it really matter, or more precisely, when does it matter?

    Perhaps advanced analytics simply represents the next evolutionary step in insight and taking analysis beyond the basic and requisite reporting that BI and visualization has delivered for the past couple of decades.  Perhaps the best way to review the value of these terms or designations is to consider them from various viewpoints. 

    For the data scientist, these terms may have mathematical nuances that need exploring and understanding.  Data scientists view these terms with mathematical implications that the non-data scientist may not fully appreciate.  The importance here for the business persona involves proper communications with the data scientists.  We need the proper understanding of how to describe the problem or the question we want tackled by the data scientist to ensure that our business explanations are preserved in translation. 

    On the other hand, when the data scientists share their finding, the business user that appreciates the various nuances of the math or methodology behind these terms better understands the results being presented.

    During the tweet up, "#ThinkChat - Advanced Analytics - What's in your future?", we had tweet opinions shared by data scientists as well as VPs weighing in on what these terms mean to them.  Perhaps worthy of a couple of highlights? 

    Data scientist, @angela_W says “Advanced Analytics is analytics on a consistent basis to manage the company off reality instead of someone's gut feelings. #ThinkChat"

    VP of Market and Strategy, @ShawnRogers said “Advanced analytics is the next level of sophistication beyond BI. taking us to predictive and prescribed insights. #ThinkChat

    Let me know what these terms mean to you and whether or not you find the distinction worthy of articulating. 

    Looking for more from @JoSchloss? Take a look at the virtual panel discussion – "Discussion with a Data Scientist

    Joanna Schloss

    About Joanna Schloss

    Joanna Schloss is a subject matter expert in the Dell Center of Excellence specializing in data and information management. Her areas of expertise include big data analytics, business intelligence, business analytics, and data warehousing.

    View all posts by Joanna Schloss | 

  • Statistica

    Statistica 13.1: Citizen data scientists are not here by accident.

    Not-the-unicorns-you-were-looking-forThe onslaught of big data is well-documented, and the consensus is that the technology that has enabled its rapid proliferation has far outpaced the ability of human study (and matriculation rates) to keep up with the resulting need for analysis.

    Big data technology has impressed many businesses, stimulating their appetites for building the future now and allowing them for the first time seriously to consider turning former data-driven pipe dreams into attainable business goals. So, the excitement has grown right along with the technology's promises, and cross-industry demand for data scientists has risen dramatically.

    As highly trained and dedicated professionals specializing in the science behind data, data scientists (a.k.a., “quants” or "data analysts") were being snatched up as the most obvious choices to satisfy these new and expanding data needs. Of course, the law of supply and demand kicked in, so these data scientists became very expensive to keep and maintain. Now the perfect, fully rounded, platform-agnostic data scientists—if the perfect ones ever existed at all—are nowhere to be found in the open market. Searching for one is like looking for a unicorn: it would be magical to find one, but good luck with that.

    Question: What is a self-respecting business to do when faced with the rising costs of resources that are deemed necessary to take revenue to new heights?

    Answer: Find a way to achieve the same results through alternate, possibly less expensive means, without waiting for the future crop of data scientists to graduate from college, despite the fact that more and more colleges and universities have responded to the skills gap with the establishment of data science degree tracks.

    Everyone was looking for unicorns. What they found was better.

    Not so long ago, in editorials written not so far away, they were called “accidental analysts,” those who would use and manipulate data without benefit of substantial, formal statistical and algorithmic training. Today the popular name is “citizen data scientists.” Whatever you call them, the rise of such data handlers is not by accident, nor is it by design. It is simply by necessity.

    Think of it like your cable or satellite bill—why would you want to pay for a pre-arranged subscription package that contains much more than you need? The lack of unicorns has brought businesses to the realization that maybe they don’t need one or two full-blown data scientists who can do EVERYTHING. Instead, they can assemble citizen data scientists who can do what is NECESSARY.

    In a recent article, Shawn Rogers, chief research officer of Dell Statistica, rightly observes the economics of the situation: “Not every company can afford a data scientist, which is a big reason why citizen data scientists will become a big part of the data ecosystem as it evolves.”

    In the same article, Innovation Enterprise’s Laura Denham states pragmatically, “Everyone in the organization needs to be able to leverage the data to some degree, and it cannot simply be left to one highly trained individual sitting at the top of the firm dishing out insights.”

    "Why not?" you may ask. Because insights would take too long! Anymore, data analytics technology has increased user expectations for rapid turnarounds in data processing as well as in decision-making. The ever-shortening patience of customers and employees alike simply won’t tolerate the turnaround times associated with traditional, centralized decision-making models. (Unless, of course, there is an assurance of more accurate results, as noted in this video chat between Rogers and Dell’s Joanna Schloss.)

    But who would be the data processors and decision makers in such a decentralized scenario? Generally, that would be the line-of-business (LOB) users who are working with data collected at critical process points. If only these people could engage effectively with the data—especially in real time—then they could provide quick decisions that improve quality, maintain efficiency and impress customers. These are the citizen data scientists, and there are probably many such potential analytics users in your own organization.

    How is it even possible to custom-train your own substitute unicorns?

    It is possible because the main ingredients for successful data analysis include curiosity and creativity, not technical expertise. For instance, Robert Murphy, managing partner at Movéo, suggests in his Webbiquity guest blog that the top six characteristics necessary for success in a data-driven marketing world include creativity, curiosity, communication skills, a knack for strategy, a desire for continuous learning and statistical/technical expertise. Notably, he listed the technical expertise last.

    Why is this? As author Simon Sinek says, "You don't hire for skills, you hire for attitude. You can always teach skills." Analysts come from all walks and disciplines; the technology is teachable, so anyone with the right aptitude and attitude is trainable. Simply put, you can’t teach curiosity or creativity as easily as you can teach enough technical expertise for your people to be effective with data analysis and model building, specialized for your business needs and performing in concert with other citizen data scientists.

    To support this burgeoning market, Dell has been retooling its Statistica predictive analytics platform to make it easier for citizen data scientists to perform their duties. In the latest 13.1 release (to be generally available in mid-June 2016), citizen data scientists will find it easier than ever to build and reuse workflows, configure in-database processing with three simple steps, compare and deploy advanced models, conduct visual analyses and drill-downs, and find patterns through new network analytics. With Statistica 13.1, Dell encourages its customers’ citizen data scientists to apply data science to the most important questions in their organizations, improving the speed and relevance of data science projects.

    To see some demonstrations how Statistica 13.1 will be a boon for citizen data scientists, register now for our free June 21 webcast, “A Day in the Life of a Citizen Data Scientist.”

  • Statistica

    Statistica 13.1: Analytics is more than math

    By 2020, some analysts predict there will be roughly 30 billion connected IoT devices – including refrigerators, racecars, robots and many other interesting items.  From connected thermostats, coffee pots, medical devices, lighting, audio speakers, TVs, factories, buildings and remote controls nearly everything will have an IP address capable of transmitting data for analysis.  

     So, if one major retailer already generates 2.5 petabytes of data in an hour of transaction data – more than 167 times the amount of data stored in the Library of Congress – what will become of the data deluge?  Should we simply wait for all of the yottabytes (1 trillion gigabytes) to show up in Bunblehive?  Or maybe wait for data science unicorns and PhD statisticians to analyze and make sense of the ever-growing mountain data? 


    Of course not. Those who wait too long may not be in business tomorrow.


    Emerging trends


    These four key trends are disrupting entire industries, and challenging the status quo:


    • The rise of the citizen data scientist: These line of business workers typically have a deep understanding of the business and a penchant for analytics. But, they’re not mathematicians.  They use numbers to make informed decisions to help drive their organization forward.  The challenge will be how to ensure best practices and enable reuse within organizations.
    • IoT analytics at the edge: To be successful, businesses can’t rely on the age-old practice of sending all the data back to the mothership for analysis.  Organizations need to deploy and monitor entire data prep and analytic workflows to gateways and endpoints so decisions can be made near the point of impact – as soon as feasibly possible.  Sure, analytic results may be sent upstream and combined with other output for more sophisticated analytics. But, sending sub-second data to tell me the light bulb is still on just doesn’t make sense.
    • Vertical market use cases: To remain competitive, companies need flexible, open, scalable, secure and customizable analytics technology platforms so they can innovate and solve specific, challenging problems within their domain.   Organizations that achieve this will gain a significant competitive advantage with their “analytic secret sauce”.
    • Analytics innovation:  These platforms combine best-of-breed methodologies and algorithms from analytic marketplaces (such as Apervita, Algorithmia and Experfy), and leverage open source technology, including R, Python and Spark.   Reaching beyond the expertise within your company and harnessing the “collective intelligence” of the world accelerates innovation.


    To address these challenges, Tim Alosi of Sanofi stated that one of the big benefits will help the “compression of time[1]” and allow organizations to act on the data sooner.  


    How Statistica 13.1 helps your organization


    The latest version of Statistica provides your organization with several new features so you can more easily adapt to these trends. We’ll talk a bit more specifically about Statistica 13.1 in subsequent blogs.  But, the key capabilities and enhancements of this newest version include:


    • Streamlined workflows for the citizen data scientist
    • Edge scoring for IoT analytics
    • Extended in-database analytics
    • Network analytics


    Interested in learning more?  Register now for our May 12th webcast about Statistica 13.1. to gain a deeper understanding of how this release will benefit your organization.

    [1] IoT Analytics And Dell Statistica Deliver Time Compression.  Gill Press, Forbes. April 15, 2016.

    David Sweenor

    About David Sweenor

    From requirements to coding, reporting to analytics, I enjoy leading change and challenging the status quo. Over 15 years of experience spanning the analytics spectrum including semiconductor yield characterization, enterprise data warehousing, reporting/analytics, IT program management, as well as product marketing and competitive intelligence. Currently leading Analytics Product Marketing for the Dell Software Group.

    View all posts by David Sweenor | Twitter

  • Dell Big Data - Blog

    Dell Statistica Recognized as a Leader in newest Gartner Magic Quadrant for Advanced Analytics Platform

    By Danny W. Stout, Ph.D.

    Earlier this month, Dell Statistica was honored to be recognized by Gartner in the “Leaders” quadrant of its Magic Quadrant for Advanced Analytics Platform (February, 2016), moving up from last year’s “Challenger” position.  The report sees this as the fastest-growing segment of the analytics market and expects that by 2020, predictive and prescriptive analytics will attract 40 percent of enterprises' net new investment in business intelligence and analytics.

    Today advanced analytics platforms are an essential tool for business analysts, statisticians, and data scientists. Gartner bases its rankings on "completeness of vision" and "ability to execute" focusing on companies with a strong track record in the market and most likely to influence the market’s growth and direction.  In this vein, Dell Statistica has a 30-year track record in the industry, enabling organizations to realize the full potential of their data in order to predict future trends, optimize business and manufacturing processes, identify new customers and sales opportunities, explore “what-if” scenarios, and reduce the occurrence of fraud and other business risks.  

    The Statistica platform is a very easy-to-use solution that requires no coding and integrates seamlessly with most database platforms or directly with Hadoop, and helps organizations simplify the process by which they deploy predictive models directly to data sources at the edge, inside the firewall, in the cloud, and in partner ecosystems.  Customers like the fact that while no coding is needed, it is possible to code anything within Statistica using Visual Basic, an industry standard language.  This allows users to customize functionality required for their business, giving them the best of both worlds - a platform where coding is not necessary, but available if needed.

    Over the past year, Dell has delivered additional innovative functionality to give customers feature updates in the Statistica product line to improve sales and marketing strategies. Gartner acknowledged the hard work and praised in particular the platform’s new strategic focus on the Internet of Things (IoT).  You can read more of the report here

  • Statistica

    Thought Leaders in the Mix - Feb. 2016

    Our subject matter experts for Statistica keep busy, staying abreast of current trends with big data and small data, IoT, predictive software and real-world analytics solutions. And they frequently comment in industry publications, professional forums and blogs--or produce videos, in some cases. Here are a few of their recent articles.
    Dell Software Joanna Schloss Analytics overload: Why data optimization needs to be balanced
    by Joanna Schloss, senior product marketing manager and BI/Analtyics SME, Dell Center of Excellence

    Discussing how data analytics can enable healthcare professionals to provide a higher level of care, Joanna offers an overview in Building Better Healthcare, where she outline how professionals can apply this tool to drive innovation without losing value.

    Dell Software Ryan G McKinney Tech Predictions for 2016: The February #ThinkChat Recap
    by Ryan McKinney, social media and communities advisor, Dell Software

    Sad to see 2015 trends slip into the past? What's new in your world? Mix together some Statistica users, talking heads and subject matter experts, combine with a healthy dose of the analytics community on Twitter, and it turns out that everyone’s got something to share! Enjoy this recap of our monthly #TweetChat discussion.  

    Dell Statistica Shawn Rogers
    Looking Ahead: Predictive analytic platforms enable businesses to best serve customers and improve business processes
    featuring Shawn Rogers, chief research officer and marketing director, Dell Statistica

    Using the 2015 Forrester Wave report as a starting point to address the widespread applicability of advanced analytics, author Cassandra Ballentine taps our own Shawn Rogers (among others) for some additional insights. Rogers emphasizes that the application of advanced analytics is best driven by business needs rather than technological capabilities. Company size and data volume are not nearly as important as understanding data so that it can be used to improve the way you do business. 

    Dell Statistica John Thompson
    An Inflection Point for Dell Statistica
    by John K. Thompson, general manager and executive director, Dell Statistica

    John reflects on the journey that Statistica has taken since its inception with StatSoft in 1984 through its aggressive accomplishments with Dell in the past two years. With its latest industry recognition as a market leader, he anticipates the real journey is just beginning and explains why Statistica is up to the task. 

    Dell Statistica Shawn Rogers
    The Denver Broncos' Analytics Game
    by Shawn Rogers (again)

    Touching on key analytics lessons learned from Super Bowl 50, Shawn discusses the importance of getting a jump on the competition, improving current processes and changing organizational culture. In this CMSWire article, he outlines five lessons about winning with analytics that businesses can learn from how Denver went about winning this year’s big game.




  • Direct2Dell

    An Inflection Point for Dell Statistica

    When Dell acquired StatSoft and its Statistica advanced analytics platform back in early 2014, we knew it was only the start of a journey toward something bigger. Certainly, StatSoft had every reason to be proud of its accomplishments up to that point. The company and its leaders spent 30 tireless years building a loyal user base, and earning a reputation for world-class customer service and support that exceeded anything I’d seen in all my years in the industry. But we still felt there was room for more - more investment, more functionality, and more innovation. In other words, we felt there was room for true market leadership.

    That’s why I was so proud last week when Dell was positioned as a “Leader” in the 2016 Gartner Magic Quadrant for Advanced Analytics PlatformsIdea. For those of you not familiar with Gartner Magic Quadrants, they assess vendors on their ability to execute and the completeness of their vision, and provide the industry with an important and objective tool that organizations can use to help evaluate the advanced analytics capabilities of vendors worldwide. I highly recommend reading not only this Magic Quadrant, but others with relevance to key areas of your business.

    On a personal level, I can’t help but feel as though this placement represents an inflection point of sorts for Statistica – an opportunity to not only look back at all we’ve accomplished as a business, but also to look forward to the many innovations we still have in store for customers.

    By committing to and never wavering from an aggressive development roadmap, in less than two years, we’ve transformed Dell Statistica from a solution that meets the needs of the top Ph. D.’s and data scientists, into one that now also meets the needs of the everyday citizen data scientists that have become the true driving force behind the use of analytics for so many companies. We did this not only by greatly enriching Statistica’s data visualization, visual discovery and dashboarding capabilities, but also by delivering a completely revamped and modernized GUI that prioritizes ease of use and visual appeal.

    As proud as we are of those enhancements, technology leadership is about more than just having all the requisite bells and whistles customers want today. It’s about charting a course that prepares them to deal with the challenges of tomorrow. And there’s no greater analytics challenge staring customers in the face than the explosive growth of IoT infrastructures and the edge devices of which they’re comprised. That’s why we moved aggressively to build out our new Native Distributed Analytics Architecture (NDAA) capability, which enables Statistica users to push predictive algorithms and scoring functionality directly to the source of data. This eliminates the time and expense required to transport data to a centralized location, and allows for immediate action to be taken in response to insights. Leadership means coming up with a new approach to address a new challenge, and with NDAA, we’ve developed today a capability that no organization will be able to live without tomorrow.

    I noted earlier that when we initially acquired StatSoft, we felt like we were at the start of journey to something bigger. With all we’ve accomplished in the two years since, it might seem as though that journey is now complete. But in fact, the opposite is true. We’re still only getting started. We just completed year one of an ambitious three-year plan to deliver continued innovation in advanced analytics. In months and years ahead, we’ll continue delivering aggressive cycles of product updates to our customers, allowing them to progress and scale at their own speed while maintaining a proactive approach to tackling the challenges of tomorrow.

    At Dell, we don’t take leadership lightly. So, while we’re extremely proud to be recognized as a leader today, you don’t need predictive analytics to know how determined we’ll be to maintain that leadership well into tomorrow.


    Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

    Idea Source: Gartner, “Magic Quadrant for Advanced Analytics Platforms,” February 2016

  • Statistica

    Dell Statistica Receives 2015 Technology Innovation Award from Dresner Advisory Services

    For Dell Statistica, a great year just got even better. Earlier this morning, Dell was recognized as a technology leader in Advanced and Predictive Analytics for 2015 in the inaugural Dresner Advisory Services Technology Innovation Awards, a new awards program that recognizes the top vendors across the company’s Wisdom of CrowdsÒ series of research covering numerous thematic areas.

    If you’re not familiar with Dresner Advisory Services and its chief research officer, Howard Dresner, well, you should be, especially if you’re serious about keeping up with all things business intelligence and analytics. Dresner Advisory Services is one of the premier providers of truly independent, third-party research and analysis. Its findings and commentaries are not driven by research sponsors, but by data and input collected from real-world technology users.

    So, it goes without saying (but I’m going to say it anyway) that we’re honored to have Dell recognized as a Technology Leader for 2015 in the area of advanced and predictive analytics by such a trusted and respected authority. The expert label gets thrown around a lot these days, but in the case of Howard Dresner, it’s absolutely warranted, and we’re thrilled to have earned the recognition of his firm.  

    Now, Statistica has been on the receiving end of more than its fair share of awards and recognition over the years, but if it seems like we’re more excited than usual about this bit of recognition, there’s good reason. As 2015 draws to a close, it’s a great time to reflect back on all that we’ve accomplished since welcoming Statistica into the Dell family. And in doing so, I can’t help but feel as though this recognition today serves as a validation of sorts for all the hard work we’ve completed, and as motivation for all the hard work still to come.

    At the time of its acquisition by Dell in the spring of 2014, StatSoft was a company with a 30-year track record of success and loyal user base for its Statistica advanced analytics software numbering in the millions. But we nonetheless had our work cut out for us, as the advanced analytics market and the needs of customers was rapidly evolving, and continues to do so. One of the primary items on our immediate technology to-do list for Statistica was to deliver enriched data visualization, visual discovery, and dashboarding capabilities. We did just that earlier this year with the introduction of the Statistica Interactive Visualization and Dashboard Engine. We also heard loud and clear that our customers wanted more visual appeal and even greater ease-of-use, and we responded by introducing a completely revamped and modernized GUI this year at Dell World as part of the launch of Statistica 13.

    But you don’t get to be a technology leader without operating on the leading edge, and we’re doing just that with our focus on what we call Native Distributed Analytics Architecture (NDAA), also introduced in Statistica 13. With NDAA, Statistica users can push predictive algorithms and scoring functionality directly to the source of data, allowing companies to take advantage of the compute power on that system while eliminating the time and expense required to transport data to a central repository. In other words, instead of pushing data to the analytics, we’re enabling customers to push analytics to the data. This concept of “analytics at the edge” is already achieving great traction with customers, and considering the explosive growth of IoT environments happening as we speak, we fully expect NDAA will soon become a must-have capability. And we fully expect Dell Statistica to lead the way in delivering it.

    Though we’ve come a long way in a short period of time, we’re really just getting started. In 2016, not only will we continue to enhance and enrich our first-to-market NDAA capabilities, but we’ll continue our emphasis on delivering vertical-specific packages designed for the specific needs of companies in industries such as healthcare, pharmaceuticals, manufacturing, and financials. And in keeping with Dell’s heritage, and with StatSoft’s, we will continue to focus on democratizing and making advanced analytics available to the masses. We’re already seeing a new breed of non-technical analytic users cropping up throughout organizations. These citizen data scientists will play an enormous role in the continued growth of advanced analytics, and we’re committed to helping them drive innovation for their companies.

    In other words, as great as 2015 was, and as happy as we are to have ended it on such as great note courtesy of the Dresner Technology Innovation Award, we’re looking forward to even bigger and better things in 2016. And beyond. 

  • Statistica

    Thought Leaders in the Mix - Dec. 2015

    Our subject matter experts (and guest bloggers) for Statistica keep busy, staying abreast of current trends with big data and small data, predictive software and real-world analytics solutions. And they frequently comment in industry publications, professional forums and blogs--or produce videos, in some cases. Here are a few of their recent articles.
    Dell Software Joanna Schloss Identifying and Overcoming the Top Big Data Challenges
    by Joanna Schloss, business intelligence and analytics evangelist

    Emphasizing the importance of investing in big data projects, Joanna describes in her latest CIO Review article (page 85) how organizations can foster IT/business alignment, develop in-house training programs and find tools that reduce complexity while managing and analyzing all data.


    Dell Software Ryan G McKinney IoT in Manufacturing: The November #ThinkChat Recap
    by Ryan G. McKinney, social media and communities advisor, Dell Software

    Mix together some Statistica users and subject matter experts, combine with a healthy dose of the Twittersphere's analytics community, and it turns out that everyone’s got something to share! Enjoy this recap of our monthly #ThinkChat discussion that addressed ten questions on how the Internet of Things is impacting the manufacturing industry.


    SMB Group Laurie McCabeMaking the Internet of Things Real For SMBs
    by Laurie McCabe, IT analyst and cofounder of SMB Group

    Statistica is ready to handle the IoT, but do small and medium businesses recognize the potential for themselves? With an eye toward the practical application of a Dell Gateway, guest blogger McCabe offers this analysis in her recent Dell World follow-up.




  • Statistica

    That Giant Sucking Sound You Hear…Is it Your Data?

    Nature abhors a vacuuum. So does data analytics. The whole idea behind collecting and analyzing data is to answer questions within some kind of context and, thus, enable decisions to be made--hopefully, better decisions than those made without the data.

    So, it only stands to reason that the more relevant data you collect, the better analysis and decisioning you can make--as long as you don't succumb to "analysis paralysis," of course. To this end, successful outcomes require data collection and preparation to go hand-in-hand with your analytics efforts.

    So, how do you collect more and better data that is relevant to your context? For starters, you might expand the number of data collection points within your current monitoring systems. You could also arrange to share or purchase data from third-party sources. Or you can re-visit previously untapped data archives that you already have on-site. Any or all of these solutions might be reasonable for your situation, and executing them over time can result in some convoluted data environments and workflows that would make Rube Goldberg proud, especially in a siloed enterprise. And it is with such environments in mind that effective data integration becomes extremely important to maintain any semblance of efficiency.

    Subscribers to our Statistica newsletter (yes, you can subscribe for free) already got a tip for an effective data integration solution, because the October/November issue contains a link to a helpful and detailed how-to article highlighting Statistica's new connection with Dell Toad: “The Smart Data Analyst’s Tool Set.” Authors Robert Pound and Scott Burk explain that analytics follows the age-old 20/80 rule that pervades all human activity: data analysis is only 20 percent of your work, while the upstream data preparation--the aforementioned collection, integration and cleansing--consumes the other 80 percent. The key to success here is the comprehensive capability of Toad Data Point and Toad Intelligence Central (TIC) which can now be accessed easily from within the enterprise editions of Statistica 12.7 and 13.0.

    The last thing you want is for your analytics projects to be overwhelmed or undermined by the complexity of so many disparate data sources. Of course, you need those sources in order to escape the pull of the data vacuuum and make the most of your decision-making context, so you probably shouldn’t ignore them. Thankfully, Statistica and Toad combine to make the process simple and relevant.

     Read the Oct/Nov Statistica Newsletter >

    Paul Hiller

    About Paul Hiller

    Paul Hiller is a marketing communications analyst at Dell Software. He enjoys bringing order out of chaos.

    View all posts by Paul Hiller | Twitter

  • Statistica

    Is Statistica 13 Really All That Great? (Duh.)

    Probably every IT department has at least one cynic who believes that every software maker touts every new release as something earth-shattering. After all, why give software a new number if it doesn’t represent a quantum leap of some kind, right? However, it is arguably true that some releases may disappoint the masses while others may justify their sequential numerations. So, skepticism may be a healthy way of self-regulating one’s expectations.


    How does this apply to Statistica 13?

    Having said all that, you probably expect that I will now claim the new Statistica 13 really is earth-shattering (it is!) and that you should simply take my word for it (you should!) There actually are specific capabilities within this release that make touting its merits a very easy assignment. However, “earth-shattering” remains a subjective term, so I should not be so crass as to insist you take my word for anything.

    Instead, I will gladly let others make that case for me, because this Statistica release is very impressive and people are taking notice. Our newsletter subscribers already received a headful of headlines about Statistica 13, big data, and the Internet of Things (IoT) generated from our recent Dell World event in Austin, TX. Maybe you've run across these headlines yourself in other venues:

    On top of Dell’s own press release, these six articles are but a drop in the bucket of media coverage, but I can tell you that what got journalists and analysts really excited about the Statistica 13 rollout is our software’s application of Native Distributed Analytics (NDA), with which Statistica saves time and effort by pushing algorithms and scoring functionality into your databases, basically analyzing your data — even big data — right where it lives. Statistica 13 distributes analytics anywhere on any platform. When it comes to dealing with streaming data and transfer limitations, NDA will fast become a busy analyst’s best friend.

    Meanwhile, you just know there other enhancements in Statistica 13 that will make the user experience more enjoyable and productive with respect to data visualization, workspace GUIs, and more. After all, we had to pack in enough newness to justify that new number 13, right?

    Today is a good day to check out Statistica 13 to see what it can do for you and your business. Also, be sure to subscribe to the Statistica newsletter to keep abreast of our latest product info and thought leadership.

    Read the Oct/Nov Statistica Newsletter >

  • Statistica

    Free Statistica at College: The Gift That Keeps On Giving

    We've all been to college at one time or another. Some of you reading this post are still in school even now. And the majority of us are probably still paying off student loans.


    Speaking of college costs, maybe you have already learned about Dell Statistica's response to students in need. Our answer: FREE academic software!

    Major Costs Add Up at School

    Ponder your college years for a moment. Good times and challenging courses. But let’s focus on the struggle of the whole college experience ROI. What are your top complaints in this regard? If they relate to costs, you are in broad company. A nationwide survey of higher education students reveals a list of popular complaints, with a measurable percent stemming from costs:

    • “The price of textbooks!”
    • “College is too expensive.”
    • “The cafeteria food is gross.”
    • “Being broke all the time.”

    Okay, we can't help you with the cafeteria food, but you'll notice the other complaints are indeed about costs.

    Additionally, a plurality (39%) of respondents to Princeton Review's recent "College Hopes & Worries Survey" said their biggest concern is the level of debt incurred to pay for a degree.

    It comes as no surprise that everything at college costs more money than we like, and it all adds up. Consider textbooks alone, the bane of every undergrad out there. Costs vary greatly from one major to the next, but assuming new book purchases are required, a study based at University of Virginia indicates that a statistics major is neither the most nor least expensive when it comes to textbooks. However, the study did find the average statistics textbook costs about $110, and students must buy multiple textbooks throughout that major's curriculum. The most expensive statistics book topped out at $342.

    And, as if that weren’t enough…students in the data sciences get to tack on the cost of basic analytics software, too. It's like buying a virtual textbook on top of the physical textbooks.

    What is the skills gap?

    Meanwhile, though it may vary from industry to industry, the data scientist skills gap is real. Even as long ago as 2011 McKinsey & Company was already reporting that there will be a shortage of talent necessary for organizations to take advantage of big data. Barring some kind of change in the human resources supply chain, they predicted by 2018 “the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.” This is great news for students looking to break into this career path.

    Change that Matters

    So, our free academic program in North America is the kind of “change” we can apply readily to impact that human resources pipeline at the university level. It may not sound like much, but remember that every little bit helps when we are talking about reducing the financial burden of students seeking a strong foundation with skills-based training and key software tools in order to increase their value in the competitive data science field.

    Think about it: The world needs more statistics and data science graduates to handle the deluge of big data challenges that are developing in every industry. Would the cost of just one more textbook—or, in this case, an analytics software package required by the professor—make or break the average student's ability to pursue the degree? Why risk it? We'll just give it away and let the chips fall where they may! If we choose to give away some software to help put more problem-solvers into the world’s workforce, then that's what we will do.

    And the value of such a program? Priceless! Not only is the free academic bundle a boon to the study of analytics in North American academia, but because it will expand the pool of graduates qualified for real-life analytical pursuits across industries, the effects of this program are literally immeasurable, with potentially world-changing impact. You just never know where the next genius case study will originate. Truly, the gift that keeps on giving.

    Read the Oct/Nov Statistica Newsletter >

  • Dell TechCenter

    Halloween Fun with the Dell Software Team

    From parties to haunted houses, trick-or-treating to giving out candy to the neighborhood kids, it’s personally always been a favorite holiday of my wife and I. We even set the date we would exchange our vows so that we would be on our honeymoon in New Orleans during Halloween. And let me tell you, if you are a fan of the holiday, I highly recommend being there for the event. 

    Halloween Fun at Dell Software

    The Dell Software team enjoys Halloween as well. As you can see, many of our families go all out on this entertaining holiday.


    Cynthia was the famous pop art piece by Roy Lichtenstein and had a great Nightmare Before Christmas pumpkin. 


    Madison hung out with friends she's known since high school at a Halloween house party. She's the  one in the center dressed as a witch in black.


    Jeanie and her husband Jeff and had a blast at her neighbors annual Halloween Dance Off party. Jeanie's daughter was a baby cheetah, and she was Momma Cheetah.


    Gio's dog has great Halloween spirit (and Gio has great photo editing skills!)


    Chris spent Halloween day coaching his daughter and other special-needs baseball players from  District 62 Little League at Angel Stadium. The Challenger Baseball Classic is an annual event  where special needs baseball teams from southern California get to play a game on the field at Angel Stadium. (amazing way to spend your time!)


    Emily went to a local Halloween maze and I think her face says it all. She had a good time.


    Amber's daughters went as Queen of Hearts and Sweety Kitty. She calls this photo “The girls just wanna have fun!”


    Ryan had a fantastic Harry Potter Party with his friends, complete with Sorting Hat and their very own Quidditch Cup!


    My son and I dressed up as hackers, while my wife, quite a fan of the horror genre, went as a character from You're Next.

    Halloween Content from Dell Software
    To top this off, the Halloween fun doesn't stop with our customs and parties, it also crept into a number of content offerings published around the season. 

    Switching Analytics Platforms – A Process Nightmare?

    The Windows group had:

    And Foglight had a great video with 2015 Foglight Dashboard refresher: