I’m often asked how the new age of big data will impact small- and mid-sized business (SMBs). Can they keep up? Can they stay relevant in the age of big data? The answer is a resounding yes. After all, big data really represents all data, regardless of what it looks like or where it resides. We’re talking social media, internet, IoT, images, and digital media, and as well as all those forms of structured data we’re quick to forget about but are still dominating the data management landscape. In no uncertain terms, the SMB is just as interested – if not more interested - in taking advantage of this pool of data. Just like their enterprise counterparts, SMBs are actively searching for the value and business opportunity hidden within data.
In many ways, the new data landscape is completely altering the old competitive landscape as it pertains to SMBs and enterprises. The growth of so-called big data hasn’t made SMBs less competitive or less innovative. In fact, it’s done just the opposite. Thanks to advancements in our ability to capture and analyze data, SMBs can now drive innovation in ways previously reserved for the enterprise. Managing “all data” gives the business, regardless of their size or budget, the ability to better understand their customers, their businesses, and their marketplaces. All of which means that in this new data ecosystem, SMBs are more competitive with bigger, richer enterprises than they’ve ever been in the past.
Which bring us to Microsoft. Microsoft, like Dell, has long been known as champion of the middle market, and, again like Dell, they’re clearly focused on taking that commitment to the next level amid the changing data landscape. You can already see how customers’ need to corral big data is impacting the way Microsoft supports the SMB ecosystem. MSFT has invested in morphing and creating many products to reflect the need to support the evolving needs of the SMB. A great example of this is the company’s aggressive investment in Azure. Other examples of Microsoft investing heavily to meet the changing needs the SMBs can be seen in the company’s Excel and SharePoint brands.
To understand that this all means for Dell (spoiler: this is a great thing for Dell), let’s look more closely at Azure. Azure opens up a cloud-based approach to big data storage, delivering opportunities for the SMB to enjoy a pay-as-you-go approach. The Azure platform opens up a means for SMBs to experience small footprints of the big data ecosystem without committing precious resources to these efforts. These smaller chunks of the data are also more representative of what an SMB might need to store and archive. After leveraging Azure storage, Azure ML allows customer to experiment with big data using machine learning, essentially setting up an analytic sand box for the organization to explore and experiment with analytic capabilities in a meaningful and “by design” method. Here’s where Dell comes into the mix. Dell Statistica allows SMBs to easily leverage its predictive power in tandem with Azure ML’s compute power to build a best-in-breed solution to address advanced analytics.
The combination of Azure and Statistica provides just one great example of Microsoft and Dell technology working together to benefit SMBS. The potential for SMBs to leverage Dell technologies to deliver value on top of their Microsoft investment is virtually limitless, and it’s one of the primary characteristics differentiating Dell in the information management space. Not only is our ability to help you get the most out of your Microsoft investments unique, but so is our willingness. For SMBs and enterprises alike, Dell is the platform-agnostic vendor. Our relationship with Microsoft is stronger than ever, but when we say all data, we mean it. So, whatever investments SMBs make and whatever path they travel down in order to get more out of their data, we can go down it with them. That’s what all data is all about.
See how data growth and new technologies are affecting the DBA ― read the eye-opening study today.
You never know what you will learn from the helpful Statistica newsletter. (Subscribe for free.) Our recent June issue brought to our readers’ attention that legacy StatSoft’s social media properties have been changed up quite a bit. You won’t find us by the same name anywhere anymore!
Well, that’s not entirely true, but readers did learn that our old Facebook page has now expanded to include all our fellow software teammates in Dell’s Information Management Group. So, now when you go visit our page, you will find our Statistica content mixed with that of Dell Boomi, SharePlex, and Toad. It’s like we suddenly discovered an extended family with whom we share much in common—primarily we share the successful end-to-end workflow of YOUR data. We think you should stop by and get to know these family members, too, and then “like” them the way you’ve always liked Statistica.
Find all our new social media links in the June newsletter >
Integration, Analytics and Process are all part of the Internet of Things (IoT) value chain. Pulling it all together is much harder than it seems and will present a significant challenge to many companies looking to derive value from IoT initiatives. Successful companies will need to employ a strategy that leverages a flexible infrastructure that manages the data and the analytics at the edge and embedded into critical applications.
In this week’s #ThinkChat segment Tom and I discuss why the Analytics of Things are more important than the Internet of Things (IoT). Neither of us discounts the value of the infrastructure or the data but in the end actionable insights are what drive the ROI and it’s impossible to get there without the analytics.
#ThinkChat Conversation with Tom Davenport Part 4 of 7
To view other segments in the #ThinkChat series click here.
The headliner in the latest Statistica e-newsletter was hard to miss, announcing the official release of Statistica 12.7. Thirty-one years in the making and our analytics platform just keeps getting better! There were no trumpets or parades, but that doesn't mean there is not some really cool stuff in there. I won’t go into details about 12.7 here —that's what the newsletter is for! (Yes, you can subscribe for free.)
By late 1987, StatSoft had expanded and integrated all its lines of software into one large statistical package called CSS (Complete Statistical System). CSS included prototypes of many of the unique input, output, and analysis control features that would later become trademarks of StatSoft’s software technology.
As CSS (and MacSS for the Macintosh) became popular, StatSoft was already devoting all R&D resources to the development of a new, advanced line of statistics software: STATISTICA, which was to offer entirely new levels of functionality not available in any other data analysis software at the time.
And yes, in case you were wondering, the trademarked product name was always presented in italicized capital letters, even in body copy.
The first (DOS) version of STATISTICA was released in March 1991, followed by STATISTICA/Mac in January 1992. Finally, STATISTICA for Windows (aka STATISTICA 4.0) was pre-released in 1993, representing the crowning achievement of StatSoft’s R&D efforts. The platform’s graphics technology and numerical integration, the flawlessness of its user interface, and the capacity and speed of its computational modules all set new standards for numerical and graphical analysis software.
The rest, as they say, is history. Subsequent releases of STATISTICA continued to address enterprise, connectivity, and scalability needs in the world economy, and the platform continued to set new performance, quality, capacity, and comprehensiveness standards for statistics, graphics, and analytic data management software. No wonder it eventually caught Dell’s attention.
StatSoft had steadily worked the software up to version 12 before the Dell acquisition in March 2014, after which the Statistica name itself got a makeover: no more italicized caps. Now the latest release this year is Statistica 12.7. And next…? Stay tuned.
These days there’s a lot of talk about big data and its effect on privacy. After all, we now work with vast amounts of data that wasn’t practical, accessible or simple to leverage in the recent past. It used to be true that companies only tapped into 20 percent of their data resources, leaving the remaining 80 percent because it was too costly and difficult to utilize fully.
Not anymore. Today, innovative companies are striving to use all of their data (#AllData). Advances in data mining and big data analytics are enabling innovation at the speed of business. We can mash-up, manipulate and mine information to do great, new insightful things. We can take advantage of derived data, which leverages several points of data to create new data about just about everything, including buying patterns, consumer preferences, business directions—the list goes on and on.
But before we get carried away with the endless possibilities, let’s remember a quote from Voltaire: “With great power comes great responsibility.” Companies that start down this path—and it’s a crowded one these days—must walk a fine line between innovation and icky.
Most everyone appreciates when Amazon makes suggestions for additional purchases based on behavior data. In other scenarios, data that is derived can come as a complete surprise—such as when a retailer uses shopping basket analysis to determine that you have a cold or a baby on the way. When Amazon uses data about you, it feels innovative. When a retailer creates data about you, it feels downright icky.
With the advent of big data technologies, there’s more and more data included in analytic work streams that simply wasn’t available before. Issues around privacy are very fluid right now. Common sense and best practices should prevail during conversations about where the boundaries of innovation and “ickiness” cross.
According to a recent AP story on high-tech fitting rooms, some retailers are testing versions of technically advanced fitting rooms with “smart mirrors” to compete with online retailers. The technology enables a brick-and-mortar retailer to collect much of the same behavior data as online retailers and then use it to recommend other products. So, would you appreciate a mirror that suggests a pair of jeans to go with the red shirt you just tried on or is that an invasion of privacy?
Consumer advocates already are voicing concerns about who ultimately has control over the data collected. Governments worldwide are starting to pass legislature and guidelines around digital privacy. It’s early, but the conversations need to continue so regulations can be developed to protect people from what they don’t fully understand.
Recently, I bought new doorknobs for my kitchen cabinets and for weeks, I was inundated with online ads for doorknobs, even if I was visiting a sports, cooking or news website. Most people don’t know that major websites share data as part of behavioral ad targeting. I personally think it’s cool when Amazon suggests a book I might like based on a previous purchase. But, a sports site trying to sell me more doorknobs falls into the icky camp.
That’s why it’s so important to understand both the context and circumstance of how data will be used. I spoke with an educator from a small school district on the east coast where analytics were being gathered on K-6 students. The goal was to data mine all available information on a student to identify the Key Performance Indictors (KPI) that would correlate to how likely he or she was to graduate high school A fantastic utilization of data, isn’t it? But there also is a potential downside. How do you share it? Or should you share it? Do the parents deserve to know? Will the knowledge affect how teachers interact with students?
According to an article in Time, a movement is stirring in about 125 schools around the country. Officials are sifting through years of grades from thousands of former students to predict what will happen to current classmates. One university uses data to determine which students would have a higher propensity of graduating while other schools have learned to minimize costs of recruiting new students who they believe are more at risk.
While big data and analytics are incredible, there is a double-edged sword around proper use of this information. For example, there’s a teachers’ union that is working with its state to change the compensation policy to one that is more performance-based. While that would seem all well and good, what if a school gathers data that reveals which students will not do well and then teachers don’t want these students because they could negatively impact their compensation? Or what if students find out their predicted fate and it turns into a self-fulfilling prophecy?
At Dell, we understand innovation comes with responsibility. We strive to keep our finger on the pulse of governance and privacy best practices so we don’t cross the boundaries from innovation to icky. Have any thoughts on walking this fine line? If so, drop me a line at email@example.com.
What do the 43rd President of the United States and I have in common? We like hanging out with healthcare technology professionals!! President George W. Bush gave the closing keynote at this year’s Health Information and Management Systems Society (HIMSS) event in Chicago and Dell was one of the Corporate Sponsors. It’s been a few years since my last visit to HIMSS and I have to say I was extremely impressed. The event is attended by over 42,000 people and seems to cover every square foot of the McCormick Center. Dell had an extremely strong presence at the program, hosting a charity in the booth, a dozen different Dell HCLS solution demo's and 3 live tweetups.
The technology themes were varied throughout the event and I was there to help lead a discussion on Population Health with Dell experts Dr. Gary Miner, Dr. Tom Hill and Dell's acting Chief Medical Officer Dr. Charlotte Hovet. We were also joined by Dr. Ken Yale, Vice President of Clinical Solutions, ActiveHealth Management. It’s interesting to see where data is playing a role in driving more consistent and higher quality patient care. Population health obviously benefits from data driven insights. Technology's like advanced analytics are helping us move beyond an understanding of large populations and to focus in on more personalized patient care via diverse data and insightful analytics. As we are able to leverage more data and a greater variety of information on specific patients the ability to personalize care and apply a customized level of best practices will result in much better overall patient care.
L-R Shawn Rogers, Dr. Gary Miner, Dr. Tom Hill, Dr. Charlotte Hovet and Dr. Ken Yale
The end result as advanced analytics drives patient care forward will be precision healthcare where care givers are able to execute specific regimes for each individual based on their specific needs, chemistry, DNA and other personalized markers and prerequisites. It's exciting to think that advanced analytics has the ability to enhance treatment and deliver personalized healthcare. Innovation isn't without its hurdles, connectivity to data and a new responsibility of patients to bring their own data into play will prove difficult. New trends will include device information on a patient’s exercise and activities, diet, location, travel history and more. Advanced analytic platforms will factor many new data points into models in order to achieve the highly specific care plans required by precision medical treatments. Look for care givers to push back a bit as the culture of human knowledge and instincts collides with automated and model driven best practices. I believe that ultimately both voices need to be heard in order to supply the best possible care. Dr. Hovet made the point that even though analytic platforms will supply a path for treatment Dr’s will still play a critical role in communicating, implementing and executing precision medical treatment. The days of the robot doctor are still way out in our future.
Having been in the data business for as long as I have, I found the themes at HIMSS to be exciting and full of promise for future and immediate innovations based on our ability to leverage greater amounts of data and a wider more dynamic range of information in order to add value to overall patient care. These are exciting data driven days for health care.
Perhaps some of you remember this successful advertising campaign of a bygone era: "The Maidenform Woman: You never know where she'll turn up." I was reminded of this while compiling the June issue of the Statistica e-newsletter.
Those of you in the know are surely raising your eyebrows by now. That's because Maidenform was promoting women's undergarments, not analytics solutions. Nonetheless, the shared concept of ubiquity is where I found the Statistica connection.
Specifically, I was prepping a lengthy list of events in EMEA and North America, everything from tradeshows and conferences to workshops and webcasts. But it was the EMEA events, both big and small, that struck me with their variety of non-analytic-sounding titles reflecting different industry verticals: HIMMS, Interop, Oil & Gas, Cloud World, IsisTech & Oxford AHSN eHealth. Of course, it helps to know what the acronyms stand for, but here's my point: Statistica's analytics solutions and data tech compatibility are applicable in just about every industry across the spectrum, so almost every business event out there is relevant.
That is why Dell Software is sponsoring these events and many more. It is important that we meet decision makers where they are and help them envision the suitability of our solutions, even at functions that—at first glance—might not seem to be practical venues for exposure. Our calendar of events is certainly in keeping with our current mission to embed analytics everywhere, empower more people, and innovate faster. And as we continue to increase our reach through such varied opportunities, Statistica becomes like the Maidenform Woman: you never know where we'll turn up!
Read (and subscribe to) the June newsletter >
Everyone loves superheroes. As if we need proof, Avengers: Age of Ultron has already raked in more than $1.1 billion. Perhaps it’s because we all love to fantasize about having superpowers. Who wouldn’t want super strength like Captain America? How about the ability to fly, so you could whip past traffic on your morning commute?
Our daydreams could go on and on, but we suspect there’s one superpower that would help you right now: the ability to quickly and easily analyze the massive volumes of data your organization collects each day. Imagine if you had Hulk-like strength to smash down data barriers. You could effortlessly collect, integrate, analyze and use all that data. But without the right powers ― and with constant demands to help generate business insights ― your data battles may seem more daunting than a faceoff with Ultron himself.
Well, that whole fantasy about ruling your data universe is about to become a reality. That’s because we’re delivering the power you need to easily turn data into actionable information. And we’re talking about virtually all data here: data coming from traditional on-premises sources as well as cloud-based data sources and modern data stores like Hadoop and NoSQL. So if you’re ready to get your analytic superpowers on, check out our latest version of Statistica, the advanced analytics platform.
Two important enhancements in Statistica 12.7 will empower you to take data analytics to the next level. We partnered with Datawatch Corporation to boost the advanced analytic capabilities of Statistica with enhanced interactive visualization and dashboarding. Rich, visual representations of various data streams will help you easily identify opportunities and hidden patterns. This release also offers self-service data preparation and real-time streaming to put data analysis power in business users’ hands.
In his recent article, “Dell Brings Advanced Visualization to Analytics Platform,” CIO’s Thor Olavsrud noted that the addition of these advanced interactive visualization tools and dashboard capabilities extend the applicability of Statistica to additional users, including business analysts.
Statistica 12.7 also integrates with our Toad and Boomi product lines, delivering connectivity with more than 164 data sources, cloud or on-premises, in motion or at rest. Further development of the Statistica big data analytics module, enhanced text-mining capabilities, natural language processing and search tools, expand the product’s ability to derive insights from unstructured data. The coolest part is that the Statistica big data analytics module brings advanced analytics to the data, rather than the data to the math.
We already have customers using the newly integrated Datawatch capabilities. Don your own analytical superpowers with a free trial of Statistica 12.7 today.
As quants become more critical to your overall analytic environment there will be growing pains between them and the line of business executives they serve. Many business sponsors see quants as alien beings, math magicians from another planet. Quants can and do deliver extremely useful insights but it remains the responsibility of the business leader to transition that insight into valuable action. Feeding the need of an executive to “look” informed is a recipe for disaster. Its critical for a business leader to stay focused on action not just the collection of information.
In this week’s #ThinkChat segment Tom Davenport and I discuss the dynamics of quants and their business partners and touch on a few companies who are doing it right and getting high value from their investments in Big Data and Quants.
#ThinkChat Conversation with Tom Davenport Part 2 of 7
To view other segments in the #ThinkChat series click here
At this year’s Dell Annual Analyst Conference (DAAC), Michael Dell was crystal clear about the immense opportunity the Internet of Things (IoT) represents. In fact, he called it the trillion dollar opportunity.
Not surprisingly, IoT was one of the big trending topics also discussed by analysts, sourcing advisors, Dell partners and customers alike. It was reflected in keynote speeches, breakout sessions and in demos. The building automation demo by Dell OEM partner KMC Controls showcased how data taken from sensors in a building, aggregated via a gateway and then analyzed can help make a building more energy efficient and safer. Next to this demo, Dell Software showed how an advanced analytics solution built on top of the KMC Controls solution can provide comprehensive data integration and predictive analytics-based insights.
The Internet of Things
Let’s step back a bit. IoT is not new and although it is often framed as an emerging trend, it is no longer a prospect of the future. Many companies already have sensors on their equipment used for predictive maintenance. The Internet of Things can be described as an ecosystem where sensors, devices and equipment are connected to a network, and can transmit and receive data for tracking, analyzing, and taking business actions. What does this data journey look like?
The data journey
The journey of the data begins at the sensor connected to a device, for example an air conditioning unit or refrigerator. Now the data has to travel via a wireless or connected medium to get to an aggregation point, either a gateway or a datacenter. Gateways, such as the just launched Dell IoT Gateway , are small, wireless or connected devices that collect, help secure and process sensor data at the edge of a network. They represent one way of collecting data and can be used as a smart device to perform real-time monitoring and analytics of streaming data.
Next, the traveling sensor data has to be integrated into a much larger pool of data, including non-sensor data such as weather, social media, CRM, business or other machine-to-machine (M2M) data. A platform like Dell Boomi allows for seamless, real-time data integration and normalization on either the gateway, datacenter or in the cloud. At this level, Boomi also adds additional value in terms of ensuring industry data compliance (HIPAA, Safe Harbor, PCI, SOC II)
The data value resides in analytics
Nicely integrated, this data is now ready for analytics (Dell Statistica). This video explains the value proposition.
In short, here is how it works: There are two ways to analyze the data: 1. perform real-time analytics on streaming data (time series, see examples 1 and 2 below) at the edge (gateway) and 2. perform deeper analytics on the historic data set that can be done in the datacenter to help predict maintenance events or forecast business trends.
An example of streaming data analytics is shown in figures 1 and 2. Figure 1 (follow link) shows the monitoring of refrigerator compressor temperature against ambient temperature in real time, setting off alerts to the building operator in case of anomalies, such as an unhealthy compressor.
Figure 2 (also shown above) shows real-time operational analytics mashing coffee pricing data (milk, paper cup, coffee grounds, etc.) with sensor data from inside the manufacturing facility to monitor the overall health of the manufacturing facility.
Figure 3 (follow link) provides an example of predictive analytics performed on data at rest. This example shows how we can use multivariate process monitoring and control statistics to predict machine health. The analytics show that there is a definite correlation between the speed of the cup movement by the robotic arm inside the coffee machine and the ice dispensing speed which points to the need for maintenance of the robotic arm.
KMC Controls, via its building automation solution, controls and monitors the devices and sensors that are installed in a building. There is an opportunity to develop a centralized view of the entire building to provide a comprehensive IT management solution which includes IoT devices and sensors as well as all IT assets that are managed inside the building.
Many devices work on their own secure network, and building automation manufacturers like KMC Controls need to consider what security measures must be implemented for detecting and blocking malicious activity over non-standard network protocols. A natural starting point is to consider extending the existing firewall technologies to comprehend these new devices.
Learn more about IoT solutions
To assist companies in overcoming the hurdles in setting up industrial IoT technologies, Dell has set up the Dell Internet of Things (IoT) Lab. Companies can come to the lab - located in the Dell Silicon Valley Solution Center in Santa Clara, CA - to receive assistance in architecting solutions to their IoT needs.
To learn more about the Dell IoT Lab, please see http://dell.com/iot. To learn more about the software solutions Dell offers in this space, please visit http://software.dell.com/
In this second part of our interview series with Dr. Charlotte Hovet, medical director of Global Healthcare Solutions at Dell, we examine what healthcare will look like in 2020 and offer tips for getting started.
What will healthcare look like in 2020?
The world of healthcare will look different in five years and significantly different in 10 years as providers and patients adapt to disruptive change. Technology, consumerism and new payment models are reshaping the delivery of healthcare, and as a result, we can anticipate better care, better health and lower costs. However, that doesn’t mean there won’t be significant stumbling blocks along the way.
I’ve traversed the globe for nearly a decade advocating change and while physicians are quick to adopt new medical devices, they’ve been slow to embrace Electronic Health Records (EHR). And, often for good reasons. New technologies must enhance the effectiveness and efficiency of clinical practice and align with people, process and policy changes. As such, challenges will linger over the next five years as phase two of the EHR continues and progress is made on the integration, interoperability and security fronts.
Full adoption of patient-centered tools will take time and patience as well as assistance to overcome steep learning curves. Moving to a digital world is certainly disruptive but the upside is tremendous—a world of true clinical collaboration and innovation. The ability to deliver integrated services will spawn new care-giving models with expanded scopes and teams outside the traditional clinic and hospital settings.
New technologies like telemedicine will emerge that enable people to have care on a daily basis where they need it most—in their homes. Care teams will be able to reach out to patients in remote areas with a focus on prevention and continuity of care. Whether a person has an acute problem or a chronic disease, the capacity for home care will be greatly enhanced. It will be an exciting time in healthcare as we experience value-based, rather than volume-based, service delivery.
Would you say that value-based healthcare is data-driven healthcare?
Absolutely! Analytics and informatics will be the primary drivers in this newly expanded healthcare view. The knowledge we derive from data changes everything—how we interact with patients and how we diagnose and treat them. Let’s look at University of Iowa Hospitals and Clinics where Dr. John Cromwell is using Dell Statistica to better predict which patients face surgery risks and then determine which medications or wound treatments would be most effective in reducing their chances of acquiring a hospital-acquired infection.
How will predictive analytics be useful in lowering healthcare costs?
Instead of focusing most of our efforts on the high-cost, high-risk group, which currently accounts for three-quarters of our healthcare spending, predictive analytics will enable organizations to focus on the rising risk—the middle group—which is often ignored. If we can identify those people who are at risk for chronic disease and actively intervene before they become high risk, we can make major headway in lowering the cost of healthcare delivery while dramatically improving quality.
What’s the best way to get started?
Healthcare transformation requires alignment of people, processes and technology, which we discussed in a recent webinar. We recommend starting with a readiness assessment to reveal where you do—and don’t—have alignment across the organization.
This can be determined by asking basic questions, such as: What clinical analytics do you need and who holds the key to that information? Who on your staff will mine the data and look for trends? What will be done with that information to change the delivery of healthcare services? What role does governance play in all of this? And, what steps need to be taken once all this knowledge is passed along to the appropriate clinical improvement teams? How will they collaborate to identify trends, turn insights into action and change care delivery?
This iterative process needs to involve the physicians and nurses who are directly involved in delivering care. Future healthcare will be highly collaborative and empower healthcare professionals to identify best practices through analytics as well as how they can use this information to improve decision making and patient care outcomes.
How is Dell helping customers accelerate healthcare transformations?
Dell does a great job of guiding customers along their data-driven journeys by bringing together hardware, software and services to address their needs today while providing an IT platform for the future. Today, we’re proud to be working with some of the leading healthcare organizations, including Dignity Health, HealthMarkets, Beth Israel, Boston Medical Center and more.
In my travels, I meet with chief analytics officers, chief technology officers and chief medical information officers. I tell them about a population analytics project with a hospital in the south. I share highlights of a recent pilot using advanced predictive analytics to identify those at risk for exasperation of asthma or diabetes and the impact on hospital readmissions. I explain how the University of Iowa Hospitals and Clinics has lowered surgery infection risks and subsequently surgery costs.
It’s exciting to share insights, ideas and engage others in healthcare transformation today, so we all can benefit from a new world of healthcare delivery by 2020.
What do you think healthcare will look like by 2020? Email me at firstname.lastname@example.org to offer your forecast.
In this interview, Dr. Charlotte Hovet, medical director of Dell’s Global Healthcare Solutions, shares her thoughts about how healthcare informatics and predictive analytics are helping to usher in a new era of wellness and disease prevention.
The benefits of agriculture IoT sound enticing, but there are architectural challenges to be addressed before deciding on a solution that turns 6000 head of cattle into a data powerhouse. Dandekar describes Dell's successful case at Chitale Dairy.
While advanced analytics is a critical component to the success of an organization, Schloss outlines in her latest CMSWire article the drawbacks of excessive analysis and the benefits of focusing on innovation rather than optimization.
Lately, whenever I hold a whiteboard discussion on collective intelligence, customers, prospects, analysts and even my fellow Dell team members give me their full attention. Now, people have talked about collective intelligence for ages, but I think what drives the point home now more than ever before is the success we’re seeing with crowdfunding and crowdsourcing.
There’s an important lesson from crowdsourcing that data scientists need to learn: there’s greater value in your information if you can share it more readily with more people. The collective intelligence you can gather will be far richer than if it had stayed within the confines of the corporate walls.
Let’s face it, intelligence is not evenly distributed in this world. But there are lots of folks who are very good at building models and want to make them available for the greater good. By tapping into this shared, group intelligence, companies of all kinds can make better business decisions. Perhaps that’s why I get similar levels of excitement whether I’m talking to a Silicon Valley start-up, healthcare organization, energy company or high-tech manufacturer.
After all, why rely on four or five data scientists in your own organization when you can turn to data scientists around the world for insight and perspective? This is the winning approach taken by Apervita, a leading health analytics community and soon-to-be Dell partner, which empowers health professionals and enterprises to capture and share health knowledge. They’re smart about facilitating collective intelligence by simplifying how people author, publish and use health analytics, including algorithms, quality and safety measures, pathways and protocols.
In February, the Mayo Clinic joined the Apervita community to share its extensive portfolio of algorithms covering specialties, such as cardiovascular, pulmonology and oncology. The goal: To make it easy for physicians to sift through all the Mayo Clinic’s cardiovascular data, for instance, so they can automatically identify patients at risk for sudden cardiac arrest, which is the leading cause of death among adults over the age of 40.
In May, the Cleveland Clinic joined the Apervita community to share its advanced prediction models and wealth of medical knowledge with a broader audience. By liberating all this data and putting the collective knowledge to work, these organizations and Apervita are making it much easier for health researchers and practitioners worldwide to have a positive global impact on health.
The beauty of Apervita’s cloud-based approach is in the simplicity and openness of its platform, which enables anyone, anywhere to create and subscribe to analytics and then easily integrate them into their workflows. This is the same approach taken by Algorithmia, which launched in 2013 with the goal of advancing the art of algorithm development, discovery and use.
Dublin, Ireland-based ExpertModels is another innovative group with an open, online platform for sharing data insights as well as building, requesting or marketing data sets, analytical models and data science expertise. The openness of these data markets and communities is what makes them an ideal conduit for collective intelligence. That’s also what truly differentiates Dell Statistica because the sheer openness of its architecture makes it possible to blend the best of these models through a common repository.
By opening our platform to other environments, Dell has empowered organizations to take models from Algorithmia, Azure ML, ExpertModels, etc., and knit them together in new workflows to increase interaction and collaboration. It’s a great example of how we’re helping customers get smarter about collective intelligence—and we’re the only company that can deliver this level of integration.
It’s one of the reasons Borden Chemical initially chose Statistica as an analysis platform at over 30 sites worldwide. A leading supplier of high-performance resins, adhesives, coatings and basic chemicals to a broad range of industries and thousands of end-use applications, Borden integrated Statistica with its SAP and Laboratory Information Management System (LIMS). By taking advantage of Statistica’s open, distributed architecture, the company easily empowered more than 150 researchers, quality control engineers and technical consultants to combine their collective intelligence worldwide to simplify complex data analyses and reporting.
I’m confident this collective intelligence message will continue to resonate, especially as we share more examples of all the amazing things we can accomplish with distributed intelligence. After all, we’ve always known that “two heads are better than one,” so think of what can be done when you amplify that with hundreds of thousands of smart people and interactive models.
How can you increase your company’s collective intelligence? Drop me a line at email@example.com with ideas on how to get smarter about collective intelligence.
Big data has made big strides in recent years. Specifically, more organizations than ever before are leveraging data and information to deliver actionable and valuable business insights. While big data problems of the past have centered on making sure infrastructure could keep up with how much data is being pulled, significant advancements in storage and other infrastructure technologies have given us a firm foundation on which companies can deploy their predictive models.
Thus far, 2015 has provided new opportunities to bring analytics directly to business users, but with it, challenges now go beyond what’s in the datacenter alone. These opportunities and challenges have already begun to present themselves and organizations are learning to address them in the following ways:
Opportunity: Enterprises are using existing technology with big data platforms to deliver ROI
While the analytics space has historically been crowded with BI, dashboarding and other tools, more enterprises have begun to use new platforms with existing analytics programs to unlock business value. To begin with, enterprises are looking for ways to incorporate data visualization with data analytics solutions to more easily interpret vast amounts of unstructured data. While the interpretation challenges still remain, those who apply visualization solutions map out meaningful insights everyone from non-technical executives to data scientists can read more effortlessly.
One of the ROI-achieving byproducts of visualization and analytics is that insights now become more accessible to a wider user base. With BI vendor offerings becoming increasingly easy to operate, business-minded users who might not have the background to use traditional systems are finally able to leverage data analytics to create new revenue streams. In doing so, they’re able to deliver better customer experiences and expand into new markets.
Challenge: Self-service and automated decision-making are influencing businesses to reorganize
While the demand for candidates skilled at interpreting data still surpasses the supply, companies are coping with this shortage by investing in self-service, automation and augmentation platforms. Ultimately, organizations are leveraging automated decision-making and data discovery tools for improved cost and efficiency. At the same time, they must be prepared to significantly restructure to achieve competitive advantages like using data to proactively cross-sell and up-sell. Many operational processes now can be completely executed automatically with data analytics. When adopting programs that automatically push successful predictive models straight to the data, organizations should spend time checking the source to ensure the usefulness and relevance of tried-and-true models. While automation can enable real-time analytics, resources still should be allocated toward making sure current models are the best ones to use.
Opportunity: The growth of Information as a Service (IaaS) is providing easier access to analytics
There is a steady movement from simple, backward-looking descriptive analytics to advanced analytics that predict outcomes, and prescribe a course of action. This creates more opportunities to democratize access to analytics. One option that is emerging as a result of both this movement and the rising popularity of “as-a-service” delivery models is “information-as-a-service” or IaaS. The availability of IaaS further breaks the barrier to entry for businesses that historically have not had the technology, finances or skills to leverage advanced analytics, as well as provide them with an additional competitive edge to bolster growth.
Challenge: You’ll find network and security challenges at the intersection of big data and IoT
The growth of connected endpoints is making more information available for extracting insight. This, in and of itself, has driven both the growth of IoT, as well as the need for analytics. With more data, however, there is more exposure to such vulnerabilities as cybersecurity threats, compliance issues and other risks. Although this creates a market opportunity for vendors offering integrated solutions covering a comprehensive list of data analytics, endpoint management, threat detection and compliance needs, the reality for IoT organizations is that there is already a struggle not only to mine new data pools, but also to securely store the data.
Organizations continue to have an opportunity to benefit from advanced analytics, as access to data only gets easier and making sense of it simultaneously grows less complex. This provides opportunity for people outside the data scientist profile – from business users who need analytics to solve a marketing problem, to small businesses that, yesterday, couldn’t afford to invest in the time or costs associated with pulling insights from their data. While other challenges have emerged and will continue to do so, improved accessibility has opened a huge window of opportunity that help businesses use their data to spike competitive advantage.
With the headline, "Get inspired by the life-changing benefits of Dell Statistica," we linked to a new case study in the April/May issue of the Statistica Monthly News. This one is all about the rare diseases unit of Shire, a global biopharmaceutical company that seeks to ensure the robust and uninterrupted supply of quality medication to its patients. The implementation of Dell Statistica has helped them conduct statistical process control, monitor processes and identify areas for improvement.
Anything that reduces a multi-day analysis down to mere minutes without sacrificing quality has got to be good, right?
Given the nature of Shire’s business and the kind of help they offer to people all over the world, this is a very inspiring story, and Dell Statistica is proud to be part of it.
Read more in the April/May issue of Statistica Monthly News >
How do you empower more people with advanced analytics for greater impact on how they do their job? How do you embed analytics everywhere so you can make data-driven decisions? How can you use analytics to innovate faster?
These questions keep us up at night, just as they keep a lot of our customers up at night. We don’t have a silver bullet for them yet, but we’re moving Statistica closer to being one, little by little.
The press and analysts are starting to notice. They’re talking to you and finding out that Statistica is meeting your needs and then some. Many Statistica customers are commenting favorably on the ease of use, completeness of solution, integration efforts with other Dell products, and ongoing investment by Dell in the product and people since the acquisition last year.
That’s encouraging news as we get ready to launch this quarter’s Statistica version 12.7, with features aimed at helping you get advanced analytics into the hands of more of your users:
We plan quarterly releases to Statistica from now on. If you already use Statistica, keep an eye out for upgrade instructions. And if you’re tired of staying up at night figuring out how to get analytics into the hands of more of your users, keep an eye out for the free trial version to download.
What do your smartphone and your office air conditioning system have in common? Surprise—they top the list of IoT data sources.
In the May issue of the Statistica Monthly News, we shared an item about a new infographic that summarizes the results of a survey Dell commissioned from Enterprise Management Associates (EMA). It’s all about the Internet of Things (IoT) marketplace.
When it comes to the IoT, I tend to think of it as something that is still theoretical, probably because I didn’t wake up this morning to learn that Skynet was suddenly in charge of everything. But the build-up of the IoT is in full swing and has been for some time with machine-to-machine (M2M) sensors and real-time automation. Perhaps it is not at critical mass yet, but EMA’s infographic identifies some of the major industries pushing that swing. Review the infographic and see what other interesting information EMA uncovered.
In this two-part series, Dr. Charlotte Hovet, medical director of Global Healthcare Solutions at Dell, shares her thoughts about how healthcare informatics and predictive analytics are helping to usher in a new era of wellness and disease prevention. Part one takes a look at healthcare informatics changes happening today while part two looks ahead to what we should expect by 2020.
Q: How do you define healthcare informatics?
Last month at HIMSS’15, I spent considerable time speaking with attendees and Dell customers about health informatics. The buzzword “informatics” is often seen as the intersection between computers and medicine. But that’s not it. Informatics is a science; it’s the study and practice of creating, storing, finding, manipulating and sharing information. Informatics drives innovation, and when applied to healthcare, it enables us to effectively use information and knowledge to improve the quality, safety and cost of patient care.
I spent more than 20 years as a practicing family physician, and that clinical experience shapes how I view patient-centered, information-driven healthcare. Additionally, I’ve spent nearly a decade advising others on the transformation of healthcare and the role of information technology.
Q: What role does predictive analytics play in accelerating this healthcare transformation?
Analytics is becoming critical to healthcare. The wealth of digital data available to healthcare providers is expanding exponentially, and having the ability to share, integrate and analyze data opens the door to population health management, disease prediction and personalized medicine.
Because medical records now are digitalized, we have the ability to share data and use it to improve decision-making. Using data to impact clinical decisions is key to transforming healthcare. With analytics, we can connect disparate and different types of information to identify trends. From those trends, we gain valuable insights, and these insights should be used to change behavior. The only way we can significantly change patient-care outcomes is by changing the way care is delivered by clinicians and the way patients manage their health.
Q: How does information-driven healthcare change the doctor-patient relationship?
With information-driven healthcare, we can move from episodic care, where patients present with a particular problem, to new models of care delivery designed to optimize the health and wellbeing of the populations we serve.
This will transform today’s fragmented, volume-based healthcare delivery model into a mobile, interconnected, value-based, collaborative care delivery model. The status quo is being disrupted by innovative digital technologies and the ability to align patient wellness with physician payment incentives. The result is the emergence of new tools and new healthcare capabilities that will lead to more personal and precise medicine.
Q: What impact will new wearables technologies and smart sensors have on doctor-patient interactions?
We’re in the early stages of wearable devices; however, it’s clear that wearables and other technologies enabled by the Internet of Things will play an important role in empowering patients to use information and take greater responsibility for their health. This happens through the ability to access lab results from your smartphone or using telemedicine to expedite a diagnosis. All of this data—whether it’s directed to the patient or the provider—leads to more informed decisions.
In the future, we’ll see greater opportunities for shared decision-making as both patients and their physicians will have direct access to information that can make a difference. Wearable sensors will do more than remind you to walk more. They’ll trigger automated functions that can be incorporated into your lifestyle to increase prevention and wellbeing.
Wearable technology will be able to combine personal and social media data with sensor data to reveal useful insights into a person’s lifestyle, such as sleep patterns and exercise levels. All of this data will be fed into cloud-based solutions where a variety of predictive analytics can be performed to deliver more personalized medicine.
Q: How is Dell driving the delivery of patient-centered, information-driven healthcare?
For the past two decades, Dell has helped healthcare organizations keep pace with dramatic industry changes with end-to-end, future-ready IT platforms that drive healthcare innovation. Our leadership in the healthcare IT space was reinforced recently by Gartner, which ranked us the No. 1 IT Services Vendor in the Healthcare Providers Category for the sixth consecutive year.
This is a great accomplishment, and no coincidence, as we’ve focused on hardware, software and services to help our healthcare customers optimize their clinical information systems and be better prepared for what lies ahead. With our cloud and analytics expertise, they’re embracing more patient-engaging strategies while moving forward with clinical analytics.
In the next five years, the adoption curve will be very rapid for new healthcare IT technologies that provide sustainable benefits. Predictive analytics and informatics will still be at the epicenter, delivering the insight and knowledge required for healthcare transformation.
We’ll continue the discussion in part two of our conversation with Dr. Hovet. Till then, drop me a line at firstname.lastname@example.org to share your thoughts on the future of analytics in healthcare.
You never know what you’ll find in the Statistica Monthly News email. The top item in last week’s issue was a blurb steering readers to the press release announcing Dell Software’s partnership with Datawatch that will take Statistica’s already robust data visualizations and dashboarding to a whole new level of interactive dynamics.
Historically, a common objection from prospects was that certain spreadsheet programs could display business data visualization with sufficient detail for all manner of reporting. So, they would ask, why upgrade to Statistica? Arguably, this might have been partially true to a point at one time, but there comes a day when market changes demand advanced analytics, and advanced analytics demand advanced visualization in order to do any good. And it is so much easier to conduct analytics with a comprehensive platform like Statistica that doesn’t require data to be output to a standalone visualization application.
I have seen impressive, real-time demonstrations of this new capability in Statistica and am excited about what the Datawatch partnership has to offer our users.
Read more in the May issue of Statistica Monthly News!
Dr. Thomas Hill Executive Director of Analytics for Dell Information Management Group recently contributed a very interesting article entitled “When Smart People Won’t Use Smart Technologies”. Intriguing title, the article looks more closely at how different people respond when exposed to innovation and new technology options and how it affects people’s attitudes toward their physicians, healthcare provides, and personal health data. One would expect quick adoption and overall excitement towards new capabilities that combine data to enhance our care– apparently this is not the case.
Dr. Hill’s article provides insights on how patients are reacting to more analytically driven healthcare and how you can help your patients embrace the new age of technology and healthcare.
Learn how you can be smarter with your technology implementation by preparing your organization with governed and integrated data that is delivering results and value to the patient.
Many businesses pursue analytics projects with the idea that they can respond ever more effectively to dynamic customer, market and business demands. Thompson describes the critical factors of analytics agility necessary to make this happen.
Applied to the preponderance of connected monitoring, social psychology research suggests that increased public awareness of the Internet of Things around us will influence our daily choices for the better. Davis ruminates on this effect.
In her latest CMSWire article, Schloss notes that the very term, "Shadow IT," portends rogue employees and covert operations, but such negative connotations are unwarranted. Shadow IT will only grow larger in a world of advanced analytics, so she examines the common myths to explain why businesses should want Shadow IT to thrive.
One way to think of "advanced analytics" might be as "complex analytics," the kind that go beyond the traditional analytics employed to produce business intelligence. However, there is another meaning that is certainly apropos in the context of anticipating circumstances, predicting trends, and prescribing actions. In such future-facing scenarios, "advanced analytics" might suitably be thought of as "analytics in advance." This is where the analytics maturity model starts to make sense.
David Sweenor, Statistica product marketing manager in Dell Software's Information Management Group, provides a practical summary of this maturity model in his recent post, "5 ways to boost your business IQ." It is worth noting that the model he touts is layered like a pyramid, with each layer built upon the solid foundation of previous layers. There is no skipping ahead when it come to maturity: every level of analytical maturity must be earned and learned in sequence.
Accordingly, Sweenor helpfully provides a quick overview of five advanced analytics techniques that should be evaluated by any organization seeking to build up its maturity: segmentation, decision trees, predictive models, text analytics, and optimization/simulation. And he describes some helpful Statistica case studies that prove the value of advanced analytics in real-world scenarios. Read David's post and see where you are in the model. You can also find more Statistica case studies under the "Resources" tab here.
The need to be more agile comes up a lot in customer conversations, especially from frustrated executives who want to be more sure-footed and flexible in moving their businesses forward. Too often, they feel stymied by a lack of useful insight, which hampers their ability to respond quickly and effectively to changing customer, market and business demands.
Luckily, this gives me an opportunity to bring up one of my favorite topics: analytics agility. With the right mindset, tools and technologies, organizations can become much more adroit about how they use the power of analytics to improve decision making. As with most things, the toughest part is getting started.
According to Dell’s 2014 Global Technology Adoption Index, 61 percent of companies worldwide have big data waiting to be analyzed—and yet only 39 percent of those polled felt they had a firm grasp of how to go about extracting value from that data. What it takes is a mix of intellectual curiosity and intestinal fortitude to develop an understanding of how your business works from a data perspective.
In my experience, there’s usually a group of naturally curious intellectuals in every company that are eager to drill down into business facts and figures to discover trends, triggers and roadblocks impacting business success. Thanks to our increasingly connected world, these data miners have more tools and techniques at their disposal than ever before to look at data from all angles.
Also critical is having a supportive, equally curious leadership team that encourages the use of data to figure out the business. I met recently with a large sportswear manufacturer that invests heavily in analytics to support a variety of marketing initiatives. The challenge for the analytics team is that when the data supports what the marketers want to hear, it’s all good. When the analytics reveal a different outcome, the marketers claim that the data is bad and do what they want anyway.
Unfortunately, having the right analytics tools and smart people driving the process won’t make much difference if company leaders aren’t open to learning and following what the data reveals. After all, the point of analytics agility is the opportunity to quickly and nimbly change direction completely or make a minor course direction before it’s too late. As we all know, however, sometimes it takes a few lessons learned the hard way to realize the data was good to begin with but the business decision was flawed from the start.
Another critical success factor to increasing analytics agility is having the support of an IT team that continually and consistently collects, manages and exposes data for a variety of analytics efforts. Historically, this has been one of the biggest stumbling blocks as traditional, centralized IT teams often were too overwhelmed with “break-fix” tasks to respond quickly and efficiently to analytics requests. In the past, many early analytics efforts died as soon as the financial, sales and marketing people generated data from separate silos of information and nothing matched up.
Thanks to continued IT decentralization and increased data sharing, it’s much easier now for IT to build an infrastructure that brings different types of data together and delivers a single version of the truth that everyone can get behind. When that occurs, the journey to analytics agility becomes a shared experience that produces tremendous insights and business breakthroughs.
And in some cases, even medical miracles. I’m still sharing the story about Dr. John Cromwell at the University of Iowa Hospitals and Clinics. As reported in the Wall Street Journal’s CIO Journal, Dr. Cromwell is using Dell Statistica to better predict which patients face surgery risks and then expedite surgery room decisions on which medications or wound treatments will be most effective. Now, that’s a prime example of the power of analytics agility.
I also recently spoke with Danske Bank, the largest bank in Denmark and one of the leading financial institutions in northern Europe. This Dell customer is doing some amazing things with Statistica and various credit scoring tools to produce real-time insight that enables cutting their credit risk exposure nearly in half. By taking advantage of analytics agility, the bank can make up-to-the-minute decisions about which markets to serve to gain a competitive edge while mitigating risk.
Today, we have the analytics tools to drive fast, flexible business decisions. And, each day, I hear about another customer with a strong IT and leadership team backing efforts to push the analytics envelope. I’m encouraged to see more companies getting a firmer grasp on what they can do with greater analytics drive and dexterity.
I’m looking for more examples of how companies are flexing their data muscle with analytics agility. Drop me a line at email@example.com to share what you’re seeing.
Smart phones were the harbingers of the connected future as they transformed from merely portals for consuming information into sensors and location-based signaling tools. But the Internet of Things (IoT) takes that to another level where relatively inexpensive devices can be connected and managed through automation suites, then mined for new information. As I write this, my Nest thermostat is reporting my energy usage, and my Enphase Energy hub is recording my solar power generation. In April, in Northern California, it is almost ideal conditions for both, with no need for climate control and ample sunshine. Finally, my Kevo locks let me know when people come and go from the house or when a door is left open.
We expect this automation trend to accelerate, too. From water sensors to warn you when your plumbing has gone awry to smart pill bottles to help insure accurate daily dosing for an increasingly aged population, the range of information that we will soon have access to will likely become overwhelming. Indeed, just managing all of the devices, protecting privacy while allowing access, and figuring out how to merge related data streams together is already becoming a growth industry.
The latter may be the most interesting and valuable part of the technology puzzle, as well as the most challenging. Data analysis traditionally falls into several areas. The most common is alerting and descriptive statistics. The smart pill bottle needs to, at a minimum, alert the patient when the dosing is incorrect. It might also provide aggregate statistics concerning compliance. Where it becomes more challenging is when those statistics are merged together with other information sources. What is the correlation of a medication with the successful treatment of the condition or even off-label impacts and adverse effects? This has traditionally been the domain of retrospective analysis with large variations in the data sets due to incomplete and inaccurate reporting and the challenge of collecting together sufficient records. While medical privacy laws like HIPAA play a part in blocking effective access to this data, reporting is at least as great a challenge.
We can see similar patterns in terms of energy monitoring and consumption, like with my solar array. We recently used connected temperature sensors in refrigerators at Dell to monitor usage and performance of the devices. An odd spike occurred in one on Friday afternoons, showing the impact of an ice cream party on the energy consumption of the device. While a humorous outcome, opening and closing refrigerators consumes around 7% of the total energy used by the devices, and broad monitoring patterns has the potential to help reduce this waste.
These kinds of outlier patterns in the data, whether in off-label effects of medications or in energy consumption by appliances, have a broad social impact. Social psychologists have known for some time that the setting a person is in can strongly affect their choices. For instance, when people see others around them picking up litter from the ground, they are much less likely to litter themselves. We can guess that the awareness that connected monitoring will bring to our lives will have a similar effect. While we may try to avoid leaving the fridge door open, seeing the impact of such actions on the building, the company, the city, the nation, and the world creates a network of awareness and expectations that reinforce better behavior.
Understanding the architecture that supports Internet of Things (IoT) projects at first glance can seem overwhelming if not impossible to emulate. There is a wide array of technologies that support these projects and help customers to wrangle the data involved in these programs. Data management, integration and analytics all play a key role in delivering innovative and responsive IoT projects.
Enterprise Management Associates (EMA) recently authored a white paper titled Demystifying the Internet of Things: Implementing IoT Solutions. The paper explores the necessary strategies to implement a project and also highlights some of the solutions that Dell provides its customer to support these projects. EMA identified the most popular data sources that companies are utilizing for IoT projects. (See figure below)
Geo-location devices are the most popular representing 14.7% of the projects researched by EMA but with that said its clear there is a wide variety of opportunities ranging from Corporate infrastructure data, manufacturing data and consumer infrastructure just to name a few. EMA’s paper explores the challenges of integrating, replication, analyzing and securing a responsive environment that moves at the speed of the business to support greater business insights and enable action. Companies adopting IoT can benefit greatly from a smarter view of their business and create competitive differentiation by using this technology. Download a copy of the full paper here and share your thoughts on IoT with me. I’d like to hear how your company is taking advantage of this new and innovative approach to data.