Innovative companies are adopting advanced analytics to take action and match the speed of their business. This is especially true in the world of manufacturing where complicated process driven activities benefit greatly from smarter, faster analytic insights and actions. Collecting and analyzing process data from sophisticated manufacturing processes requires a flexible and agile infrastructure that supports a wide variety of disparate data sources often spanning sensor and machine sources that are combined with instrumentation and testing data, machinery and production data, customer and market data, supply chain information, 3^rd party benchmarks and a wide assortment of system data.  

In the case of Pharmaceutical manufacturing systems can include laboratory information management systems (LIMS), manufacturing execution systems (MES), enterprise resource planning systems (ERP), supervisory control and data acquisition systems (SCADA) and last and perhaps most difficult to manage and integrate; paper documentation. Bringing this data together in an action oriented manner requires accurate planning and solid project management principles.

Sanofi Genzyme the 3^rd largest pharmaceutical company in the world is embracing this challenge and utilizes the following criteria when planning process driven analytic projects.

• Measure the business impact
• Measure compliance impact
• Secure the data, ensure its robust and accurate
• Deliver the data in a format that is easily consumed by analytics.

Genzyme has built an agile architecture named APEX to bring data together to support advanced analytics. APEX is designed to bridge decentralized, heterogeneous data sources and provide a centralized, secure and validated data layer for analytics. Genzyme recognized early on that they needed to leverage real-time data as well as process oriented data in order to get the analytic insights they desired, APEX accommodates these functions through the use of a traditional data warehouse working in tandem with a process oriented data store. Genzyme also took into account how important data lineage, integrity and validation are to their compliance initiatives and maintains strict control over all data sources throughout the analytic process.

The framework that Genzyme has developed creates value across the company as it supports a repeatable and scalable environment designed to meet the evolving needs of their manufacturing processes. In the end, the following themes are crucial to the success of this and future projects.

1. Alignment between business stakeholders and the analytics. Working closely with the business is a critical step in the process and ensures that value is delivered to the end consumers of the analysis.
2. Validation, auditability and traceability of all data within a regulated manufacturing process are extremely important to the overall compliance and regulatory processes of the company. This is especially true in pharmaceutical or any other regulated manufacturing environments.
3. Easy and seamless data acquisition. Automating and controlling data access throughout a diverse manufacturing environment eliminates wasted time and energy wrangling data with manual or home grown solutions.
4. Open, flexible and extensible solutions are important for driving an agile analytic environment it’s critical that the solutions used enhance the process not detract from it.
5. Comprehensive real-time capabilities deliver analytics at the speed of the manufacturing process. This is a key value component to any advanced analytic environment. It’s important that the actual workloads take place in time to deliver the business value required.

Next Steps

For more details on process driven analytics for manufacturing and the Genzyme story, watch this webcast: "Business Analytics in Regulated Manufacturing".

Statistica users > Make your voice heard in Dresner's 2015 Wisdom of Crowds® survey by March 20 and you may qualify for a complimentary copy of the study findings.

We invite you to help represent Dell Statistica in this 6th annual examination of the Business Intelligence (BI) marketplace that covers BI deployment trends and related areas including cloud BI, collaborative BI, advanced and predictive analytics, cognitive/artificial intelligence, data storytelling, and an all-new section on enterprise planning.

Statistica users in all roles and throughout all industries are invited to contribute their insight, which should take approximately 20 minutes. Take the survey yourself and share the link with colleagues and customers: www.dresnersurvey.com/TR8DLYN. We appreciate your support and thank you for completing the survey by March 20, 2015.

by Danny Stout

Manufacturers have a long history of successfully employing data - big data - to help make important and insightful business decisions. According to a recent article in CMS WiRE by Joanna Schloss, a subject matter expert specializing in data and information management at Dell, early adoption means the industry is set to be a primary benefactor of the big data analytics boom.

Schloss submits that as an early adopter of big data, with a ubiquitous presence in society, and unparalleled access to data collection, the manufacturing industry has a plethora of new revenue streams available to it. In her article, she outlines three:

• Cross-selling - the data that manufacturers collect on a daily basis is useful to a variety of related and unrelated industries. The ability to sell that information places manufacturers in a unique position for new revenue streams.
• Provide premium customer offerings - customers are willing to pay for extra services that save them time and money, or provide them a more enjoyable experience. An endless number of potential new customer offerings can be developed based on the data that manufacturers collect on customers.
• New products - insights are the cornerstone of data. Through data analysis, manufacturers have the opportunity to move into tangential markets that otherwise would have been unknown.

The potential of big data is now a reality for every industry. Manufacturing just happens to be positioned to immediately begin delivery and reaping the rewards.

You can read all of Joanna's insights here.

by Uday Tekumalla

Predictive analytics are used by companies for everything from customer retention and direct marketing to forecasting sales. But at the University of Iowa Hospitals and Clinics, predictive analytics are serving a far more noble purpose - to decrease post-surgical infections.

By utilizing  a number of different data points that were gathered from 1,600 patients, each of whom has had colon surgery performed at the University's hospitals, the medical teams have dramatically reduced the number of patients inflicted with post-surgical infections. In fact, over a two-year period, those infections were slashed by an impressive 58-percent.

That is an impressive feat. There are, after all, a multitude of variables that can lead to an infection. This analysis considered several different data points - patients’ medical history, data from monitoring equipment, data from national registries, and real time data collected while the surgery is being performed like blood loss, wound contamination, etc. The University built predictive models using Dell Statistica predictive analytics software to achieve these impressive results. Running this analysis allows the hospital to determine a patient's risk level for post-surgical infection, providing the medical team with clear insight into the medications and treatment plans to employ going forward to minimize the risk of infection.

Along with providing better patient outcomes the University of Iowa also has likely reduced medical costs. This is an exciting example of the potential of predictive analysis. Learn more about the university's results here.

At Dell’s recent Big Data 1-5-10 event, I kicked off my introduction by saying my goal is “to help customers use 100 percent of their available data all the time.” This remark caused a few heads to turn, and later prompted Jeff Frick, GM of SiliconANGLE and host of theCUBE live interview show, to ask me for more insight into what he called a “provocative statement.”

Shouldn’t we all be driving toward collecting, analyzing and utilizing data to its fullest? As I explained to Jeff, we’re nowhere near ready to deliver all the data, all the time, but we need to make steps in that direction so we’ll be ready to clear the hurdles and take full advantage of opportunities as they become available.

Technology is still siloed, unfortunately, which makes it difficult for people to build out all the analytical models today that can deliver answers to their most critical questions. Structured and unstructured information isn’t analyzed together, which creates another barrier to getting one single view of the truth. Another barrier: people doing the analytics address very specific, often narrow areas of focus.

Currently, most companies use only a subset of their data for a very specific purpose. But, you can discover so much more if you step back and take a larger view. For example, instead of only looking at revenue trends over the past 12 months, what could be learned if you look more broadly at the health of your company’s customer base or the social factors driving trends and behaviors that either accelerate or moderate a drop or move in your business?

Delving deeper into the data delivers so much more insight. At the University of Iowa Hospitals and Clinics, for instance, Dell Statistica is used to pull data from a wide variety of data sources to help lower the rate of infection for surgical patients. As reported in the Wall Street Journal’s CIO Journal, the University of Iowa takes information from patients’ medical records, and surgery specifics, such as patient vital signs during operations, to predict which patients are face the biggest risk of infection.

Armed with this valuable insight, doctors can create a plan to reduce the risk by altering medications or using different wound treatments.

Thanks to the evolution of analytics, other organizations will be able to follow University of Iowa’s lead in more fully utilizing their data. We’re at a tipping point—compute cycles now are affordable enough and can keep pace with data proliferation while plentiful bandwidth and cloud services make ubiquitous data access a reality. Today’s infrastructures enable us to do things that weren’t possible five years ago.

While environments now are ready to accommodate a more holistic view and broader conversations about data, most companies are just starting to buy-in conceptually. Sure, companies want access to all their data, all the time, but most folks I speak with see this as an aspirational goal still to be achieved. When it comes to the here and now, they’re pretty pragmatic and taking the first steps to realizing their data’s full potential.

Since focus is the hallmark of success, I recommend putting customers first. Start by taking all the steps you can to get all the data on your customers. Then, gather all the data on your product areas, supply chain, manufacturing, etc. In each respective area, there likely will be a dozen different data sources that are interconnected and interrelated. For instance, in compiling data on customers, you’re likely to encounter exposed interfaces that take you to product, which can be integrated with manufacturing, and so on. It’s kinda like assembling LEGO blocks or deciphering fractal patterns as all the data elements are nested and interwoven.

Another major step is determining how best to empower your data analysts by providing them with the right tools for producing everything from simple reports and visualizations to complex analytics. But don’t stop there. If your data is locked away and only useful for PhD modelers and data scientists, you’ll only solve part of your problems. Getting data into the hands of your subject matter experts and line-of-business decision makers is crucial because they too must be empowered to build their own analytical models.

The day when employees become their own data analyst isn’t too far out on the horizon. Once everyone has access to all the data, all the time, they can create their own hypotheses. Training your employees to think more analytically is something every organization should already be doing to stay ahead of the curve.

What steps are you taking to ensure your company gets the most from all its data, all the time? Drop me a line at john.k.thompson@software.dell.com to exchange ideas on how to unlock the power of your data.

This blog will be where members of the Statistica team--a dedicated group of trouble-shooters, project managers, subject matter experts, sales engineers and thought leaders--can freely connect with each other and with members of the broader Statistica user community.

We are excited to share our thoughts about issues and trends and products within the dynamic realm of statistics, business analytics, big data, predictive solutions, and information management. This community is also intended to facilitate your use and comprehension of the award-winning Statistica analytics platform, through the eventual sharing of blogs, webcasts, media, whitepapers, tools, solutions, and more. We appreciate your patience as we roll out relevant content here, and we recommend you visit the excellent resources already available via the Statistica product page. Meanwhile, come back and come back often to learn what’s new in the world of Statistica.

Or better yet, join the community if you’re not already a member and visit our main blog page to email subscribe to this blog. (The link is under "Options.") Feel free to give us feedback and suggestions by posting your comments on a blog post or a how-to wiki article. We want to provide tools and solutions that are practical and helpful for you.

 -- The Dell Statistica Team

As I took in the sights and sounds of Dell World earlier this month in Austin, and more importantly, soaked in the palpable and almost inescapable buzz surrounding Dell’s information management capabilities, I kept coming back to the same thought: What a difference a year makes.

This year marked the fourth annual iteration of the event, and the third I’ve had the pleasure of attending as a member of the Dell team. Since its inception, Dell World has been nothing short of a marquee industry event, its agenda lined with preeminent speakers, informative sessions, rich networking opportunities and world-class entertainers.  So it spoke volumes – about both the progress Dell has made as a leading provider of information management solutions and the growing importance of information management to customers – that big data, data analytics, and the ability of companies to transform data into insights was front and center throughout the event.

There’s no greater endorsement of an IT trend’s importance to Dell as a company and to the industry at large than when Michael Dell makes it the focal point of his opening keynote address at Dell World, and this year, that address focused on “The Power and Promise of the Data Economy.” As Michael made crystal clear in his speech, the road to competitive advantage in today’s economy is paved with data, and organizations that seize the opportunities afforded by the data economy will be the ones that succeed.

A huge part of doing that, of course, is leveraging modern advanced analytics to better understand your businesses, predict change, increase agility and control critical systems. So it should be no surprise then that Michael’s keynote was followed by a fantastic panel discussion on modern data analysis featuring Dell Software’s own Dr. Thomas Hill, one of founding minds behind the creation of the Statistica advanced analytics platform Dell acquired from StatSoft earlier this year. My opinion is biased, of course, but I thought Dr. Hill was hands down the star of that particular show.

But it wasn’t just the keynote. Information management was everywhere at Dell World. No less than 10 sessions during the event focused on the need to analyze, integrate and manage data and information, including in-depth technical sessions on realizing the power of connected intelligence with Toad for Oracle and performing customer churn analysis with Statistica, as well as broader discussions on the importance of making data the lifeblood of your business. I personally had the pleasure of dropping by the Dell World News Desk to discuss the importance of turning data into insights, and our vice president and GM Matt Wolken had a similar dialogue with our good friends at theCUBE.

Speaking of news, it wouldn’t be Dell World without some major announcements, and in that regard, information management stood out. This year, we announced two major initiatives that will drive our analytics capabilities forward. First and foremost, we announced the start of a new collaborative effort with Microsoft aimed at enabling customers to perform powerful predictive analytics capabilities within a scalable, cost-effective hybrid cloud environment. The effort enables customers to use Statistica in tandem with the Microsoft Azure Machine Learning Service. In addition, we also announced that we are upgrading Statistica with enhanced big data capabilities through integration with Kitenga.

Of course, while we’re thrilled to see information management taking on a growing importance across Dell, and more importantly, across our customer base, we really are just getting started. We have many new initiatives we’ll be unveiling – and major announcements we’ll be making – in the coming months, and by the time Dell World 2015 rolls around, I have no doubt that I’ll again marvel at what a difference a year makes. 

If you haven’t followed Dell’s growth in enterprise software, you’ll be surprised to learn that it’s making enough small, medium and large companies happy to become a $2 billion-per-year business.

Dell Software is gaining momentum as a complementary offering to Dell’s computing hardware. Through a strategy of steady development and acquisition, software has become a huge business for Dell, with the Information Management Group a strong contributor.

In an interview at Dell World early this month, Matt Wolken, vice president and general manager of Dell’s Information Management Group, described how the group has grown and changed over the last few years. Its strategy and approach have been to keep up with its customers’ ever-increasing need for tools for databases, analytics, integration and development, while supporting environments as varied as Oracle, DB2, SQL Server and Sybase, plus dozens of different data sources.

You can see the entire interview in this video, which includes these highlights:

• “As we’ve built our business, we’ve tried to remain neutral to the database tier, because customers all have their own preferences of what they need and which platform they use. So we’ve preferred to stay in the database tooling business where we enable each of the database variants.”
• “It’s not a cost to do analytics; it’s a revenue generator to find new customer segments and keep customers from leaving.”
• “You don’t pick a winner [among databases]. You have to follow where the customers go, and they have multiple databases.”
• “There are far more data analysts than there are data scientists. How do we enable the analysts to use the data without becoming an analytics expert? A lot of it will end up in code. We’ll take it to the tier where the average user, who is not a data scientist, can get value from it.”
• “Here at Dell World we have an entire track dedicated to information and data management tools for deriving insights and getting value out of data. We wouldn’t have had that just two or three years ago.”

The interview offers insight into the role of the Information Management Group and some of the upcoming moves you can expect from us in database tools and analytics.

In the world of big data, are you an analytical producer or an analytical consumer?

Don’t worry; one isn’t necessarily better than the other. In fact, analytical producers and consumers need each other, as we explain in our white paper, “Big Data Analytics in Action.” Analytical producers use data mining, predictive analytics, machine learning and natural language processing to produce models, which analytical consumers use to drive the business forward.

Big data is the plumbing; analytics is the business context.

Another important topic we cover in the paper is the difference between big data and analytics, a difference that sometimes gets blurred in the conversations between IT and business managers.

Big data is about the storage, speed, performance and functionality of hardware and software pulling information into your organization. Analytics is about enabling informed decisions and measuring impact on your business. Big data drives innovation in analytical technologies, and this white paper introduces you to the most prevalent of those analytical technologies, as shown in the diagram:

• Data Preparation – As data moves faster and in higher volume from more disparate sources, both producers and consumers need it aggregated.
• Data Exploration – With web-based tools, users can visualize and drill deep into data.
• Search – Users can also gain insight from unstructured data with tools that allow them to mine it.
• Advanced Analytics – Techniques like machine learning, forecasting and optimization reveal patterns in the data.
• Deployment and monitoring – The key to making repeatable decisions in the organization is to combine predictive analytics and business rules.

Read the white paper

The white paper also contains concrete applications of analytics and big data in marketing, finance, healthcare, pharmaceuticals and manufacturing, along with a series of tips to ensure success in your next analytics project.

Get your copy of “Big Data Analytics in Action” and keep an eye out for more content on our Information Management blog in Dell TechCenter.

What if predictions in healthcare could be as personalized and accurate as they are on Amazon or Netflix?

A new book, “Practical Predictive Analytics and Decision Systems for Medicine,” helps organizations move in the right direction by providing a step-by-step guide to applying predictive analytics to healthcare.

The book’s team of authors includes two members of the Dell Information Management Group who joined Dell following the company’s acquisition of StatSoft: Dr. Thomas Hill is the executive director for analytics in the Information Management Group; Dr. Gary Miner is the senior analyst and healthcare applications specialist in that group. StatSoft, which created Statistica software, has long provided analytics capabilities for the healthcare industry.

 In a recent Q&A with Hill, Miner and lead author Dr. Linda Winters-Miner, these writers highlight the promise of analytics for healthcare. “If you could take the so-called Amazon experience of highly personalized, accurate profiling of what someone is probably going to do next, and turn that loose on healthcare, common sense tells you there’s an incredible amount to be gained,” says Hill.

Some forward-looking hospitals are already using predictive analytics and decisioning systems to achieve key healthcare goals, such as reducing infection risks. “As a surgery is taking place, they’re inputting real-time data into a decisioning system,” says Hill. “The surgeon can then use [the insights generated] to make on-the-spot decisions about how to go about closing and treating the incision in order to reduce the risk of infection.”

Despite the great promise for analytics, the industry has a long way to go. “I don’t think we’re at a point where we can say most organizations are even dabbling in analytics for predicting individualized diagnosis and treatment,” says Miner. “There are certain exceptions, but most organizations are 15 years behind the time.”

This book could provide valuable guidance to help organizations understand how analytics is transforming the healthcare industry – from patient, to payer, to provider. It could also help invigorate the profession. “I hope it gets people excited and hopeful about their ability to change the industry,” says Winters-Miner.

 Read the full Q&A with Hill, Miner and Winters-Miner on the Direct2Dell community site, and check out the book.

Interested in exploring Dell Statistica software? Try Statistica for free!

by Joey Jablonski

Recently, insideBigData highlighted some exciting developments announced at Dell World: a new series of solutions offered by Dell to help enterprises make the most of their big data by enabling improved customer relationships and transforming data into actionable business insights.  The comprehensive, digital business services help customers redesign processes to improve operational efficiency and better engage customers.

The customer solutions focus on three important areas:

• Modernization
• Transformation
• Digital services

To meet these goals, Dell Services has launched Dell Digital Business Services.  Dell DBS helps customers better understand their end users’ preferences and transform business processes using digital technologies.  It accomplishes this by consulting with them to help assess their needs and develop a plan to address those needs by leveraging digital technologies, while offering access to Dell's comprehensive end-to-end portfolio.  Additionally, Dell DBS has built strong relationships with several partners across the digital transformation technology areas. Among them are  Kony, Apperian, Cloudera, and Informatica.

Complementing these efforts, Dell Software has also announced a new collaborative effort with Microsoft.  This collaboration will allow customers to perform powerful predictive analytics capabilities within a scalable, cost-effective hybrid cloud environment.

Additionally, Dell Software launched a new big data analytics module within its Statistica advanced analytics platform.  This new module embeds Kitenga's big data analytics capabilities directly into the Statistica user workflow.  Statistica users can now crawl various forms of unstructured data and combine it with large-scale structured data to gain a richer understanding of customer behavior and market opportunities.

You can get greater detail and learn more about these Dell World announcements at insideBigData.

You want to run your business on data and deliver results right now. Who doesn’t? But to achieve these goals, you need to bring together data from disparate sources and perform in-depth analysis. How can you achieve agile data integration while helping to ensure the quality and consistency of data at the same time?

In this Dell on-demand webcast, Philip Russom, research director on data management for the research firm TDWI, outlines three pillars for agile data integration. By building on these pillars, organizations can deliver data integration solutions sooner, better align solutions with business goals and ultimately free up resources to develop more solutions.

Pillar 1. Enable self-service data integration

The process of gathering requirements for data integration can be time-consuming, but according to Russom, it doesn’t have to be. Providing technical and business teams with self-service tools that incorporate data profiling, data discovery and data visualization capabilities can accelerate the process. Those tools help teams record requirements as they work, helping to eliminate the weeks or months of interviewing various stakeholders.

Pillar 2. Capitalize on rapid data set prototyping

Creating data set prototypes early in the data integration allows you to sustain high data quality and avoid issues down the road. Fortunately, many self-service tools enable rapid prototyping of data sets. Technical and business team members can conduct simple data extractions and transformations to produce prototypes quickly and easily.

You can achieve agile development and delivery of data integration solutions while also addressing responsible data access and preparation requirements that help ensure the quality and consistency of data.

Pillar 3. Employ data stewardship and facilitate collaboration

Data stewardship plays an important role in successful data integration. A data steward is a member of a business group who helps ensure data management efforts meet business requirements and who can deliver a rapid return on investment. When data stewards collaborate with technical staff on data integration projects, organizations can better align technical work with business requirements. The result is faster development of data integration solutions and fewer overlapping tasks that can delay project completion.

Having the right tools can make it easier for organizations to build on these pillars. In the same webcast, Peter Evans, a business intelligence and analytics product evangelist at Dell, highlights Dell software for information management that can help organizations take advantage of these pillars and achieve successful, agile data integration.

Ready to learn more? View the data integration webcast.

Do you ever feel that something is thwarting your data discovery efforts? Or are you an IT manager who thinks that business users just don’t understand data governance? Whichever side you’re on, you’re not alone. Business users and IT managers in plenty of other organizations share those sentiments.

Like many companies, yours probably has a much greater volume and variety of data at its disposal than ever before. Your business users are eager to explore and analyze that data so they can generate new insights to help your company capitalize on opportunities and meet its business goals.

But something is standing in their way. As business users see it, that something is IT. Your IT department is tasked with data governance — making sure data is accurate, complete and secure. And unfortunately, data governance processes and policies impose restrictions that can hinder business users’ ability to freely explore data and get the answers they need when they need them.

Neglecting data governance is not an option. But if business users have trouble accessing or using data, your company could miss some vital opportunities. So, what’s the answer?

A new approach: Collaborative data governance

According to the Aberdeen Group, implementing a “collaborative data governance” approach can help eliminate the conflict between IT and business groups, and improve data discovery. Collaborative data governance opens a dialog between IT and business users. IT gains greater visibility into user needs, and business users learn the proper procedures for accessing data so they can work with IT to optimize their data discovery experience. The groups share the responsibility for governing data and finding new ways to maximize its value.

In its 2014 Business Analytics survey, Aberdeen found that organizations with collaborative data governance enjoy several important benefits:

• Information, in time: In collaborative organizations, business users were able to get the info they needed within the necessary decision window about 75 percent of the time, compared with 63 percent at organizations without that collaborative approach.
• Higher user satisfaction: Perhaps not surprisingly, business users were more satisfied with the timeliness of information delivery when there was a collaborative approach in place. The Aberdeen survey found that 55 percent of those business users were satisfied, compared with 30 percent of business users working in organizations without a collaborative approach.
• Less red tape: The collaborative approach can also help boost support for data discovery and analytics initiatives among high-level decision makers. According to the Aberdeen survey, 71 percent of collaborative organizations have an executive-level champion for analytics; less than half of non-collaborative companies have similar support. Executive champions get new programs off the ground by cutting through red tape.

Creating a culture of collaboration between IT and business users can yield benefits that extend beyond specific data discovery requests. For example, as Aberdeen suggests, collaboration can help prevent data silos from forming and facilitate more enterprise-wide access to data, while ensuring data is accurate and secure.

Beyond encouraging collaboration, having the right BI tools is also essential. The Aberdeen survey showed that 72 percent of collaborative organizations use data management and data quality tools. Those tools help simplify data discovery while streamlining governance.

Whether you’re a frustrated IT administrator who’s tired of having to say “no” to new requests or a frustrated business user who’s tired of wading through bureaucracy to access data, it’s time to reach out to the other side. Working together, you can increase the value of data while maintaining the levels of governance your organization requires.

Your turn

Learn more about collaborative data governance and the advantages you can gain from this approach by reading the Aberdeen report, “Collaborative Data Governance: Peeling the Red Tape off Data Discovery.”

If you’re looking for more insight on how to bridge the divide between IT and business groups, read Peter Evans’s recent blog post, “Bringing Business and IT Together for Better Business Intelligence.”

Last month, I authored a blog on Direct2Dell outlining the great momentum we’ve seen in the advanced analytics space since acquiring StatSoft back in March. If you haven’t read it yet, it’s a good way to understand the synergy that exists between Statistica and the broader Dell portfolio, and it outlines the many integration points Dell’s customers can look forward to in the months ahead.

But don’t let all of the exciting things we have planned for down the road obscure the fact that Statistica is already one of the market’s leading advanced analytics platforms, and that right now – today – it should be at the top of the list for anyone looking to invest in an advanced analytics solution in order to facilitate better, faster decision making. Here are 10 of the many reasons why:

1 – Predictive capabilities. When it comes to performance and capabilities, Dell Statistica takes a back seat to no one, offering a comprehensive set of data mining, predictive analytics and data visualization capabilities that companies need to better understand their businesses, predict change, increase agility and control critical systems.

2 – Ease of use. User surveys confirm that simplicity has long been a hallmark of the Statistica platform. It’s simple to install and administer, features a user friendly interface, and doesn’t require proprietary coding skills.

3 – Low TCO. In keeping with Dell’s heritage as a company that makes innovative solutions attainable to the masses, Statistica is a cost-effective solution that fits the budgets of small and mid-sized companies. 

4 – Scalability. Just because it’s simple and affordable doesn’t mean it’s not scalable. Statistica offers the scalability and high-performance technology needed to work on the enormous datasets common in enterprise accounts.

5 – Integration. Statistica easily integrates with existing data stores, including traditional RDBMS, distributed file systems, and data services, as well as emerging data storage technologies. It also embraces open standards, seamlessly integrating with R, PMML and other industry standard protocols.

6 – Role-based support. Statistica was built to accommodate users of differing skills and roles, all of whom collaborate across different parts of analytic operations. The platform offers personalization options that ensure user groups have access only to the data, interfaces, and workflows relevant to their areas of responsibility. In this way, Statistica deftly manages business processes and increases data security.

7.  Real-time monitoring and reporting. Easy-to-use dashboards and Live Score™ functionality makes it possible for organizations to support of on-demand business needs, even when faced with thousands of simultaneous data inputs from line-of-business applications.

8 – Vertical and LOB prowess. Statistica is designed to meet the specific needs of customers across a variety of verticals and lines of business. It has a long, successful track record with customers in manufacturing, healthcare, pharmaceuticals, banking, and marketing.  Statistica is used to aid fraud detection, personalize marketing offers, and improve patient outcomes in the healthcare industry.

9 – Track record. Speaking of track record, Statistica has been around as long as Dell itself, and over the course of its 30-year history, has been trusted by world-class businesses and academia alike.

10 – Validation. Statistica has repeatedly been recognized as one of the leading advanced analytics platforms by top industry analysts, most recently having been recognized by both Hurwitz & Associates and Dresner Advisory Services for the significant value it delivers customers.

With more companies – including more of your competitors – than ever before now leveraging predictive analytics to gain an edge in the marketplace, there’s never been a better time to get to know Statistica. We have a team of experts ready to help you better understand how predictive analytics can drive your business forward.

Just interested in chatting about advanced analytics? Drop me an email at john.k.thompson@software.dell.com  or send me a note on Twitter at @johnkthompson60.

There’s no doubt that big data can create big opportunities. From online retailers and telecommunications firms to financial service companies and government agencies, organizations across a diverse array of fields recognize that analyzing the large volume and variety of information available to them can play a big role in achieving their goals. Insights drawn from big data can help organizations enhance the customer experience, increase internal efficiencies , improve fraud detection, identify new growth areas and more.

Given the potential benefits, what’s preventing more organizations from capitalizing on big data today?

In some cases, it’s a matter of finding the right tools — and organizations often need a fairly wide range. They need tools for data integration and data management as well as the analytics and business intelligence tools that will ultimately generate the new insights. At the same time, they need a robust infrastructure with the scalable capacity to accommodate growing collections of data plus the performance for delivering timely insights.

For midsized organizations in particular, a lack of in-house expertise can slow or stall big data analytics projects. These organizations might need assistance integrating data sources, implementing analytics solutions and deploying the infrastructure required to support big data.

Offering a unique combination of solutions

According to a recent solution brief published by Enterprise Strategy Group (ESG), Dell is uniquely qualified to address these challenges. “Dell is one of the very few companies possessing the right ingredients to really reshape the big data and analytics market,” writes Nik Rouda, senior analyst for ESG. By working with Dell, organizations can get the software, hardware and services they need from a single vendor.

• Software: Several important acquisitions — including StatSoft, Boomi, Toad, SharePlex and Kitenga — have helped Dell create a comprehensive portfolio of software tools for data integration, data management, big data analytics and business intelligence. Organizations can find the right solution within the Dell portfolio whether they are starting with a few disconnected databases or looking to transform existing data warehouses. Integrated capabilities help reduce configuration work and accelerate time to value.
• Hardware: Unlike many other big data and analytics software vendors, Dell also can provide the robust hardware foundation for collecting, integrating, analyzing and sharing information. By choosing Dell servers, storage and networking solutions, organizations can reduce the time, costs and potential problems with integrating multiple disparate systems.
• Services: From providing strategic assistance in consolidation and implementation to delivering turnkey solutions, Dell experts can help reduce the obstacles to capitalizing on big data.

Changing the marketplace

By offering a comprehensive, integrated portfolio of software along with hardware and services, all from a single vendor, Dell is bringing the benefits of big data and analytics to a wider range of organizations. According to Rouda, “The new focus on building a complete technology stack for midmarket and departmental environments will be well received by a segment of the market that has been underserved.” Big data will not have to be just for big companies any more.

StatSoft has been a part of Dell for several months, and this is a good opportunity to gauge the fit.

If you’ve followed Statistica, you’ll be glad to know that we’ve retained the Statistica brand, and we’re in Dell Software’s Information Management portfolio under Business Intelligence (BI), alongside Toad BI Suite, Boomi and Kitenga.

If you’re not yet familiar with Statistica, I’ll explain why that’s such a good place for us to be.

Data mining, visualization and predictive analytics

Once you’ve collected enough data, you’ll want to do three things with it: mine it, visualize it and use it to predict the future. Advanced analytic tools are in use across all industries and business functions. Advanced analytics allow you to:

• Identify new customers and sales opportunities
• Retain your best customers
• Forecast trends and industry shifts
• Explore what-if scenarios
• Detect fraud and mitigate business risks

Statistica is known for enabling its customers to find patterns in huge amounts of data and make decisions based on those patterns. As Gregory Piatetsky of the KDnuggets site pointed out, Statistica rounds out Dell’s portfolio of information management tools, as depicted in the image below.

Big data today will be normal data tomorrow

As a part of the Information Management product portfolio, Statistica gives Dell competitive advantages in emerging areas of advanced analytics. Thomas Hill, Ph.D., executive director of analytics for Statistica, talked about several of those areas in a KDnuggets interview:

• What we think of as big data today will be normal data tomorrow, and analyzing that much data will become routine. In fact, in sectors such as manufacturing, it already is routine.
• Data mining and predictive modeling algorithms contribute to better products – including semiconductors, solar panels and medical devices – made at lower cost with less scrap and less impact on the environment.
• In highly regulated industries like pharmaceuticals, the market rewards products that support privacy in personal information, including transparency of processes, documentation of decisions and assignment of responsibilities through approval processes. These features move the product landscape toward analytics, governance of data and the application of results in areas of personal importance, such as credit worthiness and health care.
• A huge portion of useful data no longer comes in structured, row-and-column format. Unstructured, high-velocity, continuous data streams are becoming increasingly common. The data community is still refining particular analytics and analytic workflows that will grow in importance for applications like automated dynamic learning and forecasting, identification of optimal steady states and optimization of system robustness.

That’s the big picture for big data as Statistica continues growing inside Dell. Find out more about Dell Statistica, and subscribe to the Information Management blog on TechCenter for more ideas on putting advanced analytics to work for your organization.

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