Google Cloud Dataflow Shows Competitive Advantage for Large-Scale Data Processing

Mammoth Data, a leader in Big Data consulting, today announced the findings of its comprehensive cloud solution benchmark study, which compares Google Cloud Dataflow and Apache Spark. The company, specializing in Hadoop®, Apache Spark and other enterprise-ready architectural solutions for data-driven companies, saw a lack of understanding of current cloud technologies with no available comparison of the performance and implementation characteristics of each offering in a common scenario. As a result, Mammoth Data worked with Google to compare Google Cloud Dataflow with well-known alternatives and provide easily digestible metrics.

Google Cloud Dataflow is a fully managed service for large-scale data processing, providing a unified model for batch and streaming analysis. Google Cloud Dataflow provides on demand resource allocation, full life-cycle resource management and auto-scaling of resources.

Google Cloud Platform data processing and analytics services are aimed at removing the implementation complexity and operational burden found in traditional big data technologies.  Mammoth Data found that Cloud Dataflow outperformed Apache Spark, underscoring our commitment to balance performance, simplicity and scalability for our customers,” said Eric Schmidt, product manager for Google Cloud Dataflow.

In its benchmark, Mammoth Data identified five key advantages of using Google Cloud Dataflow:

• Greater performance: Google Cloud Dataflow provides dynamic work rebalancing and intelligent auto-scaling, which enables increased performance with zero increased operational complexity.
• Developer friendly: Google Cloud Dataflow features a developer-friendly API with a unified approach to batch and streaming analysis.
• Operational simplicity: Google Cloud Dataflow holds distinct advantages with a job-centric and fully managed resource model.
• Easy integration: Google Cloud Dataflow can easily be integrated with Google Platform and its different services.
• Open-source: Google Cloud Dataflow’s API was recently promoted to an Apache Software Foundation incubation project called Apache Beam.

When Google asked us to compare Dataflow to other Big Data offerings, we knew this would be an exciting project,” said Andrew C. Oliver, president and founder of Mammoth Data. ”We were impressed by Dataflow’s performance, and think it is a great fit for large-scale ETL or data analysis workloads. With the Dataflow API now part of the Apache Software Foundation as Apache Beam, we expect the technology to become a key component of the Big Data ecosystem.”

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Source: insideBigData

Bridging the Big Data Gap in Big Pharma with Healthcare Analytics

In this special guest feature, Brian Irwin, VP of Strategy at SHYFT Analytics, takes a look at the three market dynamics driving life sciences organizations to evaluate new data analytics strategies and technologies as they transform into value-based care delivery models. He leads strategic account development and partnership initiatives while serving as a key strategist for the company. SHYFT Analytics is a leading cloud analytics company within life sciences. The company plays an integral role as the industry continues to undergo dramatic transformation to deliver more personalized and value-based medicine. Brian has over 12 years of experience in a variety of sales and leadership roles within the life sciences industry. Areas of impact and focus have included organizational leadership, executive account management, and strategic enterprise development. Most recently, Brian served as the President and Managing Director at Informa Training Partners, a company focused on Clinical and Managed Market training solutions devoted exclusively to pharmaceutical, biotech, and med device companies. Additionally, Brian spent 9 years with Takeda Pharmaceuticals N.A. in positions of increasing responsibility, leadership, and organizational development. Brian holds a BA in Biology and Natural Sciences from St. Anselm College.

Life sciences organizations recognize that Big Data is both an opportunity and a challenge for their entire industry.  However, the strategies and systems, processes, and platforms in place today are not successful and cannot contend with the demands of a rapidly evolving healthcare industry. As total spending on medicines globally reaches the $1 trillion level annually and with no end in sight to rising costs, there is tremendous pressure across the healthcare ecosystem to improve outcomes and prove value. Core to making healthcare more efficient, measureable, and patient-centric is the ability to integrate vast data resources available across this ecosystem and translate them into meaningful, actionable insights.

The demand for timely and improved use of these data creates pressure across the various channels of healthcare, leaving manufacturers, payers, and provider groups particularly vulnerable to the big data deluge. Tasked with making sense of the exponential volumes of patient-level clinical and financial data, these organizations must also capitalize on opportunities to inform both clinical and commercial strategies simultaneously.  A demand for data access across the enterprise, a changing competitive landscape tied to intense cost pressures, and the rapid influx of Real World Evidence (RWE) data is forcing the hand of every healthcare entity. Their vast network of data silos – historically housed in rigid, brittle, inaccessible systems – are no longer fit to serve as the backbone of operations in an increasingly dynamic and often unpredictable marketplace.

Let’s take a closer look at the three market dynamics driving life sciences organizations to evaluate new data analytics strategies and technologies as they transform into value-based care delivery models.

1 — Data Demands across the Enterprise

The rapid proliferation of technology and the overall shift towards patient engagement has generated an unprecedented amount of clinical and commercial data. However, overburdened internal resources have their hands tied with even gaining access to these data as well as archaic reporting processes.  Historically, getting data out of these silos and into the hands of decision makers across the different facets of a company’s operations took weeks, even months. To make matters worse the ‘reports’ that were developed and delivered for review were often incomplete, lacking the right data or the right detail to truly inform business decisions. Executives had two choices: Accept the information as they were or ask for modifications and wait another month for the final result.

Today it is clear that pharmaceutical companies no longer have the luxury of time; waiting for insights, which are subpar at best and inaccurate at worst, risks any potential first mover advantage that could be gained.  Without a faster, more effective way to manage data across the enterprise, life sciences companies cannot garner insights quickly enough to stay competitive.

2 — Cost Reductions and Increased Commercialization Costs

Life sciences companies are undergoing a massive shift in the way information is gathered, used and leveraged to drive successful outcomes in all areas of their ecosystem. At the same time, many are constrained by tighter payer controls and increased commercialization costs. According to an IMS Institute IT survey, over $35 billion in cost reductions are needed through 2017 in order for large pharmaceutical manufacturers to maintain their current levels of research and development activities as well as their operating margin levels. The same study found that almost half of survey respondents, 45 percent, confirmed they are planning cuts of more than 10 percent over the next three years. The question becomes, “how can we conduct these research activities, particularly observational research, faster and cheaper than its done today? Companies have invested heavily in all of the data they need, but lack the technology and applications required to achieve these goals.

These cost pressures sit in paradox to the revenue opportunity available from the industry data explosion; life sciences companies are struggling to find the balance between cost reductions and investment in innovations. All recognize that if they cannot take advantage of the data in front of them their competitor certainly will… and will take their market share too.

Transforming data into insights through proven analytics can support the industry’s increasing need for real-world and outcomes-based insights. By rethinking the silos that permeate their businesses they can improve the volume and value of research activities shorten cycle times across all lifecycle phases, strengthen analytical competence and drive rapid change and market differentiation.

3 — Real World Evidence will Drive Enterprise Success Factors

RWE includes elements associated with the delivery of care – electronic medical records, claims information, patient surveys, clinical trial effectiveness, treatment preferences, even physician utilization patterns. Until recently no one has been able to crack the code on RWE success– conservative market estimates suggest big pharma spends $20 million dollars on an average annually on RWE, but they are still no closer to fully understanding the real world impact of pharmacologic and non-pharmacologic treatment on patients and healthcare systems.

The typical approach to RWE – a myriad of siloed databases, services-dependent, with access restructured to just a handful of “power users” – has shown to be vastly ineffective. It simply cannot address the need to quickly access, analyze, and deliver insights from real-world data for broad use across the organization.

As pharmaceutical companies continue to invest, create, and collect real-world evidence data, each of them must be able to turn that information into actionable insights as they seek to impact treatment options, reduce costs, and improve patient outcomes. By translating RWE data into patient-centric intelligence and analytics for use across the clinical – commercial continuum, the impact of real world data can quickly go from basic theory to pervasive practice and finally deliver upon its promise to transform treatment strategies and the health of patients everywhere.

Cloud-based analytics can bridge the Big Data gap. Such solutions have the capability to translate data into patient-centric intelligence for use across the enterprise. The result is an improvement to both the volume and value of research activities, shorter cycle times across all lifecycle phases, and stronger, more complete analytical competence to drive rapid change and market differentiation. By enabling these organizations to quickly access, analyze, and deliver meaningful insights for broad use, they can deliver a better understanding of unmet patient need, create more targeted and streamlined product development, and contribute to the overall elevation of quality healthcare.

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Source: insideBigData

“Above the Trend Line” – Your Industry Rumor Central for 5/2/2016

Above the Trend Line: machine learning industry rumor central, is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz. Our intent is to provide our readers a one-stop source of late-breaking news to help keep you abreast of this fast-paced ecosystem. We’re working hard on your behalf with our extensive vendor network to give you all the latest happenings. Heard of something yourself? Tell us! Just e-mail me at: daniel @insidebigdata.com.  Be sure to Tweet Above the Trend Line articles using the hashtag: #abovethetrendline.

In C-suite news we learned that Alteryx, Inc., a leader in self-service data analytics, announced it has appointed Chuck Cory, former Chairman, Technology Investment Banking at Morgan Stanley, and Tim Maudlin, an Independent Financial Services Professional, to its Board of Directors … Another industry alignment! FICO, the predictive analytics and decision management software company, and Capgemini, one of the world’s foremost providers of consulting, technology and outsourcing services, unveiled they have formed an alliance to meet the increasing market need for analytic solutions in financial services. The alliance will provide FICO’s risk and fraud management products through Capgemini’s consulting and integration services in North America … More people movement! UK-based IS Solutions Plc, a leading AIM-listed data solutions provider, has appointed digital data expert Matthew Tod to head up a new Data Insight practice. Tod was previously of digital analytics consultancy Logan Tod and Co. which was acquired by PwC in 2012; he became Partner and later led the Customer Consulting Group, successfully building up PwC’s digital transformation strategy capabilities. He takes the new role of Director of Data Insight at IS Solutions Plc and will work with clients to overcome the “data everywhere” problem, enabling them to gain competitive advantage from their information assets … And in some M&A news: HGGC, a leading middle market private equity firm, today announced that it has completed the acquisition of FPX, a SaaS company and leading provider of platform-agnostic enterprise Configure-Price-Quote (CPQ) applications. As part of the transaction, senior FPX management has reinvested their proceeds from the sale and retained a significant minority stake in the business. Terms of the private transaction were not disclosed … Heard on the street: Narrative Science, a leader in advanced natural language generation (Advanced NLG) for the enterprise, announced the availability of Narratives for Power BI, a first-of-its-kind extension for the Microsoft Power BI community. The extension, now available for download, allows users to access important insights from their data in the most intuitive, consumable way possible – dynamic, natural language narratives. Now all users, regardless of skill-set, can quickly understand the insights from any data set or visualization, simply by reading them … ODPi, a nonprofit organization accelerating the open ecosystem of big data solutions, revealed that 4C Decision, ArenaData, and AsiaInfo, have joined the initiative to advance efforts to create a common reference specification called ODPi Core. Many vendors have focused on productizing Apache Hadoop® as a distribution, which has led to inconsistency that increases the cost and complexity for application vendors and end-users to fully embrace Apache Hadoop. Founded last year, ODPi is an industry effort to accelerate the adoption of Apache Hadoop and related big data technologies. ODPi’s members aim to streamline the development of analytics applications by providing a common specification with reference implementations and test suites … Veriflow, the network breach and outage prevention company, announced the appointment of Scott Shenker to its Board of Directors and of Sajid Awan as vice president of products. Veriflow launched out of stealth on April 5, with $2.9 million in initial investor funding from New Enterprise Associates (NEA), the National Science Foundation and the Department of Defense. The company’s software, which is designed for CISOs, network architects, engineers and operators, uses mathematical network verification, which is based on the principles of formal verification, to bulletproof today’s most complex networks. Veriflow’s patented technology, including a best-practice library of network security and correctness policies, provides solutions across the multi-billion-dollar networking market to minimize the security breaches and costly disasters that can result from network vulnerabilities … SnappyData, developers of the in-memory hybrid transactional analytics database built on Apache Spark, indicated that it has secured $3.65 million in Series A funding, led by Pivotal, GE Digital and GTD Capital. The funding will allow the company to further invest in engineering and sales. The SnappyData leadership team includes, Richard Lamb, Jags Ramnarayanan and Sudhir Menon, who worked together during their time at Pivotal to build Pivotal GemFire® into one of most widely adopted in-memory data grid products in the market … Three researchers located at Drexel University, North Carolina State University, and the University of North Carolina at Chapel Hill have been named 2016 -2017 Data Fellows by the National Consortium for Data Science (NCDS) the consortium announced. The NCDS, a public-private partnership to advance data science and address the challenges and opportunities of big data, will provide each Data Fellow with $50,000 to support work that addresses data science research issues in novel and innovative ways. Their work will be expected to advance the mission and vision of the NCDS, which formed in early 2013. Fellowships begin July 1 and last one year … Lavastorm, a leading agile analytics company, announced that it has partnered with Qlik® (NASDAQ: QLIK), a leader in visual analytics, to put a powerful, fully-integrated modern analytics platform into the hands of data analysts and business users directly through Qlik Sense®. The dynamic, integrated solution provides an intuitive, comprehensive platform that eliminates the complexity of advanced analytics while empowering business users of all skill levels to uncover unique, transformative business insights … Another industry alignment: Snowflake Computing, the cloud data warehousing company, and MicroStrategy® Incorporated (Nasdaq: MSTR), a leading worldwide provider of enterprise software platforms, today announced an alliance to bring the flexibility and scalability of the cloud to modern data analytics. This collaboration will build on Snowflake’s certified connectivity with MicroStrategy 10™ through further product integration and go-to-market collaboration, enabling businesses to take advantage of the cloud to get fast answers to their toughest data questions … New product news! Datawatch Corporation (NASDAQ-CM: DWCH) launched Datawatch Monarch 13.3, the latest edition of the company’s first-to-market self-service data prep solution. Datawatch Monarch enables business users to acquire, manipulate and blend data from virtually any source. The new product release delivers better and faster data access and data prep through advanced functionality, unrivaled simplicity and enhanced information governance … Trifacta, a leader in data wrangling, announced that Infosys (NYSE: INFY), a global leader in consulting, technology and next-generation services, has partnered with Trifacta to provide a data wrangling solution for the Infosys Information Platform (IIP) and Infosys’ other platforms and offerings. Infosys clients can now leverage Trifacta’s intuitive self-service solution for exploring and transforming data — a critical step in any analytics process. Business analysts and data scientists can integrate large, complex data sets, transform and filter the results and share valuable insights, all from data stored and processed within the IIP platform.

Platfora Further Democratizes Big Data Discovery

Platfora, the Big Data Discovery platform built natively on Apache Hadoop and Spark, announced the general availability of Platfora 5.2. The new release democratizes big data across an organization, moving it beyond IT and early adopters by enabling business users to explore big data and discover new insights through their favorite business intelligence (BI) tool.  With its flexible, open platform, Platfora makes it easy for customers to maximize and extend existing IT investments while getting measurable value out of big data. Platfora 5.2 features native integration to Tableau, Lens-Accelerated SQL accessible through any SQL client, and the option to run directly on the Hadoop cluster using YARN.

Achieving value from big data implementations has been elusive for enterprises, and connecting traditional BI tools to Hadoop data lakes has been a difficult, slow process, with many organizations doing far more work with virtually no new answers to show for it. Platfora’s Big Data Discovery platform enables citizen data scientists to conduct self-service data preparation, visual analysis, and behavioral analytics in a single platform. With this release, Platfora puts all this smart data and analysis in the hands of any business user leveraging any BI tool, so they can ask and answer the important questions for their business, like customer behavior and segmentation. Platfora provides the tools and tight iterative discovery loop to make new insights possible in a matter of minutes to hours, rather than the days or weeks it could take using an alternative solution.

Getting value out of big data is more than just slicing and dicing billions of records and it can’t only be the domain of a data scientist. It requires discovering what you have and getting the data ready for analysis to use without boundaries,” said Peter Schlampp, VP of Products, Platfora. “We are dedicated to providing flexible, open tools that can address modern data challenges, and Platfora 5.2 opens up the transformative power of big data to business users by enabling them to use the BI tools they know and love, further empowering ‘citizen data scientists’ across enterprises.”

Platfora cohort analysis

Platfora Big Data Discovery 5.2 includes a variety of new features and technical enhancements that make make it possible for both business and technical users to easily integrate with their favorite tools, including:

• Native Tableau Integration: Directly export prepared and enriched data in TDE format to Tableau Desktop or schedule data pushes automatically to Tableau Server.

• Lens-Accelerated SQL: Platfora lenses make access to petabyte-scale data 100s to 1000s of times faster than querying the data directly. Now any BI tool can query lenses live via SparkSQL and ODBC, opening big data to any business user. Compared to standalone SQL accelerators for Hadoop, Platfora’s lenses are more scalable, easier to maintain and manage, and enterprise-ready.

• Run on Hadoop Cluster: With the development and maturity of the YARN resource manager for Hadoop, it is now possible to run Platfora directly on the Hadoop cluster or in the traditional dedicated configuration. IT departments can take advantage of existing hardware investments and repurpose computing resources on-demand.

• Enhanced Vizboards™: The easiest and best way to visualize data gets better in Platfora 5.2 with responsive layout, smarter default visualizations and more consistent use of color.

Big data discovery will help advance the analytics maturity of the organization, will start training some of the future data scientists, can provide the first batch of insights that may raise awareness to new opportunities and may provide enough return on investment to justify the business case for big data analytics,” said Joao Tapadinhas, Research Director, Gartner. “It is the missing link that will make big data go mainstream.”

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Source: insideBigData

Webtrends Infinity Analytics – A New Breed of Analytics for IoT

Our friends over at Webtrends just released the infographic below to explain some of the differences between analytics solutions of yesterday vs. today and provides details into what’s coming with their new solution Infinity Analytics. Uniting scale and flexibility with speed and accuracy, Infinity Analytics harnesses big data to deliver truly actionable customer intelligence.

[embedded content]

What You Have Wrong: Three Myths about RAIDs

DriveSavers has performed tens of thousands of RAID data recoveries in our history. Yes, we’ve seen our share of RAIDs. We’ve also heard our fair share of stories—some accurate and others, not so much.

Ready to get your facts straight? Here are three common myths about RAIDs.

Myth #1: All RAIDs are Redundant

Redundancy is one of the two biggest reasons users choose to use RAIDs, the other being performance.

Most RAID setups provide redundancy, in which the same data is located on different drives. This is beneficial for two reasons: 1) if a drive fails, it can be replaced without loss of data or interruption of work; and 2) basic physical maintenance can often be performed (i.e. swapping older drives before they crash, etc.) without interruption to work.

But despite the word “redundancy” being the first letter of RAID (redundant array of independent disks), not all RAIDs actually have redundancy incorporated.

A RAID 0 setup involves “striping” data across 2 or more drives so that different pieces of a single file live on every drive in the system. RAID 0 does not include copies of the data and, therefore, is not redundant. No matter how many drives are incorporated into this setup, if just one drive experiences a physical failure, the whole RAID is immediately inaccessible and data is lost. In fact, for this reason the chances of losing data are actually multiplied when using a RAID 0 as opposed to a single drive. The more drives used in this setup, the more likely the chance of data loss.

It’s like having kids. The more kids you have, the more likely one of them will catch that flu that’s been going around. And once one of them catches it, the whole family is doomed.

So why would anyone use a RAID 0? The answer is performance. Files are always split into pieces, whether you are using a single drive or a RAID. When pieces of a file are spread across multiple drives, they can be pulled from all of those drives at once rather than just from one drive. To paint a picture, pretend you have two halves of an apple. You will be able to grab the whole apple faster with two hands than with one. This is because you can grab both halves at the same time when using two hands, but only one half at a time when using one hand. In much the same way, the more drives used in your RAID 0 the greater the data transfer rate (the rate at which data moves from one place to another). Just make sure you’re backing it all up.

Myth #2: RAIDs are Backups

RAID 5, RAID 6 and mirrored systems typically have redundancy built in, which serves to help lessen the risk of losing data when a drive fails physically. Still, these devices most certainly are NOT backups. If too many drives fail, a user accidentally erases files, the RAID gets corrupted or malicious programs take control and encrypt the contents, your data can be lost forever.

Unfortunately, many users see RAID and think they are protected. This is a terrible assumption to make. Most of the RAID systems we’ve seen over the years have had redundancies built in, but have still had multiple failures, data corruption or deletion of data (either targeted or accidental).

Something that’s good to keep in mind: when you purchase a complete RAID system, all of the drives that make it up are usually the same make, model and age. Identical drives tend to have very similar, or even identical, life spans. Don’t forget that while one failed drive may not cause you to lose data, multiple failed drives certainly will.

Myth #3: RAID Failure is Always Obvious

If a single drive fails in a RAID with parity or redundancy in place, the system will continue to run in degraded mode at lower performance speed. Since even in degraded mode, users have access to all of their data, they may not notice that anything has changed. In this case, they will carry on, happily unaware, until the next drive fails. Then, catastrophe.

A dedicated system administrator who regularly and systematically checks a RAID for any problems or concerns may recognize when one of the drives has failed and replace it before any further failures occur. But what if two or more drives fail at once, as often happens? Remember—the drives in a RAID are often all the same make, model and age with the same life span and likelihood of failure.

The Truth: Even RAIDs Need to be Backed Up

All media, from phones to thumb drives to hard drives to RAIDs, should have multiple copies of data. The end user or system administrator has to look at the risk of not having an hour-, day-, week-, month- or year’s worth of data. If the data loss would be too devastating for a specific time period, then they have to find a solution to copy data to another media, such as another RAID, the cloud or tape—anything that will ensure that the data is protected when their RAID either fails or has another data loss situation.

Contributed by: As Director of Engineering at DriveSavers, Mike Cobb manages the day-to-day operations of the Engineering Department including the physical and logical recoveries of rotational media, SSDs, smart devices and flash media. He also oversees the R&D efforts for past, present and future storage technologies. Mike makes sure that each of the departments and their engineers are certified and that they continue to gain knowledge in their field. Each DriveSavers engineer has been trained by Mike to ensure the successful and complete recovery of data is their top priority. Mike Cobb has a B.S. degree in Computer Science from the University of California, Riverside. Since joining DriveSavers in 1994, Mike has worked on all aspects of engineering as well as heading the Customer Service Department for several years.

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Source: insideBigData

Infosys Launches Mana – a Knowledge-based Artificial Intelligence Platform

Infosys (NYSE: INFY), a global leader in consulting, technology, and next-generation services, announced the launch of Infosys Mana, a platform that brings machine learning together with the deep knowledge of an organization, to drive automation and innovation – enabling businesses to continuously reinvent their system landscapes. Mana, with the Infosys Aikido service offerings, dramatically lowers the cost of maintenance for both physical and digital assets; captures the knowledge and know-how of people and fragmented and complex systems; simplifies the continuous renovation of core business processes; and enables businesses to bring new and delightful user experiences leveraging state-of-the-art technology.

Over the last 35 years, Infosys has maintained, operated and managed systems with global clients across every industry. Building on this deep experience, Infosys has recognized the need to bring artificial intelligence to the enterprise in a meaningful and purposeful way; in a way that leverages the power of automation for repetitive tasks and frees people to focus on the higher value work, and on breakthrough innovation. Today’s AI technologies address part of this with learning and information; Infosys is now bringing this together in a fundamental way with knowledge and understanding of the business and the IT landscape – critical knowledge that is locked inside source code, application silos, maintenance logs, exception tickets and individual employees.

Infosys has already started working with a number of clients:

• For a company with a large fleet of field engineers, individual productivity improved by up to 50% by utilizing the self-learning capabilities of the platform
• For a major global telecommunications firm entry effort of agents was reduced by up to 80% by automating order validation and removing the need for corrective processes
•  For a global food and beverage manufacturer, Mana assisted sales managers in automating the sales planning processes by automatically resolving maintenance tickets of recurring issues. As the system self-learned, over time it provided solutions to known problems automatically, helping reduce the time required to provide a solution to a maintenance problem

Infosys Mana

The Mana platform is part of the Infosys Aikido framework that helps companies undertake non-disruptive transformation of their existing landscapes

• Ki: capturing the knowledge within legacy systems to renew, accelerate and enable them to bring entirely new experiences
• Ai: delivering open, intelligent platforms that bring transformation – new kinds of applications, software tools, unprecedented levels of data processing, a radical new cost performance
• Dō: design-led services which bring a Design Thinking approach that starts with a deep understanding of a client’s business and IT objectives, its users and customers, to find their most critical problems and biggest opportunities

Infosys Mana is comprised of three integrated components all of which are based on open source technology:

• Infosys Information Platform – an open source data analytics platform that enables businesses to operationalize their data assets and uncover new opportunities for rapid innovation and growth
• Infosys Automation Platform – a platform that continuously learns routing logic, resolution processes and diagnosis logic to build a knowledge base that grows and adapts to changes in the underlying systems
• Infosys Knowledge Platform – a platform to capture, formalize and process knowledge and its representation in a powerful ontology based structure that allows for the reuse of knowledge as underlying systems change

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Source: insideBigData

Accenture and Splunk Form Alliance to Help Organizations Analyze Machine Data to Drive High-Impact Business Performance

Accenture (NYSE: ACN) and Splunk (NASDAQ: SPLK) entered into an alliance relationship that integrates Splunk products and cloud services into Accenture’s application services, security and digital offerings. Accenture is helping clients use Splunk solutions to improve business outcomes by mining vast amounts of application and operational data to identify trends and improvement opportunities that were previously difficult to detect.

Deployment of this new platform leveraging an intelligent delivery strategy strengthens our position as Hawaii’s technology leader,” said Amy Aapala, Vice President of Information Technology and Order Management Systems at Hawaiian Telcom. “The real-time insights gleaned from our customized dashboards enables our team to be more strategic and proactive on a day-to-day basis, which ultimately improves our customer service and increases productivity.”

Accenture is expanding its network of trained Splunk practitioners in order to meet significant client demand for operational intelligence solutions. Accenture and Splunk are also collaborating and bringing to market new packaged solutions, the first of which integrates Splunk analytics into Accenture’s Managed Security Services. Accenture will provide Security Information Event Management (SIEM) As-a-Service to clients, using Splunk Enterprise and Splunk Enterprise Security (ES) to deliver advanced threat detection, correlation, search and incident management capabilities. Additional solutions may be tailored to various business areas including digital, marketing and sales.

Hawaiian Telcom (NASDAQ: HCOM) is one of Accenture’s first clients to take advantage of its intelligent application management services, infused with Splunk technology, to help mine  business insights and expand monitoring of the company’s core IT ecosystem.

Our alliance with Splunk is another strong example of how Accenture is impacting our clients’ businesses with ‘new IT.’ By mining and analyzing machine data from back-end systems, call centers, web traffic, inventory levels, shipments and more, IT can play a greater role in influencing business performance, not just IT performance,” said Bhaskar Ghosh, group chief executive, Accenture Technology Services. “We’re integrating Splunk’s platform for operational intelligence into our global application service offerings and delivery teams, bringing robust new capabilities to our clients at scale. We can also deliver this capability through our new intelligent automation platform, Accenture myWizard, to help turn data into critical insights that drive improved business outcomes.”

Accenture Technology Services adopted Splunk internally, with a focus on helping IT organizations become more business-centric. Using Splunk® Enterprise, Splunk Cloud, Splunk Enterprise Security, Splunk User Behavior Analytics and Splunk IT Service Intelligence, Accenture is developing and rolling out operational intelligence solutions that span the entire software development lifecycle. Additionally, any team can build their own Splunk-based application and host it with a central app store for other teams to use. Accenture has developed numerous Splunk applications to date, spanning software development, IT operations, security monitoring and business operations.

As one of the largest global systems integrators, Accenture will help broaden access to Splunk’s platform for operational intelligence to organizations that have not yet tapped the power of machine data,” said Doug Merritt, President and CEO, Splunk. “By analyzing machine data, organizations gain end-to-end visibility into operations and make better informed business decisions. We are thrilled to work with Accenture and leverage its innovative best in class technological experience to further deliver operational intelligence around the globe.”

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Source: insideBigData

The Agile CFO: Navigating Economic Uncertainty with Data Insights

Adaptive Insights, a leader in cloud corporate performance management (CPM), released its global CFO Indicator Q1 2016 report, revealing that while chief financial officers (CFOs) remain worried about growing economic volatility, the vast majority of the 377 CFOs surveyed remain confident in their forecasts, and believe that the combination of big data, analytics, and scenario planning will likely be the key to navigating their organizations through the current financial uncertainty.

Enabling them to accelerate the pace of change and remain agile, scenario planning is viewed by 48% of CFOs as the activity that will provide the most strategic value to their organizations during a downturn. Seventy-eight percent believe that applying financial data analysis to achieve profitability will provide the most strategic value overall to their organizations. Other key findings in the report include:

High forecasting confidence: 85% of CFOs feel moderately, very, or completely confident in their forecasts for the first half of 2016
Strong trust in the value of big data and analytics: 43% believe big data and analytics will have the single biggest effect on their future role
Agility highly valued: 59% rank transformation and innovation experience as the second most beneficial attribute to their performance — second only to technical and analytical skills (65%)

A strategic CFO is an agile CFO — one who can pivot on a dime and react decisively regardless of what the future holds,” said Robert S. Hull, founder and chairman at Adaptive Insights. “When the financial outlook seems unclear, planning for multiple scenarios can significantly contribute to agility. While transformation and innovation experience helps inspire myriad possible outcomes, technical and analytical skills can be applied to more rapidly model multiple actionable plans — helping financial leaders to remain agile as they navigate even the most turbulent markets.”

Technology Savvy CFOs Chart Course in Turbulent Markets

Driven by such factors as potential changes in regulatory requirements and taxation laws, economic uncertainty is the only certainty for today’s CFO. In fact, potential changes in regulatory requirements alone is driving 64% of CFOs to plan for multiple outcomes. It’s no surprise then that scenario planning has been identified as a key task for driving strategic, agile decision-making.

To support strategic activities, 56% of CFOs indicated that they were either very or completely likely to invest in dashboards and analytics and identified cloud-based, or SaaS, solutions as their preference. According to the study, CFOs estimate that 33% of their IT infrastructure is SaaS today, and they forecast this to grow to 60% of their infrastructure in 4 years. CFOs cited collaboration (24%) as the top reason for implementing SaaS solutions, followed by less reliance on IT (21%), and significant cost savings (17%).

And with growing reliance on technology and scenario planning, CFOs are not only feeling confident in their forecasting capabilities but also in their use of technology. Ninety-three percent report they are moderately, very, or extremely proficient in technology.

Shifting Priorities for CFOs

While most CFOs (93%) see compliance as their top priority today, only 62% see this as a priority three years from now. Rather, over the next three years, they expect to begin focusing on talent management (78%) and transforming financial data into intelligence to drive growth (77%). Tied for third in the study, both at 74%, are understanding how to leverage IT to take advantage of evolving market opportunities and overseeing corporate governance.

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Source: insideBigData

dPaaS: A Disruptive Force in the Integration Space

In this special guest feature, Rob Consoli, Senior Vice President of Sales and Marketing for Liaison Technologies, makes a case for the benefits of a data-centric approach to integration called dPaaS (Data Platform as a Service), an integration and data management platform that is fundamentally different from traditional approaches and offers a number of advantages. He brings over 25 years of technology industry experience and has a demonstrated track record of successfully building teams and helping growth-oriented companies navigate cultural and process transitions as they expand operations and global reach. In this pivotal role, Rob leads Liason’s North American efforts to strategically position and sell the company’s cloud-based integration and data management solutions, as well as increase its sales to meet the company’s growth objectives.Consoli holds a Master of Science from Southern Methodist University and a Bachelor of Science from Auburn University.

Let’s daydream for a moment: if you were to hit the lottery tomorrow, what would you do with the money? Would you bury it in small amounts all over the back yard? Maybe stash some under the mattress or in the freezer?

Or, would you invest it in some well-thought out mutual funds, stocks or maybe even a worthy startup as a venture capitalist? Perhaps even donate to some worthy non-profit causes?

My guess is you’d opt for the latter approach, recognizing that simply having the money isn’t nearly as valuable as putting the money to work in a smart way. The fragmented approach of scattering a few hundred dollars here and there would provide no value, prevent you from realizing the full benefit of the windfall and put you at tremendous risk of theft or loss.

Surprisingly, many modern businesses are still operating with a fragmented approach to their data akin to stashing it away in small amounts all over the backyard and around the house. With multiple applications all collecting and storing their own data within these specific silos, the data is locked away inside these applications.

In aggregation, the data has tremendous value. The ability to make correlations and comparisons across various data sets and characteristics could reveal incredibly valuable insights to improve business success.

But, the reality is, making those integrations to aggregate and use the data effectively is extremely difficult, and it becomes increasingly more so every day as new applications and their related data sets get added to the mix.

While every company knows their data is an extremely valuable asset—it has even become THE most valuable for some—many are discovering that more data doesn’t necessarily equate to more “information.” In fact, without proper handling and analysis, data is just bits and bytes offering almost no value on its own. It must be elevated, analyzed and understood with context in order to have value. This is why, despite an abundance of data, information is still scarce for a lot of businesses.

Many factors have contributed to the data explosion over the last few years: the deconstruction of large, monolithic applications which are being replaced or supplemented by multiple niche SaaS applications; the explosion in mobile data and social media, and the Internet of Things are some of the biggest.

Unfortunately, this influx is making it harder than ever to perform the integration and data management operations required to turn data into actionable information. I blame the traditional application-centric integration methodology for this, which requires that businesses grow ever-increasing numbers of tentacles between applications. The problem with this approach is that converting data into information is extremely complex, requiring monumental effort in order to extract, cleanse, de-duplicate and harmonize the data from multiple applications in preparation for running analytics algorithms against it. A couple of months and a million dollars later, you finally get the information that you anecdotally already knew.

And, worse yet, this only solves the temporary challenge. With every new application or data set added to the technology stack, the entire process must be repeated. You’re right back to square one, and every single initiative requires the same large investment of time,  resources and capital to achieve the stand-alone goal. Nothing about this process is future-ready.

Fortunately, a change is in the air. A new way of approaching integration—a data-centric methodology—is picking up steam and poised to fundamentally answer the question, “How can I better integrate my systems to produce actionable information and not be continuously distracted by the minutiae of integrating application tentacles?”

This new data-centric approach to integration is called dPaaS (Data Platform as a Service). dPaaS is an integration and data management platform that is fundamentally different from traditional approaches and offers the following advantages.

• Integration delivered as managed services – This includes people, platform and processes to manage businesses’ integration needs, whether that’s integrating end points behind the firewall to external businesses, or connecting application to application in the cloud. Why integration as managed services? So that businesses can focus on what matters most: producing and acting on information versus being in the ever-changing business of integration.
• Unified integration and data management– Traditionally, integration and data management at large organizations have required two Centers of Excellence, two teams and two sets of tools. But with today’s mandate to find insights in real time, this is no longer tenable. dPaaS has an underlying schema-less Big-Data-based repository that overcomes the challenges of storing and processing unlimited data in order to elevate it to actionable information at the right time, for the right people.
• Compliance – The protection of personally identifiable information (PII), payments-related data and personal health information (PHI) are paramount in any business application. A dPaaS solution offers baked-in functionalities, infrastructure, processes and controls that comply with government and industry standards such as PCI DSS and HIPAA. Again, this built-in compliance posture future-proofs the investment: you may not need compliance now, but when you do add data that requires protection, the dPaaS platform will be ready.
• Data visibility – In an ideal scenario, data should be fluid so that, with the right context, it can be molded into an information model that is defined at consumption time (much like water that assumes the shape of its container). However, the data consumer should always have full visibility into how the data assumed its current shape. Data lineage that stores the different states of the data in an immutable log is fundamental to dPaaS architecture and a valuable differentiator as compared to traditional data management solutions.

If yours is among the smart businesses looking to use information, and not merely data, as a disruptive and innovative force to gain an edge over competitors—which you should be—now is the time to give dPaaS a look. By providing a comprehensive, end-to-end approach to data integration and management, dPaaS allows your company to leverage the efficiencies and insights today, and have a future ready solution to tackle the challenges of tomorrow.

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Source: insideBigData