NewPush Recognized as Top 20 VMware Cloud provider 2017

CIO Review recognition

NewPush started using VMware technologies from its inception in 1999. At the time the first dot com boom was just heating up. Many virtualization technologies were emerging for the Intel platform. Over the years we kept focusing on providing enterprise grade infrastructure. Meanwhile, we have kept increasing the role of VMware as we understood that for Intel based hardware VMware provided the most reliable enterprise soluitons. As a result, we have moved the use of VMware from our development labs to our production systems and data-centers. Since the 2010’s we are formally a VMware partner providing VMware Cloud solutions. Most noteworthy, the last few years have shown a tremendous growth in the capabilities VMware Cloud delivers. Therefore it is our pleasure to announce that once again, CIO Review has recognized NewPush as a top 20 VMware technology provider.
20 most promising VMware Cloud solution providers - 2016

VMware Cloud Solutions

Important milestone for NewPush

This recognition for the second time in a row is a milestone that is important to us. We have worked hard to pioneer and to be successful deploying state of the art VMware based cloud technologies, and we have worked harder even to maintain a leadership position in this crowded space. Our work continues to focuses on NSX, vSAN, and the vRealize suite. As we continue our quest to provide the best cloud services to our customers, we look forward to deploy advanced analytics capabilities centered around Splunk Enterprise security essentials.

Forward looking posture

Cloud technologies keep changing at an ever increasing pace. In this year’s edition of CIO Review, we dive deeper in iGRACaaS, identity governance, risk and compliance as a service. Companies who stay ahead are going to continue to have a competitive advantage, by providing a better customer experience. By partnering for technology decisions with NewPush, you can spend more time with your core business, while ensuring that you have a trusted partner with a proven track record to help you keep a competitive edge on the IT front. If you would like the NewPush advantage for your company, please do not hesitate to get in touch today. We are here to help 24 hours a day, seven days a week.


Customer Journey Analytics: Mining Behavioral Data to Boost Retail Sales

by Barbara Thau –

Customer journey analytics examines an increasingly complex path in the modern retail landscape. Today’s typical path to purchase includes not only in-store interactions, but also online research and browsing, as well as mobile actions. Research shows that 56 percent of modern customer engagements take place over multiple days across multiple channels.

This evolving journey from initial interaction to purchase makes it hard for retailers to gain definitive insights on consumers. According to eMarketer, 35 percent of marketers admit that they don’t understand the customer journey and more than 60 percent said their incomplete data prevents them from personalizing communications.

For this reason, retailers are increasingly turning to data analytics for a nuanced read on shoppers’ distinct purchasing paths. Companies that tap into insights from behavioral data have the opportunity to customize merchandise offers, drive store loyalty and boost sales and profit margins.

Retailers struggle with customer analytics

Behavioral data and other customer analytics can be extremely influential when retailers are revamping marketing or operational strategies, yet many companies still struggle to effectively gather and parse this information.

“Many [retailers] still do not leverage customer analytics for operational decisions, often because of a lack of fully integrated customer data as well as integration into other operational data,” Dave Nash, director of customer experience at West Monroe Partners, explained to CMSWire.

Companies need a solution that allows them to collect data through all steps of the buying journey, from the first brand interaction to online browsing, social media interactions, mobile application use and ultimately purchase, whether it’s online or in store. Only by aggregating and analyzing this information will retailers be able to fully map purchase paths. As a result, many organizations are adopting advanced customer journey analytics solutions.

 

Read more here:

http://www.ibmbigdatahub.com/blog/customer-journey-analytics-mining-behavioral-data-boost-retail-sales


Financial Data Analytics Play a Significant Role in Effective Capital Allocation

by Martin McInnis –

Financial data analytics enabling more effective capital management has become a critically important element in banking and financial services. A recent McKinsey & Company survey, which looked specifically at German banks, highlights capital issues facing all banks:

  • In recent years, banks have markedly increased capitalization, both from a regulatory and an economic standpoint. Further increases are likely.
  • Banks have improved their capital-management practices. For example, leading banks are reallocating capital more frequently than the traditional annual cycle in order to optimize returns.
  • Efficiency of capital deployment and balancing risk-taking against profit-seeking have taken a more prominent and pervasive role in banks. Capital management has evolved to include more functions in the bank than the finance function and traditional associated committees.

Capital is expensive, and management of bank capital is becoming increasingly complex. Appropriate metrics and analytical models are needed to properly measure and maximize returns on capital under these challenging conditions.

Balancing profit-seeking and risk-taking

Banking, by its very nature, is a risky business. Banks actively seek out opportunities to take on and manage risk to generate profits.

A primary example is lending money to a customer: The bank stands to earn interest on the loan, but there is risk of non-repayment. This simple example quickly becomes complicated when you consider that each potential borrower represents a unique level of repayment risk. Further, each loan product, such as an unsecured credit line versus an insured mortgage on an owner-occupied home, represents a different level of exposure to loan default.

Performance metrics that do not appropriately account for risk can result in the destruction of bank capital. In extreme cases, this can even lead to the destabilization of global financial markets, as seen in 2008. The question that needs answering, then, is how do banks determine which opportunities provide the best returns despite the risks?

 

Read more here:

http://www.ibmbigdatahub.com/blog/financial-data-analytics-play-significant-role-effective-capital-allocation

 


Service Provider Data: 5 Ways to Store, Access and Analyze Useful Information

by Jeff Bertolucci –

With big data set to grow exponentially in the next decade, telecommunications service providers face several challenges. Not only will providers grapple with escalating smartphone and tablet usage, they’ll also need to find innovative ways to manage service provider data generated by a new class of Internet of Things (IoT) sensors and gear.

How many devices are we talking about? 451 Research estimates that IoT and machine-to-machine (M2M) connections will nearly quadruple globally in the next several years, from 252 million in 2014 to 908 million in 2019. So what’s a provider to do? Here are the top five ways service providers can store, access and analyze their big data over the next 10 years.

1. Software-defined networking

Telecommunication providers in the U.S. and around the world are transitioning to software-defined networks (SDN), which enable them to proactively anticipate and respond to their customers’ needs. By transforming hardware-intensive legacy networks into programmable solutions, an SDN can streamline operations and deliver new services. It also creates a centrally managed network that dynamically senses and reacts to changing workloads. AT&T, for instance, recently implemented an SDN to build a streaming cloud environment for its mobility network data centers.

“This means that we can deploy new functions into our network almost instantly with a software update,” writes AT&T Senior Executive Vice President John Donovan of Technology and Operations on the company’s Innovation Space blog. “Previously, we had to install new hardware each time we added a new functionality. Today, we can upgrade in minutes rather than months.”

2. Improving efficiency

Companies are always looking for new ways to store and manage data; cloud-based and hosted services are strong alternatives to building expensive new data centers. According to Verizon, Citi slashed the number of data centers it ran from 70 in 2009 to 20 in 2014, while boosting the utilization of the remaining centers from 5 percent to about 45 percent.

 

Read about a quick case study about  Consolidated Communications, Inc. (CCI), a leading business and broadband communications provider here:

http://www.ibmbigdatahub.com/blog/service-provider-data-5-ways-store-access-and-analyze-useful-information


CIOs Facing Tsunami of Disruptive Technologies

By Gary Matuszak –

CIOs are facing a tsunami of converging technologies: cloud (24×7 accessibility, storage, computing power), Internet of Things (data producers), mobile (access point 24×7, anywhere), and data and analytics (developing insight from massive amounts of data), with cognitive computing all coming into play.

The interrelated rise of cloud, Internet of Things, mobile and data and analytics will continue to drive unprecedented transformation in enterprises in the next three years.

There is also the challenge of both global consistency and variation. While some innovations are emerging globally, others vary by region. A CIO’s company may be U.S.-based but pressure can come from other countries. For example, in China biometrics is one of the technologies expected to have the greatest impact in driving business transformation in next three years. Also in China, financial services is the industry, after technology, which is expected to see the greatest transformation due to emerging technologies.

Disconnect between innovation and adoption

Innovation may be happening faster than can be assimilated and utilized effectively, before the next iteration. The greatest challenges for businesses to adopt the Internet of Things and data and analytics are technology complexity and lack of experience in the new technology and business model. These findings indicate CIOs could be overwhelmed by the rapid innovation cycles. CIOs will want to draw on the collaborative thinking of their internal or external resources to help evaluate the technologies and business benefits.

The most successful businesses will be those that can most effectively prioritize, evaluate and implement emerging technology opportunities as part of their overall company strategy. This is why  CIOs are advised to:

  • Create an enterprise-wide technology strategy
  • Get senior management on board
  • Establish a comprehensive governance approach
  • Develop an agile development process
  • Invest in talent

Successfully adopting emerging technologies is a dynamic, ongoing process.

 

Read more here:

http://www.cio.com/article/3028129/it-industry/cios-facing-tsunami-of-disruptive-technologies-over-the-next-three-years.html


Multi-Channel Consumer Engagement Tips for CPG Brands

by Ritika Puri –

The need for a multi-channel consumer engagement strategy is stronger than ever. Consumers want the latest news about products and promotions, but they are often overwhelmed by the amount of information delivered to them via myriad channels.

In their quest for the best deals and shopping opportunities, consumers often subscribe to product updates on social media, email and mobile applications. This dynamic creates an opportunity for CPG marketing teams, but also another challenge: Attention spans are fleeting, and it’s getting tougher for brands to reach their target audiences, especially for CPG brands that may not have direct relationships with end users. According to Media Post, a new survey shows only 19 percent of consumers remain loyal to brands.

It can be challenging for CPG brands to reach their target audiences and inspire brand loyalty, but a multi-channel consumer engagementstrategy can help. Here’s how companies can use big data analytics to reach their target audiences, improve loyalty and increase engagement.

Digital content

CPG leaders like Coca-Cola and Red Bull have launched digital magazines and content portals to share entertaining or informational resources with consumers, according to Sprout Content. These content strategies allow companies to create a dynamic presence across social media, mobile, niche online communities and other communication channels. The resulting web traffic then enables CPG brands to gather data about end consumers to create more tailored and engaging marketing campaigns.

Volkswagen recently launched its free Real Racing GTI smartphone application, which allows gamers learn about the brand’s cars, get a feel for the driving experience and share results with fellow consumers. The automobile manufacturer can use the data gathered as an asset to inform future campaigns and consumer-targeting initiatives.

Online content is more than a tool for building brand awareness: It’s a mechanism for gathering data and learning about consumers’ interests and buying habits.

 

Read more here:

www.ibmbigdatahub.com/blog/multi-channel-consumer-engagement-tips-cpg-brands

 


Start your Internet of Things Project Today

by Sushil Pramanick –

As the number of connected devices grows astronomically, many more manufacturers are jumping into the fray. IDC estimated that the install base for Internet of Things units was 9.1 billion as of the end of 2013. And it expected the installed base of Internet of Things units to grow at a17.5 percent compound annual growth rate (CAGR) during the forecast period to 28.1 billion in 2020. From Nest Learning Thermostats to water sensors to connected homes, firms such as Morgan Stanley predicted not surprisingly the number of connected Internet of Things devices to approach 75 billion by 2020.

Testing the limits of possibility

Use cases for the Internet of Things are limited only by imagination. Consider Internet of Things applications for the insurance industry. Insurers can leverage their policyholders’ driving data as part of telematics strategies. Or with connected homes, insurance companies can monitor exposure to everyday household risks.

Perhaps one of the largest impacts on business is the weather. Did you know that routine weather events generated costs of over 500 billion dollars in 2014 in the US alone? One of the use cases for utilizing weather data is to provide policyholders with weather alerts. Weather alerts have the potential to save insurance companies by helping policyholders prepare for weather-related events that may damage property. For example, insurers can text their policyholders when a hailstorm is on its way so that policyholders can move their vehicles into a garage or other covered parking. That way, damage to the vehicles and future claims can be minimized or prevented. Imagine the improved experience for your policyholders.

Diving deeper into the Internet of things

What do these use cases mean for manufacturers? What do they need to consider when laying out their connected device strategies? The Internet of Things is still at an early stage; the connected market started over a decade ago to monitor and control every piece of information from physical and social environments.

In the past, many of these units were hardwired together in a complex system, but with analytics, cloud computing, mobile computing, telematics, wireless connectivity and other technology advancements, the birth of the Internet of Things transpired. For example, the Nest Learning Thermostat performs the basic function of an ordinary smart thermostat: it monitors, adjusts and maintains a predetermined configuration. But this Nest thermostat also senses humidity, activity and light, and its built-in intelligence learns how and when the user likes to adjust the temperature. It can even optimize the house’s temperature for energy efficiency.

 

Read more here:

http://www.ibmbigdatahub.com/blog/start-your-internet-things-project-today

 


How to Make Better Product Decisions Using Big Data

by Ritika Puri

Innovation in the CPG industry is critical. Market fragmentation is on the rise, which means that established companies need to create a competitive edge by building new business lines, making informed product decisions and adapting to meet consumers’ expectations.

Big data and analytics play a crucial role in this process. The following are three strategic opportunities that CPG executives may want to take advantage of at their organizations.

1. Customer feedback

In the retail setting, where purchase paths are becoming increasingly complex, consumer feedback remains a top priority. It’s not enough, however, to simply know what consumers think about their purchases after the fact. CPG leaders need to reinvest this information into future product development, and big data can help with this objective.

CPG leaders should engage with consumers on a regular basis. In addition to conducting market research and opinion studies, organizations can use social media data for real-time feedback, uncovering what shoppers are thinking and feeling in the moment. It’s this type of sentiment analysis that can inform decisions about new products and incremental improvements.

CPG brands can also use real-time feedback to identify the products and features that shoppers value most and bring those concepts to market faster. This approach to continuous innovation can expedite the research and development process so products are in the hands of customers sooner.

2. Social media data

Thanks to social media platforms like Facebook, Twitter, YouTube and Pinterest, CPG leaders have access to a wealth of consumer-generated data. Many organizations are collecting this information but are unsure of how to use it. A case study presented at The Lean Startup Conference from CPG startup Mighty Handle may help shed some light onto this problem…

 

Read more here:

http://www.ibmbigdatahub.com/blog/how-make-better-product-decisions-using-big-data

Related Content:

3 ways behavioral analytics can drive business growth (by Gaurav Deshpande):

http://www.ibmbigdatahub.com/blog/3-ways-behavioral-analytics-can-drive-business-growth

 


Consumer Market Analysis: For Stronger Predictive Modeling

by Ritika Puri –

One of the biggest challenges that leaders in the consumer packaged goods (CPG) industry face is lack of predictability. Even with in-depth planning and market testing, there’s no guarantee that a product will succeed once it reaches store shelves. Brands need a way to outsmart these uncertainties, and big data fuels consumer market analysis capabilities that brands need to launch high-performing products.

“What-if” scenario analysis is a technique that’s gaining popularity. The idea is simple: Rather than making general forecasts, brands can tailor their analyses to specific situations and contexts. As a result, CPG brands are able to modify their actions in a more focused manner.

The following are three tips to keep in mind when designing your brand’s predictive analysis engine.

1. Ensure that your data-modeling foundation is airtight

There are several prerequisites that predictive analytics programs need. For effective customer market analysis, data must highlight the right market demand signals. Before building a predictive analytics layer, take the time to streamline your data collection process and automate it as much as possible.

Through automation, teams can free up bandwidth to introduce a predictive analytics layer. As Nari Visawanthan, the vice president of product management for integrated business planning at River Logic, points out, companies very rarely have “a single version of the truth” when conducting sales and operations planning. Data science, as a result, is often a matter of interpretation. Your analysts need to build the best possible models for capturing and acting on market demand signals if your brand is going to create the most accurate predictions.

Visawanthan recommends asking the following questions when developing your “what-if” scenario analysis:

  • Do you have mechanisms in place to model operational and financial data simultaneously?
  • Is it possible to perform optimizations across strategic, tactical and operational time horizons?
  • Is there a way to rank-order and prioritize certain demand signals above others?

 

Read more here:

http://www.ibmbigdatahub.com/blog/consumer-market-analysis-tips-stronger-predictive-modeling

 


Consumer Product Warranties

3 Ways Big Data Can Optimize Value

by Ritika Puri –

 

News of poor service reaches more than twice as many people as praise for a good experience, according to the White House Office of Consumer Affairs in a Better Business Bureau report. That’s why CPG brands need to see their sales through for years to come. With consumer product warranties, companies can back the value of the goods they sell. Shoppers, knowing that their purchases are covered, can rest assured that they’re safe from investments gone wrong.

The challenge, however, is optimization. It’s impossible for brands to know ahead of time whether or not a product will fail and generate unforeseen costs. Not to mention, consumers may not be aware of the warranties that come with their purchases. Here are three ways big data can help.

1. Synthesize field product performance

Sheila Brennan, a program manager for IDC Manufacturing Insights, explains that by “synthesizing field product performance, service and customer data from multiple sources,” companies will be able to detect potential problems earlier, optimize spare-parts planning and improve forecasting accuracy.

CPG leaders can reinvest this knowledge into product enhancements. When flaws are caught early enough, companies can make faster incremental changes to their product pipelines. Field product performance in the moment expedites information transmission through a big-data feedback loop.

Over time, this process will become faster and more efficient. The key is to use consumer product warranties as mechanisms to listen and learn.

2. Optimize price and duration

A paper in the Journal of High Technology Management points out that warranties fulfill two marketing needs. First, they offer promotional value in attesting to a product’s reliability. Second, they provide assurance to consumers who may experience post-purchase remorse.

There is, however, a tipping point where warranties stop being cost effective, and where expenses outweigh sales revenue. Determining an ideal time limit and coverage scope can feel like a game of darts in the dark. Over time, though, companies improve their precision.

Big data improves the prediction process. According the Journal of High Technology Management paper, by evaluating multiple variables such as optimal price and warranty length, analysts can estimate the overall maximum profit for a particular product. From there, organizations can build models to optimize warranty price points and durations.

 

Read more here:

http://www.ibmbigdatahub.com/blog/consumer-product-warranties-3-ways-big-data-can-optimize-value

Related content:

IoT Security: Prevention and Management Tips for CPG (by  Ritika Puri):

https://newpush.com/2016/02/internet-of-things-security-prevention-and-management-tips-for-cpg/

Consumer Goods Industry Trends: How Companies are Driving Product Sales via Big Data (by Barbara Thau):

https://newpush.com/2016/02/consumer-goods-industry-trends-how-companies-are-driving-product-sales-via-big-data/