Network Optimization

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With the rise of new technologies, such as cloud, on top of the new and varied ways customers and employees interact with your company, today’s networks need to be in peak condition to meet these new demands.  Applications are now delivered from many different sources putting more stress on your wide area network (WAN).  It isn’t as simple as delivering the necessary information or application to an endpoint – delivery must also be fast, at any time and efficient.

Wide area network optimization tools are used widely to increase visibility and control and to optimize bandwidth to improve performance. BizTech Magazine outlines the following ways WAN optimization tools typically work below:

  • Compression – Network optimization tools use an algorithm to remove redundancy from data flows reducing size
  • Deduplication – This is another form of data compression, but it conserves bandwidth by reducing the number of bytes transferred between endpoints
  • Transport Control Protocol (TCP) Acceleration – this technique focuses on the network itself.  TCP Acceleration works to improve throughput on an internet link
  • SSL Optimization – this method allows SSL traffic to be accelerated across the internet connection without sacrificing security
  • Protocol Optimization – older protocol may not work well over WAN. This process adjusts these older protocols to reduce latency
  • Application Optimization and Control – this technique is application related and focuses on ways to improve performance and delivery of applications

Khalid Raza on Network World further adds, “the primary role of WAN optimization is to overcome application performance bottlenecks associated with network architectures that were designed for data center, not cloud-based, applications.” As your IT environment expands, network optimization tools can help you achieve your best performance and manage the peaks and valleys of demand.

Big Data: The Road to Advanced Analytics

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We are all familiar with the potential benefits of big data analytics to a company’s future success, but how do we start laying the groundwork to support it?  Here are some starting points from Information Management.

Data. First, define all your data sources. Determine where all your data currently lives and how it flows through your operation. You should also prioritize and categorize your data, identifying the critical pieces.

Infrastructure. How will your system move and support all of this data? How does your system handle structured and unstructured data? Evaluate the infrastructure in place and what would need to be developed or integrated to ensure data flows well between collection, processing and ultimately to your analytics applications for decision making. Another key part of infrastructure is its processing speed. Your platform also needs to be able to support rapid processing of raw data to ready it for analysis.

Storage.  The sheer volume and formats of big data and the rate at which it can change can be daunting.  You need a solid storage strategy in place. Utilize solutions that help you manage capacity, performance and cost.

Security. Part of building a solid foundation means thinking through data security. Be sure to explore the processes for securely transferring data in, out and around your data center and big data analytics applications.

Checks and balances. Analytical insights are only as good as the data put into the analysis. Discuss what oversight and review is needed to ensure data quality and integrity.

Big data analytics gives companies the ability to make more informative, real-time decisions, but it all starts with a solid foundation to operate on.

When to Think About Cloud

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Is cloud still thought of as “separate” from your other operations and projects?  Allwyn Sequeira  of The Data Center Journal advises that we must break free from that mind-set and instead think of public cloud as a system that works with your data center and supplements what you are running on-premise today. Essentially, cloud is a key piece of a hybrid operation that can help improve performance, agility and scalability. Mr. Sequeira provides several scenarios where cloud adoption should be considered.

Equipment upgrades and refreshes.  Do you have local equipment close to reaching capacity or a new project that requires your system to scale?  It may seem automatic to request a price for new hardware to meet the new demand, but actually, this is a great time to explore what cloud options are available and how they would work with your data center. Cloud can be provisioned faster than new physical hardware, and long term cloud will allow you to scale and adapt to future needs quicker.

Mergers and acquisitions.  When a company merges with another or acquires new businesses, this is a great time to think through cloud adoption. The cloud can be a cost-effective solution for integrating new systems and it can reduce transition time versus building or installing new hardware.

Pilot projects and testing.  Many companies adopt cloud solutions for development and testing of new projects.  The cloud removes the need to provision specific hardware to the project reducing time and cost.

Look at cloud as another tool in your IT toolbox.  It should be considered like any other tool when you are creating solutions to meet business and market needs.

Testing Your Data Recovery Plan

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Whether it is human error, a weather event or a technical issue, a disaster is likely within the lifetime of your systems and data. Being prepared for disaster recovery is important to ensure your data and operations are protected and that downtime is minimal.  Backup and recovery is not just about having a plan on paper or a phone tree of contacts or a script for customers.  Preparedness comes from frequent testing of your backup and recovery solutions.

How often should tests be conducted? The answer to this question depends on your industry, regulations, compliance standards and the nature of your data and processes.  Think through these factors and plug in business leaders to determine the appropriate testing plan.

Quality is important. Each backup and recovery test should be treated as the real thing. Formal recovery procedures should be followed and proper documentation should be part of each test.  Be sure to review your testing plan against industry best practices or work with a trusted business partner to develop a solid test plan. After each test, a review should be conducted on the results of the test and what worked well and what did not.

Understand the environment. As part of your backup and recovery plan think through which business or IT situations put company data at risk for a disaster.  This will allow your team to conduct more real-life testing scenarios.  This exercise can also help you better understand periods in the year when risk may be at its greatest and the types of business functions, IT processes and customer interactions that open data up to risk.

Practice makes perfect. One of the biggest benefits of frequent testing is that your team gains a level of experience and confidence with your backup and recovery plan. By practicing they can react more quickly in an actual disaster event.

Backup and recovery planning is an ever-evolving function of the business. As your business changes so does your data backup and recovery needs. Make this critical function a key part of your team’s agenda.

Cloud Security – Questions to Ask

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Cloud services can provide any-sized business efficiency and cost savings. Cloud services can also help free up time to allow for innovation and speed up time to market. For some companies cloud services can also provide better security.  IT teams wear many hats to keep their systems in tip-top shape as the world demands always on service.  Many teams are looking to trusted partners who offer solutions to help them manage.

When it comes to adopting a cloud solution, many put security on the top of their list of concerns.  In fact, IBM recently found that 76% of CIOs consider IT security their biggest risk. Security just like other criteria should be evaluated thoroughly when looking at a new IT service whether that service is cloud related or not. IBM Cloud pulled together these questions to ask when evaluating a cloud service:

  • Who is responsible for security? Understand who owns what once the cloud solution is in place. You may find that the provider does not take full security responsibility or uses a third party.  Depending on the skills  of your team, you will need to decide if you can handle the added security needs or perhaps you can work on a shared responsibility setup or you may want a vendor who has full cloud security expertise and handling
  • How do you evaluate if the security is adequate?  When it comes to security don’t stop your evaluation at the certificate level. Dig in and find out what and where the certification covers and what it means to your business. Better yet, look for a cloud service provider that covers security for the entire infrastructure and can help you manage regulatory compliance standards
  • What happens if something goes wrong? You must understand the provider’s disaster recovery process. In today’s always on world, quick recovery is crucial not only to your customers and employees but also to your bottom line

Finally, once the decision is made to move forward, be sure to clearly document the process, procedures and the division of responsibility for managing your cloud service.

Big Data Predicts

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Big data analytics is providing many new insights for companies around the world.  Bernard Marr reports for Forbes that predictions from big data analytics are being made every day by some of the most well-known companies in the world – buying habits on amazon.com, Netflix movies and even dating sites.  There are many other situations that big data is being applied to that you might not realize.  Mr. Marr points out several:

  • Weather. Always a topic of conversation in Minnesota, weather affects not just our weekend plans but also the operations of many businesses.  More accurate weather reports, especially in rural areas, are now possible due to the multitude of sensors and increase in mobile devices. More data allows for better weather predictions down the road
  • Health and disease.  From diabetes to cancer, big data analytics is being utilized to identify risk factors and early signs.  Big data analytics is being used to improve patient outcomes as well as find ways to combat rising healthcare costs
  • High school dropouts. Mr. Marr reports that nearly one in five students in the U.S. do not complete high school on time.   Many states and even districts have their own programs in place to increase graduation rates, but what if a larger approach could be taken using big data methods. The Center for Data Science and Public Policy at the University of Chicago is doing just that – applying data discipline to identify dropout risk factors to help more youth complete high school
  • Cyber attacks. A timely topic in recent news, big data analytics tools can actually be utilized to help detect and predict security threats

Big data analytics has many applications. From consumer buying behavior, patient symptoms, public policy and even the weather, the insights it can provide are on track to make long lasting impacts.

MN Perspective: Big Data & Healthcare

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A recent article on the Star Tribune by Joe Carlson brought the conversation about healthcare and big data analytics close to home. Several Minnesota-connected companies led discussions on working across the industry to utilize data from insurance, hospital, pharma and medical device sources to drive better patient outcomes and manage rising costs.  Working with big data analytics tools has been key to making this possible.

The first example comes from Medtronic and UnitedHealth. Many might think medical device and insurance companies might not always have the same goals in mind but not true.  The two companies partnered together to discover the value of the features of a Medtronic insulin pump vs. other pumps on the market.  Mr. Carlson reports they discovered that there was a decrease in hospitalizations due to complications from diabetes when the Medtronic pump was used, thus decreasing overall insurance costs.  This led to a change of thinking.  Dr. Richard Migliori, chief medical officer of UnitedHealth explains in the article, “It caused us to stop looking at line-item cost figures and start looking at, what is the total value? Because we saw a total value, we then began to wonder; shouldn’t we be (paying) on the basis of total value?” In 2016, UnitedHealth announced that Metronic would be its preferred insulin pump provider.

In Mr. Carlson’s article, he also gains insight from Mayo CEO John Noseworthy.  Mr. Noseworthy adds that the industry has changed in the last five years and the atmosphere is one of “partnership, trust and transparency” in order to work together to bring down rising costs and improve patient outcomes.

Finally, St Jude Medical is working on a solution to reduce chronic pain by using a medical device versus pain medications. According to Mr. Carlson’s article prescription opioid painkillers are not only addicting but they lead to 15,000 deaths per year.  Using big data analytics, St. Jude is looking to make the case for using its device which has a larger upfront cost but may reduce overall costs long term.

The power of big data analytics and a great spirit of partnership has helped make these connections possible.

Early Adopters Report Cognitive Benefits

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IBM explains cognitive computing as follows, “cognitive technology augments human expertise to unlock new intelligence from vast quantities of data and to develop deep, predictive insights at scale.”  Cognitive computing is opening up more real-time opportunities  for many businesses across many industries. In fact, analysts expect investment in cognitive technology to grow from $2.5 billion in 2014 to $12.5 billion in 2019. IBM recently surveyed over 600 decision makers who are early adopters and asked about the benefits they are experiencing.

First, who are the early adopters?  Below is a great graphic from IBM outlining where these early adopters fall within their cognitive uses:

The cognitive early adopters

Here are also some of the reasons why they are investing in cognitive:

  • 65% of early adopters view cognitive technology as very important to their organization’s strategy
  • 58% feel that cognitive is essential for digital transformation
  • 58% reported that they feel strongly that cognitive will become a “must have” in just a few short years

62% of cognitive users are already reporting that their projects have exceeded expectations.  For the most part these early adopters are finding success in three categories: customer engagement, productivity and efficiency and business growth. Here is a break down from the survey:

  • 49% report more personalized customer/user experiences
  • 42% said they have been enabled to respond faster to customer/market demands
  • 46% have seen improved decision making and planning
  • 46% report improved security and compliance
  • 41% have used cognitive to expand the company into new markets
  • 45% report a reduction in costs due to productivity and efficiency gains

Business growth - IBM Cognitive Graphic

The road to adoption isn’t always easy. Many of the decision makers surveyed sited data management and an employee skills gap as challenges. Turning to outside expertise has been key to helping them overcome both challenges.

Cognitive computing is helping many early adopters unlock new opportunities. As more practical use cases develop, we can expect more companies to put cognitive technology to work for them.

Industry Spotlight: Manufacturing & Big Data

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68% of manufacturers are currently investing in big data analytics in the next twelve months and 67% are moving forward with these investments even as they cut in other areas. Mr. Louis Columbus of Forbes breaks down for us a recent analytics/IoT Honeywell survey of 200 North American manufacturing executives.

According to the survey, 46% of respondents no longer see big data analytics as “optional.”  Instead, the benefits of big data analytics are clear to many in the industry:

  • Equipment performance.  Unscheduled downtime was sited as a top threat to revenue.  51% of respondents agree that the combination of the Industrial Internet of Things (IIoT) and big data analytics will help to predict equipment downtime, maintenance needs and breaks/repairs
  •  Supply chain management. 46% of the executives surveyed agree that big data analytics will help with supply chain performance by allowing them to better plan for and use resources more efficiently
  • Safety. 47% of respondents agree that access to better equipment and operational data through big data analytics could help detect possible safety issues
Big Data Benefits - Forbes
Source: Forbes.com

Mr. Columbus reports most executives are fairly confident on the analytics path they are on. In fact, 65% report being on the “right track” or “above the curve” when it comes to their use of big data analytics. In summary, the results of this survey show that big data analytics will be key to allowing those in the manufacturing industry to improve revenue and operations for their companies.

Modernize Your Data Warehouse

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Entrepreneur reports that by 2020 every person online will create 1.7 megabytes of new data every second, and according to a study on  Computerworld, by that same year $72 billion will be spent on big data hardware, software and professional services. Data is growing and its coming in many forms both structured and unstructured, and companies are looking for the tools to help them manage and utilize it.

Big data can provide a more complete picture of your customer, marketplace or product/service. But before you can take advantage of big data, you must first have the right systems and tools in place.  Now is the time to consider your own infrastructure to make sure the right workloads are working on the right technologies.  In fact, it may be time for your company to take a deep look into how your data warehouse architecture can be modernized, so you are ready to move ahead with big data analytics projects.

Modernizing your data warehouse can increase your ability to deploy new workloads and handle new data sources.  You can also simplify your operations. Modern appliances can be integrated with your data warehouse, filling service gaps and making deployment and management of data easier.  Finally, a whole new set of tools for big data analysis are available. These analytics tools can integrate into your system allowing you to crunch massive amounts of data to form actionable insights.

So how do you get started on the right path? A trusted partner can help you develop a plan for the changes needed to modernize your data warehouse architecture and can help identify the right big data analytics tools for your company. Evolving Solutions takes the time to fully assess your data and business objectives, develop customized software solutions based on your needs, and we provide ongoing support.  We take the time to understand your goals and help you to accomplish them. Contact us today to start discussing how to modernize your data warehouse.