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SMB Nation has been serving the Bainbridge Island area since 2001, providing IT Support such as technical helpdesk support, computer support, and consulting to small and medium-sized businesses.

Top 10 Hot Big Data Technologies

Gil Press, Contributor

As the big data analytics market rapidly expands to include mainstream customers, which technologies are most in demand and promise the most growth potential? The answers can be found in TechRadar: Big Data, Q1 2016, a new Forrester Research report evaluating the maturity and trajectory of 22 technologies across the entire data life cycle. The winners all contribute to real-time, predictive, and integrated insights, what big data customers want now.

Big Data



Here is my take on the 10 hottest big data technologies based on Forrester’s analysis:

  1. Predictive analytics: software and/or hardware solutions that allow firms to discover, evaluate, optimize, and deploy predictive models by analyzing big data sources to improve business performance or mitigate risk.
  2. NoSQL databases: key-value, document, and graph databases
  3. Search and knowledge discovery: tools and technologies to support self-service extraction of information and new insights from large repositories of unstructured and structured data that resides in multiple sources such as file systems, databases, streams, APIs, and other platforms and applications.
  4. Stream analytics: software that can filter, aggregate, enrich, and analyze a high throughput of data from multiple disparate live data sources and in any data format.
  5. In-memory data fabric: provides low-latency access and processing of large quantities of data by distributing data across the dynamic random access memory (DRAM), Flash, or SSD of a distributed computer system.
     distributed file stores: a computer network where data is stored on more than one node, often in a replicated fashion, for redundancy and performance.
      

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How Big Data is Revolutionizing Employee Training

Companies of all types must handle problems on a constant basis, but these challenges are more difficult for a small business. Having limited resources makes it difficult to compete with larger enterprises. Companies of all types must handle problems on a constant basis, but these challenges are more difficult for a small business. Having limited resources makes it difficult to compete with larger enterprises. 

In the past, access to something as powerful as Big Data was limited due to the costs and skills required. In recent years, however, cloud vendors have begun providing data solutions that are simpler and more affordable. For SMBs, this usually requires no more than a subscription fee to begin leveraging the benefits of big data. One report forecasts that big data use by small businesses has increased by 42 percent over the past year.

Even if you run a small proprietorship, you can still utilize big data – for example, to discover patterns and trends in your internal operations or supply chain, identifying new prospects, developing new products, improvements to efficiency, and otherwise making informed decisions to drive faster growth. 

One area where big data is proving highly effective is in employee training. In this article you'll discover how data analysis will improve your workforce and your bottom line.

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Meeting the Expectations of Your Employees

Big data will help you to pinpoint the skills your workers need to perform better, whether on an individual, team, or departmental basis. You can then prioritize and implement a training program that targets the skills and knowledge your employees need most. 

You can establish training courses in a variety of formats according to your needs and resources. These might involve mentor programs with veteran employees, online courses, demonstrations and lectures, or traditional classroom settings. You'll be able to determine which combination of training situations works best and how to schedule them around employee work hours.

You will need to identify which information and materials are required for your training sessions, such as instructors, video, printed handbooks, and smartphone or computer apps. If you can find the right materials and environments to suit your workforce, employees can actually become excited about attending training sessions.

Using big data to track your training programs, you can focus on the most effective training aids, developing better lesson plans, and designing new content and exercises. This way, you'll be able to provide the training that your employees will find the most engaging.

Assessing the Effectiveness of Training

Tracking the effectiveness of your training program will provide the data you need to keep making improvements for greater results. You can identify employee preferences in comparison to the rate of progress. This will help you establish more interesting and even personalized learning activities.

Big data can help you adapt your training to each employee's specific needs. Advanced e-learning technologies can improve the learning experience for everyone to provide optimal returns on your time and investment.
Immediate correlation of test results with demographic data can help you easily chart overall development as well as where individuals may be struggling. People learn at different rates and in different ways, so that some may need special attention or fresh approaches. You'll be able to determine which learning modules are most productive for which employees.

Big data analysis of training activity can also help you to spot employees who demonstrate special talents or capabilities. It would be in your company's best interests to develop these talents as much and as quickly as possible, or prepare gifted individuals for leadership roles. 

Learning through a cloud-based big data solution will provide real-time feedback for employees, instructors, and management. You can take corrective actions to improve the process as training continues. 

The cloud provides a highly responsive and flexible platform for a mobile, connected workforce. The easy accessibility of cloud computing technology has made the perk of working from home possible for an increasing number of people, so now it’s easy for remote employees to get all the benefits of education without having to leave the comfort of their home.


Minimize Employee Turnover

In the U.S., corporate spending on employee training amounted to $70 billion in 2014 and continues to grow. Your small company may not be able to commit a huge sum, but the benefits of developing a better workforce are clear. 

Employees who receive ongoing training are happier in their jobs and more committed to their employer. More capable workers are more confident, more comfortable, and more likely to remain with your company long-term. 

Zoe Weintraub of Guild Education maintains that employees who receive job training are up to 40 percent more likely to stay with their employer than those who don't receive extra training.

A business culture that involves employee skills training benefits both the company and the individual worker, which makes it well worth the investment. Your employees will understand that their efforts are important to the business and its mission. They'll have a greater sense of job satisfaction knowing that you are prepared to invest in their future.

One of the rewards of incorporating big data into your small business comes from its applications in employee training. Cloud-based systems allow you to improve and adapt training programs in real time to get the best from your workforce. At the same time, it creates a sense of involvement and loyalty in your employees. This double benefit of ongoing training makes it a shrewd investment for companies of all sizes. 

Author Bio:Jasmine Williams covers the good and the bad of today's business and marketing. When she’s not being all serious and busy, she’s usually hunched over a book or dancing in the kitchen, trying hard to maintain rhythm, and delivering some fine cooking (her family says so). Tweet her @JazzyWilliams88

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Harry’s New Side Hustle in Analytics

Pre-recession, my basic mo·dus op·e·ran·di (MO) was to take fun seriously in business. I’ve emerged battle-tested over the past decade and have adopted a tougher stance on life: Lead…or follow…or get out of the way. I still try to have fun where I can find it but the economy isn’t as fun as a decade ago and neither am I!

First, before you proceed, I’d ask you to peruse my LinkedIn profile HERE so you  bigdatacan get the foundation to understand the context I’m about to present. Hopefully you’ll note that I’m committed to education both formal and semi-formal (that would be my technology-related certifications). Second, my goal is to lead by example and have your follow along and join the parade. Third, as I’ve opined many times over the past few years. Small Business Server is GONE and it’s time to reinvent ourselves. You’ve done it before; you can do it again.

Side Hustle
It’s all Karl Palachuck’s fault. About 20-months ago at the Microsoft Worldwide Partner Conference (2016), it announced a “degree” in Data Science. Karl signed up to participate in this and I openly questioned whether it could be called a “degree” as Microsoft is not an accredited University. Fast forward the movie and the program has been rebranded a professional certificate (which is appropriate) and the title is Microsoft Professional Program. There are three majors: Data Science, Big Data and DevOps. Note that these are “earned” certificates; not honorary. These are the real deal.

I’m pursuing the Big Data certificate for a few reasons. It’s how I’m wired (I’m not a developer and flunked out of C++ years ago). I was a SQL Server MCSE in the late ‘90s to support my employer (Clark Nuber) and its vaunted Microsoft Great Plains Dynamics accounting consultant practice (once Great Plains Dynamics abandoned Btrieve on the NetWare platform, it adopted SQL Server as the engine on a Windows NT Server network). The Big Data certificate is a natural extension of my background in this area. Finally, many readers know I recently exited a Seattle-based Big Data startup in Predictive Analytics and I want to go all in and double down in this area as the New Harry!

Program Referrals

You’d be amazed concerning the support I have received when I have made mention of my latest education side hustle. After a brief mention in one of the recent MSP Tech Talk lectures (you can sign up HERE for Spring quarter where one of the lectures is a deeper dive on marketing analytics), I received several inquiries about the program and the sign-up link. Ditto a catch-up coffee last Friday with Brandon from Bainbridge Technology and his wife (she has a data analyst background). Finally, there was my friend who works for a State of California’s I-Bank (Infrastructure and Economic Development Bank) and is seeking to take his career to the next level with his passion concerning alternative energy such as solar power (yes – Big Data plays nicely in science to).

Just ‘Da Facts
I know. I know. Get to the fricking point Harry!

In the Microsoft Professional Program Big Data certification HERE https://academy.microsoft.com/en-us/professional-program/ – there are ten required courses that take 12-30 hours each to complete. The education outcome is to train you in eight new skills. Each course runs for three months and starts at the beginning of a quarter. January—March, April—June, July—September, and October —December. The capstone runs for four weeks at the beginning of each quarter: January, April, July, October. Accordingly, I have budgeted two years to complete this journey. Not only do I want to acquire new skills along the way but I want to demonstrate forward professional progress. Again, I implore you to join me right here right now.

Last missive. This is essentially free for Microsoft Partners. I consider this to be in the neighborhood of a several thousand-dollar subsidy compared to what you might pay for other programs. You can pay $99 USD to receive a completion certificate suitable for framing – something I’ll treat myself to upon successful completion.

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3 Data Science Methods and 10 Algorithms for Big Data Experts

Data Science

One of the hottest questions in Information Management now is how to deal with Big Data in all its applications: how to gather, store, secure, and – possibly most importantly – interpret what we collect. Organizations that are able to apply effective data analysis to massive amounts of data gain significant competitive advantages in their industries.

Organizations no longer question the value of gathering and storing such data but are far more heavily focused on methods to make sense of that all the valuable information that data represents. Although security and storage remain critical issues for IT departments, organizations are finding that their commitment to Big Data can’t stop there – they must be able to make sense of their data, to know what data is valid, relevant, and usable, as well as how to use it.

The more data an organization has, the more difficult it is to process, store, and analyze, but conversely, the more data the organization has, the more accurate its predictions can be. As well big data comes with big responsibility. Big data requires military-grade encryption keys to keep information safe and confidential.

This is where data science comes in. Many organizations, faced with the problem of being able to measure, filter, and analyze data, are turning to data science for solutions – hiring data scientists, people who are specialists in making sense out of a huge amount of data. Generally, this means making use of statistical models to create algorithms to sort, classify, and process data.

What is Data Science?

Data science has been a term in the computing field since around 1960 when it was first floated as a substitute for the term “computer science”. Over the next twenty years or so, it gradually came to mean that blend of statistics and methodology that specifically pertained to data analysis. However, it was not until the much more recent emergence of Big Data and its role in organizational development and direction, that data science began to be a fundamental requirement of any organization working out how to analyze such massive amounts of data.

Data science is interdisciplinary, incorporating elements of statistics, data mining, and predictive analysis, and focusing on processes and systems that extract knowledge and insights from data. It is also known as “analytics transformation” because the goal is to “transform” raw data into usable insights. It has also been called “industrial analytics” because the context is industrial rather than scientific – to analyze data for competitive or quality improvements that can be gained by having a better understanding of one’s customers, potential customers, service model, and almost any aspect of the organization that can be represented in bytes.

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3Q2016 MSP Report

Last week we enjoyed our largest audience of the year on the weekly webinar for good reason: it was the 3Q2016 MSP Report with industry updates and forward looking statements. I was joined by respected blogger and analyst karl and harryKarl Palachuk as we talked about yesterday, today and tomorrow. Here are some select findings (to capture the entire conversation – please replay the webinar here).

Big Data
Even in the SMB sector, you can have the larger conversation about Big Data. My own thoughts including using this as a “gateway drug” to get embedded into other departments at your clients. For example, technology decisions are increasingly being made in the Marketing department (where the money is as Willie Sutton said about bank robbing https://en.wikipedia.org/wiki/Willie_Sutton). Many of us have a SQL Server background and know Excel well. Those two cool tools translate very well to Big Data analytics and visualization so you are well on your way to going from “Computer Guy” to “Data Nerd” and making a few bucks along the way.

Karl’s take on Big Data was different. He contends that the RMM tools are collecting snitz loads of operational data the is predictive for MSPs. You can forecast maintenance based on mean failure rates and the like to proactively serve your clients. Huge value adds.

Finally, our friend Anurag at TechAisle, our research partner, is blogging up a storm on the Big Data opportunity for MSPs from a business model vantage point. You need all three opinions (myself, Karl, Anurag) as you explore this potential new line of work.

Ageism
Part of the report was to share our more of our annual survey results. I covered off on education, income, economic attitude and our demographic. Karl really keyed in on how our majority of our community is skewed to the “mature” side to put it mildly. We’re landing in the 45+ age range folks. And as Karl articulated, we’re not getting younger nor is the partner community. We’re not attracting youngins’ to an IT Pro/MSP lifestyle. Karl has literally started a populist uprising proclaiming that ageism has to end and we need new blood and new communities. You can listen to Karl on this topic by replaying the webinar here).

Other topics in the webinar report include

  • Mergers and Acquisitions
  • Gossip
  • Industry Earnings
  • 2nd Half Trends
  • And a Veeam moment
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Defining the 'I' In Small Business CIO

Story by Tom Flynn - 

Big data and analytics market growth is far outpacing overall IT market growth, and that means the need for IT professionals who know what to do with the piles of data collected by businesses is growing just as fast. In fact, the McKinsey Global Institute predicts as many as 190,000 data scientist positions in the United States will remain unfilled by 2018. On top of that, some 1.5 million managers and analysts will lack the skills to understand and make decisions based on big data analysis.Big Data

“To capture the full economic potential of big data, companies and policy makers will have to address the talent gap,” McKinsey says. Otherwise, there will be a lot of unfilled potential in U.S. businesses to leverage big data and analytics to hone processes, develop new products, accelerate go-to-market strategies and measure customer sentiment.

The sector most likely to suffer from this skills shortage is the SMB market, which typically lacks the resources to vie for talent against large, deep-pockets competitors. For organizations in that situation, the answer could be to partner with a managed services provider (MSP)--one that delivers easy-to-use data analytics tools that enable SMBs to compete at the level of the big players.

And the time to act is now, since the big data and analytics market is poised to grow at a 26 percent yearly rate to $41.5 billion in 2018, six times faster than the overall IT market, according to IDC projections. That means if you can’t figure out how to leverage data analytics soon, others surely will – and some of them might just eat your lunch.

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Parallels Summit Day Three: Company News and Insights from Serguei Beloussov

Parallels Summit LogoWednesday marked the final day of the Parallels summit, and I wanted to take some time to not only give a quick summary of that morning’s keynote from Arconis CEO, Serguei Beloussov, but to also cover Parallels’ company announcements.

I’ll begin with the news Parallels released earlier this week in the form of a press release. Parallels announced that it has completely redesigned its Parallels Cloud University, an online (browser-based) training program for business and technical roles in service providers’ companies. According to Parallels, this training allows service provider partners to quickly onboard new employees.

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CompTIA Study: Big Data Promises Businesses to Evaluate Data Needs

Comptia logoCompTIA’s second annual Big Data Insights and Opportunities study confirms growing familiarity with the concepts surrounding big data, which has sparked greater levels of interest across the C-suite. A full 78 percent of organizations say they feel more positive about big data as a business initiative this year compared to a year ago.

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IBM Acquires UrbanCode to Help SMBs Quickly Deliver Mobile, Cloud, Big Data

IBM said that it has acquired UrbanCode Inc., a provider of automated software delivery to help businesses quickly release and update mobile, social, big data, cloud applications. 

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