Data Science can improve existing business processes, expand business capabilities, or eliminate waste in current practices – depending on how it is used. Like any good business solution, the value of data science is directly proportional to which it is applied. Most every business has data that is not fully utilized (think: data versus useable information), processes that are slow or could be improved (think: efficiency, automation, optimization), or adjacent areas that your business could expand into (think: innovation, core competency expansion, adjacent market movement.) Data Science has a wide berth of capabilities and a proven track record in each of these spaces. So, generally speaking, data science has the potential to improve any or all of these areas, it just requires a focused effort of the right team and technology to bring about real-world gains.

As a blend of computer science/IT, math/statistics, and business domains/knowledge, modern data science is a relatively young and growing field, but one which has a number of readily-available tools and techniques that make getting started in Data Science easy and straightforward.  Getting started requires essentially things:

  1. Have a specific area of focus or a specific problem / challenge area.
    Too often, initial data science efforts feel like (or become) “solutions in search of a problem”. Because data science can do many things in many areas, scope creep – even at the very early stages, is a real challenge.  So, the most important first step is knowing what you want to accomplish and keeping that initial goal firmly in sight.

  2. Have the resources on hand, ready to go.
    In this context, resources include hardware (e.g., GPU-enabled analysis systems), software (data science has over 200 open-source and commonly-used tools to tackle most any problem), and – most importantly – a team of data science focused experts that can help get you started. The good news here is that you don’t have to build up your staff, if you have the right partners and relationships.

  3. Have access to experts in the data science community that can help get you started.
    You are joining a rapidly evolving area that has been going on for several years: experts and communities already exist. There is a tremendous amount of information readily available (Kaggle, YouTube videos, extensive online references and tool-specific communities, etc.)

On the one hand, having such low entry barriers is fantastic, as it truly democratizes some of the most powerful tools that will shape businesses, industries, and organizations for years to come.  However, it can also be a bit daunting: so much opportunity, so much information, so many possible directions.


  1. Have a partner that understands your data science goals, knows where you are / where you want to be, and can help you develop and travel your roadmap towards your future data science success.
    As a long-time, trusted technology partner for a wide array of companies, Future Tech has unique insights into how data science can help your company and industry. We have worked closely with major industry leaders in their initial (and, later, more fully developed) successful data science activities.  Our perspective has always allowed us to see the industry trends through the lens of your unique business needs and bring to you the latest technological innovations that would specifically improve and enhance your business. 


Future Tech is now focusing this same business acumen on data science, to help bring this new technology to bear inside your organization.  As your technology partner, Future Tech stands ready to translate the power of data science into a real-world solution that will improve your business.  To accomplish that, we will follow the three steps above: Help you define the problem, ensure you have the right hardware and software, and ensure you have the right experts from across our industry partners to ensure your data science success.


The easiest way to get started? (TL;DR)  Join Future Tech’s Data Science demo program.

Future Tech Enterprise, Inc., works with AI leaders such as NVIDIA (Elite Partner), Dell Technologies (Titanium Partner) and HPi (Platinum Partner), to execute demo programs focused on documenting the power and performance of GPU-enabled mobile Data Science Workstations (mDSW).  Our demo programs are tailored to enterprise-level organizations.

The mDSW used in our demo programs are truly the world’s first work-from-anywhere data science systems.  They combine the mobility of a laptop with the power of GPU-enabled data analysis tools, and enable data scientists to accelerate numerous tasks, including:

  • Define / Refine advanced algorithms wherever they are
  • Perform preliminary data cleaning and analysis
  • Develop and train models on discrete data sets
  • Test and verify AI/ML/DL solutions from a mobile system
  • Create models and solutions that can be easily transported to larger scale / cloud solutions

Our partnerships with industry-leading equipment manufacturers enables Future Tech to make a limited number of demo systems available for qualifying participants who want to experience  the power of the mDWS fleet.  Your data science team, working with Future Tech and our partners, will identify a specific, real-world business challenge within your organization to solve.  You will receive a powerful, fully configured data science workstation, pre-configured with GPU-powered parallel processing software, and have access to top-tier expertise from NVIDIA, Future Tech and our OEM partners.

Together, we will define the problem, map out the solution, and ensure your team’s success in bringing AI/ML, Deep learning and the full power of data science to resolve the defined challenge. 

Our jumpstart demo programs typically lasts 8-10 weeks, with weekly check-ins and working sessions. 

If you would like to apply to the program, please visit http://ads.ftei.com/NVIDIA and apply today! 

Top Level: This is a surprisingly common question and there are passionate arguments going on for each of the answers above.  Some organizations focus on the “data” side, others focus on the “science” – both views are accurate, depending on your business’ organization.  However, in the long run, it is less important who “owns” data science than it is who is “part of” data science. 

Organizations with effective and successful data science efforts typically view data science as a collective effort spanning IT, product lines, business management, technology and strategy groups as well as the data science area. 

Truly successful data science is a team effort: a coordinated, business focused integrated team of professionals bringing their specific areas of expertise and concerns together to use data science tools and technology to solve long standing and often seemingly impossible business challenges. 

Having the data science team “start at the top” is helpful, to show the executive sponsorship needed to swiftly clear conflicts (priority, resource, turf, etc.) and ensures that everyone, across all functions,  stays problem- and business-focused. 

A common attribute we see in successful data science groups is the alignment between all functions. 

It’s a collective whole approach – exemplified by an equal partnership between business leadership, IT, security, product lines, etc.  Why? Because the true nature and power of data science is to solve problems and enhance capabilities – which typically lie in the other domains (business, products, etc.) 

Challenges and opportunities exist throughout the business, so the data science team should be equally capable and comfortable working with anyone anywhere in the organization to bring new insights and capabilities to bear. 

Too often, data science starts in a product area as an improvement opportunity.  It starts in isolation, guided by a business objective that is not well communicated among various stakeholders.  It then fails to gain traction with enterprise IT due to unique hardware and software requests, becomes seen as a potential budget impact from the business community, and effectively starts a long journey as an uphill battle. 

Often, the challenge to data science adoption is not technical or technological, it is helping focus a multi-faceted team 

Common roadblocks and questions that come up include:  


  • I want to use AI/ML/DS in my organization, but I don’t know where to start? What does AI / ML / Data Science need, special hardware? Expensive software?  
  • I want my business to be more competitive and hear AI/ML/Data Science is the answer. How many data scientists do I need? We’ve tried data science before, it didn’t work well or only produced limited results. Why should we try again? 

As we always say, organizations that are successful at data science – do not work in isolation.

They pursue data science with a collaborative approach, with equal partnership among business leaders, IT, security, product lines, etc. 

Technically yes but realistically, no. 

There has been a game-changing shift in hardware (and software) supporting data science workloads and CPUs (central processing units) have been significantly outpaced by GPUs (Graphics processing units) by orders of magnitude.   As a result, modern Data Science software really needs modern (i.e., GPU-powered) hardware. 

Oversimplifying it, think of CPUs as single processing queue, like a one lane highway or a store with a single  checkout person. 

GPUs are designed to be parallel processing, so you have a multilane highway and hundreds of checkout lines at the store.  But GPU advancements also include embedding algorithms directly into the hardware, so the analogies breakdown a bit: the multilane highway now also supports planes, trains, rockets, and bullet trains – all without mixing and matching the two. 

And the check-out lanes?  Your purchases are already tagged with RFIDs, so we don’t need a checkout person and in fact, drones working with the GOU systems have already delivered your groceries to the house and the GPU-savvy smart-AI refrigerator has loaded them in. 

Modern DS software assumes GPU processing is available. While many of the tools may work on traditional HPCs (at least the ones that primarily use CPU-based processing), the true power and speed of modern data science tools requires GPU (graphics processing unit) capabilities. 

This has been a foundational change over the last few years: data science processing (including AI, ML, Dl, etc.) has shifted away from general purpose CPU computing and is fully focused on GPU-enable processing.  The hardware on GPU-based systems is fundamentally different and has been specifically designed to support data science workloads. 

Don’t let the “graphics” part of GPU fool you, the GPU is not just driving video for great, detailed images.  GPUs are designed for data science workloads: optimized for parallel processing, they have specifically built components (such as TensorCores) which are built to process data science algorithms (such as TensorFlow).