Businesses are in a constant struggle to stay relevant in the market and change is rarely easy — especially when it involves technological overhaul.

Think about the world’s switch from the horse and buggy to automobiles: It revolutionized the world, but it was hardly a smooth transition. At the turn of the 20th century, North America was a loose web of muddy dirt roads trampled by 24 million horses. It took a half-century of slow progress before tires and brakes replaced hooves and reins.

Businesses are in a constant struggle to stay relevant in the market and change is rarely easy — especially when it involves technological overhaul.

Think about the world’s switch from the horse and buggy to automobiles: It revolutionized the world, but it was hardly a smooth transition. At the turn of the 20th century, North America was a loose web of muddy dirt roads trampled by 24 million horses. It took a half-century of slow progress before tires and brakes replaced hooves and reins.

Just as driverless cars hint at a new era of automobiles, the writing’s on the wall for modern analytics: Companies will need to embrace the world’s inevitable slide toward Open Data Science. Fortunately, just as headlights now illuminate our highways, there is a light to guide companies through the transformation.

The Muddy Road to New Technologies

No matter the company or the technological shift, transitions can be challenging for multiple reasons.

One reason is the inevitable skills gap in the labor market when new technology comes along. Particularly in highly specialized fields like data science, finding skilled employees with enterprise experience is difficult. The right hires can mean the difference between success and failure.

Another issue stems from company leaders’ insufficient understanding of existing technologies — both what they can and cannot do. Applications that use machine and deep learning require new software, but companies often mistakenly believe their existing systems are capable of handling the load. This issue is compounded by fragile, cryptic legacy code that can be a nightmare to repurpose.

Finally, these two problems combine to form a third: a lack of understanding about how to train people to implement and deploy new technology. Ultimately, this culminates in floundering and wasted resources across an entire organization.

Luckily, it does not have to be this way.

Open Data Science Paves a New Path

Fortunately, Open Data Science is the guiding light to help companies switch to modern data science easily. Here’s how such an initiative breaks down transitional barriers:

  • No skills gap: Open Data Science is founded on Python and R — both hot languages in universities and in the marketplace. This opens up a massive pool of available talent and a worldwide base of excited programmers and users.
  • No tech stagnation: Open Data Science applications connect via APIs to nearly any data source. In terms of programming, there’s an open source version of any proprietary software on the market. Open Data Science applications such as Anaconda allow for easy interoperability between systems, which is central to the movement.
  • No floundering: Open Data Science bridges old and new technologies to make training and deployment a breeze. One such example is Anaconda Fusion, which offers business analysts command of powerful Python Open Data Science libraries through a familiar Excel interface.

A Guided Pathway to Open Data Science

Of course, just knowing that Open Data Science speeds the transition isn’t enough. A well-trained guide is equally vital for leading companies down the best path to adoption.

The first step is a change management assessment. How will executive, operational and data science teams quickly get up to speed on why Open Data Science is critical to their business? What are the first steps? This process can seem daunting when attempted alone. But this is where consultants from the Open Data Science community can provide the focus and knowledge necessary to quickly convert a muddy back road into the Autobahn.

No matter the business and its existing technologies, any change management plan should include a few key points. First, there should be a method for integration of legacy code (which Anaconda makes easier with packages for melding Python with C or Fortran code). Intelligent migration of data is also important, as is training team members on new systems or recruiting new talent.

While the business world leaves little room for fumbles, like delayed adoption of new technologies or poor change management, Open Data Science can prevent your company’s data science initiatives from meeting a dead end. Open Data Science software provides more than low-cost applications, interoperability, transparency and access — it also brings community know-how to guide change management and facilitate an analytics overhaul in any company at any scale.

Thanks to Open Data Science, the road to data science superiority is now paved with gold.