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How Data Science is Driving Digital Transformation Now

Index of Contents

  • Introduction: A Clear Digital Vision
  • The Digital Revolution
  • Data Science: Approaches and Solutions
  • Tips to Manage Data Science Projects
  • Summary

1. Introduction: A Clear Digital Vision

In an increasingly competitive world, we should have a deep understanding of the business in which we operate, how it is evolving, and the new innovations that we could embrace or build to remain competitive and conquer new market segments. To do this, we must be able to develop a clear vision of transformation that takes us to another level of performance.

By embracing Digital Transformation, we will deal with artificial intelligence, machine and deep learning, virtual reality, and a lot of other innovative technologies. At first sight, it might even sound fearful to lead the business in such a complex and intricate direction. With this in mind, we will consider some strategies to better understand and take competitive advantage of the huge streaming of data in the current era of the digital revolution.

2. The Digital Revolution

Nowadays, the world is increasingly full of new possibilities offered by advanced technologies. Our society has become filled with the incredible capabilities of AI, IoT, robots, drones, machine learning capabilities, augmented reality and so on. Digitalization and new technologies have been rocking in the past few decades and humankind has faced the era of the fourth industrial revolution. The Fourth Industrial Revolution, or Industry 4.0, is the theorization of a manufacturing paradigm based on the concept of the “Cyber Physical System” (CPS), where advanced computer systems can interact with machines augmented with computational, communication and control capabilities.

Four Industrial Revolution

Fig.1 – Changing the world through Industrial revolutions

Let’s take a quick look at the digitalization progress. How can we comprehend it? One idea is to use the concept of FLOPS. FLOPS is a measure of calculations per second for floating-point operations. Floating-point operations are needed for very large or very small real numbers or computations that require a large dynamic range. It is, therefore, a more accurate measure than instructions per second.

Computing capability growth

Fig. 2 – Computing capability growth (Source)

As computing capabilities continue to increase and handle higher complexity, computers are able to run applications that take into account the features of human brains. Recent research on Intelligence Process Automation shows us that the implementation of digital automation could increase the success rate of transformation projects by 70%. For example, the use of Robotic Process Automation (RPA) can help organizations earn up to 4 times the ROI (Return On Investment), and the conscious and effective adoption of AI can increase business productivity by up to 40%.

In order to remain competitive, modern companies must keep pace with the digital revolution, exploiting the use of these smart technologies and integrating them into corporate digital networks. The huge amount of data generated can be processed and transformed into strategic information for the benefit of production and, therefore, of the business.

But how is it possible to transform the collected raw data into valuable commercial business information?

When predictive analyses are considered important for business purposes, those in the interdisciplinary field must use scientific methods, processes, algorithms, and systems to extract knowledge and insights from both structured and unstructured data. We call this field Data Science and it is related to data mining and Big Data. A Data Scientist is responsible for extracting, manipulating and generating predictions from the data.

Here the question will arise: why should businesses utilize data scientists? There is enormous value in data processing and analysis—and that is where a data scientist steps into the spotlight. An example of adding real value for a company is the creation of predictive models that combine data from all connected areas. In this way, data scientists know how to get actionable insight out of gigabytes of data and thus unlock its power to drive business value.

How Data Scientist add Value

Fig. 3 – How Data Scientist add Value

3. Data Science: Approaches and Solutions

The complexity of data and finding meaningful solutions for businesses creates the need for Data Visualization. Visualizations with properly implemented algorithms give us the ability to absorb information more quickly, improved insights, comprehension of the appropriate next step, ability to maintain the audience’s interest and many other advantages.

Data Visualization chart

Fig.4  Data Visualization chart

Data scientists need to effectively manage their projects to lead to higher output and faster results. During their investigations, they might use tools like Python or R to develop an algorithm, explore the data and make charts or use a couple of other data visualizing applications. Software applications like Jupyter Notebooks allow data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and more. Software like Julia is specifically designed to quickly implement the basic mathematics that underlies most data science, like matrix expressions and linear algebra. However, working with large datasets on a local machine may result in the overheating of machines from the simplest of machine learning tasks. A great way to solve this is to move work to a cloud-based data science enterprise platform. This gives data scientists the tools to do everything they want: seamless collaboration, effortlessly-scalable computational resources, and easy analytics. One of the best solutions available is performed by Saturn Cloud, which is supported by the AWS high-availability infrastructure.

Saturn Cloud allows data scientists to deploy, manage, and scale the PyData stack using Jupyter Notebooks in the cloud. This infrastructure is optimized to support DASK, which is a flexible library for distributed parallel computing in Python. It provides ways to scale Pandas, Scikit-Learn, and Numpy workflows more natively, with minimal rewriting. Many data scientists appreciate DASK essentially because it is a very versatile tool that works with a wide array of workloads.

Mind map in Data Science context

Fig. 5  Mind map in Data Science context

4. Tips to Manage Data Science Projects

Let’s take a look at some good tips that will help us better deal with our Data science initiatives.

Tips to manage Data Science projects

Fig. 6 – Tips to manage Data Science projects

Technology and Business Strategy

Enterprises gain certain advantages in implementing modern, innovative technology. However, technology implementation and usage must reflect the strategic vision of the business. It becomes evident that companies must have a clear awareness of what they are trying to achieve with technology to ensure a positive outcome.

Skillful Staff

It is obvious that entering into this new digitalization era will bring us new jobs and the digital transformation requires completely new skill sets. Therefore, companies should put their efforts into training existing staff members to master these skills and/or recruiting new people with different competencies.

Intelligent Approach

We need to try to sharpen our approach by focusing our intellect on further innovation and data-driven decision-making that can give us a potent edge.  We must develop predictive models based on new business intelligence that requires the right combination of human and artificial intelligence.

Data Operating Platform

Data processing and storage should be cloud-based. It is recognized that businesses see an improvement in performance and security after switching to the cloud. Saturn Cloud is one of the most adaptable cloud-based platforms for Data Science. Data Science teams, as well as Enterprises that had chosen a cloud-based platform, don’t need to spend time on administration for the infrastructure and instead can benefit from effortlessly scalable computational resources.


Data science covers a very wide field and therefore its applications are countless. Various sectors such as banking, transportation, e-commerce, healthcare, and many others are using data science to improve their products and services. By choosing the right platform, we can make our data science projects as effective as possible. For example, a platform that gives data scientists the tools to build scalable projects that can handle massive datasets, like DASK, puts them far ahead of their competitors.

Security Considerations

Given the challenges and risks, we need to make some considerations concerning cybersecurity and data protection that remain the primary concerns of CISOs when businesses decide to move to the cloud.

For a CISO, information security and privacy are of paramount importance, though from the point of view of a data scientist, security means automating version control so that you never risk forgetting to commit. Indeed, the ability to manage data versioning alongside code versioning ensures the science is always reproducible.

5. Summary

Any organization can be innovative, fast to deliver, and engaged with each new venture. Driving implementation at a rapid pace is increasingly accessible as long as businesses embrace and benefit from the opportunities of the digital age. With all the possibilities, tools, and empowering, innovative technologies coming out every day, organizations need to sharpen their business opportunities based on the right data-driven business models.

Data-driven decision-making is more effective and realistic as the decisions are based on actual information and not assumptions. One of the important aspects of data visualization is that it does not just take into consideration past data, but also anticipates the future based on various holistic factors.

For this reason, data science must be a fundamental component of any digital transformation effort.