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Driving Digital Transformation with Data Science: What, How and Why?

  • Neelam Tyagi
  • Jan 09, 2020
  • Updated on: Apr 01, 2021
Driving Digital Transformation with Data Science: What, How and Why? title banner

In the fast-paced competitive world, digital transformation has become remarkable technology. Several companies have initiated their transformational journey through implementing digital transformation in terms of novel business models, processes and technologies, even advanced organizations are witnessing the strength of digital transformation by their drives around potent innovations.


Not signifying the digital technology only, the digital transformation is all about the facts that technology (digital in nature) enabling people/ organizations to work out on their conventional problems, i.e., adopting digital solutions over old solutions.  


It is the integration of digital technology across all realms of a business while diverging on how an organization operates and renders values to their customers.


“You can’t delegate digital transformation for your company… You and your executives have to own it! Executives need to engage, embrace, and adopt new ways of working with the latest and emerging technologies.” — BARRY ROSS, CEO AND CO-FOUNDER, ROSS & ROSS INTERNATIONAL




An immense volume of data is waving into and out in today’s classes of business, but it becomes more complex to know how to convert this data into actionable insights.


On the other side, data science has an incredible perspective for all types of businesses to design models that further define trends and use them as the foundation for transformative software, i.e. from locating IoT devices to predictive analytics.


These models are used to augment customer experience, processing efficiency, user engagement, possible conditions where data can crack difficult problems. 


The market for Data Science services is increasing with the speed of light, it plays a vital and crucial role in helping to transform your business digitally when many companies are looking to unlock the strength of business data that lacks with the demanding proficiency and support. 


In the scope of this blog, you get to learn about the concept of digital transformation, and how data science is upgrading the transformation of business digitally.



Digital Transformation


Digital transformation is the all-embracing transformation of multiple activities that an organization control to leverage opportunities produced by digital technologies and data. It touches the ubiquitous era of digitalization regardless of the size and worthiness of the industry. Moreover, 


  • It reflects the digital trends in terms of operations and policies that make severe changes in how businesses control and assist customers.
  • It depends on organizational data to achieve targets more efficiently and abandon values to customers, but how we catch in the next section. 

An image shows a digitally transformed area in terms of infrastructure, operations, cultures, etc. Analytics Steps, analyticssteps

Digital Transformation overview over a broad area of network-connection

Looking substantially, the intrinsic components that are very likely to transform are its business models, operations, infrastructures, culture, sorted quantitative and qualitative modes of searching for new sources of customer values. 


It has extensive applications in many industries including Banking and Finance, Healthcare, Insurance, IT, Travel and Tourism, and Retail. Along with that, it leaves an impact on industries in the following ways:


  1. Digital Business Representations: Many organizations have changed the way they find, create and introduce a new business with the implementation of various business models deployed digitally.

  2. Digital Operating and Utilization Models: Enterprises are learning new approaches and methods in digitally organized manners for controlling and operating different organization’s tasks.

  3. Digital Expertise and Facilities: The requirement of sustained, developed and captivated talent and skills as the fundamental component are in demand in order to competitive conduction of digital mode of business conduction.

  4. Digital Traction(Purchase) Metrics: It is necessary to make digital traction in all the cooperative groups for fast, safe and authentic traction. In some companies, it is also noticed that traditional KPI is longer worth to work in digitalized modes of businesses.



How does Data Science Benefit to Business?


No wonder, Digital transformation covered all the domains of business regarding product innovations, operations, finance, retailing marketing strategies, customer services, etc.


The term “DIGITALIZATION” not only speed up the business process and performance but also deliver business opportunities. It also improves the outpace of digital disruption and fixes the position of a person in the fast-growing business environment.


Consider the situation where an individual wants to recognize

  • Which sections need to be transformed,
  • How to drop the risk factors,
  • How to withdraw unwanted pitfalls from resources.


Most of the industries have chosen data-driven approaches to digitally transformed their businesses, infact various big data technologies are avialable to follow the appropriate data-driven approaches.


In short, companies are using data science and associated technologies in order to make the environment completely digital, and BI for gathering, computing and interrogating their business data that moreover can be turned out into actionable insights.


The latest surveys show that more and more organizations are embracing data science as a service to reach a large resource of data experts for enhancing their decision making.


Experts are able enough to generate digital strategies and plans either in terms of increasing revenue and reducing costs or improve efficiency. 

Displaying five ways data science helps as a service across various sectors.

 5 ways: Data Science as service tools


“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”– Geoffrey Moore


So, it becomes essential to drag out desire outcomes and benefits from digital technologies. Given below are the multiple ways when data science acts as services to add value in business. 


1. Authorizing decision-making via a data-driven approach:


Like data science, digital transformation is a convoluted process, i.e, customer data combined with appropriate business operations can leverage to make informed conclusions while restricting unwanted risks. With data science capabilities, you can find out how to transform business digitally and which area of business needs to transform. 


Data Science as service demands companies to hire a professional provider that is the required resource and help you to keep this transformation faster and ahead of you in the competition.


2. Classifying warnings, opportunities, and scopes via data-insights


The volume of available information and insights are rapidly growing with the increased volume of data which indirectly initiates the opportunities and hence scope to grow for business as well as the individual.


Data science services make organizations capable to cope with the deficiency of data experts and give a detailed description of their business environment.


Data science is a technique that enables next-generation outcomes to predict what is going to happen and how to preserve it from risks if any. 


For instance, from the customers’ data, it can likely be predicted that what would be the next purchase of customers, to forecast which customer will buy similar products, etc. 


For such process, the method of recommendation system and customer behavior analytics are used to interpret what customers are likely to pruchase.       


Data science enables organizations to have real-time visibility about their customers, support in making decisions to optimize the internal process for larger activity, expanded flexibility and reduce the cost.


3. Adding more values with Machine learning:


Being a major part of the data science ecosystem, machine learning can stimulate digital transformation more effectively in bioinformatics and other industries. It supports to break massive data to identify trends and exceptions. 


One impactive approach is Artificial Intelligence which uses machine learning algorithms to deliver insights, designing timelines models and anticipating chances where disruptions occur. 


For example, additive analytics as a solution can leverage ML algorithms to identify which patients are at high risks to readmission, using a suitable predictive model, hospital staff can estimate emergency room admissions for patients and thus improving patient care results and reduce time and costs.





No matter what industry you are in, either the fashion industry or food industrydata science can help you to transform your business digitally, CIO defines digital transformation as

the acceleration of business activities, processes, competencies, and models to fully leverage the changes and opportunities of digital technologies and their impact in a strategic and prioritized way.” 

4-steps Analysis-Fusion of the combined model of Digital Transformation and Data Science. Analytics Steps, analyticssteps, analytics steps

Analysis-Fusion: Digital Transformation and Data Science

Data science has brought the capability to transform industries, potential to change long-running traditional business models on their heads. What basically needs to address is how and where to utilize maximum data into actions.


The analysis of data trends lets organizations develop models to forecasts future predictions under numerous possibilities. Along with multiple benefits, data science implementations in driving digital transformation have their own risks and challenges.


(Suggested blog: AI to change digital marketing


One should keep in mind that it doesn’t provide immediate assistance to the business unless model accuracy meets the requirements. Data must be used correctly as it is acquired, managed and exchanged in real-time, etc. 

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