Data Science and Digitalization

The fewest companies are still questioning the need for a more digital business approach, especially if they want to stay competitive and keep market shares.

One of the best-proven ways to ensure the above in 2021 is automating and digitalizing some of the daily manual tasks so employees can focus more on the company’s core competencies, customer service, and company’s primary value proposition.

Every day company’s are producing an immense amount of data. All this data makes it more complex to decide which data contains the value and the actionable insights -> Introducing data science.

Data scientists use the company’s data to identify patterns and trends to use the results as the foundation for transformative software, such as real-time dashboard analysis and predictive analyses, which are part of becoming digitalized.

For the business executives, the easy to access information from the data science analysis helps them improve customer experience, processing efficiency, user engagement, etc.

If you want to read more about data science for business executives, I will refer you to this article: Data science for business executives.

What is digitalization?

Digitalization is the term for digital transformation, which covers the converting activities that an organization controls to digital platforms or automatic processes. By this, the companies leverage opportunities produced by digital technologies and data. Digitalization touches the ubiquitous era of digitalization regardless of the size and worthiness of the company.

Digitalization can cover all business domains regarding product, innovations, operations, finance, retailing marketing strategies, customer services, etc.

The most common areas for digitalization in a company are business models, operations, infrastructures, culture, sorted quantitative and qualitative modes of searching for new sources of customer values [1].

Analytic Steps have formulated the most typical ways digitalization impacts a business[2] :

Digital business representations:

Many organizations have changed how identity creates and introduces a new business with various business models by becoming digital.

Digital operating and utilization models:

Enterprises are learning new approaches and methods in digitally organized manners for controlling and operating different organizations’ tasks.

Digital expertise and facilities:

The requirement of sustained developed and captivated talent and skills as the fundamental component is in demand for competitive conduction of digital mode of business conduction.

Digital traction and purchase metrics:

It is necessary to make digital traction in all the cooperative groups for fast, sage, and authentic traction. In some companies, traditional KPI is no longer worth working in digitalized modes of business.

Digitalization and data science

When a company becomes digitalized, it speeds up the business process and performance and delivers new business opportunities. With digitalization, companies often become more data-driven, and it is here data science meets digitalization.

By using data science and the associated technologies in the digitalization process, companies can gather, analyze, and translate their data into actionable insight. Digitalization allows easy access and sharing of real-time reports quicker and faster than ever before.

Implementing data science in digitalization helps the whole organization make better decisions, which in the button end will lead to an increase in revenue, reducing cost, or improve efficiency.

To use the value of data science, is it essential to drag out outcomes and benefits from digital technologies.

The next will cover the multiple ways data science can be incorporated in a digital company as a service to add value to the business.

Decision making

Digitalization is a convoluted process, f.x if you want to combine customer data with proper business operations to leverage informed conclusions and restrict unwanted risks. Using data science, you can identify how to transform your business digitally and which digitalization will have the most significant impact. F.x; is it more profitable to automate and digitalize some of the tasks in customer support, or will the profit be higher if HR got a new digital salary-payment system?

Classifying warnings and opportunites

Digitalizing how a company is collecting, handling, translating, and presenting data makes it possible to predict what will happen and preserve it from risks with data science. F.x. from customer data, is it possible to predict what is going to happen as the next purchase for the individual customer, customer churn, or the best suited cross-sell product for the specific customer.

Companies can access real-time information about their customer’s shopping behavior by implementing a new digital analyses structure of their data.

Conclusion

The combination of digitalization and data science can be used in all types of industries.

Managers and staff can easier access real-time information about the company, customers, or departments by working in a digitalized company.

Data science has brought the capability to transform industries. It pushes the CEOs and managers to reconsider how they use the traditional business models to consider how and where to utilize the data into action.

The results from the analyzed data let organizations develop models to forecast predictions under numerous possibilities.

Originally published at https://www.borbaki.com on September 18, 2021.