Skip to main content

Digital transformation

Digital Transformation will focus on the critical aspects of building a digital-ready business.

Digital Transformation is leveraging the drivers of transformation to build a digital business strategy, to lead organizations, and help them navigate through the digital disruption.

Digital transformation is the process of using digital technologies to create new or modify existing business processes, culture, and customer experiences to meet changing business and market requirements.

This reimagining of business in the digital age is digital transformation.

Digital transformation is the adoption of digital technology by a company. Common goals for its implementation are to improve efficiency, value or innovation.

Examples for Digital Transformation:

  • Domino’s chatbot implementation
  • Nike’s eCommerce strategy
  • Use of Virtual Reality (VR) for patientcare in Healthcare
  • E-commerce implementation during COVID
  • AUDI’s innovative showroom using tablet

key attributes creating good digital transformation:
Plan a digital business strategy – It is vital to outline an effective digital transformation strategy that defines the end goals of your business.
Proper customer engagement – Your business should engage with customers by understanding their needs and align service to their requirements. 
Define the process – A process plays a very important role in creating successful digital transformation examples.
Implement the right tools & technology – It is essential to adopt the right tools and technology based on your business needs to deliver an excellent customer experience.
Make data-driven decisions – Using data and analytics provide actionable insights that help in evidence-based decision making.

Comments

Popular posts from this blog

New way of product development

Today is the era of fast-paced world and competitive world. Companies are realizing that the old sequential approach to developing new products won’t get the job done and product can’t be reached to market when compared to competitors. The 4 stages of product development are as follows – R&D, Growth, Maturation, and Decline. Instead of sequential approach, companies are using holistic approach – as in rugby game, the ball gets passed within the team as it moves as a unit up the field. This holistic approach has six characteristics: 1)     Build-in-instability 2)   Self-organizing project teams 3)   Overlapping development phases 4)   Multi-learning 5)   Subtle (very clear and strong) control 6)   Organizational change to explore and learning The above six characteristics forming a fast and flexible process for new product development with advantage of act as a change agent, creative, market driven ideas, flexi...

Product Manager vs Product Owner

Both the product manager and the product owner work towards a common goal, to build and improve products that create meaningful value for customers and all stakeholders within the company. This usually happens by delivering and optimizing product features. Product Manager Product Owner The product manager discovers what users need, prioritizes what to build next, and rallies the team around a product roadmap. The product owner is responsible for maximizing the value of the product by creating and managing the product backlog. This person creates user stories for the development team and communicates the voice of the customer in the Scrum process.      Product Manager and Product Owner's work on below vacuum. Product manager focus on: Business Strategy Long term Product Vision Long term Product Strategy Product Roadmap Alignment with Product Owner Product owner focus on: Release Plan (Product Backlog ie: ...

Data & Analytics

Data and analytics is the management of data for all uses and the analysis of data to drive business processes and improve business outcomes through more effective decision making and enhanced customer experiences. Four Types of data analytics: 1.         Predictive data analysis Predictive analytics may be the most commonly used category of data analytics. Businesses use predictive analytics to identify trends, connections between data, and relationship between data. 2.        Prescriptive data analytics Prescriptive analytics is where AI and big data combine to help predict outcomes and identify what actions to take. Prescriptive analytics can help answer questions such as “What if we try this?” and “What is the best action?” You can test the correct variables and even suggest new variables that offer a higher chance of generating a positive outcome. 3.        Diagnostic data analyti...