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digital technology

Data Science Dojo
Natasha Merchant
| June 6

You needn’t go very far in today’s fast-paced, technology-driven market to witness the results of digital transformation. And it’s not just the flashy firms in Silicon Valley that are feeling the pinch. Year after year, developing and expanding technology displaces long-standing businesses and whole markets. 

In 2009, Uber came along and revolutionized the entire taxi business. Amazon Go, a cashier-less convenience store that debuted in 2019, is just one instance of how traditional industries are undergoing a digital upheaval. 

In today’s dynamic and constantly evolving business landscape, digitization is no longer a matter of debate but a crucial reality for businesses of all shapes and sizes.  

The question at hand is – what’s the path to get there? 

In this piece, we’ll delve deeper into each of these areas and explain why they’re critical for modern businesses to thrive in the digital age. 

Understanding the basics of digital transformation strategy

Digital transformation strategy guide
Digital transformation strategy guide

 An organization’s digital transformation strategy is a plan to optimize all aspects of its use of digital technology. The goal is to enhance operational effectiveness, teamwork, speed, and the quality of service provided to customers.  

The term “digital transformation” is broad enough to encompass everything from “IT modernization” (such as cloud computing) to “digital optimization” (such as “big data”) to “new digital business models.”  – Gartner

While innovation and speed are essential, digitizing the enterprise entails more than just introducing new technologies, releasing digital products, or migrating systems to the cloud. It also necessitates a radical transformation of the organization’s culture, processes, and workflows. 

ALSO READ: The power of AI-generated art to innovate the creative process 

Why is digital transformation strategy important?  

There are various motivations that could lead an entrepreneur to embark on the digital transformation journey.  

Survival is the most obvious motivation.  Now let’s discuss the significance of digital transformation.

Achieving competitive advantage  

Companies must consistently experiment with new ideas and methods to survive in today’s fast-paced, cutthroat economic climate. By harnessing the latest technologies, companies can innovate their products and services, streamline their processes, and reach new demographics. This can lead to the creation of fresh revenue streams and a superior customer experience, setting them apart from rivals. 

For instance, a business that uses AI to automate and streamline its procedures can save a lot of money compared to its rivals, who still use antiquated methods. Similarly, firms that employ data analytics to learn about their customers’ habits and likes can tailor their offerings to those consumers.

Improving operational efficiency  

Efficiency gains in business operations are another benefit of digital transformation. Using automation businesses can save huge time and money while reducing human error. For instance, robotic process automation (RPA) software can handle routine tasks like data entry and invoice processing to free up employees’ time for more strategic work. 

In addition, digital transformation can facilitate enhanced teamwork and communication inside businesses. Employees can work together effectively no matter where they are located, thanks to cloud-based collaboration technologies. This not only improves output but also helps businesses retain talented individuals who place a premium on work-life balance.

Enhancing customer experience

Businesses may benefit from digital transformation and better serve their customers by allowing for consistent and individualized service across channels. To better serve their customers, businesses can use machine learning algorithms trained on consumer data to understand their client’s tastes and preferences better.   

Customers may be more satisfied and loyal to a company if it offers self-service choices; this is made possible by digital transformation. Organizations can enhance customer satisfaction and shorten wait times by introducing simple digital channels.

Steps to develop a digital transformation strategy  

After learning what a digital transformation strategy is and why it’s important, you can begin developing your own strategy. To help you succeed, we’ve broken it down into five easy steps. 

Conducting a digital assessment  

You may begin building the groundwork for your approach after you have buy-in and a rough budget in mind. Assessing how well your business is doing right now should be your first order of business.  Planning your next steps requires knowing your current situation.  

A snapshot of the current situation can aid in the following: 

  • Analyze the ethos of the company. 
  • Assess the level of expertise in the workforce. 
  • Create a diagram of the present workflow, operations, and responsibilities. 
  • Find the problems that need to be fixed and the possibilities that can help.

A common pitfall for businesses undergoing digital transformation is assuming that it is easy to migrate existing technology to a new platform or system (like the cloud or AWS). You may better plan your digital operations and allocate your resources with the data gleaned from a current status assessment.  

ALSO READ: How big data revolution has the potential to do wonders in your business? 

Setting up vision and goals

After conducting a digital audit, the next stage is to formulate a mission and objectives for the digital transformation plan. You may determine your objectives and the steps to take to reach them with the assistance of a digital transformation strategy. 

Each company will undergo digital transformation in its own unique way, and as a result, its goals will vary. But every company needs to keep in mind the following minimum standards: 

  1. How could you improve your service to your customers? 
  2. Is it possible to improve productivity and cut costs by implementing cutting-edge strategies and tools? 
  3. How can you make your accounting firm flexible and open to new ideas? 
  4. Do you have a process for mining analytics to obtain data for making quick judgments?

Asking yourself these questions can help you zero in on the parts of your plan that need the most work or the parts of your approach that should be tackled first.

Implementing the strategy

You’ve finished planning, and now it’s time to put your strategy into action. However,  there are probably a lot of elements to your idea. Don’t try to cram in all of your changes at once; instead, take a breath and work in iterations. 

Only 16% of digital transformation initiatives achieved their desired results.” – a study conducted by McKinsey & Company 

That’s a staggering statistic that highlights the need for effective implementation. 

It is recommended to implement measures in stages, beginning with low-risk projects and working up to more ambitious plans. Talk about how things are going, make sure you’re not going outside the project’s parameters, and assess any issues to see whether they require a strategy adjustment. 

Making steady, substantial progress without introducing sudden, overwhelming, and disruptive change is possible by implementing a plan in manageable pieces.

Monitoring and measuring the results  

Every initiative must focus on measurable outcomes. For example, let’s say you want to implement a new company model that boosts revenue by 3% while improving operational efficiency by 15%. Creating a baseline won’t be too difficult if you already have data on some aspects of your business.  

Project success depends on stakeholders agreeing on how to measure aspects of the business for which no data exists. Measuring and metricizing new business models is difficult.   

  1. Is this revenue growth coming at the expense of other business units, or is it generated independently? 
  2. Is the revenue increase due to acquiring new customers or selling more to existing ones? 

As the business landscape undergoes significant changes, it’s crucial to gather valuable insights that can help predict long-term shifts. Companies can adapt to the changing market by anticipating trends and making informed decisions.

As such, evaluating your inventory and making necessary adjustments is necessary while also identifying logistics and technological changes required to address these shifts.

In order to better manage your progress toward transformation, metrics can be employed to help improve the entire team. Each member of the team needs to have a firm grasp on how progress is being tracked. Everyone should feel like they have a stake in the outcome (“win together”).  If you haven’t already, incorporate data tracking into every facet of your company immediately.

Conclusion

These three issues need to be addressed by any digital transformation strategy worth its salt.

  • Strategy: What do you hope to achieve?
  • Technology: How will you implement technology?
  • Marketing: Who will spearhead the transition?

 

The “what,” “who,” “how,” and “why” of any digital transformation strategy are the answers to fundamental business questions. Answering these issues is essential in developing a digital transformation strategy that can propel businesses forward.

A digital transformation strategy’s primary advantage is that it provides a road map that helps all teams work together to achieve what’s most important to the company and its consumers. Staying on track and giving your business the ability to evolve and drive innovation is possible with a solid digital transformation framework.

The key to success is mastering the intricacies of digital change. Enable your company to streamline its strategy implementation and shorten its time to market.

Seif Author image
Seif Sekalala
| February 13

Simplify complex modern life with problem-solving tools. Digital tech created an abundance of tools, but a simple set can solve everything.

In last week’s post, DS-Dojo introduced our readers to this blog-series’ three focus areas, namely: 1) software development, 2) project-management, and 3) data science. This week, we continue that metaphorical (learning) journey with a fun fact. Better yet, a riddle. What do ALL jobs have in common?

One can (correctly) argue that essentially, all jobs require the worker in question to accomplish one basic or vital goal: solve (a) problem(s). And indeed, one can earnestly argue that the three interdisciplinary fields of this series (software-development, project-management, and data science) are iconic vis-a-vis their problem-solving characteristics. 

 

Advanced problem-solving tools for a (post-) modern world

One of the paradoxes of our (post-)modern era is this fact: our lives have become so much easier, safer, and much more enjoyable, thanks to digital technology. And yet simultaneously, our lives have gotten so complicated, with an overwhelming glut of technological tools at our disposal. 

 

And I suppose one can view this as a “rich person-problem,” akin to a kid in a candy store, indeed. In any case, here is the good news: “as luck would have it,” we can utilize a simple (set of) tool(s), with which we can both solve problems expansively, and/or simplify our lives as needed. 

 

To the rescue (!): Google, checklists, algorithms and data structures, and project-management

Incidentally, a Google search using search terms related to the topic at hand suggests a consensus vis-a-vis best practices for solving problems, and/or simplifying our lives.

Ultimately, we can use two or three vital tools: 1) [either] a simple checklist, 2) [or,] the interdisciplinary field of project-management, and 3) algorithms and data structures.

 

Here’s a fun question for you, dear reader: can you think of a tool that can simplify both simple and complex tasks such as i) grocery shopping, ii) surgery, and iii) safely flying an airplane? If you answered, “a checklist,” you’re correct. 

 

But for more complicated problems, the interdisciplinary field of project management might be useful–i.e., via the 12 (project-management) elements introduced in last week’s post. To recap, those twelve elements (e.g. as defined by Belinda Goodrich, 2021) are: 

  • Project life cycle, 
  • Integration, 
  • Scope, 
  • Schedule, 
  • Cost, 
  • Quality, 
  • Resources, 
  • Communications, 
  • Risk, 
  • Procurement, 
  • Stakeholders, and 
  • Professional responsibility / ethics. 

 

In addition to the mindful use of the above twelve elements, our Google-search might reveal that various authors suggest some vital algorithms for data science. For instance, in the table below, we juxtapose four authors’ professional opinions with DS-Dojo’s curriculum.

 

What problem-solving tools next digital age has to offer 

Thanks to Moore’s law (e.g., as described via the relevant Wikipedia article about Moore’s law and other factors, the digital age will keep producing hardware and software tools that are both wondrous, and/or overwhelming (e.g., IoT, Web 3.0, metaverse, quantum-computing, etc.).

In this blog post, DS-Dojo provides a potential remedy to our readers vis-a-vis finding easier solutions to our world’s problems, and the avoidance of that “spoilt for choice” dilemma.

By using checklists and tools derived from the three interdisciplinary fields of this blog series, we can solve our world’s ever-growing/evolving problems, and/or simplify our lives as needed.

 

Sample Overview of Data-Science Dojo’s Curriculum:

  • Weeks 1 to 3: Introduction to Quantitative Data-Analysis
  • Weeks 4 to 8: Classification
  • Week 9: Applications of Classification
  • Week 10: Special Topic: Text Analysis Fundamentals
  • Week 11: Unsupervised Learning
  • Weeks 12 and 13: Regression
  • Weeks 14 to 16: More Applications of Previously-Learned Concepts
VS.
Tech-Vidvan’s 

“Top 10”:

  1. Linear Regression
  2. Logistic Regression
  3. Decision Trees
  4. Naive Bayes
  5. K-Nearest Neighbors
  6. Support Vector Machine
  7. K-Means Clustering
  8. Principal Component Analysis
  9. Neural Networks
  10. Random Forests
P. Zheng’s “Guide to Data Structures and Algorithms” Parts 1 and Part 2

1) Big O Notation

2) Search

3) Sort

3)–i)–Quicksort

3)–ii–Mergesort

4) Stack

5) Queue 

6) Array

7) Hash Table

8) Graph

9) Tree (e.g., Decision Tree)

10) Breadth-First Search

11) Depth-First Search

12) Dijkstra’s Algorithm

Disha Ganguli’s Top 10

  1. Linear Regression  
  2. Logistic Regression  
  3. Decision Trees  
  4. ID3 Algorithm  
  5. Cart Algorithm  
  6. Naïve Bayes  
  7. K-nearest neighbors (KNN) 
  8. Support vector machine (SVM) 
  9. K-means clustering 
  10. PCA Algorithm
Data-Quest’s Top 10:

5 Supervised Learning Techniques: 

1) Linear Regression 

2) Logistic Regression

3) CART 

4) Naïve Bayes 

5) KNN

3 Unsupervised Learning Techniques

6) Apriori

7) K-means 

8) PCA

2 Ensembling Techniques

9) Bagging with Random Forests 

10) Boosting with XGBoost.

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