Trending March 2024 # Top 10 Data Science Myths That You Should Ignore In 2023 # Suggested April 2024 # Top 9 Popular

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Debunking the top 10 data science myths that you should ignore in the year 2023

In the world of Big Data, there are numerous job profiles available, such as Data Engineers, Data Analysts, Data Scientists, Business Analysts, and so on. Beginners need clarification on these profiles, as Data Scientist is the most popular and sought-after. They require assistance in determining whether Data Science is a good fit and identifying the best resources. There are several misconceptions about data science myths. As a data scientist, there are several data science myths to ignore for a successful career.

Transitioning into data science is difficult, not because you need to learn math, statistics, or programming. You must do so, but you must also combat the myths you hear from others and carve your path through them! In this article let us see the top 10 data science myths that you should ignore in 2023.

Myth 1 – Data Scientists Need to Be Pro-Coders

Your job as a Data Scientist would be to work extensively with data. Pro-coding entails working on the competitive programming end and having a strong understanding of data structures and algorithms. Excellent problem-solving abilities are required. Languages like Python and R in Data Science provide strong support for multiple libraries that can be used to solve complex data-related problems.

Myth 2 – Ph.D. or Master’s Degree is Necessary

This statement is only partly correct. It will be determined by the job role. A Master’s or Ph.D. is required if you want to work in research or as an applied scientist. However, if you want to solve complex data mysteries using Deep Learning/Machine Learning, you will need to use Data Science elements such as libraries and data analysis approaches. If you do not have a technical background, you can still enter the Data Science domain if you have the necessary skill sets.

Myth 3- All Data Roles are the Same

People believe that Data Analysts, Data Engineers, and Data Scientists all perform the same function. Their responsibilities, however, are completely different. The confusion arises because all of these roles fall under the Big Data umbrella. A Data Engineer’s role is to work on core parts of engineering and build scalable pipelines of data so that raw data from multiple sources can be pulled, transformed, and dumped into downstream systems.

Myth 4 – Data Science Is Only for Graduates of Technology

This is one of the most crucial myths. Many people in the Data Science domain come from non-tech backgrounds. Few people are transitioning from computer science to data science. Companies hire for data science and related positions, and many of those hired come from non-tech backgrounds with strong problem-solving abilities, aptitude, and understanding of business use cases.

Myth 5 – Data Science Requires a Background in Mathematics

As a Data Scientist, being good at math is essential, as data analysis requires mathematical concepts such as data aggregation, statistics, probability, and so on. However, these are not required to become a Data Scientist. We have some great programming languages in Data Science, such as Python and R, that provide support for libraries that we can use for mathematical computations. So, unless you need to innovate or create an algorithm, you don’t need to be a math expert.

Myth 6- Data Science Is All About Predictive Modelling

Data scientists spend 80% of their time cleaning and transforming data, and 20% of their time modeling data. There are numerous steps involved in developing a big data solution. The first step is data transformation. The raw data contains some error-prone values as well as garbage records. We need meaningful transformed data to build an accurate machine-learning model.

Myth 7- Learning Just a Tool Is Enough to Become a Data Scientist

The Data Science profile requires a diverse set of technical and non-technical skills. You must rely on something other than programming or any particular tool that you believe is used in Data Science. We need to interact with stakeholders and work directly with the business to get all of the requirements and understand the data domain as we work on complex data problems.

Myth 8- Companies Aren’t Hiring Freshers

This statement made sense a few years ago. However, today’s freshmen are self-aware and self-motivated. They are interested in learning more about data science and data engineering and are making efforts to do so. Freshers actively participate in competitions, hackathons, open-source contributions, and building projects, which aid in their acquisition of the necessary skill set for the Data Science profile, allowing companies to hire freshers.

Myth 9 – Data Science competitions will make you an expert

Data Science competitions are ideal for learning the necessary skills, gaining an understanding of the Data Science environment, and developing developer skills. However, competition will not help you become a Data Scientist. It will improve the value of your resume. However, to become an expert, you must work on real-world use cases or production-level applications. It is preferable to obtain internships.

Myth 10 – Transitioning cannot be possible in the Data Science domain

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Top 10 Data Science Software Businesses Should Use In 2023

Data is huge and complex at the same time. The requirement for software that makes the best use of the data available always persists. This is exactly where data science software comes into play. As organizations rely heavily on data, choosing the right software for the same is critical. We have come up with a list of the top 10 data science software businesses should use in 2023. Have a look!

Keras

Keras, a programming interface, enables data scientists to easily access and use a machine learning platform. An interesting feature to note is that it is an open-source deep-learning API and framework that is written in Python.

Integrate.io

How about a platform that brings all data sources together? Well, this is exactly what chúng tôi has in store for you. It is data integration, ETL, and an ELT platform that can bring all your data sources together. This is just the right software you need to build data pipelines.

Tensorflow

Tensorflow is that one data science software that lays emphasis on deep learning. This software is launched by Google and is written in C++ and Python. What’s so special about Tensorflow? Well, its capabilities include ML model building either on-premise, on the cloud, in-browser, or on-model.

Alteryx

Ever since it was launched in 2024 by MIT data science researchers, Alteryx has evolved to become a proprietary software platform. What has made businesses rely on it is the fact that its most popular open-source tool, “featuretools,” allows the creation of automated feature engineering.

Data Robot

If you are looking for a platform that aims at automated machine learning, then Data Robot is all that you need. In addition to providing an easy deployment process, it allows parallel processing and model optimization. It is because of this reason that this data science software is used by data scientists, executives, software engineers, and IT professionals.

Trifacta Wrangler

This excellent data science software will help you in exploring, transforming, cleaning, and joining the desktop files together. How amazing is that?

KNIME

This remarkable software enables data scientists to blend tools and data types. It is an open-source platform that allows users to use the tools of their choice. Not just that – they can expand them with additional capabilities. KNIME stands for the ability to work with many data sources and different types of platforms.

Apache Spark

Apache Spark is an open-source data processing and analytics engine that is all you need when the objective involves handling large amounts of data. Additionally, the ability of this data science software to rapidly process data has led to significant growth in the use of the platform.

Python

Almost all the businesses rely on Python as it comes with a large standard library. This high-level programming language has the features of object-oriented, functional, procedural, dynamic type, and automatic memory management. The fact that Python is extensible makes it way more accepted.

RapidMiner

RapidMiner is yet another open-source data science tool that has gained wide recognition in time. Its self-explanatory drag-and-drop application is one among the many remarkable features that make RapidMiner a part of the top 10 data science software businesses should use in 2023.

Top 10 Universities Teaching Data Science For Free In 2023

Data science has created a plethora of job opportunities for aspiring data scientists and many more professions in recent years. Students and working professionals are highly interested to have a strong understanding of different aspects and elements of data science. They constantly look for online courses with 100% placement in reputed companies from different institutions offering courses on data science. Courses on data science are providing a sufficient and deep understanding of all key concepts and hands-on experience with real-life projects to candidates. Let’s explore some of the top universities providing data science courses.

MIT, OpenCourseWare

MIT is one of the leading institutes for both teaching and research in the field of modern computing. In 2001, the university launched its OpenCourseWare platform. The aim of which is to make lecture notes, problem sets, exams, and video lectures, for the vast majority of its courses, available for free online.

Columbia, Applied Machine Learning

Andreas C. Muller, one of the core developers for the popular Python machine learning library Scikit-learn, is also a Research Scientist and lecturer at Columbia University. Each year he publishes all material for his ‘Applied Machine Learning Course online. All the slides, lecture notes, and homework assignments for the course are available in this Github repo.

Stanford, Seminars Free Online Courses, Harvard

Harvard University publishes a selection of completely free online courses on its website. The courses are mostly hosted by edX so you also have the option of pursuing certification for each course for a small payment.

Purdue University: Krannert School of Management

Offered through the Krannert School of Management, Purdue University’s Master of Science in Business Analytics and Information Management is a full-time program that starts every year in June and runs for three semesters. The graduate program offers three specializations in supply chain analytics, investment analytics, and corporate finance analytics.

DePaul University

DePaul University offers an MS in data science that promises to equip students with the right skills for a career in data science. The program includes a graduate capstone requirement, but you can choose between completing a real-world data analytics project, taking a predictive analytics capstone course, participating in an analytics internship, or completing a master’s thesis.

University of Rochester

The University of Rochester offers an MS in Data Science through the Goergen Institute for Data Science. The program can be completed in two or three semesters of full-time study, but the two-semester path includes a rigorous course load, so it’s recommended for students who already have a strong background in computer science and mathematics. For those without a strong background in computer science, you can take an optional summer course that will help get you up to speed before the program starts.

New York University

New York University offers an MS in Data Science (MSDS) and offers several concentrations to select from, including data science, big data, mathematics and data, natural language processing, and physics. You’ll need to earn 36 credits to graduate, which takes full-time students an average of two years to complete.

Carnegie Mellon University

Carnegie Mellon University offers a Master’s in Computation Data Science (MCDS) through the Tepper School of Business. During your first semester, you will be required to take four core courses: cloud computing, machine learning, interactive data science, and a data science seminar. By the end of the first semester, you will need to select from three concentrations, including systems, analytics, or human-centered data science. Your concentration will help inform the courses you take during the rest of the program.

North Carolina State University – Raleigh, North Carolina

North Carolina State University offers an MS in Analytics (MSA) program that’s designed as a 10-month cohort-based learning experience that focuses on teamwork and one-on-one coaching. The graduating MSA class of 2023 had a 95 percent employment rate by graduation, with an average base salary of US$98,200 per year, according to the university.

Top 10 Data Scientists Of 2023: Blowing Minds You Should Follow

Ever since the importance of artificial intelligence (AI) and machine learning skyrocketed, data science has seen constant hype in the technology sector. Data science has introduced attention that perhaps no other tech revolution has managed to. The technology is popular among computer science students who have a knack for mathematics and analytics. More and more people are also showing a big interest in data science roles, especially, for data scientist. Data scientists are responsible for tasks right from interpreting large datasets to putting them to use for making data-driven decisions. Analytics Insight has listed top 10 data scientists who made their way as maestros to 2023 and from whom the aspiring people can take cues.

Top 10 data scientist of 2023 Dr DJ Patil

Dhanurjay Patil is a former LinkedIn executive (from 2008-2011) who later served as the White House’s Chief Data Scientist under President Barack Obama. He is well-known as the first-ever data scientist in the US. He played a big role in digitizing the administrative works in Obama’s period. Remarkably, Patil coined the word ‘data scientist’ and was in limelight for over a decade. In 2012, he co-authored a paper titled ‘Data Scientist, the sexiest job of the 21st century.’ Patil earlier served as the Vice President of Products at RelateIQ, which was later acquired by chúng tôi Chief Product Officer of Color Labs, and Head of Data Products and Chief Scientist of LinkedIn. Twitter- 

Dean Abbott

Dean Abbott is the Co-Founder and Chief Data Scientist of SmarterHQ, and President of Abbott Analytics in San Diego, California. Abbott is an internationally recognized data mining and predictive analytics expert with over two decades of experience in applying data mining algorithms, data preparation techniques, and data visualization methods to real-world data-intense problems including fraud detection, risk modelling, text mining, personality assessment, response modelling, survey analysis, planned giving, and predictive toxicology. He is the author of ‘Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst’ and co-author of the ‘IBM SPSS Modeler Cookbook.’ Twitter- 

Randy Leo

Randy Leo is an innovative data scientist who has his own blog site ‘ClaoudML’ to publish useful data science and machine learning resources free of cost. Besides, he is also a data science mentor at an E-Learning platform called Data Science Dream Job where he helps aspiring data scientists in the learning process and getting jobs. LinkedIn- 

Eric Webner

Eric Webner is a Senior Data Scientist at LinkedIn, an online educator and an epitome of lifelong learning. Webner holds a Bachelor degree in Mathematics from the University of Wisconsin, two Masters degree in Math and Business Analytics at Arizona State University and the University of Minnesota, and a PhD from ASU. Previously, he held roles such as the Director of Data Science and Analytics for HealthEast, Principal of Data Management and Data Science at CoreLogic. LinkedIn- 

Yoshua Bengio

Yoshua Bengio is a full professor of the Department of Computer Science and Operations Research, head of the Machine Learning Laboratory (LISA), CIFAR Fellow in the Neural Computation and Adaptive Perception program, Canada Research Chair in Statistical Learning Algorithms. He also holds the NSERC-Ubisoft industry chair. Bengio’s main research ambition is to understand the principles of learning that yield intelligence. LinkedIn- 

Corinna Cortes

Corinna Cortes is the Head of Google Research, New York, where she is working on a broad range of theoretical and applied large-scale machine learning problems. Prior to Google, Cortes spent more than a decade at AT&T Labs- Research, formerly AT&T Bells Labs, where she held a distinguished research position. Cortes’s research work is well-known for its contributions to the theoretical foundations of support vector machines (SVMs).  

Sarita Digumarti

Sarita Digumarti is the Co-founder and COO at Jigsaw Academy. She has over ten years of extensive analytics and consulting experience across diverse domains including retail, healthcare, and financial services. Digumarti has worked in both India and the US, helping clients tackle complex business problems applying analytical techniques. LinkedIn- 

Yann LeCun

Yann Lecun is the Director of AI Research at Facebook, and a Silver Professor of Dara Science, Computer science, Neural Science, and Electronical Engineering at New York University, affiliated with the NYC Center for Data Science. As a professor, LeCun has been working on machine learning, computer vision, robotics, artificial intelligence, computational neuroscience and related topics. LinkedIn- 

Nando de Freitas

Nando de Freitas is an Associate Professor in the Department of Computer Science at the University of British Columbia. Freitas is a specialist in machine learning with an emphasis on neural networks, Bayesian optimisation and inference, and deep learning. As the principle scientist at Google DeepMind, he helped the organisation in its mission to use technologies form widespread public benefit and scientific discovery, while ensuring safety and ethics. LinkedIn- 

Alex Sandy Pentland

Alex Paul ‘Sandy’ Pentland is an American computer scientist, the Toshiba Professor at MIT, and serial entrepreneur. He directs MIT Connection Science, an MIT-wide initiative, and previously helped create and direct the MIT Media Lab and the MediaLab Aisa in India. He is one of the most-cited computational scientists in the world.

Dhanurjay Patil is a former LinkedIn executive (from 2008-2011) who later served as the White House’s Chief Data Scientist under President Barack Obama. He is well-known as the first-ever data scientist in the US. He played a big role in digitizing the administrative works in Obama’s period. Remarkably, Patil coined the word ‘data scientist’ and was in limelight for over a decade. In 2012, he co-authored a paper titled ‘Data Scientist, the sexiest job of the 21st century.’ Patil earlier served as the Vice President of Products at RelateIQ, which was later acquired by chúng tôi Chief Product Officer of Color Labs, and Head of Data Products and Chief Scientist of LinkedIn. Twitter- @dpatil LinkedIn- DJ Patil Dean Abbott is the Co-Founder and Chief Data Scientist of SmarterHQ, and President of Abbott Analytics in San Diego, California. Abbott is an internationally recognized data mining and predictive analytics expert with over two decades of experience in applying data mining algorithms, data preparation techniques, and data visualization methods to real-world data-intense problems including fraud detection, risk modelling, text mining, personality assessment, response modelling, survey analysis, planned giving, and predictive toxicology. He is the author of ‘Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst’ and co-author of the ‘IBM SPSS Modeler Cookbook.’ Twitter- @deanabb LinkedIn- Dean Abbott Randy Leo is an innovative data scientist who has his own blog site ‘ClaoudML’ to publish useful data science and machine learning resources free of cost. Besides, he is also a data science mentor at an E-Learning platform called Data Science Dream Job where he helps aspiring data scientists in the learning process and getting jobs. LinkedIn- Randy Lao Eric Webner is a Senior Data Scientist at LinkedIn, an online educator and an epitome of lifelong learning. Webner holds a Bachelor degree in Mathematics from the University of Wisconsin, two Masters degree in Math and Business Analytics at Arizona State University and the University of Minnesota, and a PhD from ASU. Previously, he held roles such as the Director of Data Science and Analytics for HealthEast, Principal of Data Management and Data Science at CoreLogic. LinkedIn- Eric Weber Yoshua Bengio is a full professor of the Department of Computer Science and Operations Research, head of the Machine Learning Laboratory (LISA), CIFAR Fellow in the Neural Computation and Adaptive Perception program, Canada Research Chair in Statistical Learning Algorithms. He also holds the NSERC-Ubisoft industry chair. Bengio’s main research ambition is to understand the principles of learning that yield intelligence. LinkedIn- Yoshua Bengio Corinna Cortes is the Head of Google Research, New York, where she is working on a broad range of theoretical and applied large-scale machine learning problems. Prior to Google, Cortes spent more than a decade at AT&T Labs- Research, formerly AT&T Bells Labs, where she held a distinguished research position. Cortes’s research work is well-known for its contributions to the theoretical foundations of support vector machines (SVMs).Sarita Digumarti is the Co-founder and COO at Jigsaw Academy. She has over ten years of extensive analytics and consulting experience across diverse domains including retail, healthcare, and financial services. Digumarti has worked in both India and the US, helping clients tackle complex business problems applying analytical techniques. LinkedIn- Sarita Digumarti Yann Lecun is the Director of AI Research at Facebook, and a Silver Professor of Dara Science, Computer science, Neural Science, and Electronical Engineering at New York University, affiliated with the NYC Center for Data Science. As a professor, LeCun has been working on machine learning, computer vision, robotics, artificial intelligence, computational neuroscience and related topics. LinkedIn- Yann LeCun Nando de Freitas is an Associate Professor in the Department of Computer Science at the University of British Columbia. Freitas is a specialist in machine learning with an emphasis on neural networks, Bayesian optimisation and inference, and deep learning. As the principle scientist at Google DeepMind, he helped the organisation in its mission to use technologies form widespread public benefit and scientific discovery, while ensuring safety and ethics. LinkedIn- Nando de Freitas Alex Paul ‘Sandy’ Pentland is an American computer scientist, the Toshiba Professor at MIT, and serial entrepreneur. He directs MIT Connection Science, an MIT-wide initiative, and previously helped create and direct the MIT Media Lab and the MediaLab Aisa in India. He is one of the most-cited computational scientists in the world. LinkedIn- Alex ‘Sandy’ Pentland

Top 10 Free Online Data Science Courses For Beginners In 2023

The top 10 free online data science courses for beginners in 2023 are in high demand

One of the most in-demand talents in today’s technology industry is data science, and a variety of businesses are searching for qualified data scientists. You’ve come to the correct spot if you want to learn data science and analysis and are seeking some free online data science courses to get started learning this valuable skill.

To learn Data Science from start, enroll in one of these free online data science courses. If you currently work with data analysis, they can help you fill in any knowledge gaps, and the best part is that they are free. In this article, we have listed the top 10 free online data science courses to become a data scientist. So, let’s dive into the best data science courses for beginners in 2023 that have been created by experts and trusted by many developers around the world and they are made free by their instructor for educational purposes.

1. An Introduction to Data Science [Udemy Free Course]

One of the top free Data Science courses on Udemy is this one. This course is the ideal choice for you if you learn best visually. You will learn how to manage the data effectively with this training. The fact that this course is brief, easy to follow, and full of value is its most significant quality. More than 2000 students have enrolled in this 44-minute video course so far.

2. Essentials of Data Science [FREE Udemy Course]

Another free online course from Udemy to study and comprehend data science is available here. You will realize how significant they are if you delve into the data business. This course is meant to provide you with an overview of the three core areas of data science that are crucial for success and that every effective data scientist should be knowledgeable about.

3. What is Data Science? [Coursera FREE Course]

You will be thrilled to enroll in this free course on Coursera, one of the top online learning platforms and likely the best site to learn Data Science and Machine learning, if you are seeking a free introductory course on data science.

4. Data Science Specialization by CodingInvadors (Not FREE)

You may enroll in this live data science training program in 2023 to pick up all the skills you need to succeed as a data analyst. The course covers the foundations of data science, and you will gain knowledge of essential tools including Python, PostgreSQL, Jupyter, Statsmodel, Pandas, Scikit-Learn, PowerBI, and Git.

5. Intro to Data for Data Science [FREE Udemy Course]

You may enroll in this additional online course in data science from Udemy for nothing. You will interact with data in data analysis, of course, and you can’t succeed as a data analyst if you can’t grasp the data. You will learn more about science and data in this course.

6. Introduction to Data Science using Python [Free Course Udemy]

One of the simplest data science online courses you’ll discover online is this one. Python programming, the most widely used programming language for data scientists worldwide, is covered in this 2.5-hour video course on data science.

7. IBM Data Science Professional Certificate [Coursera]

The IBM platform was especially considered when creating this Coursera course. You will learn all the fundamentals of Data Science in the IBM cloud environment with this course. Although this course is comprehensive, it will serve as your one-stop shop for all your data analysis needs.

8. Data Science for Everyone [Free Course by DataCamp]

You may enroll in an excellent free online course to study data science. DataCamp, one of the top interactive learning platforms for data-related skills including data analysis and data science, is the provider of this course. There is no coding required for this course’s introduction to data science.

9. Learn Data Science with R Part 1 of 10 [Free Udemy Course]

While Python is undoubtedly the finest computer language, it is not the only one that can be used for data science. This course will teach you how to utilize R for data science. This free course on Udemy is undoubtedly a fantastic resource to attend if you want to master Data Science using the R programming language.

10. NumPy for Data Science Beginners: 2023 [Udemy Free Course]

Top 6 Digital Transformation Myths In 2023

Digital transformation (DX) is taking over almost every industry in the current global market. Businesses of all shapes and sizes are leveraging digital to come out of the dark pit dug by the pandemic. As digital technologies spread across the globe, many misconceptions and myths rise with them. These myths can create hesitations in adopting digital and can also lead to wrong implementation and project failures.

You cannot solve a problem you don’t understand. In a digitally accelerating world, business leaders need to understand these myths to overcome them.

This article explains 6 digital transformation myths and misconceptions that business leaders need to avoid to ensure digital success. 

Myth 1: DX is all about buying digital tech

Thinking that DX is all about purchasing expensive digital tech is a popular myth. Undoubtedly, digital transformation involves the purchase and implementation of digital technologies, but they should only be considered as tools that harness the potential of digital. Digital transformation is a much bigger picture than just buying expensive digital software. DX involves: 

Transformation of culture: This requires organizations to adopt a digital mindset that encourages innovation and approach digital as the way forward

Requires collaborative leadership: Successful DX requires adopting a digital mindset from top-to-bottom. Without the support of the leadership and the top-level management, digital initiatives and projects will fail.

Transformation of the human resource: Enhancing the people of the organization is as important as enhancing the technology. A company’s level of digital dexterity can be the determining point of failure and success. To learn more about digital dexterity, check out our comprehensive article.  

Transformation of vision and mission: Failure to incorporate digital in the organizational vision and mission can be damaging and can lead to DX failure.

To streamline your DX journey and avoid failure, check out our comprehensive article on the digital transformation framework.

Figure 1. A 5 step digital transformation framework

Watch how DBS bank focused on all aspects of digital transformation to achieve excellence.

Myth 2. DX has no tangible value

Another misconception that business leaders need to overcome is understanding that digital transformation has no measurable value. 

Digital technologies benefit almost every business component, including financial benefits, operational benefits, and sustainable benefits. Organizations are leveraging digital technologies such as artificial intelligence (AI), Internet of things (IoT), Automation, blockchain, etc, to achieve higher efficiency, productivity, and sustainability.

The benefits of digital transformation are real and there is an abundance of evidence. Check out our article on digital transformation case studies and success stories.

Check out our comprehensive article on DX Key performance indicators (KPIs) to measure your business’s DX progress and benefits. 

PTC also provides a framework to identify value in your digital transformation initiative and process (see Figure 2).

Figure 2. A 5 step framework to identify value in digital transformation

Source: PTC

Myth 3: Going big is better

Thinking that going big with digital transformation will bring out big results is also a misconception. DX does not necessarily require big initiatives to succeed. Instead, a more effective process is approaching DX as a process of small achievable objectives.

Initiating large-case and high-risk digital projects will not necessarily return big value. They can also result in failure and substantial losses. Business leaders need to get a clear understanding of what creates value for them and then design their DX strategy based on achievable objectives which contribute to the long-term goals. 

According to BCG, having an objective-based and agile approach is important for the success of DX:

Myth 4: One size fits all

Thinking that there’s a single formula for digital transformation that is appropriate for all organizations is another misconception. Business leaders need to understand that a DX framework that worked for one organization might not work for another.  

Every organization needs to ask these 3 questions before creating its digital transformation strategy.

Source: Deloitte

Myth 5: DX is a quick fix

While there are off-the-shelf digital packages and solutions that can be purchased to elevate an organization’s digital capabilities, they are just a part of the never-ending DX journey. This process will have many hurdles and moments of success, and since technology is always changing and upgrading, companies will have to keep improving their digital strength. 

Myth 6: DX is optional

Digital transformation is not an option anymore if businesses need to survive in the post-pandemic world. According to a recent PwC study, 60% of business executives consider digital transformation as a top growth driver for 2023 and beyond (see Figure 3).

Figure 3. Top growth drivers and focus of investment by companies

Source: PWC

Watch how digital transformation has become a necessity for Northrop Grumman, an aerospace and defense company:

To maximize the value of your digital transformation investments, you can consult a digital transformation expert. Check out our sortable/filterable list of digital transformation consulting companies to find the option that best suits your business needs.

Further reading

To accelerate your digital transformation process, check out:

Shehmir Javaid

Shehmir Javaid is an industry analyst at AIMultiple. He has a background in logistics and supply chain management research and loves learning about innovative technology and sustainability. He completed his MSc in logistics and operations management from Cardiff University UK and Bachelor’s in international business administration From Cardiff Metropolitan University UK.

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