Adding a prefix “Dr.” is certainly an exciting prospect for any professional. It adds a lot to the personal satisfaction and brand. However, most business folks believe (almost rightly) that a doctoral degree makes one too theoretical to be of any value in the “Real world”!
It is true that even universities have always looked at PhDs as a breeding ground for the next generation faculty. The unsaid doctrine is that a BS gets you a job, an MS/MBA moves you ahead in career and a PhD makes you a professor who can create more BS and MS.
Business schools are the first to realize that there is a huge requirement in business for people who are as scientific as PhD holders.
Obviously, researchers in business are not expected to publish papers or guide students as their academic counterparts do. They are looked up to analyze complex business problems methodically as a scientist does.
They are expected to make suitable approximations and define some simple parts in the complex whole and attack them using known repeatable, robust principles and techniques. The output may not be a research paper (it could be actually). However, it definitely is a framework or a template that can be used by business practitioners to enhance productivity, lower the costs, and propel their business ahead of the competition.
Business schools went ahead and created doctoral education for working professionals precisely with these goals. Doctor of Business Administration (DBA), Doctor of Management, etc., are designed for working professionals to make them scientific problem solvers in a business context.
For a better understanding of the concept it is important to discuss the and understand the following points:
● A Doctor in an Analytics Business: The need of the hour
● DBA vs DBA for a Data Scientist
And From our experience in the field of Data Science what can we device:
- Digital has to become the philosophy and nerve center of the curriculum
- Applied and Hands-on Thesis is not just good to have but is a must-have
- A Dr. can potentially make you a high-demand thought leader in a digital organization
A Doctor In An Analytics Business: The Need Of The Hour
Data Science and Analytics are traditionally (very wrongly) seen as specialized programming jobs. So, universities, influenced by the industry, first started with data science programs as minors in computer science and statistics for undergraduates and graduates. They recently realized that the business need is far deeper and wider, and hence designed dedicated specializations in Masters, Bachelors, PhD, in Machine Learning has always been a favorite field in computer science and it is becoming super important off late, even in engineering and business.
The career lines for these graduates were data scientists (after Bachelors), senior data scientists (after Masters), and ML Scientists (after PhD).
However, soon business and academic communities realized that many critical leadership roles in the analytics industries are not benefited with any of the skills taught in the above programs.
Let us say, a large IT services company wants to fill leadership roles in the data science consulting practice. This person should have enough technical depth and the ability to identify the business gaps, communicate with the clients and most importantly build solutions that provide measurable business value (interestingly, this last skill is never considered a core competency in any traditional PhD in AI or other Masters and Bachelors courses).
Let us say, an IT product company decides to smarten its application and wants leadership that can take them to the market quickly and profitably. The leaders should have the skill to define the product, design the technicalities, and lead the data science and DevOps teams compassionately and efficiently for rapid design and development.
Hence, A leader in data science is not necessarily a technical expert who worked in the company long enough or a business leader who is a taskmaster! It is a lot more than that and companies who want a 100% digital transformation in the next decade are struggling to find that ideal fitment.
DBA Vs DBA Or A Data Scientist
We, Rennes School of Business, and INSOFE brainstormed for months, before designing the world’s first DBA for Data Science leaders. The goal was simple
“We wanted to take business or technical managers and transform them into thought leaders in Data Science”
Our experience can be summarized as follows:
1. Digital has to become the philosophy and nerve center of the curriculum
AI and ML leaders must be prepared for the digital world (industry 4.0). So, traditional organizational behavior, strategy courses that are mandatory for traditional DBA must be fully re-developed keeping the problems and issues of digital organizations in mind.
The organizational behavior should talk about inertias, fears and ego aspects employees normally display when they have to take the help of machines in decision making. The strategy should talk about how they should create practical digital blueprints for their department or enterprise.
The candidates should develop sufficient confidence in the jargon, positioning of not just AI and Machine Learning but all other digital technologies like IoT, Blockchain, Cloud, Big Data.
Storytelling is another extremely important skill for a strategic leader who has to deal and win the confidence of business colleagues. It is not enough if they communicate with just the tech teams they lead.
Essentially, course work should prepare the leaders to harness the Data and digital assets to the fullest.
2. Applied and Hands-on Thesis is not just good to have but is a must-have
A DBA always demands an applied thesis. For example, in a PhD, a thesis about analyzing the changes in the radiation in the first 4 nanoseconds of the Big Bang is very acceptable. However, DBAs do not pick such topics. They focus on Hands-on projects.
Traditional DBA thesis can, however, be just an analysis (deep and insightful it should be. But, nevertheless, just an analysis). The impact of the Pandemic on logistics across the USA in the pharmaceutical industry is a valid and good DBA thesis. It makes one a thought leader in an important field (Logistics in Pharmaceutical) and trains them to solve complex problems in the field.
The thesis work of the DBA for Data Science leadership must add one more component to the thesis, namely, Hands-on. One of the primary goals of such a DBA is to make the graduate confident enough to lead large scale data science projects and super smart teams confidently.
After a lot of deliberation, we realized that unless one is given hands-on knowledge of execution aspects of data science, one cannot lead confidently. This turns out to be the most challenging and delicate balance to strike.
Obviously, the goal of a DBA is not to create high-quality ML programmers or engineers. At the same time, they have to be able to confidently work with a super smart engineer without losing their fort. Any DBA thesis should strike this balance.
3. A Dr. can potentially make you a high-demand thought leader in a digital organization
So, that’s it. If planned and executed well, the Doctoral degree you do is not just about adding a sexy prefix to your name. It is actually more about a bottom-up transformation.
In three years, you will learn how to approach business problems scientifically and create meaningful and innovative solutions.
More specifically, a Doctorate Degree in Data Science ensures that
- You learn how to identify the important problems to solve while digitalizing an organization.
- Build and lead teams that can implement technical solutions to the problems you identified.
- Deploy them in a way that they provide true and lasting value to an enterprise
If you believe true thought leadership in this exciting field can help your career and help you make some fundamental contributions at a business level and are willing to work hard for 3 years, take a look at the DBA in Data Science program or feel free to write to [email protected] or RSVP for an Info Session this Saturday i.e. 12th September, by clicking on the link below.
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Srishti currently works as Associate Editor at Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures.