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#ICYMI: These days many companies have embarked on Digital Transformation. Unfortunately, not many have found good success. A recent study in Forbes, says over 84% of Digital Transformation initiatives in business enterprises fail. While lot of articles talk of Digital Transformation including reasons for the poor failure rate, critical success factors, etc.., here is my take on what is NOT Digital Transformation. Here are the top 5 aspects (based on what I have seen) where the means have taken more importance than the end (goal).

* Digital transformation is NOT Analytics (and Artificial Intelligence or Machine learning). Digital transformation is using quality data to question your assumptions and challenging the status-quo. Do not get carried away with fancy dashboards and colourful infographics if you have little insights on how the data is collected for the dashboard. An article in Harvard Business Review, states just 3% of the data in a business enterprise meets quality standards.
* Digital Transformation is NOT about data capture and storage. You don’t capture and store data just because RAM or storage is cheap. Data management should be tied to improving the business process and every data filed should have a business utility. Studies by Forrester says that over 70% of data in an organization is never used for any strategic purposes. Digital Transformation is about data consumption to improve decision making, compliance, and operations. 
* Digital transformation is NOT an IT initiative. Most Digital Transformation projects easily camouflage into IT or cloud migration projects. A few years back, cloud deployment was a disruptive technology. Today, many cloud services are provided by those who delivered on-premise solutions a couple of years back thereby making the cloud offering a commodity service. Most cloud migrations today is practically data center migration. Digital Transformation is opportunity or problem-driven; it is enabling the business for a sustainable competitive advantage. Digital Transformation is focusing on the business goals and outcomes and not the technology; technology is the means to the end.
* Digital Transformation is NOT an outsourcing initiative (to India) or engaging an army of highly paid management consultants (for their fancy PowerPoint slides). Digital Transformation is having a simple, scalable, and flexible digital infrastructure to serve the business effectively.
* Finally, Digital Transformation is NOT celebrating failure in the name of “learning”. Digital Transformation is a serious, expensive, and strategic business initiative and failure is NOT an option in today’s business environment. Digital Transformation should be tied to business results such as productivity, market access, innovation, compliance, and cost reduction.

Ultimately, success in Digital transformation is about change and realizing any change needs strong leadership and culture. The change will be painful and this change is inevitable if the organization needs to survive in today’s volatile business environment.  Winston Churchill once said – “If you are going through hell, keep going.“  Hell, here represents the struggle; if you keep going when it is hard, you will reach paradise.

Prashanth H Southekal is the Managing Principal of DBP-Institute. He specializes in monetizing business data for insights, compliance, and operations. He brings over 20 years of Information Management experience from companies such as SAP AG, Shell, Apple, P&G, and General Electric. He has published two books on Information Management including the most recent – Data for Business Performance. For more details on DBP-Institute’s service offerings, please visit www.dbp-institute.com.

  https://goo.gl/MJowQf #DataScience #Cloud

#ICYMI: As Data Science becomes highly popular, more and more people are interested in pursuing a career in the field. It is not only about exploiting a multitude of opportunities but Data Science is actually an intriguing subject. It is incredibly stimulating for the mind and a perfect activity for those who like thinking outside the box.

For the same reason, Data Science is now being taught at the level of high school. Young minds can be honed for this skill and prepared for a more complex global environment. The aim of teaching Data Science to teenagers is not just limited to helping them make a career in a steadily growing field but it is also quite nourishing for their brains. Learning Data Science can help professional excel in related fields as well.

Data Science is Natural Subject to Teach to Teenagers

It is an undeniable fact that over time, the human brain has evolved. Many factors are involved in this evolution that has resulted in current teenagers being smarter than the ones before them. Today’s kids have a grown up already in a competitive world; one that is also rich in technology. So part of their brains and personalities is already prepared to take on the complex business world.

Besides kids’ brains having become advanced, most of them have the natural ability to collect and interpret data. And they do it with interest because children are naturally curious as well. For example, kids like collecting day to data like who is competing in the latest hit singing or dancing show. They can predict results and vote smarter based on insights from following the show. They apply the same concepts to their high school competitions and the way they will choose their college options.

Why Focus More on Data Science and Not Coding?

High schools necessarily teach a curriculum for computer science. However, the type of curriculum may vary from school to school. However, computer science is a field that keeps evolving. Many high schools stay up to date by introducing students to basics of newer technologies. Others may only focus on computational thinking; leaving the rest to be pursued as specialities at college.

However, we now live in a global village where students should be prepared from a very young age to face the challenges of the future. Therefore, updating curriculums is almost a necessity. Talking about updating courses, one has to admit that certain technical knowledge is becoming less important to learn and others more. For example, coding is to be mostly taken over by artificially intelligent machines in the future and Data Science becomes a more opportunistic career.

Importance of Teaching Data Science at High School

As mentioned earlier, Data Science is not just a technical subject to be taught for career-based purposes. In fact, it is almost a social science. Most of the careers, especially those directly related to the business world, are becoming integrated with big data.

An understanding of Data Science will develop better collaborative skills despite what careers the students choose in the future. Data collection is part of almost all types of industries these days. Even if a certain profession has nothing directly related to applying data analytics, they will still need to understand the insights provided by Data Scientists for their fields at some point. If there is already an existing foundation for Data Science, the communication will become easier and result in more effective perspectives.

Tips to Teach Data Science to High School Students

Data Science is a complex field. One has to remember to develop a curriculum that applies to the needs of the high school students. The concept is to prepare them so they can understand its importance and decide if this is the career they might pursue in the future. Or how they may use it in relates fields.

Here a few things high schools must focus on when planning to teach a Data Science course:

* Clarifying course objectives is a necessary step. Both computer and Data Science are not cut and dry subjects. There are different dynamics when you go beyond the basics. Therefore, courses should have defined goals. Based on these goals, staffing and student support should be decided
* Courses should complement the current trends in the field of Data Science and employment opportunities as well. High schools should consult career counselors, colleges, and Data Science experts for accomplishing this effectively
* Many skills are integrated with multiple school courses. For example, public speaking, writing, and presentation. Data Science should also be treated as a similar skill. Understanding and interpreting data sets should be associated with any course that involves problem-solving
* Courses should include collaborations with universities and businesses in order to expose students to the more practical applications of Data Science

We are already witnessing the way data is changing the business world. Data has almost become a commodity and data scientists are highly respected professionals. It is safe to assume that the future will only offer more complex challenges when it comes to competition in the business world. Therefore, the importance of collecting and interpreting data will only increase. Furthermore, the pace we are experiencing, another safe assumption is the development of smarter technologies for solving data sets. This is why we need to prepare young minds for the future. They should be able to understand the steadily advancing technologies and that big data is the key to success.

Moreover, a foundation in Data Science will add to their soft skills; increasing their chances of becoming well-established professionals no matter what careers they pursue. It is significant to mention that it is a street-smart skill; usable even outside of the professional world. https://goo.gl/KtHdC3 #DataScience #Cloud