What the Industry Expects from a Modern Data Leader

Being a leader in any field of business isn’t easy, but when it’s a technical or mathematical field, the job becomes ever so much more complicated. To be a successful data leader in today’s highly data driven business economy takes certain qualities and, according to experts, the following attributes are shared by every one of them.

The Right Education

Leadership is often an inherent trait, which can be further nurtured into maturity through training and experience, but that alone isn’t enough when it comes to data leaders. As it’s a highly technical and mathematical field at once, the right educational foundation is extremely important, without which, none of one’s leadership skills will amount to anything in the industry.

An online statistics master’s program developed by Michigan Tech has been formulated with exactly this aspect in mind. It further sharpens the analytical and technological knowledge of a data professional to prepare him with the skills that the industry demands.

When someone has the skills to solve actual, work problems in the data business, on the basis of his/her education and experience, it’s a sign of a future leader in the making. Without a proper understanding of how the business works and how to solve its various problems, leadership cannot succeed.

Those that have an undergrad degree in any field of statistics or mathematics, should certainly check out an online statistics master’s program to further prepare themselves, should they wish to take up more responsibilities down the line.

Understanding the Business Outside the Core Work

There is actually a significant difference between work and business, which a veteran business leader understands all too well. We have already discussed the importance of the knowledge base for being a data leader, but that’s the work, not exactly the business.

Of course, without a leader understanding the work, he/she cannot ever hope to tackle the business that surrounds the work, but the two are not one and the same. Therefore, it is not a real mystery as to why business leaders are often the ones with both the technical knowledge and a deep understanding of the trade that the company conducts.

An additional course in business management, or significant experience with the business aspect of the data industry is once again, a common trait for most data leaders.

The Data Culture

The data culture is hard to define without practical examples, but consider it to be similar to building any other kind of work culture in an office environment, but predominated with a push towards encouraging agility, fluidity, innovation, strategic alterations and more from any member of the team.

A leader opens up opportunities for every data professional under him/her, so that they can approach the leadership with new ideas, algorithms, processes, analytical techniques or just totally non-technical improvement suggestions. The old saying about two heads are better than one is quite true in this sector and a good data leader maximizes the available pool of intelligence, experience and human analytical power at the company’s disposal.

Recognizing What is What

As the job of the data scientists, statisticians and analysts of the world primarily involves making calculated predictions, a data leader is only successful when he/she is able to seclude actual facts from hypes without basis.

A team cannot be expected to calculate the estimated total value of the autonomous vehicle industry by 2020, if the key leading figures in the team are unable to determine what’s hype, what’s hyped fact, and what’s pure fact. Erroneous predictions may ruin businesses which rely on such estimations, and that in turn will ruin the analysis and data research firm as well.

In all honesty, no data leader can be 100% accurate all the time, but given how far we have come in terms of predictive Ai utilization in detecting trends and making predictions, accuracy is expected. Even then, AI is at its nascency, which is why data leaders are the ones with the big brains to figure out what even they cannot.

Data Leaders are Naturally Good at Formulating Meaningful Questions

No survey or questionnaire would be able to provide any meaningful insight, if it wasn’t carefully designed to ask the right questions.

Each and every one of the questions should be designed in a way that the answer manages to satisfy any one or multiple criterion’s of the findings that the data is supposed to provide. In other words, there should be a clear connection between what is asked and what the goal of the survey is. It is easy to lose track of the goals sometimes and get sidetracked with some of the questions, as different goals are often so closely linked to each other.

The Relationship Between the Data and the Commerce

This is once again, one of those scenarios where the surveys and questions must have a clear connection with the business aspect of the company. Are the goals of the current surveys and analytical reports beneficial to the client in any way? Even if it is beneficial, is the investment necessary to conduct the survey adequately proportional to those benefits?

Data analysis needs to have a good ROI for clients, or there would be no point, and successful data leaders realize that fact quite well. They are often found to engage as much time in figuring out the most beneficial datasets, as they do in interpreting them.data leader and commerce

Finally, it should be mentioned that irrespective of the nature of a business, if there is no vision in sight, the effort is wasted. Therefore, the clever data leader utilizes their resources carefully to only gather the data that would be, or at least could be relevant, instead of gathering huge data sets that have no apparent connection to the ongoing projects or future visions of the team.

It’s a wastage of valuable resources, which further diminishes the return on investment, making it a fruitless endeavor. Managing resources is after all, a core part of every successful business and the data business is not an exception either.


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