The economy is starting to shift due to data. Look at Amazon’s rise to the top, and the effect the company has had on retail. It’s an impact that other fields, such as the civic sector, are now trying to replicate.
Why you should study data analysis is simple: Data analysis is the future, and the future will demand skills for jobs as functional analysts, data engineers, data scientists, and advanced analysts.
As the CEO of Allied Inventors—a fund that owns approximately 5,500 granted and filed patents, as well as equity stakes in startups—I witness future trends. Much of what’s coming lies in data. Growth in productivity will arise from better collection, analysis, and interpretation of data.
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All of this is possible today, and will soon be even more efficient, because the low cost of processing data—rather, computing costs—warrants its analysis. Further, today’s flood of information requires the ability to refine and understand data in an efficient way. Without such efficiency, the cost of sorting through mountains of data will overwhelm any benefit from it. Hence the need for better data analytics.
I recently came across a Bloomberg article about MoviePass, a company that plans to offer unlimited movie tickets for $10 per month. In the piece, Ted Farnsworth, CEO at Helios and Matheson—a data firm that’s invested in MoviePass—said:
The goal is to amass a large base of customers and collect data on viewing behaviors. That information could then be used to eventually target advertisements and other marketing materials to subscribers. … The more we understand our fans, the more we can target them.
MoviePass is willing to subsidize your trips to the movie theater in return for more data about you. But the company will only make money if they analyze the data properly.
The key point is that data can be turned into money. To do that, however, careful and efficient data analytics are required.
Here’s a look at how retail and the government are currently being affected by analytics.
Several stores have given up on competing directly with large online retailers like Amazon—which itself became a behemoth because it understood the data surrounding consumer needs.
At least for the immediate future, certain retailers should focus on how to market their products effectively within Amazon and other networks by undergoing deep data analysis. I suspect that the data gleaned from transactions, both potential and realized, will be mountainous in volume. If such retailers can parse through the data effectively, they will survive, and even thrive, in today’s new retail environment.
One government program of personal interest is food stamps. I can’t comprehend how one in five children in the U.S. reportedly go hungry at some point during a given year even with this program. I accept that there are significant abuses within the program and that public policy is partially to blame. Nevertheless, I believe that improved data analytics could increase the efficiency of the program.
If Amazon knows when you need a new bar of soap, we can surely figure out when children will be hungry and why, as well as find a data-driven solution. We owe it to them.
Simply being able to analyze data mechanically will be insufficient in the long run, however. To be an effective data analyst, you must understand the goals of the analysis and how the analysis can be applied efficiently. Only when you understand the business question can you provide insight that will help to reduce risk in decision making.
I believe that, over time, simple data analysis will be computerized. What the world needs are smart analysts who can add value to what they’re analyzing—professionals who possess superior analytics agility and excellent communication and visualization skills. Success in data analysis will require hard work, but the payoff will be worth it.
This post was written by Thomas Kang, CEO of Allied Inventors.