Leveling The Field — Data & Analytics
Creating A Better Education Path Through Artificial Intelligence
There are two simple secrets to learning data & analytics. It is a matter of both quality and quantity. With these two steps, any aspiring analyst will easily propel themselves toward a successful and lucrative career.
- Engage With Great Mentors
- Immerse Yourself In A Plethora Of Real-World Data
High standards for both are critical. Those new to the field are liable to put faith in sources that do not meet the right standards. But with a little research and education, mentors are plentiful, even those of high quality.
Data is much more difficult. It most often lives behind corporate firewalls. Like so many things in the universe, it is a matter of having the right access. To make things more interesting, data quality is often misunderstood. The real-world is full of data with major quality issues. If your goal is a solid education, you will want to concern yourself more with the quantity of your data.
People with experience from other industries might expect it to be harder to find good mentors. Certainly that is the rule in many other fields. But in DS&A (Data Science & Analytics), it is data that is harder to come by. Data in quantity — doubly so. It is real-world data that divides those aspiring to learn analytics from those who actually practice it.
Acquiring real-world data is a difficult process. It is not as simple as downloading a zip file. Data must be collected, transformed, stored, and organized. It requires considerable infrastructure and an understanding of the underlying business that it represents. It is not something you spin up in your spare time, nor something that organizations which possess it are likely to be giving away. It is a matter of security, privacy, technology, and competitive advantage.
Only those with a job in data & analytics have access to real-world data. For those trying to break in, it is an uphill battle.
Each year, numerous companies and organizations make headlines with announcements of ground breaking partnerships. With an affinity for five year commitments, we learn of impressive deals to open corporate data platforms to universities and other institution with the expectation of creating greater data access. Within a few months, these arrangements quietly fade into the archives. Donations are made. Funding is secured. But nothing else ever materializes.
Real-world data is powerful. It is also dangerous.
It is only after the deal is done that these donor corporations begin to tackle the nuances of data security. Compliance, legal, and information security quickly temper all enthusiasm. At worst - plans are soon made for sampling, anonymizing, and encryption. The data quickly loses its real-world attributes. It is thinned. It is removed. It is left … well, unreal. At best - it is locked inside university firewalls. Access becomes limited to a select few. These folks simply become extensions of the existing corporate team. Eventually the limits of either of these approaches leads to a quiet end to the process.
Leveling The Field
If you want to build a better an more inclusive path to analytic education, you will need an approach capable of leveling the playing field. More aspiring analysts need access to real-world data. This is very different than open data — you can read more about that here.
Using artificial intelligence, it is very possible to simulate real-world data in a way that would allow open access. Data of this nature is effectively cloned. It mirrors the format and flavor of real-world systems, software, and platforms. But through AI and other innovative technology, this data can be imbedded with all the same attributes which define real-world sources.
Real-world data has a certain… terroir. This creates significant distance between open and sample data sources and their real-world cousins. Real-world data is flawed (near always). It is never perfect in quality. What real-world data has is context, connection, depth, and bias. When these aspects are missing, any experience or education provided will be thin at best and often absent entirely.
If done well, with the right standards — as noted earlier, and combined with solid mentoring, a powerful education platform could create massive access and opportunity for the world’s aspiring analysts. If done really well, this platform could revolutionize analytics entirely. It would start with education, but a simulation engine of this strength and magnitude would be a powerful tool for even seasoned and experienced practitioners.
So where do you find one?
Fair question. In just a few days, Corsair’s will be unveiling our TradeCraft platform. It is our effort to deliver on the potential outlined in this article. For those interested in early access — click on the link below to signup for our Beta community. Invite and more information are coming soon.
