Build vs Buy in Energy Analytics

My Quants are Better than Your Quants

People always ask me, “Who does your company compete with?” There are a few vendors that do energy analytics like cQuant.io. But mostly, we compete with energy companies that believe they are going to succeed by building bespoke analytics themselves. Almost always, they fail. Here is why.

Let me start by telling you what we do. cQuant.io is an energy analytics company. Our team of quantitative model developers (mostly PhD mathematicians) and software developers build robust analytic solutions for the energy industry. Those models are delivered though a cloud-native platform.

Most of the time, we meet with energy companies that already have a quantitative team. Those quants are trying (usually without much success) to keep up with the analytic demands of their company. Those demands include valuing new contracts, forecasting thermal or renewable generation, analyzing market dynamics and assessing the financial risks inherent in volatile energy markets. This is very hard to do well, but it’s relatively easy to do if accuracy is not important. I often hear, “Our models are good enough. We don’t have that much risk in our portfolio.” Then a major weather or pricing event occurs, and those companies have lost millions, or worse.

What happened? Why did they fail? Could they have forecasted the risk of that event with better analytic models. The answer is yes.

Build vs. Buy

Sometimes I will shock an executive by asking, “Who has better quants, my company or yours?” Then I will say something like, “I am 100% sure my quants are better than yours.” Before I get thrown out of their office, I quickly explain why.

It’s not that my quants are smarter than yours, we just have significant advantages.

My team is 100% dedicated to building analytic solutions for the energy industry. My team builds analytic solutions that are used by dozens of companies and hundreds of analysts. At cQuant, our customers will give us ideas on how to make the models more useful under a variety of business use cases, market contexts, geographies, and portfolios. That information feeds our development, improving our analytic solutions at a rapid pace.

For example, one of the hottest energy markets right now is renewable energy and battery storage. Market participants are demanding exceptional analytic tools to value and assess the risk associated with a massive explosion in new market opportunities in clean energy. Every week our team is receiving requests to enhance our models to capture a new contract type or asset type. We are successfully keeping pace with these requests. This is our only job.

Your quantitative team has multiple jobs. Your quants are likely not keeping pace with internal requests for analysis. If they build a model, it is typically only accessible by a small handful of other quants. It will not come with a GUI and database that can be accessed by multiple analysts. Building in-house models creates additional problems. Where are the analytic results saved? How will that model hook into your other software systems for trading, risk, and treasury? Who will support that model when things go wrong? What happens to your model if your quant leaves the company? Are you sure that your quant’s model is right? The list of questions goes on and on.

It’s not that my quants are better than your quants, we just have undeniable advantages.

The Secret Sauce Argument

This is one of my favorites. I often hear, “We want to develop our own analytics to gain a competitive advantage. We have our own secret sauce.” I have been doing this work for 20+ years and I have yet to see a single bespoke analytic model offer any advantage at the total portfolio level. Very occasionally, a bespoke model will have an advantage in a narrow context, but often creates headaches for anyone trying to understand value and risk in a holistic way. When it comes down to it, the secret sauce is in the quality of your team and their ability to use advanced analytics to support business action at the portfolio level.

More often than not, companies that engage in building bespoke models end up with a mess of various models, built in different technologies, whose data requirements are not aligned, that do not work with each other, and that provide no actionable intelligence at the total portfolio level.

This mess of models can also be dangerous. If your teams are taking market positions based on models that have only been vetted by your internal quants, this could be a ticking time bomb waiting to explode. This is typically called “model risk”.

Model Risk

Model risk is a well-known type of risk that occurs when a financial model is used to measure quantitative information such as a firm’s market risks or portfolio value and the model fails or worse, performs inadequately and leads to adverse outcomes for the firm.

When was the last time that you had a quantitative audit of your internal analytics? Do you have an internal team reviewing your quantitative models on a regular basis to assess their quality? Do you have a good sense of the short-cuts that were taken when that model was built? For most companies, the answer is “no”.

How do your bespoke models benchmark against industry leading analytic solutions? For most companies, the answer is “I do not know”.

The cQuant.io Difference

At cQuant, we deliver robust analytic solutions that have been vetted by dozens of companies and hundreds of analytic users across the energy sector. Our solutions are being used across different businesses, markets and portfolio types. This means that we have likely already seen and solved the analytic challenges that you are facing today.

We make your analytic teams more effective. Using our tools, your quantitative team can finally catch up with internal requests to value and analyze new opportunities. One customer, a global trading house, told me that we improved their deal execution by 10x.

cQuant is an analytics “platform”. A platform is a software & hardware architecture that acts as foundation or base upon which other applications, processes, or technologies are developed. cQuant’s platform allows our customers to access their analytic solution from anywhere at any time. Our users create custom workflows, save their work, collaborate with other team members, analyze market data and even build their own models, directly inside of cQuant’s platform.

cQuant is future proof. Energy markets are changing rapidly, especially in clean energy. Cloud computing has made older software seem ponderous and archaic. The best data security infrastructure is now found within SaaS solutions that partner with AWS, Azure and other security experts. In addition to analytics, real time data feeds, data libraries, error messaging, custom dashboards, scalable architecture, compatibility with other software systems speed your teams’ analysis and decision making. All in one place.

At cQuant we are 100% dedicated to delivering the most robust and powerful analytic solutions to our customers in a modern cloud-native platform. This is our only focus, and we are good at it.
It’s not that our quants are better than yours, we just have unmatched advantages.

I think you owe it to yourself and your company to take a look.


David Leevan is the CEO of cQuant.io, a leading SaaS platform for energy analytics. cQuant.io serves as the primary analytics platform for independent power producers, load-serving entities, retail energy providers, renewable developers, corporate energy buyers and global trading houses.