“There is surely nothing quite so useless as doing with great efficiency what should not be done at all.” – Peter Drucker

Data volumes increase every year. All this data should yield better decisions, but in many cases more data isn’t helping. That’s because data needs a business context and continuous testing for validity. Applying context and validation to raw data is the job of an analyst.

A good analyst can come from many domains: economics, engineering, finance, social sciences, among others. The key skillsets that separate an analyst from other quantitative disciplines are communication and vision. An analyst works with decision-makers to provide the insight they need. Analysts should reliably and noticeably make managers’ and clients’ lives easier and lead to better organizational decision-making.

The analyst role is more of a mindset than a toolkit. Sometimes analysts use the tools of the data scientist, sometimes plain old least-squares regression, sometimes there’s no data analysis but an Excel financial model. Whatever the quantitative tool, analysts present a view of the world that helps make better decisions. The analyst maximizes their value at the point of maximum uncertainty.

Analysts must embrace risk, even though it means they won’t always be right. Analysts who are rarely wrong are also rarely useful, because predictions have no value when they are obvious.

At Orennia, we train our analyst team to answer the most important question in decarbonization: what will be the best use of capital? By focusing on different facets of this question, we make our clients’ lives easier and more profitable. When we spend time with our clients’ management and analyst teams, in addition to answering questions, we transmit our mindset.

We make our clients more effective as a result, because they end up working their best opportunities.

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