Category: New Investment

At FirstMark, we believe that every company is going to become not just a software company, but a data company.

For that to happen, it is essential that technologies that leverage data be democratized. For the foreseeable future, the global demand for digital innovation will continue to vastly outweigh the number of developers, engineers and scientists. Therefore, some of the technical complexity must be abstracted away to enable a broader group of people to build data-driven products and companies.

Billions of dollars have been invested in the rise of data science and machine learning as mainstream disciplines in the world of business, one of the most exciting tech trends of the last (and next) decade.

In the enterprise, many of the applications of data science and machine learning ultimately produce a prediction: which customers are the most likely to buy? Or churn? Which transactions are most likely to be fraudulent? What part of town is likely to place the most food deliveries tomorrow afternoon?

However, powerful though it may be, there is one thing machine learning generally doesn’t tell you: once you have a prediction, what do you do with it? For example, once you have predicted high demand for food delivery in a certain part of town, how do you decide which delivery team member to dispatch where and when, to optimize for efficiency and maximize revenue and customer satisfaction?

Enter decision science. While the term has not crossed over to mainstream consciousness like its data science cousin, decision science has been around for decades. Also often known as Operations Research, it encompasses a variety of advanced analytical methods and quantitative models to help with decision-making and efficiency, including simulation, mathematical optimization, queuing theory, etc.  

Business planning is, of course, one of the vital functions in the enterprise: hard to run a successful company beyond a certain size without a clear sense for objectives and resources.

Yet, to this day, business planning is a often a cumbersome, rigid and time-intensive process. Typically led by the finance team, it is largely done through email, excel spreadsheets and meetings. In large companies involving multiple business units and geographies, the process can take several months. As a result, business planning tends to effectively happen once a year.

Great power, great responsibility: as the data revolution continues, and companies big and small are increasingly able to collect and analyze massive amounts of data, they are facing rapidly mounting pressure to become much better data stewards. Security, privacy and compliance were largely an afterthought, but there are becoming crucial topics. Regulation is rapidly spreading around the world – witness for example GDPR, CCPA and the even more ambitious New York privacy bill.

As abundantly evidenced in the news, corporations are woefully unprepared to handle the challenge of identifying, managing and securing sensitive data in its various forms — PII, PHI, attorney privileged information, etc. The problem keeps getting worse as the amount of unstructured data keeps increasing in the enterprise. Too often, corporations tend to deal with it after the fact, once a crisis has occurred, in the context of litigation. Only a very small minority is able to address those issues proactively.