Background

Data Science Disappointment

Is your enterprise data team spinning its wheels?

Dealing with massive volumes of data, enterprises continue to drive data science projects that involve machine learning (ML). They recognize that manual processes are unable to keep up with the sheer volume and complexity of this data. Enterprises are putting more skin in the game: 83% increased ML budgets year-over-year.ⁱ

Figure 1 Source: Algorithmia 2021 Enterprise Machine Learning Trends

These growing investments show that executives increasingly recognize the strategic importance of ML. Most enterprises defined more use cases and at least half increased their use of ML in existing use cases. Barriers to entry for ML adoption continue to fall and savvy executives realize they need to develop the ability to leverage new opportunities provided or risk falling behind the competition.ⁱⁱ

The percentage of respondents who indicated they have more than five AI/ML use cases has increased 74% year-on-year

Figure 2 Source: Algorithmia 2021 Enterprise Machine Learning Trends

That said, too many AI/ML projects never live up to their promise: 85% fail to deliver and only 53% make it from prototype to production.ⁱⁱⁱ

The failure rates do not mean data science is a bad investment. Rather, enterprises aren’t executing on data science effectively. Enterprises that successfully integrate ML into their business see a host of benefits and often realize returns of two to five times their initial investment.^iv

Figure 3 Source: Deloitte: Business Impacts of Machine Learning