Top 5 Mistakes Companies Make with Data Science

Top 5 Mistakes Companies Make with Data Science

Top 5 Mistakes Companies Make with Data Science. What to avoid in your journey towards data-driven decision-making

Becoming a data-driven company is one of the hardest things to strive towards. This is far from an exhaustive list, but are some of the main issues I see companies experience during their data science journey,

  • Not Having Defined Metrics
  • Making the Wrong Hires
  • Being Buzzword Focussed
  • Not Addressing Data Quality Issues
  • Misapplication of Agile Management

Put simply, the problems tend to stem from not addressing more fundamental issues within the business, which may only become apparent once data, and related concerns, take centre stage.

Not Having Defined Metrics

To correctly act on collected data it is required to know what set of actions it is informing and how to interpret their results. Metrics are a means of contextualising data in such a way. Without metrics in place, it can become anyone’s guess as to what inputs inform what outputs, meaning that a company doesn’t really understand the value of their data. In many cases, this can lead to a situation in which each new analytical question leads to a deep dive into each and every available data point, which is clearly not sustainable. With a new data science hire, who will typically not have specific domain knowledge to be able to contextualise the data independently, these issues will be amplified that much more. Defined metrics should act as the foundation upon which a data-driven organisation is created, allowing transparent and available reporting of relevant company data.

data-science data business-intelligence startup data-analysis

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