Summary

We saw in this chapter how data analytics evolved over time as technology advanced and as business needs changed. One of the main objectives of walking through this history is to realize that, once again, we are at the cusp of a change. Data-driven organizations are putting pressure on data products to deliver faster innovation to keep the company ahead of the competitive curve. We also saw how data preprocessing has become critical to modern-day analytics, which uses machine learning for accurate predictions and forecasting. Clean, curated data itself becomes like a product that other products can consume to get innovative insights. This drives the need for a more collaborative and agile analytical environment where data can be discovered and used to build data products, as opposed to the centralized dashboards and reports of the past. A data mesh is one of the ways to bring this agile and collaborative framework to life.

However, a data mesh is a long-term strategy and...