Over the past decade, organizations have seen a rapid growth in data. Harnessing insight from that data is crucial to the growth and sustenance of these organizations. Yet, groups chartered with extracting value from data fail for various reasons. In this chapter, we will cover how organizations can avoid the potential pitfalls of data science.
There is a larger discussion about the quality and governance of data, which we will not be covering here. Experienced data scientists recognize the challenges with data and account for them in their processes. In general, some of these challenges include the following:
- Poor data quality and consistency
- Silos of data driven by individual business teams
- Technologies that are hard to integrate with other data sources
- The inability to deal with the Vs of big data: volume, velocity, variety, and veracity
In some cases...