Most mid-market companies make one mistake or another when investing in a data analytics platform, not understanding the many intricacies associated with preparing their data to get the best results. Some of the most common mistakes include:

• Not realizing they need to build pipelines to get the data from their multiple data sources to the analytics platform
• Tasking IT with implementing a data analytics solution, when the IT department does not have data science skillsets
• Basing analytics on data that is riddled with errors, incomplete, or stale, which compromises quality of decision-making due to the inaccuracy and tardiness of the underlying data.
• Relying on the reporting function of one data source and not taking into account data beyond that source for decision-making
• Using dashboards that provide insights into the past only, and not the future – a gap that needs to be bridged to compete with larger enterprises