10 Costly Mistakes In Data Integration Projects (And How To Avoid Them)
What are some of the most common costly mistakes in data integration projects? And more importantly, how do you recognize and avoid them in the future?
Whether it's starting too quickly, having a business owner who isn't engaged, or having error-handling processes that aren't up to the job, there are many common data project mistakes. Make sure you're not making them.
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00:00 Introduction
01:35 Starting the project too quickly
02:50 Not thinking about scale
04:47 Getting the project lifespan wrong
06:43 Technology decisions
08:45 Not properly understanding what your users need
12:41 Not having a business owner and champion
13:44 Not focusing on what your data tools actually need to do
15:26 Skipping training
17:50 Deciding how much to DIY
20:27 Misjudging the cost
22:20 Choosing the wrong architecture
26:38 Mishandling a POC (proof-of-concept)
28:37 Lack of transparency
29:15 Not planning for bad data
30:20 Not enough focus on testing