Dashboards are a useful tool, but it's important to know their limitations, especially in data quality. So if you're starting up a new data quality program, make sure you ask your team's candidates this important question: https://t.co/b79hhbMDbr
On this day in 1804, the Lewis & Clark expedition gets nowhere. But turning around when you find you're on the wrong path is all part of the process for making true progress, & that goes for data projects too. It's why we use the trailblazer approach: https://t.co/kOPo3aZ3C5
Have you tried our free desktop data wrangling tool, MIObdt? Our team uses it all the time! Here's a video of a real request from a real customer that showcases how MIObdt makes it easy to do something that would be difficult in Excel. Watch it here: https://t.co/5FArjJRnXh
If there are no standard #dataquality dimensions... where do you start? There's always DAMA, but here's what we've found to be most broadly applicable: https://t.co/nEenMyuZTx
If you talk to any of MIO's engineers, the phrase "entity resolution" is going to come up sooner or later... probably sooner. It's fundamental to our approach to data and makes some really cool things possible. How we do it so effectively → https://t.co/Aig2VlWXxD
How many dimensions of #dataquality are there? It's a great question... with no definitive answer. Find out why in our presentation! https://t.co/cKUXv9gJbD
The concept of #dataquality is great... but when it's time for the rubber to hit the road, where do you actually put it? Our rundown of your options: https://t.co/9GcSHY1epf
Is your data lake landscape getting more complicated than you were planning for? Don't worry: you can still get high-quality data, starting with our 4 keys for #dataquality on a #datalake (or lake of lakes): https://t.co/s7bqFaI1Lx
Do you have a project that needs really, really high-speed #datamatching? Ludicrous speed, maybe? Our consultants can get you up and running with our architecture that does just that! Learn more in our article & get in touch: https://t.co/8glqdqI7A6
Get insight into #dataintegration for customer context in our upcoming #webinar! These projects can be complex, but there are ways to set yourself up for success. Register here: https://t.co/ivuNwJV8p8
We love projects where we get to invent something new & solve a problem for the first time! Here's a look into one of our more recent technologies: a ludicrous-speed data matching architecture. https://t.co/8glqdqI7A6 #bigdata#datamatching
The trailblazer approach is how we consistently deliver solutions that actually do what you need them to. Check out our breakdown of 1) what "trailblazer" has to do with anything and 2) how it helps with #dataquality projects in particular: https://t.co/kOPo3aZ3C5
Find out what happened when we put MIObdt head-to-head vs Excel using a real scenario from a recent project—filtering on 7.2 million records! Spoiler: MIObdt was faster and found more. Get all the details: https://t.co/b7h2slM4PE
Data lakes contain multitudes... and sometimes the multitudes are also data lakes. The more layers you have, the more important data quality is if you want to get things done. Here are our 4 keys for #dataquality on a #datalake (or lake of lakes): https://t.co/s7bqFaI1Lx
Whether you call it #customer360, customer context, or something else, #dataintegration on all your #customerdata can be challenging at best. In our #webinar on 5/24, learn some of our keys to success with these complex projects! Register here: https://t.co/sHDNafoXQO
We put MIObdt head-to-head vs Excel using a real scenario from a recent project—filtering on 7.2 million records. How did it go? Find out in our newest post on Medium. (Spoiler: MIObdt was faster and found more.) https://t.co/b7h2slM4PE
Did you catch the news of our new #opensource#python release, capture files? We interviewed the capture file #pythondeveloper Bert Barabas to get the rundown.
PS: Keep an eye out for our upcoming piece on using capture files with #kafka
https://t.co/67TtisKEk2
Our first open source release is here: capture files, Python edition! Use capture files to create a record-by-record replica of your API or queue stream for replay, backup, & testing. Learn more about them in this interview: https://t.co/67TtisKEk2