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Data Analytics
JOIN US: Discuss the challenges, potential and best practices for data analytics.
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#eWeekChatExpanding Your AI Deployment JOIN US: Discuss strategies for building out your enterprise AI usage.
Andi Mann
A3.Also, getting enough #data to be effective - most orgs don’t centralize enough data to get a full picture from #analytics.
James Maguire
And then there's the issue of quality of data...
Rex Ahlstrom
challenges de-duplicating and harmonizing data sets from disparate applications creates big #dataquality issues
Andi Mann
@JamesMaguire So true. Never is 'Garbage In, Garbage Out' more applicable - it is exponential at the scale and speed of #analytics !!
Tapan Patel
A3 I would biggest pain point(s) are not even related to technology! They are related to poor data literacy, culture challenges to accept change, skills/staff shortage. #eweekchat #DataScience #analytics #AI #datamanagement
Andi Mann
Aha!! Always the biggest issues. Technology is easy - people are hard!! 😁
Rex Ahlstrom
A1. Data Fabrics are becoming a priority. This will help cross application analytics access more data in a faster and simpler way.
James Maguire
I hear a lot about data fabrics. Almost a "topic du jour"
Rex Ahlstrom
A lot of hype around #DataFrabric but also a lot of potential if implemented properly.
Rex Ahlstrom
A #DataFabric is also not something you just "buy and install". It is a design pattern that needs to be considered alongside existing systems & technologies.
Tapan Patel
A5 - Myth: AI is all about deep learning. Reality - Majority of problems are still solved using traditional ML methods and techniques.
#AI #analytics #datamanagement #datascience #artificialintelligence #eweekchat
James Maguire
Thanks for bearing with us.
Bruce Kornfeld
A5 Data analytics eliminates human bias - depends on how and how it was set up

(edited)

Andi Mann
@brucekornfeld LOL, mostly exactly the opposite - #analytics frequently reinforces human bias!
Tapan Patel
A4 - Data Science teams should not be building models or working in isolation. Collaborate with your peers in data management (i.e. data engineers) and IT (e.g. ITOps) for model deployment. #AI #analytics #datamanagement #datascience #artificialintelligence #eweekchat
Tapan Patel
A3 Lack of involvement, support and ownership from business stakeholders is another major barrier. #eweekchat #DataScience #analytics #AI #datamanagement
Rex Ahlstrom
"I hired a data scientist, why don't I get good analytics results"... executive sponsorship, #dataengineers, #dataanalysts, data-driven culture... spot on
Rex Ahlstrom
A3. @Chris_Ehrlich I completely agree. Simple access to the high-quality data they need while keeping an eye on #governance and #compliance.
Rex Ahlstrom
A2. Many #analytics programs are delivering value but falling short. Still too siloed, departmentally focused, limited by access to high quality data. Proliferation of compliance issues also making it much harder to get the job done and stay out of the courts.
Rex Ahlstrom
@Syniti is constantly being asked by customers how to make it easier to source high quality data for #analytics, avoiding the garbage in garbage out challenge.
Andi Mann
A2. Think most are comfortable w/ elementary analytics. e.g. in #video, #performance is easy to analyze, take action on insights to improve #UX