Follow Up Blog from My #DataCred Twitter Chat

I hosted a twitter chat on 23rd of July on the topic of Data Credibility. If you missed this great twitter chat, here is a quick analysis and some of the highlights.

Twitter Statistics

The one hour chat had nearly 400 tweets sent with #DataCred hash tag.
Twitter chat had more than a million (1.1Million) impressions.
Experts from all around the world turned up to the chat. UK, Denmark, New Zeeland, France, Canada, Mexico, Finland, Japan, South Africa and United States of America to name the few.
Total participant count stands at 59

Data Credibility Twitter Chat – Distribution of Participants

Highlights and Top #DataCred Tweets

Q1: I will go ahead and kick things off by asking the first question: What is Data Credibility? How do you define it? #DataCred

— Prash Chan (@MDMGeek) July 23, 2014

@AxelTroike A1 #DataCred: I’m going to offer “degree to which people BELIEVE the data is reliable for the intended purpose.” — Alan David Duncan (@Alan_D_Duncan) July 23, 2014

A1 And the intended purpose must be clearly defined. It is to support the execution of Business Function. #datacred — John Owens (@JohnIMM) July 23, 2014

A1: simply put ‘good usable data’ @MDMGeek #datacred — Athar Afzal (@atharafzal) July 23, 2014

A1: With #DataCred we have the good old #DataQuality theme about purpose of use vs real world alignment — Henrik L. Sørensen (@hlsdk) July 23, 2014

Q2: How do you see Data Credibility impacting businesses in today’s #BigData & #SocialMedia rich world? #DataCred

— Prash Chan (@MDMGeek) July 23, 2014

A2: Unreliable data can have a catastrophic impact on businesses that rely on the outcome. #DataCred #SocialMedia

— Casey Lucas (@CaseyCrl) July 23, 2014

A2 well free flow of info is always good – hard part is analyzing which is correct & not correct @MDMGeek #datacred — Athar Afzal (@atharafzal) July 23, 2014

#DataCred A2: There is three kinds of crap: Crap, damned crap and big data – not at least social data 🙂

— Henrik L. Sørensen (@hlsdk) July 23, 2014

Q3: What are the primary reasons of failure for your #Analytics projects? Where do you put #DataCred in that list?

— Casey Lucas (@CaseyCrl) July 23, 2014

A3: In my opinion, quality of data used for analytics is a primary & single most reason for a failure. #DataCred — Casey Lucas (@CaseyCrl) July 23, 2014

A3 Not having a clearly defined goal of what the project is meant to be achieve. #DataCred — John Owens (@JohnIMM) July 23, 2014

A3 #DataCred : No functioning enterprise-wide #MDM in place. — Axel Troike (@AxelTroike) July 23, 2014

#DataCred A3 Credibilty is #1 reason of failure. If your client doesn’t trust you, you are out of the game — Juan Carlos Ore (@juancore) July 23, 2014

A3 #DataCred : #Analytics in their core require #MDM based on a model of Master Entities (dimensions!) #DataModeling — Axel Troike (@AxelTroike) July 23, 2014

Q4: How do you create complete, validated, verified & trusted customer information backbone for your analytics system? #DataCred

— Prash Chan (@MDMGeek) July 23, 2014

A4 #DataCred : #MDM using data that can be linked / compared via unique identifiers such as email address, twitter handle, phone number

— Axel Troike (@AxelTroike) July 23, 2014

A4. Again, I’d turn to transparency. Commonly agreed definition of #datacred for cust data, cont.measurement, follow up, corrections. — Kimmo Kontra (@kimmokontra) July 23, 2014

#DataCred A4 A #DataGovernance structure implemented with the correct biz sponsors

— Juan Carlos Ore (@juancore) July 23, 2014

@IBMSocialBiz @MDMGeek A4: Steps: Collaborate, Communicate, Co-operate, Cajole, Coerce. #DataCred — Alan David Duncan (@Alan_D_Duncan) July 23, 2014

A4: A well built #MDM system is like having trusted data, a confidence score of 720+ slapped on every piece of information #DataCred

— Prash Chan (@MDMGeek) July 23, 2014

@nailloyd 100% #DataGovernance? Human beings are involved, so not possible! #DataCred #MDM — Alan David Duncan (@Alan_D_Duncan) July 23, 2014

Q5 If the people doing the job are responsible for the quality of their own data then you can achieve 100% #DataCred

— John Owens (@JohnIMM) July 23, 2014

Q5 If you are trying to exert governance fro outside then you cannot achieve 100% governence #DataCred — John Owens (@JohnIMM) July 23, 2014

Q6: In the context of #MDM & #BigData, how do you see one benefiting from the other? #DataCred

— Prash Chan (@MDMGeek) July 23, 2014

A6 #DataCred : #BigData can help to enrich Master Data (#MDM) via unique identifiers such as email address, twitter handle, phone number — Axel Troike (@AxelTroike) July 23, 2014

A6 #MDM brings context to #bigdata, enabling more objective #datacred measurement. — Kimmo Kontra (@kimmokontra) July 23, 2014

@hlsdk I hear – Bad Data is why the God created #MDM. MDM can be a solid foundation for #BigData analytics projects #DataCred — Prash Chan (@MDMGeek) July 23, 2014

@hlsdk “#BigData” needs #MDM more than MDM needs “BigData”. MDM > CONTEXT, but doesn’t confer PURPOSE. #DataCred — Alan David Duncan (@Alan_D_Duncan) July 23, 2014

#DataCred A6 #bigdata is an enabler, as long as the whole process can provide credibility in the data — Juan Carlos Ore (@juancore) July 23, 2014

I agree with u guys @atharafzal @CaseyCrl MDM being a central point for information management will be enriched by big data #datacred,#MDM. — E #BigData #MDM (@nailloyd) July 23, 2014

Q8: What are some of the good and bad aspects of #SocialMedia data you have come across? #DataCred

— Prash Chan (@MDMGeek) July 23, 2014

A8: Social data – major catalyst for customer insight: Influence, intimacy, speed & low cost. Best thing since sliced bread! #DataCred

— Prash Chan (@MDMGeek) July 23, 2014

Great question!! A8 @MDMGeek – #SocialMedia data = and how useful it is to get closer to your customers!! #DataCred — Casey Lucas (@CaseyCrl) July 23, 2014

Q9: There is a perception that big data and data quality are mutually exclusive. What is your take? #DataCred

— Casey Lucas (@CaseyCrl) July 23, 2014

@CaseyCrl #datacred If you have #data, you need quality, is not an option, even if it’s #bigdata — Juan Carlos Ore (@juancore) July 23, 2014

A9: #DataQuality certainly is not out of fashion. Never will be. In fact it’s very important for #BigData analytics project #DataCred — Prash Chan (@MDMGeek) July 23, 2014

A9 #DataCred : Purpose-oriented #DataQuality is a prerequisite for any data, structured or unstructured.. — Axel Troike (@AxelTroike) July 23, 2014

Active Participants

Here are some of the key participants who drove discussions sorted alphabetically.
@Alan_D_Duncan
@AramarSolutions
@arcogent
@atharafzal
@AxelTroike
@AskariSI
@BDQTweets
@CaseyCrl
@davidpatrick
@HansruediF
@hlsdk
@IBMSocialBiz
@InspectorDhola
@jasonburrows
@JohnIMM
@juancore
@kimmokontra
@microstrat
@MidmarketIBM
@nailloyd
@nidhi_chauhan
@northconsult
@ocdqblog
@Profitecture
@RobinScharpf
‏@wiseanalytics

Cheers and a big thank you too all of you again for tuning into the #DataCred. I hope to arrange more such informative and successful twitter chats in future.

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