01. Miriam Friedel, Elder Research, visits #theCUBE!. (00:17)
02. Observations from the Data Science Panel. (00:35)
03. Challenges for Data Scientists. (01:02)
04. Elder Research and IBM. (01:33)
Track List created with http://www.vinjavideo.com.
--- ---
ETL reality check: If your data science model seems to good to be true, it probably is | #DataFirst
by R. Danes | Oct 7, 2016
Technology companies are launching data science platforms of widely varying complexity. Some claim their tools are so simple they will make “citizen data scientists” out of folks with little or no experience. TheCUBE, from the SiliconANGLE Media team, recently spoke with an actual research scientist who offered a caveat for everyone working with data: Before you jump up and down about the new model you made, check your Extract-Transform-Load.
Miriam Friedel, research scientist at Elder Research Inc., spoke to Sam Kahane (@Sam_Kahane), during the IBM DataFirst Launch event about the current state of data science.
Having just attended a data science panel, Friedel remarked on some universal snafus practitioners encounter. “It was really interesting to hear about some of the common challenges that we all face working on different types of data science problems,” she said.
Building data models that walk the walk
“One thing that we struggle with a lot is it’s not simply enough to build a model that’s really interesting or technically advanced,” she explained. “You need to put it into production, deploy it, and that’s what brings value to your customers. And a lot of other people on the panel had spoken about similar things.”
Keeping one eye on ETL
Data science is intricate — and those working with it — be they Ph.D.s or small business owners — need to heed this to be successful, Friedel advised.
“If you happen to be building a model and it looks too good to be true, it probably is, so make sure you check your ETL and your modeling that you’re not doing something crazy,” Friedel concluded.
Forgot Password
Almost there!
We just sent you a verification email. Please verify your account to gain access to
BigData NYC 2016 | New York. If you don’t think you received an email check your
spam folder.
In order to sign in, enter the email address you used to registered for the event. Once completed, you will receive an email with a verification link. Open this link to automatically sign into the site.
Register For BigData NYC 2016 | New York
Please fill out the information below. You will recieve an email with a verification link confirming your registration. Click the link to automatically sign into the site.
You’re almost there!
We just sent you a verification email. Please click the verification button in the email. Once your email address is verified, you will have full access to all event content for BigData NYC 2016 | New York.
I want my badge and interests to be visible to all attendees.
Checking this box will display your presense on the attendees list, view your profile and allow other attendees to contact you via 1-1 chat. Read the Privacy Policy. At any time, you can choose to disable this preference.
Select your Interests!
add
Upload your photo
Uploading..
OR
Connect via Twitter
Connect via Linkedin
EDIT PASSWORD
Share
Forgot Password
Almost there!
We just sent you a verification email. Please verify your account to gain access to
BigData NYC 2016 | New York. If you don’t think you received an email check your
spam folder.
In order to sign in, enter the email address you used to registered for the event. Once completed, you will receive an email with a verification link. Open this link to automatically sign into the site.
Sign in to gain access to BigData NYC 2016 | New York
Please sign in with LinkedIn to continue to BigData NYC 2016 | New York. Signing in with LinkedIn ensures a professional environment.
Are you sure you want to remove access rights for this user?
Details
Manage Access
email address
Community Invitation
Miriam Friedel, Elder Research | BigDataNYC 2016
01. Miriam Friedel, Elder Research, visits #theCUBE!. (00:17)
02. Observations from the Data Science Panel. (00:35)
03. Challenges for Data Scientists. (01:02)
04. Elder Research and IBM. (01:33)
Track List created with http://www.vinjavideo.com.
--- ---
ETL reality check: If your data science model seems to good to be true, it probably is | #DataFirst
by R. Danes | Oct 7, 2016
Technology companies are launching data science platforms of widely varying complexity. Some claim their tools are so simple they will make “citizen data scientists” out of folks with little or no experience. TheCUBE, from the SiliconANGLE Media team, recently spoke with an actual research scientist who offered a caveat for everyone working with data: Before you jump up and down about the new model you made, check your Extract-Transform-Load.
Miriam Friedel, research scientist at Elder Research Inc., spoke to Sam Kahane (@Sam_Kahane), during the IBM DataFirst Launch event about the current state of data science.
Having just attended a data science panel, Friedel remarked on some universal snafus practitioners encounter. “It was really interesting to hear about some of the common challenges that we all face working on different types of data science problems,” she said.
Building data models that walk the walk
“One thing that we struggle with a lot is it’s not simply enough to build a model that’s really interesting or technically advanced,” she explained. “You need to put it into production, deploy it, and that’s what brings value to your customers. And a lot of other people on the panel had spoken about similar things.”
Keeping one eye on ETL
Data science is intricate — and those working with it — be they Ph.D.s or small business owners — need to heed this to be successful, Friedel advised.
“If you happen to be building a model and it looks too good to be true, it probably is, so make sure you check your ETL and your modeling that you’re not doing something crazy,” Friedel concluded.
