DataAutomated

Smart Data Lifecycle
Io-Tahoe comes together with Ethos, Pure Storage, and Cohesity to talk solutions and strategies that leverage data automation to optimize all stages of your data lifecycle.
   a year ago
#DataAutomatedEnterprise Data AutomationJoin us for a panel discussion on enterprise data automation with Io-Tahoe & Webster Bank on June 18th at 9 AM (PDT). Sign in with your LinkedIn or Twitter account, and contribute your thoughts on CrowdChat live. Register here: https://iotahoe.typeform.com/to/zJrTBO
Dave Vellante
Q5.
How can the cost to perform data analytics be improved within a Smart Data Lifecycle?
https://www.crowdchat.net/s/662tk
https://www.crowdchat.net/s/662tk

Yusef Khan
A Smart Data Lifecycle can massively reduce the cost of performing high quality data analytics. You can get to a single source of the truth much more easily, you'll have less duplicate data and you can deploy self-serve analytics to business users with confidence.
Gareth D Miles
@dvella 60-70% of a Data Scientists efforts are often spent wrangling data rather than developing revenue generating algorithms, Io-Tahoe flips this on its head automating the discovery process, increasing the accuracy value of data, giving scientists and users time to make money
Yusef Khan
Data Analysts and Data Scientists will spend less time wrangling data and more time on value add analysis and building advanced models. You'll have less, better reporting - no longer multiple slightly different copies of the same report!
Patrick Smith
The Smart Data Lifecycle helps organisations understand their data; it’s key to driving analytics, making sure the datasets are optimal, delivering cost efficiency, improved time to insight and ultimately competitive differentiation.
Yusef Khan
How many times do you see businesses buy the next greatest MI tool only to be let down by the underlying data?!
ajay vohora
is this also about recognizing that not all data is the same or has the same value? Moving the data that you need to the right platform to execute workloads is part of working smarter with data.
Dave Vellante
so true Yusef, and with skills shortage the last thing you want is wasting time of a top resource
Ezat Dayeh
Consolidation of data into a single platform where analytics can be run, or at least provisioned from, reduces cost significantly
Adam Worthington
By reducing the Total Cost of Ownership cross all elements – through automation reduce the amount of time Data Scientists need to spend chasing and understanding data.
Adam Worthington
By reducing infrastructure and application silos
Adam Worthington
By reducing the copies of data within, and across infrastructure and application silos
Dave Vellante
Q2.
Why do organizations need to think about making the data lifecycle work smarter?
https://www.crowdchat.net/s/762sb
https://www.crowdchat.net/s/762sb

David Piester
The need for automation and using ML algos across the data lifecycle is a must with the ever growing data challenges and massive data growth an enterprise organization faces doing business in the age of IoT and the growing need to migrate to the cloud to manage data te
Senthilnathan Karuppaiah
Data integration, harmonization, customization and self-service are key to meet diverse requirements which otherwise impossible to perform manually.
David Piester
Having a smart data lifecycle approach end to end, will enable organizations to quickly adapt and gain insights into their data assets and realize value much more quickly.
ajay vohora
Smart Data Lifecycle – leverages all the metadata related to the data, in order to perform simple repetitive tasks that contribute to reduced fragmentation, . . . .
ajay vohora
redundancy, duplicates, anomalies and also to improve data quality, data governance. So it's this valuable metadata that is used to 'inject' automation on these repetitive
tasks, and serves all Data Consumers to improve business agility and performance.

(edited)

Dave Vellante
I'm going to come back to reduction in end-to-end cycle times - creating an efficient data factory that works for the entire organization, not just one department or division
ajay vohora
The data life cycle by itself is a sequence of stages that a particular unit of data goes through from its initial generation by Data Producers through to serving Data Consumers.
Yusef Khan
Otherwise it's duplication, duplication, duplication all the way!
ajay vohora
@ajay_vohora The 'secret sauce' is employing these statistics to drive automation of repetitive tasks upon the underlying data files and records. . . . .
ajay vohora
@ajay_vohora These repetitive tasks are commonly indexing, ML based classification, pattern recognition, anomaly detection, data quality scoring, sensitive data tagging etc.

(edited)

Lester Waters
Isn't everyone tied of capturing it all in Excel? :)
Mark Pittman
"Death by Excel"
Adam Worthington
Because it’s a key area of competitive advantage (or disadvantage). A smarter approach to data directly and significantly reduces cost and risk whilst improving agility and creating an environment for innovation.
Adam Worthington
Tech and business are now inextricably linked; those that don’t recognise this and leverage the power of their data are going to be left behind...
Jeff Frick
And don't forget democratization. give more people more data with more tools to work the data, and authority to do something about it...... opens up innovation
Lester Waters
We all have a CDB to track IT assets... so why is it we don't track our data assets?
Tom Poole
Because it's difficult, too much friction.
ajay vohora
There's definitely a manual overhead to be considered in trying to tie together the valuable data across different systems and application siloes
ajay vohora
@tom_poole Sometimes the effort required to address the challenges seems so daunting it's easy to just do the basic 'superficial' things and keep going on the same old way.
Tom Poole
Yes, and tagging/classifying can be limited to the perspective of the classifier/tagger. I.E., a silo'd view
Lester Waters
@tom_poole Indeed. Breaking down the silo is letting the other businesses see what data you already have... maybe they can leverage it or supplement it
Lester Waters
It certainly is an exciting journey. I learn something every day about the challenges various businesses face and its exciting to try and solve these challenges
Ezat Dayeh
@tom_poole agreed it is a difficult process but its something that should be done.....slowly. With increased compliance requirements & ever increasing security threats, its a choice between doing it now in your own time or rushing to do it when its most likely too late!
Dave Vellante
Q3.
How has the pandemic’s effect on the economy changed the need for Smart Data Lifecycle initiatives?

https://www.crowdchat.net/s/662st
https://www.crowdchat.net/s/662st

Mark Pittman
So many IT teams are having to do-more-with-less... #DataAutomation is no longer a want, it's a need.
ajay vohora
There is more pressure now than ever to put data to work to help drive the customer experience.

(edited)

Dave Vellante
automation has become a mandate - what used to scare people is now compulsory - digital transformation is largely about leveraging data - no digital, no business in this COVID era
ajay vohora
@ajay_vohora The demand and the need for working smarter with data is being driven by the customer, customer behaviours and expectations.
Lester Waters
The pandemic has been a forcing function for change. We need to share knowledge faster and smarter among a more disparate group of poeple.
Gareth D Miles
We've entered a major thrift economy now more than ever before and need to to keep the lights on, need to do more for less in triple quick time to keep things going. Automating data is a must now to ensure we make the most of the dollars available.
Jeff Frick
Hadn't heard that term "thrift economy" - definitely have to do more with less, but isn't this SOP for IT?
Jeff Frick
What are the "highest priorities" that you see with customers since mid march 2020 ? "House on Fire" concerns that need to be addresses ASAP ?
Dave Vellante
Q1. What is “smart data”? What makes a data lifecycle “smart”? What are the critical aspects of a Smart Data Lifecycle?
https://www.crowdchat.net/s/062rw
https://www.crowdchat.net/s/062rw

ajay vohora
Smart Data is data from which metadata i.e. signals and patterns have been DISCOVERED (i.e. generated and extracted) and performed by software functions and algorithms.
ajay vohora
Smart Data can be acted upon and sent to a downstream for further data consolidation and analysis.
Dave Vellante
@ajay_vohora and what's different today is this metadata generation and classification can be automated fully
John Furrier
Smart data is about data being "addressable" for both infrastructure and applications. To me lots of devops principles like horizontally scalable and vertically integrated architecture is key to success
ajay vohora
By the way - we're not talking about scanning tables and columns to find table and column names - that's a very superficial technique and has limited value to being able to solve challenging problems to do with understanding the underlying data values, content, context
ajay vohora
. . . . .and semantics e.g. reading the the actual email addresses, zip codes, addresses it where valuable statistics can be generated to then apply ML methods to these stats.
ajay vohora
The rise of Smart Data has come from the challenges thrown up by “Big Data” i.e. massive data sets where automation married with metadata enhances the ability to perform data processing, advanced analytics or Machine Learning at scale.
Tom Poole
optimized placement based on need/outcome/workload and at a reasonable approved cost.
Gareth D Miles
I think Smart Data is an automatic capture and assignment of meaning, location, importance and movement associated to data that would not otherwise be found in metadata or people knowledge alone.
Lester Waters
"Smart data" is obviously a broad term. Its more baout being smart about your data. We invest heavily in IT infrastructure management, when the real value is in our data. We need to understand what we have and how we can drive value from it.
Adam Worthington
A Smart Data Lifecycle prioritises simplicity, flexibility and efficiency whilst improving data visibility
Jeff Frick
@ajay_vohora > In some cases, the metadata becomes more valuable than the actual data
Dave Vellante
@JeffFrick and sometimes more voluminous!