eweekchat

Thanks for hosting, James!

@brucekornfeld Thank you Bruce!

Great discussion everyone. Look forward to the next time @JamesMaguire.

@RKs2cents Thanks Radhika for your insight!

Thanks @JamesMaguire! Indeed, see you all in the market!

@Chris_Ehrlich So glad you were here Chris! Great thoughts!

Hey, thanks! It's a great forum!

Thanks a lot for the vibrant chat @JamesMaguire

Everyone -- thanks a lot for a great discussion!

@CogniteData Thanks for adding to the discussion!

Great discussion, thank you for hosting James

Thank you for hosting such a relevant conversation during these times.

@samlakkundi Thanks Sam! I appreciate your participation!

A10. As #ITandOT converge, the demand for #DataOps will end up serving more interconnected applications, devices, algorithms and #MachineLearning models that drive #digital experiences. #IoT

A10: We will continue to see a rapid pace of innovation as modern services come together / converge. And I believe it will continue to increase its importance, because digital transformations largest challenge is all about data(ops).

A10: DataOps will evolve from a practice and methodology conversation into a SaaS platform discussion in 2022 already - thus significantly expanding both the market and value delivery of #DataOps to enterprises

@BasDutch1 I like this: "digital transformations largest challenge is all about data(ops)."

A10: despite all the innovations in everything hyperscale-cloud providers do and everything startups are launching to add to the mix of capabilities, one of the hardest hurdles remains data: trying to get data out of a system and knowing what that data means.

A10. ML/AI integration for automatic tuning/performance improvements and creating more business value with less human intervention.

10A: In terms of the future of DataOps, it will become a standard enterprise discipline that does for data what DevOps did for software. #DataOps #DataManagement

A10. Ever-increasing sources of valuable data will intensify the need for smart, automated data analysis. Automation via #AI #ML will make this diverse and dynamic data be economically manageable. (1/4)

@BasDutch1 #DigitalTransformation is definitely a catalyst for implementing #DataOps as part of an enterprise wide change. We often see our customers building a #data operating model to serve their new #digital customer experience. #CX

@samlakkundi A10. Organizations will have to turn to both open source and commercial components that can address the complexity of the modern data supply chain, and they must be integrated into end-to-end solutions. (2/4)

A10: In 3 years time, enterprise CDOs and digital transformation executives in industry alike will wonder how they ever managed to support their business without an Enterprise DataOps Platform as part of their enterprise architecture

@samlakkundi A10. Organizations must consider crowdsourcing for data discoverability, maintenance, and quality improvement. The people required to make data unification effective aren't data engineers, but SMEs who enable a new level of productivity in data delivery. (3/4)

@RKs2cents 100% agree - and I think it is more and more understood that the Digital transformation is not merely about launching new services but also overcoming the technical debt that keeps a team anchored in yesterdays capabilities.

@RKs2cents and hence #DataOps is the ability to overcome and increasing the value of the data that is harnessed in those legacy applications whilst creating the services that will make us successful in the future.

@samlakkundi A10. On a human element other IT roles and responsibilities will shrink, DataOps will grow rapidly. Professionals with skills, vision & talent will be in high demand, & it's safe to predict salaries for DataOps professionals will rise rapidly to match demand (4/4)

@BasDutch1 and operationalising and scaling the new services that are deployed/piloted somewhere. Large disconnect there today...

A9. To create auditable paths of how the data has been thinned and serviced so to insure the integrity and decision process and to track data from the point of entry to its end of life.

A9. Automating large volumes of repetitive tasks such as #DataClassification and #DataProtection, can improve productivity by using business policies applied to volumes of data. Automation to curate #Metadata and analysis helps to augment human effort and drive #DataOps.

A9: . Think about this formula for a second: (data * Model) ^ People. In other words: by making data insightful through a model (context rich and business driven preferably) many more people will be empowered to take smarter actions.

A9: As core best practice for DataOps, focus on understanding what DataOps really can do for your business, not just you individually. For those in industry, our free book offers a solid guide to this for all those working in industrials. Download at https://content.cognite.com/the-definitive-guide-t...

A9: In terms of a core best practice for DataOps, it is dedicated staffing allocation to the discipline. #DataOps #DataManagement

A9. Assess your environment by starting small. Create a data operations department, align with the organization. Educate your team, make it cross-functional, build for reuse & automation, & use data development tools.

A8 I am biased (appologies), but industries with complex supply chains and/or manufacturing operations at a global scale will reap the highest rewards.

A8: As Cognite is only focused on the industrials verticals with our Industrial DataOps Platform, we cannot say much about other industries - but the benefit rating for industrials struggling with operationalising and scaling their digital transformation programs from PoCs is BIG

@BasDutch1 Care to name a couple?

@BasDutch1 A8 Industries like Oil and Gas, Consumer packaged Good or Life Science will benefit the most because the ability to react faster to a supply chain disruption is key in increasing competitiveness and achieving results

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A8. Industries that are at the edge (retail, manufacturing, smart cities, healthcare) have unique data needs in that too much "noise" is being gathered. I think these would benefit the most.

@BasDutch1 Got it, makes sense.

A8. Every company, no matter the vertical, will greatly benefit from DataOps, however, I think the customer sectors will see a larger boost. Harnessing insights directly from big data can provide significant competitive advantage.

A8. We are seeing demand increase across all industries with the most exciting #DataOps innovations in #Banking, #Technology, #Consulting, #Government, #Telecommunications, #Insurance, #Healthcare.

A8. Generally we have seen that companies in banking, retail and healthcare are more likely to have larger data plays.

8A: Banking, finance, health care, transportation, travel, manufacturing, energy, retail and technology. Those industries where the competitive imperative to harness the full operational and revenue value of data is inherent now. #DataOps #DataManagement

A7. It’s not losing clarity. DataOps platforms are primarily used by analytics and data teams within an organization; they’re cross-functional and can be used across verticals. IT operation teams can reduce storage infrastructure and increase staff productivity.

A7. Far from diluting the value of #DataOps, we are seeing the more widespread DataOps becomes, the more powerful the organization becomes at managing and governing #data well. #DataGovernance

A7: Looking at the transformative potential of an Enterprise DataOps Platform, there are in fact surprisingly few vendors really focusing on building a unified, seamless UX DataOps Platform (not just relabelling some former product portfolio as DataOps)

A7. I think it's lost some of the focus. Many vendors have created lock-in approaches to their integrated technologies that require manual processes for a market that needs a simple yet company agnostic work-flow. Still a wide-open market for innovation.

A7: Also, it feels very natural to expect differentiated DataOps Platforms to emerge, as different verticals have very different data types and use case portfolios a DataOps Platform needs to elegantly support, not just support with a set of workarounds...

7A: In terms of the clarity of DevOps, as a segment ripe for entrants, it will be further grayed. Buyers, however, will recognize the need to clarify problems and spec solutions, tech- or talent-related, and in doing so, define the segment. #DataOps #DataManagement

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@brucekornfeld "company agnostic work-flow" is an elusive quality.

@samlakkundi At @BMCSoftware, it's clear for us given our numerous customer engagements in this space.

A7: probably true, probably a lot of vendors do focus here because it's such an interesting problem to solve in the bigger context of the industry challenges. We had Magnitude decide to focus and it's all about the ERP data for us.