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A5. It’s both and necessarily so. Challenge is how we effectively #collaborate in a large organization, how we create processes to add end-to-end visibility, what tech do we use for scalable #DataManagement. #UX #AI

A5: It’s a combination of both: technology hasn't quite advanced to do it all, and human capital is not infinite in its capacity. And so, between those two, the challenges are probably about equal. The answer is automation.

A5. Human element is DataOps’ greatest challenge. We need a different approach to DataOps where self-service and low-code/no-code data pipelines are a fundamental principle. Only practical solution is democratization of data integration enabled through automation

A5: In terms of DataOps’ greatest challenge being human or technical, it is human-related. DataOps must compete as another sub-discipline for internal budgets and tech talent and a field for pros to specialize in over other more mature data fields. #DataOps

@samlakkundi Well said Sam

A5. Technological. It's just not easy to create work-flows for ingesting, processing, moving, and storing data for all customers' use cases - but the ones that solve this will have true market disruption.

A5: The greatest challenge with all new technology remains the product maturity until that is overcome. Human, organisational, cultural, etc. topics are just more trendy to discuss. For example, all talk on the "cultural" issues with smartphones ended after iPhone @JamesMaguire

@RKs2cents I agree! Might have been a trick question :)

@samlakkundi A5. At our BMC Innovation Labs, we believe while technology challenges can be difficult to overcome, some of the human barriers around their DataOps initiatives are even more pressing. #BMCInnovationLabs

@brucekornfeld Truth spoken. And not necessary to solve universally either, within specific verticals is a great place to start - and is in general the mega trend across enterprise software

@samlakkundi We agree with democratization point along with enablers of #DataGovernance at scale

A4. The reality is 87% of #DataScience projects never make it into production and the majority of #DataLakes have become unmanaged and ungoverned. See the report: https://htchivantara.is/3DtKLXc. #DataOps provides better practices to bring back value to data.

A4: DataOps’ greatest challenge is leadership buy-in for an organizational commitment to specialized DataOps functions — and not pushing the discipline as an add-on function on data professionals with other core priorities. #DataOps #DataManagement

A4. Effective communication among key stakeholders, yet poor coordination can make building, deploying, and maintaining data pipelines more difficult than needed. Many #DataOps teams don't understand how data pipeline works.

A4: Technology immaturity combined with very real change management is always a challenging combo... as much as many love talking about culture being the greatest challenge to all new things, genuine fit-for-purpose technology platform (SaaS) availability is alike @JamesMaguire

@Chris_Ehrlich Totally agree on the leadership buy-in. That's crucial to sustain the transformation required.

A4. Generic vs. Specific. It's hard to build a data solution that works across all industries - it'll be too generic. But, building a solution for a particular industry is so narrow that it won't get widespread traction. I wonder if this is slowing innovation???

A4: There's just something magical that happens when the right user is given access to the right product that actually makes their life meaningfully easier - it takes care of a lot by itself, incl. change management and plenty of executive leadership support need @JamesMaguire

@Chris_Ehrlich Couldn't agree more on leadership buy-in.

@brucekornfeld There will be vertical specialization for sure within DataOps Platforms, simply as the needs in different markets are so very different, as is the data alike

A4: I think there are 3 major challenges: 1: Data silos, 2: proprietary technology stacks; and 3: the state of automation.

A4: Lastly the lack of intelligence within BI is a scaling challenge: there is not a whole lot of self-learning algorithms that are helping you interpret data at the BI layer of the architectural stack.

A2. Absolutely not! It's a great theory and there are some vendors that are talking about it, but we are a long way from mainstream.

A2. One of the biggest pain points companies experience today is getting the right data in the hands of the right people at the right time. DataOps is on the rise, especially in IT, because it unleashes the full power and potential of the business.

A2 .1) #DataOps practices are not yet mainstream in today’s enterprise. However, what is mainstream is the recognition amongst our customers that adopting and applying DataOps is required to become a #DataDriven organization.

A2. To become data-driven, real-time insights & agility are key. DataOps enables both as it allows orgs to move data as it’s changed in real time using automation. Flexible integration solutions let IT change a source/target without disrupting the infrastructure to stay agile.

A2: Yes, DataOps as a concept is mainstream at large enterprises. At a minimum, as practices, if not a defined team-based structure. #DataOps #DataManagement

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A2. 2) As IT increasingly becomes hybridized across #CloudComputing, #IoT and on-premise systems, #DataOps practices are rising to the forefront to serve how data is created, managed, governed and consumed.

A2: Not quite :) There is a lot of market education to be done still, and we're happy to be joined by you all here today pulling your weight to grow awareness for the benefits of DataOps. For those in industry, we've written The Definitive Guide To Industrial DataOps in the topic

@Chris_Ehrlich I agree, Chris.

A2: For those looking to understand more how DataOps plays a transformational role in Industry 4.0, we invite you to visit https://content.cognite.com/the-definitive-guide-t... @JamesMaguire

A2: It’s not mainstream yet. We do see interesting examples that leverage automation more and more. It’s an evolution I think, not that different from autonomous vehicles: first we had cars, then we have cruise control in these cars that was quite good at keeping t

in my view, organizations are streamlining processes so that data moves along the pipeline much more quickly, continuously yielding actionable insights and demonstrable value to the business. #DataOps is key to this line of thinking thus mainstream.

#DataOps is also a key theme at our annual virtual event #BMCExchange this year. In fact, @BMCSoftware CTO @RamChakravarti2 will be diving into how to unlock DataOps to drive maximum business value.

@brucekornfeld, we are hearing about it more and more from our customers in helping them put #DataOps into practice.

A2 We continue to see this gap be underestimated at some of our customers: "If I just could get the data from this system into this platform, the world of analytics and smart insights opens up to me".

A2: And after this IT driven integration work it finds a lack of adoption, because the context of which data is used and the business purpose is missing and transparent in the analytic solution.

A3. To unlock value, applications need access to that data to contextualize it and correlate it across different datasets from 1000s of different sources coming in different formats all the time. #DarkData

A3. There is just too much data being generated (video, IoT, etc...) and not all of it is needed. DataOps will help organizations accomplish "data thinning" so only the important stuff is kept.

@brucekornfeld We are drowning in data....

Yes! I can't image what being an IT Pro is like these days...how do they keep up????

A3: DataOps is important, because organizations that don’t commit to a DataOps methodology will be at a competitive disadvantage and never truly view and leverage big data across departments. They will effectively mismanage data. #DataOps #DataManagement

@brucekornfeld Seriously!

A3: Our data landscape is not getting any less overwhelming, and as about all enterprises are struggling with making data truly available and useful for their data consumers, continuing with former rigid data management to business handover is simply not an option@JamesMaguire

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A3. We’re living in a time when data is generated in enormous numbers - by us, our devices, and the networks that transport it. It's a data-driven world, and as such, the data-driven business is sure to follow. (1/2)

A3. Being data-driven is a tenet of an Autonomous Digital Enterprise, where manual effort is minimized to capitalize on human creativity, skills, and intellect. (2/2)

@samlakkundi We've been talking about the "growth of data" for so many years now - I just think its really coming to a head.

@brucekornfeld Agree that there is a lot of talk about the 'grwoth of data' - but is that really the issue in the context of dataops and enterprises - My point being that companies are data rich for decades, just insights poor

A3:
dataops to me is trying to translate that data rich enterprise into a insights rich
enterprise.

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A3: Lets go back to ‘purpose’ – our desire and ability to analyze data serves a purpose – probably something like ‘enabling better decisions that ultimately create a competitive advantage for the organization at large’. And that means either increasing revenue or re

A3: The ability to make smarter decisions, to analyze better, is in a large part constrained by the amount of data scientists that you can hire, the amount of people that you can find that understand it all.

A3: And so therefore data output, data ops, in that context is a force multiplier. If I can automatically interpret, I can scale the number of insights that I can extract and the speed in which I can make these insights.