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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.

A6.1 ) #DataOps isn’t a noun, it’s a verb i.e. it’s something that is performed day-in, day-out as the new norm. A company can always procure new tools and technologies and then buy in or even hire in new expertise to run it. #DataCulture #DataCitizens

A6. Not quite. There are a lot of vendors that play in the space, but that means you’ll will swim in a sea of tools and APIs. To get the most benefit, organizations will need robust orchestration and automation to bring the pieces together to best serve their needs.

A6. Today is a process and a philosophy. In the future, products will emerge that end users can actually deploy to solve these difficult data problems in moving and using data for maximum value. I don't see this as a SaaS model - at least for data at the edge.

@samlakkundi But "robust orchestration and automation" is a buy-able solution, right?

A6.2) Being successful means #DataOps practice is understood, adopted and performed by a critical mass of producers and consumers across the enterprise.

Spot on. However, not without heavy customization.

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A6 within context yes on both. This is what we do at Magnitude. Specific to business apps, we are taking a lot of the proprietary IP / knowledge hurdles and enabling the customer's workforce and putting it into software code

DataOps is shaping up into a SaaS play for sure, incl. verticalized versions addressing different markets. For DataOps to become transformative at scale, it needs to become a true product experience with UX that makes the previously impossible easy.

6A: DataOps is a process before it’s a tool. More ultra-niche tools, particularly around automation, will be developed, but DataOps ought to be fundamentally process-driven to reach goals. #DataOps #DataManagement

A6 In '21 of course the automation, or engine if you will, is provisioned as a Service in a broader analytics architecture.

A6 but that is a key point today - Dataops is not an objective, its a core element in the ability to transform to a 'data driven organization'. Doing dataops right will help secure success, but its only one element

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.

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.