eWeekChat

A5: #DataOps is an intelligent approach that offers agility and automation of data management. Through collaboration focused on the outcome, we bring cross-functional data and IT teams together to deliver the right data to the right people at the right time with the right quality

A5. Evaluate #opensource and open algorithms. Find less complex tools. Allocate budget, people, and time. Start with proven use cases (#UBA, #AIOps). Plan to fail, plan to learn!

A5 : Data analytics stack must be designed in a flexible way to accommodate this data growth. This includes managed data platforms, integrated DevOps, and scalable and managed compute resources.

@vamsipaladugus1 A5:Re: talent issues, build training and certifications & encourage employee initiative, idea ownership, and innovation that has them thinking outside the box.

.@AndiMann Failing fast is key to learn from any data project

A5: Introducing DataOps is an enabler to successfully adopting enterprise-wide data analytics. Orgs that realize the benefit of analytics often employa multi-horizon approach, focusing on using DataOps for a small set of high value use cases before scaling across the company.

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A5: Invest in data governance and data management platforms for data analytics. #eWEEKchat

A5: We often emphasize the tools aspect of #DataOps, but critical to remember the org aspect is just as critical. Intentional alignment and accountability of how individuals can engage in the #DataCloud makes a HUGE difference. It won't happen 'by accident'

@madhoop Exactly! I advise my clients and teams - fail fast, fail small, fail cheap, fail forward. Make space for #ContinuousLearning!

A5 (1/2): 1. Work with technology that puts flexibility and openness at the forefront. 2. Avoid lock in with proprietary vendor storage and compute — Your data services need to work for you as required on any cloud, not trap you.

Prioritize enablement and training, invest in data governance, select tools that reduce siloed data sets

@KatySalamati I wish I had more upvotes for this. Data governance is its own reward, not just in compliance or privacy, but the agility to reliably use the right data at the right time!

.@jeffhollan Organizational culture is front and center to any data problem. #DataOps is fundamentally a cross collaboration between #Data and #Ops teams.

A5: Know the relationships between data management, data science, and data analytics and commit to investing in those functions as a competitive advantage

A5 (2/2): 3. interoperability is essential. Regardless of what system you use, your tooling should enable streamlined and secure end-to-end workflows. 4. Avoid proprietary formats — If you're data is in an open usable format it gives your business more agility to stay ahead

@AndiMann, couldn't have said it better!

@madhoop Right on regarding #DataOps — Automation and smart tooling will in many ways define the winners and losers this year

.@namessanti - #2023 will be the year of #DataOps

@madhoop You heard it here first!

A8: “only large companies with a lot of data need data analytics”. #eWEEKchat

A8 (1/2): When people first start looking at all the #data they’ve collected, it’s not unusual for them to think, “We should use all this data!” While I applaud the bravado, just adding more data is rarely the right answer.

A8:One big myth is that quantity is more important than quality. While it's smart to save and use all the data that we can, data quality is just as important as data quantity

A8 (2/2): It’s not about quantity. It’s about having the right #data at the right time with the right quality will always yield better results.

@vamsipaladugus1 A8:The other myth is that big data will solve all our problems. Without subject-matter expertise on interpreting the data and data governance, we won’t get the most value out of big data.

A8. That it works? I kid – but only sorta. There is a lot of ‘management by magazine’ in #data and #analytics right now. NTTAWWT! 😉

.@AndiMann "management by magazine" sums it up about right!

A8. (cont) I’m just saying … maybe some leaders would do well to take less 'advice' from @WSJTech and more from @eWEEKNews LOL!!

@andimann Well said sir!! :)

A8 (1/2): That there's only one path forward. Data analytics continues to be a wild west. Every business has different needs and questions to be answered and there is no formula or "silver bullet" to go from zero to 100 like everyone else.

A8: (1/2) Myth - data analytics is only for data scientists and analysts. If data analytics is restricted to a ‘data’ function, the business is unlikely to derive the expected value from it. Critically, the business needs to be able self-serve & consume data themselves

@madhoop LOL, thanks! Not mine, but I have always loved that expression. Been on the receiving end of it more than once!

A8: (2/2) An additional big myth is that data analytics solutions will automatically deliver benefit. Unfortunately, this is also not true. Businesses need to focus analytics on delivering insights that can influence/improve business decisions.

@A8 (2/2): Fist identify what your end state looks like and formulate your own tailored journey to get you there

@BMCSoftware That is a great myth - I agree to some extent - but IMO we need easier tools to democratize #data and #analytics. Do you think the tools are good enough for that yet?

"We just need to grow / add a data analytics team and we'll solve our problems" #DataCloud will permeate across many teams / personas when done right - not to mention the consumers of data need to be heavily invested too!

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@KatySalamati Great point. In many ways, smaller orgs can do more and better, because they have smaller and more complete datasets.

@BMCSoftware Agreed that it takes time, but this is also where a slow and low approach can yield early successes. Start with problems that others have solved for you to quickly gain value, then expand and grow from there into complex and formerly impossible analytics & AI

@jeffhollan Yes, good on Jeff - the myth that #analytics is fully prime time also extends to throwing too many resources at ultimately unrealistic goals.

@AndiMann broadly there is work to be done in the technology space to make it easier for non-technical users to consume data effectively. Most tooling remains focused on the needs Data Scientists and Data Engineers.

A8: That it's a mature business function

@BMCSoftware Yes, agree - that is exactly where I recommend clients start too. Much lower barrier to entry for the more technical use cases today. Hopeful for tomorrow tho!

A1: Data & AI governance, data fabric, applied observability, decision intelligence, MLOps, Synthetic data, Trustworthy AI, Augmented Analytics, Democratize AI, AI engineering are the 2023 trends. #eWEEKchat

A1:Three trends have momentum. 1) Data analytics are increasingly being asked to drive greater data freedom (freedom to use and to be used) in the multicloud. 2) Data democratization will be the number one competitive advantage in 2023.

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A1 (1 of 4) Organizations will continue to look for ways to reach greater operational efficiencies with #dataanalytics – including how they collect, curate, and activate their #data – to drive business in 2023.

A1 (2 of 4) Data integration will expand to include OT stream ingestion, creating a richer near-real-time analytics ecosystem. Organizations are looking for a single pane of glass approach across structure, semi-structured and unstructured #data categorization.

@Vamsipaladugus1 Data democratization is a big one!

A1. Availability and maturity of tech and skills – in 2023, easier than ever to start using analytics is up, but availability of skills is getting tighter every day

A1 (3 of 4) Data trust will be key with #data catalogs continuing to grow in importance. They provide a centralized intelligence function across the entire organization and are fundamental to our journey of #DataGovernance, quality and curation.

A1. (cont) Increasing heat to adopt analytics in more accessible areas for existing techos – like AIOps, MLOps, etc.

A1 (4 of 4) Automating decision-making will reduce manual decision-making, mitigate bias and speed up business processes that may have been stalled by human decision-makers.

A1. (cont) Business leaders – sales, marketing, even product – definitely hungry for the hype, but use cases for IT and Engineering will grow much faster in 2023

A1: (1/2) #EdgeComputing will shift analytics activity from the core to edge; increased use of AI/ML will deliver actionable real-time insights from data & we’ll see more focus upon the delivery of agility through adoption of #DataOps to improve access, quality and transparency

A1: Data governance - organizations are working to be more productive by have purposeful accountabilities and quality across data. This is especially critical because it's not just the data, it's the logic and applications that turn data into insights

@KatySalamati Great topics Katy - the governance of ML, AI, Analytics in general is still well behind the use cases. Many issues there with compliance, privacy, etc.

A1: (2/2) Combined, these trends will support the growing availability of self service and collaborative use of data.

@jeffhollan Do you see resistance to governance? Or not really?

@AndiMann And I absolutely agree on Trust. One of the preeminent challenges in tech generally, but esp. in Data Analyrics

@AndiMann Trust is key! More and more decision makers find them not trusting the data they are served.

not as much resistance as just not getting overwhelmed. The scope of what organizations expect from data is accelerating - AI / ML / applications. One reason we see a ton of momentum around the #datacloud to bring more together in a single platform

A1: I think the big one is repatriation of workloads from major cloud providers. Companies should be able to run workloads whenever they make the most economic sense and deploy where it can serve the greatest part of the company.

@jeffhollan Jeff, defo seeing a lot of this as a challenged. Great add! The rise of #DataOps & #MLOps is in response to just this. Still a looooong way to go!

A1: Working to move beyond data management, being led by data science, and providing actionable department-level business intelligence

@namessanti I still find repatriation of workloads from major cloud providers to be mildly shocking.

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AI and Data governance, but also we'll see more of a push with privacy and users wanting more protection of their data

@vamsipaladugus1 A1: AI ethics will be a key consideration for DevOps leaders.

@AndiMann Yes, there is a lot of work that needs to be done, especially around privacy, never enough!

@Vamsipaladugus1 great point on ethics coming up fast and urgently

@madhoop I say everything starts with data Data Governance!

@jeffhollan well said!

@KatySalamati So true, we see this every day with leaks - ML/AI turbocharges the damage to privacy if we will allow it to.

@KatySalamati For sure, this is a super challenging balance. Unfortunately we are leaving it mainly to individual engineers to make the call on how much is too much. That has to change, IMO.