eWeekChat

A7: Data governance is a vital part of any successful data analytics project. #eWEEKchat

A7 (1/3): If ever there’s a phrase that’s as hated as it’s loved, it’s #DataGovernance. Data governance sets internal and external standards for how #data is gathered, stored, processed, and disposed of. Every organization needs data governance.

A7 (2/3): To satisfy everyone from customers to regulators, we must adopt a #DataGovernance framework with defined roles and responsibilities and set up a cadence to discuss/debate the policies/procedures to get the most out of your #data the right way.

A7 (3/3): #Data catalogs are a great tool for beginning your governance journey.

A7:Data governance is essential. Data classification is an essential tool that data analysts bring to the table to help companies ensure proper data governance.

A7:New data privacy laws and governance regulations mean organizations are held to higher customer and legal standards than ever before. Good data governance helps companies unlock innovation by driving efficiency.

@madhoop "If ever there’s a phrase that’s as hated as it’s loved, it’s #DataGovernance." True!

A7: (1/3) Data governance and security is #1 in terms of what enterprise businesses need to protect and grow. Almost always, businesses have data all over the place, in silos, and different systems, and old servers, and new clouds etc. How do you keep this data secure? Accurate?

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.@vamsipaladugus1 #Classification is vital for any governance programs. Know your data before you govern it!

A7. Aha! Yes, but IMO you just need to find the right people, coz data governance is a huge, growing, and potentially very exciting space - for the right minds!

A7 (2/3): Fundamentally, businesses need systems that enable self-service secure and managed access to data, as well as secure and governed movement of data and workloads anywhere the business needs.

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A7: (1/2) Data governance is critical to any successful data analytics activity. Having the capability to provide data discovery, lineage & full data transparency across the data pipeline is vital to both existing analytics and in-flight / future AI initiatives.

A7 (cont) I have been lucky enough to have teams that were awesome with this, and it has been amazing to see how much #givernance can not just drive compliance, but data usability and analytics effectiveness too.

A7 (3/3): This is made even more important when we're talking about timely data as in the other question. The more seamless you can make your governance experience, the faster people can get to work.

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A7: (2/2) As pipelines become more complex, collaborative & real-time, governance will continue to be an enabler of success. It is also important that organizations understand the level of governance for any #datapipeline, to avoid unnecessary complexity, cost & risk.

A7. (cont) Coz good #data #governance is sooooo much more than storage ad security Like good Cybersecurity programs, it is good hygiene that drives better outcomes

A7: This is forcing companies to manage their data at scale, showing them how they can perhaps apply it elsewhere

A6 (1/2): Data culture simply means that day-to-day #business decisions are based on facts and #data. To get there, organizations must be data-driven with high levels of data literacy, so people understand data, what it means, how to get and use it for their decisions.

A6: Many companies have started to acknowledge the need to set up “data culture”. #eWEEKchat

A6 (2/2): Data intelligence is key for an organization to build a data-literate culture among their workers and prioritize metadata management solutions to turn intelligence about #data into knowledge.

A6:Companies are trying to morph themselves into data-centric operations because it has been proven to accelerate innovation. When companies provide teams with unrestricted access to data, employees are empowered to unlock the rich insights

A6: High performing companies are demonstrating the benefits of a #data driven enterprise & delivering a culture of data driven decisions. This is by no-means the standard for every company w/ many orgs exhibiting a fragmented approach to data + data literacy challenges.

A6. Ima say no? At least, there is a lot of room to improve, esp. in leadership. Many still do not understand what #data & #analytics are, let alone what they can do.

A6. (cont) ofc, many pockets of ‘data-driven’ culture. But tend to be in geeky spaces – clinicians, engineers, researchers – not as much in leadership or other core business areas.

Agree @Vamsipaladugus1, it still feels like there's plenty of room to go. The realization is high, but I see the majority of orgs still have a ton of untapped potential. Which is ok - like any culture, it's not a checkbox, but a constant effort

A6: (1/2) I think COVID-19 was a good forcing mechanism for many organizations to start their "data culture" journey. Before it was something many large organizations were looking at (from adopting new tech to embracing data in the company DNA) but not acting on.

Definitely there is plenty of room to grow

A6 (2/2): The past two year have been a make-it-break-it moment for this and will only continue to be come an essential pillar of business

No, those in the data market have and some enterprises. Otherwise, it remains a loose goal to be data-drive culture and apply untapped analytics to all decisions

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!

A4: Not knowing their own data and not having data governance in place are two of the major challenges that companies face with data analytics. #eWEEKchat

A4:As we move into 2023, data sets have become massive and continue to grow rapidly, making the scalability of data analytics platforms an ongoing challenge.

@vamsipaladugus1 A4:Hiring and retaining the right talent can also be a major challenge. In AI/Ml space , the market is competitive, it can be hard to retain talent

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A4. Skills obvs, but also reliability, predictability, verifiability, actionability, and ultimately, trust. Research shows over and over that leaders do not trust ‘black box’ analytics!

A4: (1/3) Pipeline complexity – increasing data volumes, multiple types & sources & growing use of real-time data are driving pipeline complexity. This is further magnified by the lack of coordination/collaboration across teams involved in data pipelines.

A4. (cont) Too many engines promise the world but never show their working, so insights are unverifiable, unpredictable, and do not drive action.

A4: (2/3) Governance & Transparency – traceability, observability of data requires new processes & skills to deliver at scale & at the pace that the business require. Use of AI analytics requires greater end-to-end transparency of data, beyond those provided by traditional tools.

A4: More and more people are coming into the "data analytics" tent in an organization, so finding ways to scale the tools for that level of collaboration. DataOps is increasingly critical here

A4. (cont) No wonder leaders typically say their biggest roadblock to using #data #analytics is #trust!

A4: (3/3) Benefit realization – new technologies (such as #edgecomputing) are being implemented without clear approaches to define, measure and realize value from the data. Many #analytics projects are still failing to deliver expected outcomes.

A4: Having fragmented data sets and not being able to easily correlate data between multiple tools

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A4: (1/2) Secure collection and movement of data in a timely or real time way. Too often we see businesses working of analysis that took long enough for data to be outdated and not yield the best results

A4 (1/2): #DataGovernance remains a significant challenge for companies. It’s been estimated that businesses with low #data quality data are losing around $3.1 trillion in the U.S. alone – or 20% of their revenue.

A4 (2/2): Being able to know your #data, understand its meaning and apply policies to automate #DataGovernance is critical to getting it in the hands of the business to drive data ANALYTICS.

@BMCSoftware Spot on there too - these are all additional elements in building trust. Great add!

A4: (2/2) Automation is a key factor here that cuts both human and resource cost drastically.

@namessanti Santiago, definitely see that too. Privacy, confidentially, regulations, and more often lead to an inability to use data in any real and meaningful way.

@vamsipaladugus1 this is spot on. How can you increasingly do more with less.

.@BMCSoftware Simplifying pipeline complexity is key. But also more real time processing of data and moving away from #Batch

A4: Never first having a handle on data management, particularly with growing AI and IoT data, to realize organization-wide analytics

@jeffhollan DataOps is a key to any successful data analytics project. otherwise it is garbage in garbage out!

@AndiMann Ditto. Timeliness of insights is the most critical variable to decision making — with blockers at the data ingest point, the analytic technology, and the regulators etc.