eWeekTweetChat

A5 Artificial intelligence is changing the game for everyone. On the #data side, it’s helping us determine the best use cases for #DataOps and automating the management and processing of it. There’s so much more potential with #AI. But human oversight remains key.

A5: Leverage #DataOps technologies to automate the data capture, cleansing & transformation so the data science & engineering can work on manipulating the data to drive business results such as higher customer retention, reducing $$, or creating new digital offerings.

@RKs2cents 100% agree with #AI changing the game across every use case.

A5: Engage & interact w/ insights – tell #datastories that make an impact. Enhance engagement & understanding with 3D & immersive data visualizations to clearly illustrate complex findings. Improve collaboration & bring business and #datascience teams together in a virtual space

Q5 - Go from being IT central to business focused and federated. Data literacy is NOW just as important to traditional literacy in the workplace. Data sharing, federated data, and data literate organizations will be the most successful.

A5:The extra for best-in-class results is about ensuring reproducibility of data engineering and ML processes. This includes continuous model training, data and model drift analysis, feature engineering, CI/CD integrations. DataOps/MLOps is the Key

A5. Focus on the use cases that business can't easily or accurately get answers to today. Solving problems that are already solved, just using newer technology, doesn't move the needle. What can new tech enable you to do today that you couldn't before?

Make data privacy and compliance an integral part of your data analytics strategy. While the democratization of analytics is a current trend, we need to put an emphasis on data privacy, confidentiality and compliance. #DataAnalytics #eWEEKchat @CIOStraightTalk

A5. Retain great people. You will need them in ways you cannot predict.

@rqrivera #DataLiteracy and #DataOps go hand in hand. You need to be literate of the data to the agility to succeed in the market.

A5: With the third party cookie losing steam, we've learned that finding non-competitive or complementary brands that are suitable for data-sharing can complete the picture of your customers' behavior. 1/2

A5. Sounds obvious but...the data strategy needs to be aligned with the business strategy rather than managed separately. This means data leaders have to become more business literate & understand how the data strategy is going to contribute to business success

@kklive Completely agree, privacy, security and compliance are often afterthoughts in data strategy

A5: Automate everything you can. The faster you can move data from point of origin to a real-time dashboard, or ML model, or AI app, the more effective your business will become.

A5: By sharing anonymized, aggregated customer data, collaborating organizations can gain a clearer understanding of who their customers are and what motivates them. (2/2)

@jonnio20 Given the huge gap in supply of talent that won't be solved any time soon, this is the right answer.

A5: Choose tools and platforms that are additive over time. eg: In the #DataCloud we have #DataSharing (yay no more copying data!) but this feature will work with all the new features coming out too. Platform scales with data and tech changes seamlessly without effort.

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A5 Still on the 'trust' tip ... use open (incl. Open Source) ML algorithms. Black boxes are damaging for trust and effectiveness. Open algos drive up understanding, trust, and ultimately make data more actionable because decision-makers believe it.

@RKs2cents We should be focusing on Augmented Intelligence powered AI, not just powered by AI

A5: Prioritize and mandate data visualization and data democratization, so actionable data is not only, in fact, captured, but applied independently by teams for their goals.

@RKs2cents AI Ethics is long overdue, Already can see the black box ML products of the last AI hype period are losing favor in the democratization of AI - now we need to solve labeling problems (plug for @snorkelai)

A5: Working with massive data is still hard. Find approaches like universal data distribution with DataFlow and automated ETL for powering analytics and AI downstream. Look to unify your data lifecycle and move away from siloed and expensive solutions

@rqrivera Absolutely. Having an #AI powered data catalog is one way to solve it.

@namessanti You know, I normally "Automate all the things!!!" And #MLOps etc. can automate mundane tasks. But I would advise caution, coz #automation can do very bad things at massive scale and speed, but ML is not an exact science.

@fblissjr I would advise that you start with the simplest UC with the lease complexity that provides the most value in the shortest time. Then scale from there.

@hmschwenk 100%

A5 Data literacy needs to be a priority. People in the org need to understand the data value chain across the business and the part they individually play in it, for eg when a customer on boards

So true!

Automation is essential. From data ingest to insights. It's really the only way to stay competitive

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A4: Every pipeline has #data producers & data consumers– understand the requirements for each. Start w/ a customer-back approach to #dataanalytics by understanding end users & their needs for business solutions – then work back leveraging data to solve the problem.

A4: Bring together your data and analytics in one seamless experience. The more disparate systems you need to manage, the bigger the silos, the greater the challenges and margin for error — things your business can't afford

A4. Hire the right leaders that elevate the team. Simple but applicable to anything. And start small, but think big.

A4 - Intentionally creating a culture of being fact driven. This is hard and needs to be a strategic initiative at the CXO level. #eweekchat

A4:A key practice is implementing scalable data analytics architecture—cloud native architecture—because it allows you to scale compute and storage separately.

A4. Automate, automate, automate. It's the only way to free up smart people to deliver meaningful data products.

A4 To optimize #data analytics, we must first catalog and centralize all #metadata. This helps us understand the data and where it sits. It allows us to contextualize data across separate silos, truly unlocking #value while increasing #DataLiteracy and #DataCulture

A4: Let complex data speak for itself. Embed AI/ML into your #analytics practice. This improves decision-making and ensures you’re identifying the right problems, and implementing the most effective data-driven solutions. We call this
#IntelligentExploration

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A4 - @rqrivera - now 92% -- attribute the “principal challenge to becoming data-driven” to people, business processes, and culture, with only 8% identifying technology. #eweekchat

"Business insight driven/oriented data discovery and analysis" is the best practice mantra that can ensure organizations make the best use of data analysis investments. #DataAnalytics #eWEEKchat @CIOStraightTalk

A4: Focus on building a data fabric on an Open Data Lakehouse that enables you to work with any dats exactly as required by the business — no work arounds or hopping between point solutions. Silos and lock-in are your enemy. #DataAnalytics #eWEEKchat @CIOStraightTalk

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A4: Be picky with your data. Trying to employ #ML or #DataScience on bad data yields bad results. Choose tools that help you focus on finding quality data easily and reliably (less effort on security, etc.) so your hard work on top of it is not skewed or faulty.

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@RKs2cents 100% agree, metadata capture, normalization and management is crucial

Clean and consolidate data into a single, actionable view. Siloed data, retained in different systems and platforms, has little value. #DataAnalytics #eWEEKchat @CIOStraightTalk

A4. Understand the limitations of your analytics. Especially the limits of your dataset and algorithms. Don't follow blindly, verify the outcomes. Treat data as augmentation for intelligent humans, not replacement.

A4: Organizing data. Without sorting, labelling, and cleaning your data in a way that everyone can agree on, you're not going to get results you can replicate. Having results that can be replicated creates trust and buy-in in your insight.

@rqrivera Agree, but it's also about having a #DataCulture within your organization.

@RKs2cents metadata and semantic layers will be extremely important in the coming years, if not now.

@namessanti I love that concept. I wish there were better tools and better integrations - especially for seamlessly analyzing datasets across multiple silos.

@RKs2cents 100%.

A4: Executive buy-in to see analytics as distinct within data and dedicate the budget to build out and focus the function, structuring it as a critical strategic engine to compete and win

A1: The truth here is that getting it right for your business can be challenging. Every business challenge is different, every data set unique. Do everything possible to have your cake and eat it too — enable fast collaboration across hybrid and multi clouds

@rqrivera "In god we trust; all others bring data!"

A4. One for the data teams Benchmark your data maturity to know where you are, what’s the state of your data, how bad is it? Get metrics around it, so you can measure it but also understand how you can improve the data. That itself will identify where your weaknesses may lie.