Nagesh Koganti

David Vellante since your posts mentions "next data platform – i.e. the 6th data platform", note that while centralized cloud-based data analytics platforms have gained popularity over the past decade, 'computing at the edge' is gaining momentum over the past years, so I would expect the next data platform will more more distributed edge analytics, including running small LLMs at the edge, where for privacy and governance reasons, as well as real-time performance, data cannot be shared with 3rd party analytics platform. See https://lnkd.in/g5hi_rSV and https://lnkd.in/gUQc4V_V #edgeanalytics #LLMs #distributedcomputing #governance

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1/ Uncomfortable $10B question for $DELL 😳 Analyst Toni Sacconaghi asked a simple yet brutal Q: Why didn't operating profit change when $DELL added $1.7B worth of servers for #AI services? #earnings #tech 2/ The implication? $DELL's operating margin on #AI servers is effectively zero! 😲 A single Q led to a $10B drop in market cap. Ouch! #investors #stockmarket 3/ Are chipmakers the real #winners of the #AI hype? 🤔 While tech giants chase AI, semiconductor companies rake in profits from high demand. #analytics #bigdata 4/ $DELL's situation reminds us that not everyone benefits equally from new tech trends. 📉 The road to #AI domination is paved with costly investments. #riskvreward #innovation 5/ A $10B question indeed! 💸 One analyst's query exposed potential cracks in $DELL's #AI strategy. Will they course-correct or get left behind? 🔭 #disruptor #futuretech 6/ In the world of finance, simple Qs can have massive impacts. 💥 $DELL's case shows why public companies must be transparent & prepared. #accountability #shareholdervalue 7/ The AI wave is rising, but who'll be the real surf champions? 🌊 Tech titans or component suppliers? An exciting race to watch! #competition #disruption 8/ Perhaps $DELL's AI conundrum serves as a wake-up call. 💡 Pivoting to new tech requires careful planning & execution. No room for rookie mistakes! #strategy #leadership 9/ While some rejoice at $DELL's $10B stumble, others see an opportunity. 👀 A shake-up could spur innovation & realign priorities. #transformation #pivot 10/ In the end, the $10B Q highlights the complexities of the #AI revolution. 🧩 Not every player will emerge a winner, but the game's just begun! #futureisnow #techbuzz

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Our Genieepro family member Data geniee ., An Video abouts its features ., what it is., #ai #data #ml #conversationalanalytics #database #databot #aibot #aichatbot #analytics #finsight #datageniee #genieepro #CA #finance #excel #msexcel #aiforexcel #aiinexcel

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DATA_LAKEHOUSE = best_of(DATA_LAKE, data_wareHOUSE) Forgive the non-standard variable name cases, but the pseudo-equation is a good way to remember what a data lakehouse is.

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Imagine running a successful retail business, but feeling like you're constantly chasing your tail. Sales reports pile up, customer data remains a mystery, and keeping pace with the latest trends seems impossible. You know the power of data science, but building an in-house team feels like a expense. There's a solution: outsourcing data science. it's a strategic move. Read our latest blog to know more Data Science Outsourcing - https://lnkd.in/gkugdP6X

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The Data Conundrum I was recently in a conversation with a CDO of a Fortune 100 company and it was an interesting conversation on the conundrum that his enterprise has, on how to leverage the massive data that they have in their ecosystem. A study in 2022 by a consulting firm found that only about 1 in 4 data executives in Fortune 1000 companies were confident that they had built a “data-driven organization”. The same firm revealed earlier this year that this number had changed to just under 1 in 2 people holding the same belief. While a dramatic improvement over the earlier result, this is still quite underwhelming. That’s especially so when you factor in that enterprises have been building various kinds of data initiatives for over 15 years now. There’s no doubt that enterprises have the data. So, what conclusions are we to draw from this?   One interesting idea that is doing the rounds is that there’s a possible disconnect between the data businesses need and the data being created and presented to business users. An interesting solution being proposed to address this is to “productize” the data. That would ensure that ”product” teams would take on the responsibility of understanding the data needs of their customers (business users). They would then ensure the data being served to them would meet their needs exactly in all ways. They would work to maintain the data product, update it as customer needs evolve, and adopt the technology and tools they need to do all that. The focus shifts from the data itself to what business users need it for. Addressing that need can perhaps drive meaningful business outcomes and move the needle towards greater confidence in becoming data-driven.   #DataDriven #DataProduct #DataAnalytics 

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𝐏𝐚𝐫𝐭 𝟑 𝐨𝐟 𝟓: 𝐆𝐚𝐫𝐛𝐚𝐠𝐞 𝐈𝐧, 𝐆𝐚𝐫𝐛𝐚𝐠𝐞 𝐎𝐮𝐭! Why Quality Data is the Lifeblood of Successful AI ? No matter how advanced your AI system is, it can only perform as well as the data it’s given. Investing in data quality isn’t just a step in the process; it’s the foundation of successful AI implementation. 𝐖𝐡𝐲 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: • 𝑨𝒄𝒄𝒖𝒓𝒂𝒄𝒚 𝒂𝒏𝒅 𝑹𝒆𝒍𝒊𝒂𝒃𝒊𝒍𝒊𝒕𝒚: High-quality data ensures that your AI models are accurate and reliable, providing insights you can trust. • 𝑬𝒇𝒇𝒊𝒄𝒊𝒆𝒏𝒄𝒚: Clean, well-organized data reduces the time and resources needed for data processing and analysis, speeding up your AI initiatives. • 𝑩𝒆𝒕𝒕𝒆𝒓 𝑫𝒆𝒄𝒊𝒔𝒊𝒐𝒏 𝑴𝒂𝒌𝒊𝒏𝒈: Quality data leads to better predictions and recommendations, empowering your business to make informed decisions. 𝐒𝐭𝐞𝐩𝐬 𝐭𝐨 𝐄𝐧𝐬𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲: 𝟏. 𝑫𝒂𝒕𝒂 𝑪𝒍𝒆𝒂𝒏𝒔𝒊𝒏𝒈: Conduct a one-time data cleansing project to remove duplicates, correct errors, and fill in missing values. This is the foundation of quality data and can be done in parallel with the AI project. 2. 𝑫𝒂𝒕𝒂 𝑽𝒂𝒍𝒊𝒅𝒂𝒕𝒊𝒐𝒏: Implement checks to validate data accuracy and consistency before it’s used in your AI models. This step helps catch issues early, and you can even use AI to assist with this.  3. 𝑫𝒂𝒕𝒂 𝑰𝒏𝒕𝒆𝒈𝒓𝒂𝒕𝒊𝒐𝒏: Ensure data from various sources is integrated seamlessly, maintaining consistency and accuracy across your datasets. 4. 𝑶𝒏𝒈𝒐𝒊𝒏𝒈 𝑴𝒐𝒏𝒊𝒕𝒐𝒓𝒊𝒏𝒈: Implement processes to monitor data quality continuously, catching and rectifying issues as they arise. This proactive approach helps maintain high standards over time. 𝐀 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐒𝐭𝐨𝐫𝐲 One of our clients, a retail company, started a small AI initiative, but the output quality was unsatisfactory to the business users, with accuracy below 50%. When we stepped in, we quickly identified that the issue was due to inconsistent terminologies used across regions and multiple languages in play. After addressing these issues, we significantly improved their data quality, boosting AI output accuracy to over 80%. This improvement enhanced inventory predictions, reduced stockouts, and increased customer satisfaction. Have you faced data quality issues in your AI projects? What steps have you taken to improve your data? Share your experiences in the comments or reach out to us for more insights! Microland Limited Kini D K Anshu Shariq Mark Meeta Sunil Anand Arjun Sonny Ravi

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At Confluent's Kafka Summit in Bangalore, Shaun Clowes, Chief Product Officer, and Jay Kreps, Co-Founder and CEO, discussed Confluent's strategic approach to enhancing enterprise data infrastructure amidst the AI revolution. "Every time you have a new data need, what do you do? You connect the destination point-to-point to all the different sources needed. Over time, your data infrastructure turns into a giant, tangled mess of interconnections," shared Shaun Clowes. “Kafka often serves as the data feed into this area. However, when considering the various tools available, they generally run on not just streams, but largely on extensive tables of data. This is the essence of a data warehouse or data lake," Jay Kreps explained. #BigData #DataManagement #DigitalTransformation

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Exciting news for all leaders looking to drive synergy and strategic clarity within their organizations! StratifyPro really is a game-changer in aligning Vision with the desired Outcomes. This AI-powered platform not only brings clarity to each team member and shows them how their work contributes to the strategic success of the organization, but also creates a clearly defined roadmap for execution - with measures and metrics - that actually delivers results. Find out what other companies are doing to create an edge over their competitors and don’t miss out on this chance to transform how your organization operates. Register now and start your journey towards a more integrated and successful future! #V2O #Vision-to-Outcomes #Success #BusinessGrowth #Strategy

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New McKinsey & Company data shows #value of building out a #productoperatingmodel for long-term success & growth-“gap between top & bottom performers is largest in 5 specific capability areas: #interaction between product, engineering & operations teams; #productmanagement practices; backlog prioritization; funding; technical debt management”

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Appreciate the shout out! ✅ Lean, simple, gives you answers fast ✅ Built for operators (and to drive impact from your data)

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Domo card of the day - Grouped + Line Bar Chart A Grouped + Line Bar Chart is a hybrid visualization that combines the features of a grouped bar chart and a line chart. This type of chart displays multiple sets of data using bars, with each group representing a different category. Additionally, it overlays a line chart to show trends or correlations among the data points across the categories. In a Grouped + Line Bar Chart, the vertical axis (y-axis) typically represents the values for both the bars and the line, while the horizontal axis (x-axis) displays the categories. The bars are grouped by category and color-coded to differentiate between data sets, and the line runs across the groups to provide an overall trend. I use this always when I need to show #sales compared with previous year sales and the growth in the sales numbers, it's so convenient and powerful. Benefits of Using a Grouped + Line Bar Chart: 1. Multidimensional Data Visualization: It allows you to visualize multiple type of data simultaneously, making it easier to compare and contrast different categories. 2. Trend Analysis: The line chart overlay helps in identifying trends and patterns across the grouped data sets, providing deeper insights into the data. #trends #multidimesionnaldata #domo #salesvspy #sales #barchart #linechart #businessintelligence #datavisualisation

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Businesses are demanding more from their data infrastructure. Fresher data, more concurrent consumers and more challenging processing. In this world, traditional cloud data warehouses such as Snowflake begin to creak. They were not designed for low latency data access or to be used as an application backend. One implication of this is that the cost model also doesn't scale well, and the cloud data warehouse becomes a very expensive proposition. Check out our recent blog on this topic to learn more about this trend and how it can be combatted.

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Simplify Billsbay integration in 4 simple steps: 1. Create connections between Billsbay and targets. 2. Prepare a pipeline between Billsbay and targets by selecting tables in bulk. 3. Create a workflow and schedule it to kickstart the migration. 4. Share your data with third-party platforms over API Hub. #Integration #DataMigration #API #Workflow #DataSharing #Lyftrondata‍M‍‍igratio‍‍‍‍‍‍‍n

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A quick overview of the new low-code AI/Bi dashboard tool from Databricks

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