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DATA LEADERS: Is your organization #GenAI-ready? According to research, only 5% of organizations have implemented generative AI at scale in production environments. Unlike traditional AI, #GenAI requires diverse, high-quality data that is: ??Accessible, ??Thoroughly cleaned (especially unstructured data), ???Properly labeled & ??Secured with granular controls. To learn how to easily prep your data for #machinelearning, read this blog post: https://ow.ly/XMXR50Vg1WQ

Do you know how much dirty data is costing your enterprise? The 1-10-100 rule of data quality states: ??It costs $1 to verify a record, ????$10 to fix introduced errors & ??????$100 if errors go unaddressed. Dirty data isn't just a technical problem—it's a bottom-line issue that affects every aspect of your business. Read the full insights here: https://ow.ly/grZp50Vc8sQ #datascience #bi

You can’t manage and optimize what you can’t see. ?? ?? Getting a handle on data observability—including freshness, quality, volume, and lineage—helps your team keep your data products useful. This blog post makes the case for using centralized metadata management to ensure #dataquality. https://lnkd.in/gb-9pD7K #datamanagement

A #datadriven culture isn’t just a competitive advantage—it affects the bottom line. Research shows organizations with mature data cultures experience 2.5x higher business outcomes, including revenue and profit growth. To build a data-driven culture, organizations must: ? Secure leadership buy-in, ? Invest in role-based data literacy training, ? Enable self-serve access to data & tools, ? Foster collaboration across teams & ? Recognize & reward data-driven initiatives. If you want to build a data-first mindset, this blog post offers six steps to achieve that: https://lnkd.in/gicQvTUY

Choosing the right #dataprocessing method depends on several key factors, such as: ?How fresh must the data be? ?What are your bandwidth & storage constraints? ?How large are your datasets? In this blog post, you’ll learn about the advantages and disadvantages of eight methods of #data batch processing, including five real-time data processing methods, and when to use them. https://lnkd.in/gghz3fVJ

If you want to accelerate #AIgrowth, making data more accessible to more of your organization can help. ?? This blog post shows how to address common challenges to data democratization, including how to: ?? use data sharing to break down silos ?? share unstructured data effectively ?? replace assumptions with data & ?? use #AI for governance. https://ow.ly/SuEq50UYPJr

For those with a hybrid or a hybrid #multicloud environment, how do you unify, govern & optimize your data? ?? ?? This blog post shows how to use federated queries & internal data products as part of your unified #datastrategy in hybrid #multicloud environments. https://lnkd.in/g9XnecrB

In what instances is it beneficial to your organization to share data externally? ?? This blog post suggests several common use cases for external data sharing, along with five key risks to consider. ?? https://lnkd.in/gPe_KpCZ

In the age of #AI, how is your data quality strategy holding up? If you need to revisit or haven't yet developed a #dataquality strategy, this blog post offers guidance, including: ?? Dimensions for measuring data quality ?? How to foster a data quality culture & ?? How to establish and monitor your data governance framework. https://ow.ly/5AKV50UQKlp

As we welcome the New Year, we want to extend our warmest wishes to you. May the coming year bring you joy, success, and exciting new opportunities. Thank you for being a part of our journey, and here's to a prosperous year ahead! #HappyNewYear #Welcome2025