This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
There is, however, another barrier standing in the way of their ambitions: data readiness. Strong datastrategies de-risk AI adoption, removing barriers to performance.
As companies prepare to meet this demand with a structured approach towards data modernization , their success relies on a foundational alignment between datastrategies, AI adoption initiatives, and the overarching goals and objectives of the business. How to Create a DataStrategy Aligned With Business Goals?
But first, enterprises need a future-proof AI datastrategy. That starts with deploying infrastructure on a flexible platform that’s positioned to support data growth. Deploying in AI-ready data centers We all know that AI requires data centers. Your future-proof AI datastrategy begins here.
How do datastrategies work and do companies even need them? A key factor in achieving this goal is the effective use of data: it allows companies to identify efficiency reserves in processes and to better understand customers to adapt products and services or even develop new offerings.
How replicated data increases costs and impacts the bottom line. How a next-gen data lake can halt data replication and streamline data management. What to consider when implementing a "no-copy" datastrategy. How Dremio delivers clear business advantages in productivity, security, and performance.
When organizations attempt to build advanced analytics or AI capabilities on shaky data foundations, the results are predictable. As Forrester's 2024 AI Implementation Survey notes, "Companies that prioritize datastrategy before AI deployment are 3.2 times more likely to achieve positive ROI from their AI investments."
How to ensure a quality data approach in AI initiatives Building successful AI initiatives starts with a strong data foundation. That’s why our platform is designed to make it easier for organizations to ensure data quality at every step. From curation to integration, we help you align your datastrategy with your AI goals.
AI a primary driver in IT modernization and data mobility AI’s demand for data requires businesses to have a secure and accessible datastrategy. AI applications are evenly distributed across virtual machines and containers, showcasing their adaptability.
His focus areas are MLOps, feature stores, data lakes, model hosting, and generative AI. Anoop Kumar K M is a Data Architect at AWS with focus in the data and analytics area. He helps customers in building scalable data platforms and in their enterprise datastrategy.
These tools empower users with sector-specific expertise to manage data without extensive programming knowledge. Features such as synthetic data creation can further enhance your datastrategy.
But because of the infrastructure, employees spent hours on manual data analysis and spreadsheet jockeying. We had plenty of reporting, but very little data insight, and no real semblance of a datastrategy. This legacy situation gave us two challenges.
The first section of this post discusses how we aligned the technical design of the data solution with the datastrategy of Volkswagen Autoeuropa. Next, we detail the governance guardrails of the Volkswagen Autoeuropa data solution. Finally, we highlight the key business outcomes.
He is a strong advocate for creating seamless data experiences, transforming complex requirements into efficient, user-friendly solutions. When he’s not building new features, Khalid enjoys collaborating with his peers and cross-functional teams to advance and shape BMW’s datastrategy, ensuring it stays ahead in a rapidly evolving landscape.
Scaling your datastrategy will inevitably result in winners and losers. Some work out the system to apply in their organization and skillfully tailor it to meet the demands and context of their organization, and some don’t or can’t. It’s something of a game. But how can you position yourself as a winner? Read on […]
Joel Farvault is Principal Specialist SA Analytics for AWS with 25 years’ experience working on enterprise architecture, data governance and analytics, mainly in the financial services industry. Joel has led data transformation projects on fraud analytics, claims automation, and Master Data Management. Yogesh Dhimate is a Sr.
As someone deeply involved in shaping datastrategy, governance and analytics for organizations, Im constantly working on everything from defining data vision to building high-performing data teams. My work centers around enabling businesses to leverage data for better decision-making and driving impactful change.
CIOs who struggle to make a business case solely on this driver should also present a defensive strategy and share the AI disasters that hit businesses in 2024 as an investment motivator.
By adopting these advanced open table formats and using cloud platforms such as AWS, organizations can build more robust and efficient data ecosystems. Appendix 1: Create a new Delta Lake table with UniForm You can create a Delta Lake table with UniForm enabled using the following DDL.
He specializes in assisting enterprise customers with their data and analytics cloud transformation initiatives, while providing guidance on accelerating their Generative AI adoption through the development of data foundations and modern datastrategies that leverage open-source frameworks and technologies.
Chaitanya is responsible for helping life sciences organizations and healthcare companies in developing modern datastrategies, deploy data governance and analytical applications, electronic medical records, devices, and AI/ML-based applications, while educating customers about how to build secure, scalable, and cost-effective AWS solutions.
Business intelligence consulting services offer expertise and guidance to help organizations harness data effectively. Beyond mere data collection, BI consulting helps businesses create a cohesive datastrategy that aligns with organizational goals.
Implementing AI-driven data quality management can automatically identify and resolve inconsistencies in real time, enhancing efficiency and providing trustworthy insights. Embedding trust in your datastrategy requires integrating data quality practices into all business processes.
His interests are in all things data and analytics. More specifically he loves to help customers use AI in their datastrategy to solve modern day challenges. Sohaib Katariwala is a Senior Specialist Solutions Architect at AWS focused on Amazon OpenSearch Service based out of Chicago, IL.
His interests are in all things data and analytics. More specifically he loves to help customers use AI in their datastrategy to solve modern day challenges. Mark Twomey is a Senior Solutions Architect at AWS focused on storage and data management.
But how can delivering an intelligent data foundation specifically increase your successful outcomes of AI models? And do you have the transparency and data observability built into your datastrategy to adequately support the AI teams building them?
AI is reshaping enterprise datastrategy The AI revolution is forcing a complete rethink of enterprise datastrategy. As TechCrunch recently reported , “There is a complete reset in how data is managed and flows around the enterprise. One without the other is worthless.
AI Co-pilot: The co-pilot empowers data teams with a real-time, unified workspace that automates, optimizes, and interprets scripts while providing immediate insights into data lineage. It allows users to mitigate risks, increase efficiency, and make datastrategy more actionable than ever before.
AWS provides Amazon Managed Workflows for Apache Airflow (Amazon MWAA) as a managed alternative for customers that want to reduce management and accelerate the development of their datastrategy with Airflow in a cost-effective way.
With deep expertise in datastrategy, data warehousing, and big data systems, she helps organizations transform their data landscapes. A passionate technologist and people person, Konstantina loves exploring emerging technologies and supports the local tech communities.
However, embedding ESG into an enterprise datastrategy doesnt have to start as a C-suite directive. Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams.
Data is one of the most valuable resources a company has, and HEMA is determined to democratize its role by building an efficient data organization, who relies on the most advanced data governance solution on the market.
The independent “Data Company” or “DataCo” is a separate corporate entity whose sole objective is to manage and extract greater value from its parent company’s data. Creating a DataCo provides several strategic benefits.
When working with data labeling platforms, AI companies often share sensitive datastrategies and invest significant capital to develop and maintain a skilled annotation workforce.
Contact Jen Stirrup Consulting to discuss your AI and datastrategy needs. Partnering with independent AI consultants—like Jen Stirrup Consulting—can help define a tailored strategy for integrating intelligent augmentation into your business. Join our Newsletter Make Your Data Work - One email at a time!
Joel Farvault is Principal Specialist SA Analytics for AWS with 25 years’ experience working on enterprise architecture, data governance and analytics, mainly in the financial services industry. Joel has led data transformation projects on fraud analytics, claims automation, and Master Data Management. Lionel Pulickal is Sr.
Virtual data provides the voice of the users can be measured in terms of KPIs for the metaverse, as we find in business practices now. However, it is best to think about a datastrategy and important concepts such as data governance for your virtual environments earlier in the process, rather than an optional add-on at the end of the project.
Read the full paper here: [link] Next Steps If you’re interested in developing a strategy for your organisation’s AI workflows or would like to discuss how AI could benefit your business, I’d love to hear from you. Stay updated with the latest insights on AI, datastrategy, and digital transformation by subscribing to my newsletter.
The first pivot was moving to become an agile organization, getting into the hyperscaler model, pivoting our services toward that, and unifying our datastrategies to get ready for the next wave of transformation,” Moisant says. Overhauling the company’s data architecture was a top priority.
Align datastrategies to unlock gen AI value for marketing initiatives Using AI to improve sales metrics is a good starting point for ensuring productivity improvements have near-term financial impact. Successful selling has always been about volume and quality, says Jonathan Lister, COO of Vidyard.
Attempting to build advanced analytics or AI capabilities on shaky data foundations is akin to constructing a skyscraper on quicksand—it may look impressive at first, but it simply won’t stand the test of time. The Solution: DataStrategy as well as AI It is tempting to think that the answer to succesful AI is to implement more AI!
Smart Data expansion demonstrates institutional commitment at scale, while the commitment to value and license government-held data assets represents a fundamental reimagining of how public resources can drive private sector innovation.
For decades, a fundamental divide has shaped enterprise datastrategy: the absolute separation between operational and analytical systems. On the other, the strategic brains: the online analytical processing (OLAP) platforms that sift through historical data to support planning and strategy.
Sources: Gartner’s 2024 AI Business Value Forecast McKinsey’s 2025 State of AI Report MIT Sloan Management Review (Spring 2025) Forrester’s 2025 AI Value Report IDC Future Enterprise Survey 2025 Healthcare AI Adoption Study 2025 Shape your datastrategy Book your appointment in a few simple steps.
As organizations grapple with exponential data growth and increasingly complex analytical requirements, these formats are transitioning from optional enhancements to essential components of competitive datastrategies.
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content