Acquiris Digital

To determine the potential of AI technologies across the Business Capability Model (BCM), Chief Enterprise Architects will need to develop an understanding of the kinds of problems AI can solve. Build a model that defines and classifies the types of AI technologies: https://gtnr.it/43R8px8 ?? Follow Gartner for IT for more actionable, objective insight. #GartnerIT #EnterpriseArchitecture #ArtificialIntelligence #AI

Procrastination is not a time management problem. It’s an emotion management problem. You’re not avoiding work. You’re avoiding tasks that feel boring, frustrating, or stressful. Postponing prolongs pain. The dread of starting is quickly outweighed by the joy of progressing. Illustration Janis Ozolins

Efforts matter. Sucess follows.

The 3 Machine Learning Cats ?? ?? ??

Types of data There are various types of data one needs to understand So that one can plan well regarding data capture, management, and usage. Below are some of them:- I have purposely left one box missing, Let me know what data type you feel is important. Structured data - Typically in some form of structure, stored in a tabular form. RDBMS databases like Oracle Geospatial data - Data Related to locations on earth Streaming Data - The output of a lot of data in large volumes continuously, like gaming, videos etc Synthetic Data - Tools or AI technology artificially create this data, which is utilized for testing, research, and machine learning. Binary Data - Data which is usually stored in the format of audio, video files Sentiment Data - Data like emails, chats, and comments are used to analyze the emotional tone of the message Spatiotemporal data - This data includes location and time, which enables tracking, like cab tracking Open data - The data that is free for the public to use, edit, and share for any purposes Real-time Data - With 5g technology, this data is captured in real-time and analyzed in real-time, which is valuable for traffic emergency systems. High-dimensional data - The number of variables (or dimensions) is significantly larger than the number of samples, for example, used in IOT smart farming and facial recognition. Transactional Data - Data generated due to various transactions like payment, e-commerce shopping, Batch Data - Data in high volumes is collected and processed Dark Data - Information that is collected and stored but not used in any meaningful way Machine Data - Data generated by the systems automatically across an organization's data center Genomics Data - Data about the organization and operation of an organism's genetic material. For example, analyzing DNA of patients Big Data - Large and complex data sets that doesn't fit the traditional RDBMS Biometric - Data that deals with biometic data like face, finger prints and voice Sensor Data - Data collected from various sensors like temperature, Gyroscope sensors Social Media Data - Data collected from all the social media applications and further leveraged for various purposed Semi-Structured - Data which is in a format like JSON and which doesn't conform to data model Unstructured Data - Text files in PDF, email that doesn't follow any structure or data model Time series data - Data captured over interval of time continuously and consistently #data #dataanalytics #engineering #machinelearning

New Deloitte report on the AI infra opportunity: (page 7 for value chain breakdown) Deloitte?released?a 20-slide report on seizing the "once-in-a-generation" opportunity in data centers and AI infrastructure. Aspects of the report focus on Southeast Asia, but the fundamentals apply globally to nations, enterprises, and investors. 1\ Gen AI applications rely on foundational models that demand vast amounts of power and compute for both training and inference. 2\ By 2028, AI inference is expected to drive 85% of global GPU demand, while AI training will account for the remaining 15%. 3\ Consequently, global data center electricity consumption is projected to nearly double from 536 TWh in 2025 to 1065 TWh in 2030. 4\ Governments, enterprises, and investors are urged to treat AI infrastructure as critical assets and act fast to build them on their shores. 5\ For investors, data centers offer stable cash flows, generating $1.5-2M per MW yearly, while GPU cloud models can yield $15M per MW. Security, sovereignty, and sustained prosperity require governments, enterprises and investors to act on AI infrastructure. P.S. Link to report in comments. --- ?? Repost to help your network level up with Generative AI! ?? Want the top 1% of generative AI for enterprises? Get on the list: https://lnkd.in/eHEzF_PF

Effective CIOs make it easier for others to lead digital and build digital solutions with IT. Watch our complimentary on-demand webinar as Gartner experts delve into how successful CIOs worldwide manage strategic business and technical and leadership priorities: https://gtnr.it/4i4aESf ?? Follow Gartner for IT for more actionable, objective insight. #GartnerIT #CIO #Strategy

Hiring alone won’t close the talent gap in a rapidly evolving AI-driven world. Leading companies are taking a more strategic approach—leveraging internal talent, reskilling, and tapping into the right global talent pools. See what’s working (and how you can apply it). https://mck.co/4kQ4bfj

By Master Stress

By Generative AI