WHY IT MATTERS: Digital Transformation

The core idea behind data lineage is the ability to fully understand how data flows from one place to another within the infrastructure that was built to house and process it. It seems like it should not be a difficult problem, but it is. In fact, it is a huge issue for organizations as they face 2022 and beyond. If organizations do not know where their data comes from or goes, they have uncontrolled environments that have risk on many different levels. Having uncontrolled data environments means that it is also very difficult to extract value from data, and data is the new oil or new gold. Organizations that cannot extract value from data stand a good chance of being outcompeted and replaced by organizations that can.

An agile approach to overhauling data architecture improves speed, flexibility, and innovation.

To succeed with data and analytics initiatives, enterprises need a comprehensive view of critical technology capabilities. We believe these Hype Cycles draw a holistic view of technologies — including the new addition of lakehouse architecture as a trigger, which can make data and analytics programs modern, broadly applied, richer in functionality and successful.

Get the report and gain insight into:
-Changes in this Hype Cycle — including the addition of Lakehouse
-The current state of data management maturity
-The Priority Matrix

Business intelligence white papers on BI industry trends, and the latest BI and analytics analyst reports from Gartner, Dresner, BARC and Forrester.

At first glance, the data fabric and the data mesh concepts sound quite similar. Meshes are often made from a type of fabric, after all, and they are both malleable items that can be lain atop things–in this case, your IT systems that are subject to the ever-growing data crush.

But there are fundamental differences to these two approaches, so it’s worth taking some time to learn their differences.

Gartner Top 10 Data and Analytics Trends for 2021

12 technology trends that will help shape the future of digital business.

An agile approach to overhauling data architecture improves speed, flexibility, and innovation.

Expert data scientists and other professionals working in data science roles require capabilities to source data, build models and operationalize machine learning insights. Significant vendor growth, product development and myriad competing visions reflect a healthy market that is maturing rapidly.

Finding shabby abodes like these and making them respectable is the load-bearing wall of Amherst’s strategy. Amherst depends on humans to find cities, towns, and neighborhoods where fixer-uppers can become profitable, then relies on automation to pick individual homes. Negri, 31, heads the human team. He spends 150 days a year on the road overseeing Main Street ­Renewal’s operations from Atlanta to Denver, searching for “sweet spot” neighborhoods that combine affordable rents with a strong middle-income employment base.

Instacart is excited to announce our first public dataset release, “The Instacart Online Grocery Shopping Dataset 2017”. This anonymized dataset contains a sample of over 3 million grocery orders from more than 200,000 Instacart users.

For each user, we provide between 4 and 100 of their orders, with the sequence of products purchased in each order. We also provide the week and hour of day the order was placed, and a relative measure of time between orders.

100 million rides and runs, 220 billion data points visualizing the best roads and trails worldwide.

Training materials obtained by the Electronic Privacy Information Center show Palantir plays a role in a far-reaching customs system