For reliability, accuracy and performance, both AI and machine learning heavily rely on large sets. Because the larger the pool of data, the better you can train the models. That’s why it’s critical for big data platforms to efficiently work with different data streams and systems, regardless of the structure of the data (or lack thereof), data velocity or volume.
However, that’s easier said than done.
Today every big data platform faces these systemic challenges:
Compute / Storage Overlap: Traditionally, compute and storage were never delineated. As data volumes grew, you had to invest in compute as well as storage.
Non-Uniform Access of Data: Over the years, too much dependency on business operations and applications have led companies to acquire, ingest and store data in different physical systems like file systems, databases and data warehouses (e.g. SQL Server or Oracle), big data systems (e.g….
The Associated Press delivers 2017 Virginia gubernatorial primary results with Microsoft Power BI publish to web.
For more than 170 years, The Associated Press (AP) has told the world’s biggest and most important stories, from the assassination of Abraham Lincoln to the fall of the Berlin Wall, and more recently Brexit and the 2016 U.S. presidential election.
Today, when news is frequently accompanied by massive quantities of data, reporting means making sense of all that data, and AP continues to lead the way in data journalism. The news agency, with locations in more than 100 countries, provides newsrooms worldwide with data-driven reporting on everything from politics, business, environmental science and public safety to sports, education and more. Recent AP data journalism efforts uncovered water quality issues in public and private water systems, analyzed the history of refugees from the seven countries included…
When the term “big data” first burst onto the scene about seven years ago, experts predicted that organizations could dramatically improve how they operate by capturing and analyzing vast arrays of rapidly growing information.
Fast forward to 2017. It turns out that “big data” wasn’t just another buzzword. Now an established term in the IT and business lexicon, big data is bigger than ever. By some estimates data volumes are doubling every three years.
But organizations have yet to fully capitalize on the value of data for more informed decision-making, operational efficiencies, and personalized systems of engagement with customers and partners.
“Most companies are capturing only a fraction of the potential value from data and analytics,” according to a recent McKinsey Global Institute study, “The Age of Analytics: Competing in a Data-Driven World.”
Connecting Data for Competitive Advantage
For organizations that want to survive and thrive…
On June 7, 2010, Christopher White attended a kickoff meeting in suburban Washington D.C. for a project to rapidly develop and deploy big data analytics and visualization tools to aid the war effort in Afghanistan.
“In Chris’s mind, he was going to come to D.C. for two weeks during the summer, work on this program he literally didn’t know anything about, and that’s it,” says Randy Garrett, who was the program manager for Project Nexus 7 at the Defense Advanced Research Projects Agency (DARPA).
“Well, it didn’t quite turn out that way.”
White, an expert in training computers to extract information from troves of digitally processed information, had just finished his first year as a postdoctoral fellow at Harvard University. His advisor was a DARPA contractor, which gave White a sought-after opportunity to transfer computer science research into real-world applications. The process just…