Last month, thousands of big data professionals came to the Strata Data Conference in New York to discover the latest tools and technologies. Cambridge Semantics’ executives and staff were waiting for them with a booth and a demo of the new Anzo Smart Data Lake (ASDL) 4.0.
Only with a rich and interactive semantic layer can the data and analytics stack deliver true on-demand access to data, answers and insights - weaving data together from across the enterprise into an information fabric. By developing Anzo Smart Data Lake on knowledge graphs, Cambridge Semantics has built the foundation for a truly disruptive Semantic Layer.
Deploying a big data platform as a data warehouse replacement offers compelling cost and agility benefits. However, it is not an "out of the box" experience. To manage the data lifecycle from source to user, organizations face the daunting task of integrating point tools from a diverse array of vendors. And the resulting data flow often looks a lot like a legacy environment, with all of its limitations, just running on cheaper infrastructure.
I recall a senior executive of one of the world’s largest consumers of data proudly proclaiming in a much-celebrated announcement that went something like this: “We’ve made the bold decision to migrate our data to the cloud. This move will dramatically improve our information sharing because the contributing data will be stored in a common environment.” That was in about 2012, a long time ago in IT years! No one seemed to notice, however, that it’s not the physical location of data that enables “information sharing.” What happened is that multiple “data clouds” stood up, and data within each of these clouds was still isolated. That is, myriad data collections resided in the cloud(s), but were not related; they remained disconnected. One could say “data islands in the ocean called the cloud.”
We have been humbled by the number of industry awards Cambridge Semantics has garnered in recent weeks. We know they can only be the result of the hard work and ingenuity of the Cambridge team.
Smart Data Lake platforms providing semantic layers are automating data access and data management for accelerated insight. These platforms, based on knowledge graphs, allow organizations on-demand access to all relevant data, internal or external, regardless of the source format or type (i.e. structured, unstructured or semi-structured). All resulting in faster answers to questions impacting an enterprise. Now IT organizations can easily deliver harmonized diverse data sets with full richness allowing various stakeholders to conduct interactive high resolution analytics on the multi-layered rich data. Stakeholders can also use these Smart Data Lakes with semantic layers for on-demand discovery of right data and data sets to be used with business intelligence, machine learning or advanced analytics systems without any IT set up or preparation. The result - better insights, improved time to market, and faster and timely decision making.
May 25, 2018 has become a sword of Damocles hanging over the heads of any companies conducting business within European Union (EU) countries. Because that’s when the EU’s General Data Protection Requirement (GDPR) goes into effect. The GDPR requires all firms to enhance their protection of personal data, whether the firm is located within the EU or not. Organizations must be completely compliant from the first day or suffer the consequences of large fines – potentially up to 2 to 4 percent of a firm’s global revenue.
Utilities are implementing big data discovery and analytics tool to better manage energy flow and distribution, among other key benefits. But what are the benefits to the utility’s customers - government, commercial and residential energy users - as the power grid integrates these “smart data” enhancements?
Topics: Smart Data
A biomedical professional is certain to have come across some variant of the headlines mentioned in the visual above. Combinations of trending buzzwords in technology and healthcare form half of my news feed. The other half merely mirrors the first half.
Many retail industry observers are asking why e-commerce giant Amazon purchased the brick & mortar grocery chain Whole Foods. The unanimous answer is: access to real-world customer data that is richer and more robust that what Amazon can acquire from online purchases.