Good question. What is the point? The point is to create measurable business value from enterprise data. Of course, before measurable business value comes insight. The Modern Data Architecture (MDA) recognizes that insight can lie hidden in data of all types—structured or unstructured, messy or modeled, historical or realtime.
Attivio’s latest patent covers one of the most interesting features in the Semantic Data Catalog and the one that always gets wows when we demo it – the automatic join finder. Under the hood, the technology replaces manual processes that could take hours or days with a quick, easy process that takes minutes.
If your organization is going to win on analytics, it needs to view all of its information as a strategic enterprise asset. This includes not just the 10% you know about, but the 90% of dark data that hides in information silos. There are big challenges on the path to surfacing all of your enterprise information for business intelligence. The biggest challenge is not in storing data, or in analyzing it, but actually finding the right data. But why is it so hard? Here are the top three reasons:
It's pretty obvious to anyone who follows analytics and Big Data that an end-to-end solution for the Big Data stack will require best-of-breed technologies from multiple vendors. No single vendor can develop all the technology pieces on its own. New applications and data processing frameworks emerge and change much too quickly. As enterprises strive to create a modern and flexible hybrid data infrastructure, they look for technologies that are easy to embed and extend.
That's why Dell EMC™ chose the Attivio Data Unification Platform for its Analytic Insights Module. The Attivio platform is definitely OEM friendly. Its architecture is open, scalable, and API-accessible, which makes for secure and seamless integration with other systems.
With the release of the Dell EMC™ Analytic Insights Module, enterprises now have a platform that can address the full analytics lifecycle. Analytic Insights Module is engineered to combine self-service data analytics with cloud-native application development into a single cloud platform, eliminating the months it takes to build your own.
As a business development representative at Attivio, I regularly speak to companies about their big data management challenges and possible technology solutions. Many of their pain points would resonate with organizations across a wide array of industries.
Shopping for a Data Catalog Solution
I recently had an exploratory call with the Senior Manager of Enterprise Data Management and the Director of Data Governance at a large, multinational financial services corporation. They are searching for technology to manage metadata with a focus on data quality; data governance; closing BI and analytics gaps; and enhancing their existing big data and cloud environments.
If you missed the Strata+Hadoop Conference in New York City last week, here’s a quick recap.
From September 26-29, 10,000 experts came together to share best practices, innovative technology news, and network with their contemporaries around all things data—data science, big data, and data in the enterprise. Sessions and keynote speeches focused on a wide array of topics. Members of the Attivio team had a few favorites, including:
Are you looking to modernize your data architecture? If so, you’re not alone. According to Gartner, 90% of an enterprise organization’s information is siloed and unleveragable across business processes, meaning it takes too much time to find and understand the connections between data sources.
Our clients report a troubling side effect of this situation: People end up doing analysis on only the 10% of data they're familiar with to avoid the time and cost of trying to access and analyze the rest of the hidden, but potentially valuable information. Needless to say, the demand for a holistic, unified data view is high.
Do you spend too much time and money trying to locate and understand your data? And do you trust the accuracy of what you find?
Data-driven organizations know that the key to success through data is to make it available to as many people across the organization as possible. But every organization’s data is growing at exponential rates and is getting more complicated. This growth and complexity make current data preparation processes more challenging and often inefficient.
To get the right data out to the right people as quickly as possible organizations need to focus improvements on reducing the time required to gather and prepare data.
As Dan Woods points out in a recent article for Forbes, technology marketplaces cycle through predictable stages as they mature. He applies this insight to the component versus platform decision that organizations face when adopting new technologies.