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.
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.
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.
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.
We talk a lot of about the amount of data organizations capture and how that data comes in many sizes and formats, from structured to semi-structured and even unstructured. We also know that much of that data isn’t used for decision-making, hidden in silos across the organization. All of this makes it difficult to build a unified view of your data.
But the challenge with building a unified view is only partly due to data silos.