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.
The other day I Googled, “the problem with a modern data architecture.” Of course, at Attivio we’re big evangelists for an MDA, but it’s always interesting to see what the contrarians have to say. There were over three million returns, but none on the first two pages said a word about problems. Lots of articles about how to develop an MDA or how to optimize an MDA or why you had to have an MDA. You get the picture.
Once Chief Data Officers have identified, confronted, and hopefully overcome the challenges at the bottomand in the middleof the Big Data stack, what’s next? As Andrew Brust of Datameer notes, “At the top of the stack, there are seemingly endless choices. Whether Enterprise BI stalwarts, BI 2.0 challengers, or big data analytics players, the number of vendors and their similar positioning makes it really hard for customers.”
The scope and scale of today’s enterprise challenges are unprecedented in terms of their complexity. Vast volumes of information are scattered across the ecosystem and around the globe, and enterprises often are expected to dynamically retrieve precisely the right data set on a moment’s notice to win a customer’s loyalty. If not, other vendors eagerly await to win that customer’s lifetime business. Every transaction is a lifetime transaction.
In this hyper complex and hyper competitive environment, no single vendor can do it all by themselves. The success of one enterprise is inextricably linked to the quality of its partnerships. OEM partners, by nature, often form the critical core of another’s customer-facing solution. And so it is with TIBCO.
Writing on the O’Reilly.com site back in August, CEO Jessie Anderson of Smoking Hand, a training company for Big Data technologies, commented on the overall complexity of Big Data, NoSQL technologies, and the distributed systems that deploy them.
Chief Data Officers certainly have first-hand knowledge of this complexity and the hurdles it presents to extracting the maximum value out of business data. Complexity takes a variety of forms throughout the Big Data stack. Let’s start at the bottom.
Chief Data Officers have a lot of things on their plates. And one of those things is giving users freer access to the data they need. This is what we call "data democracy." Most CDOs like the idea of data democracy in theory. But in practice, the CDOs we talk to find that efforts to create a data democracy face at least four common barriers:
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.