Informatica Making Big Data Manageable

As an analyst covering data management, a single day does not go by without a healthy discussion or debate on the meaning or impact of big data. It is quite comical in one sense, and quite inspiring in another. We are all intrigued by this concept of having access to information so that when we get THE question, we will find THE answer. And hopefully the answer is more than "42!" (that was for my son who is currently reading "Hitchhiker's Guide to the Galaxy" by Douglas Adams - I couldn't be more proud).

This week was no different. I was fortunate to have the opportunity to attend Informatica's Analyst Days. While Informatica is leading the charge for data integration, they are also taking a thought leadership position in how to conceptually view an organization's return on its data. Combining aspects of value, cost, and the impact of big data, I saw it as an elegant way to say - hey, look - you can keep all the data you want to derive value, but there is a trade off. There is ultimately a cost. That cost can be measured hundreds of different ways - but there will be consequences. So we have to prioritize and put controls in place to maximize ROI.

Now let's say this data - this big data set - can provide incredible value - but only for today or for a week. Then, after that shelf life, the value drops. Then what?

That is where information lifecycle management (ILM) fits in. If upon creation, you assign a time-based value model associated with that class of data, you can now optimize your cost structure. So it's not just a one-time calculation, it occurs throughout its lifecycle. I was pleased to see so much of Informatica's ILM portfolio integrated with their big data messaging. By making big data cost-effective, it becomes more manageable.

This was just one of the dimensions that made the event so insightful and worthwhile - but one I want to keep on top of mind. ILM is no longer just about tiered storage - but about maximizing value and minimizing cost and risk of information. Production apps, test and development copies - on premises or off - ILM applies to transaction processing sources, legacy applications, data warehouses, and especially data stored in a Hadoop cluster.

There is huge potential here for Informatica to take its forward thinking initiatives with big data and its investment in the ILM portfolio to help organizations make the math work - even if the answer is ultimately 42.

Topics: Data Platforms, Analytics, & AI