Specifically, we are in a world of connected data and so it’s a lot different than it was in the 90s, or even in the early 2000s. The data is not just structured relational tables anymore. Now it includes data from different sources, from connected and IoT devices, from digital devices and so, data has been exploding. In fact, we’ll see more about this in the future part of this session. Let’s talk about data as a key strategic asset.
So we recently talked to Keystone Research which is part of the Harvard Business school, and what they found was that organizations that use their data to the most transformed way actually receive $100 million more in profit than their peers. So data was certainly seem as a key strategic asset. We all kind of know this. A lot of us, as we think about our data investments, we know this. But we know that there are also inherit challenges to the way we currently manage our data for analytics today.
- Some of these challenges are that the data characteristics have essentially changed. Instead of again the data that’s being stored in relational format only, it’s now changing to include non-relational data, as well as data that’s increasing in volume such that analyst companies like IDC has seen that or predicted that data will grow to 44 zettabytes by 2020, and so data is certainly growing faster than we can keep track of it.
- The other challenges that we’re facing is this always this trade-off between performance and price, and so often times we want more performance but we also don’t want to pay the increasing price that’s associated with it.
- The third challenge is around fragmented architectures. Now and now, it’s not just a single offering that’s on premises, with the Cloud you have to think about hybrid scenarios as well as incorporating open source technology as part of your solution and so, there’s fragmentation all over the place as it relates to your architecture.
- And the final challenge is the need to support new audiences and have new insights, so no longer is it just your traditional business analyst writing operational reports, but you also have to be able to accommodate data scientists who are running experimentation, or even developers who are doing intelligent applications and may need access to data and so for sure as a technology leader we need to incorporate all of these different new types of audiences and so what we need is a single solution, a data management platform for analytics, something that can take in relational data as well as non-relational data.
Be able to abstract the complexities of that data so that you can do either type of processing, either data warehousing or big data analytics, and make that available across any BI tool, any advanced analytics solution and any language, and finally make that available both on-premises and in the Cloud.