If you had to characterize your organization's relationship with data, would you say it's one of trust or fear? That's a critical question for these times, framed as they are by the imperative to establish data-driven decision making as the business norm.
More critical yet is the same question viewed from an end-to-end perspective. It's not enough that the marketing organization trusts its data and sales has confidence in its data. That will only get you so far... and not very, at that. Rather, each department and business unit needs to accept the trustworthiness of any and all corporate data.
I'm not telling anybody anything new here (or, at least, I sure hope not). The smart, modern company encourages information sharing through open communication and collaboration, understands data to be an asset and the value of integrating that data, and knows the importance of making decisions in real-time based on data, not gut. That calls for a lot of trust.
But all too often, the opposite rules the day. Fear of one another's bad data hinders interdepartmental collaboration and information sharing. Nobody in their right mind wants to send their good, clean data into the wild, only to be lost or corrupted. After all, cleaning up and getting data in shape for business reporting is no small effort.
Traditional IT infrastructure and processes offer little comfort, either. Can you trust IT to deliver timely data -- as in, at the moment of customer contact, if required? Chances are pretty high that you can't, considering the data enters IT's domain from any number of autonomous systems, sits in a legacy enterprise data warehouse, and gets pulled together for your report through batch processes that run on... and on.
Maybe things aren't quite so bad at your company. Maybe data sharing among disparate business users is common. Fantastic. But is the reporting cohesive? If sales does its thing with marketing's data -- aggregating and analyzing it and presenting it in reports -- those results might not look quite the same as those produced when marketing aggregates, analyzes, and presents the data itself. Examples of successful data integration are few and far between.
Data challenges of this sort may seem insurmountable -- easier to continue working around than addressing head on. But if you can build a centralized, controlled data layer, that doesn't have to be the case. Lest you think that's not possible, consider the story spelled out in a new case-study e-book, "Federated Data Checks In," from SAS, this site's sponsor (registration required).
I could leave off with a cliffhanger here, but I won't. The e-book tells how Westwood Vacations, a fictitious hospitality company that could easily be any number of actual businesses, used an emerging technology called data virtualization to solve some of its biggest data challenges. As depicted below, data virtualization software establishes a controlled data layer between batch and real-time data sources and consuming applications. This ensures "that all business units are accessing and processing the same data with consistent formats and processes."
How Data Virtualization Works
You could consider it a "data-as-a-service" approach, as SAS does with its SAS Federation Server. Version 4.1, shipping this quarter, provides access to big-data resources like Hadoop and SAP HANA, traditional databases like Oracle and DB2, and other data sources, as the company announced last month. The aim is to provide "easily consumable access to shared, secure enterprise data to speed and simplify data preparation." Toward that end, SAS also said it has enhanced Federation Server's security, data masking, and data governance capabilities to "ensure proper policies, access and restrictions for sensitive data."
I'll leave the nuts and bolts of how data virtualization works for another post. For now, I'd rather circle back to that modern enterprise and how the technology works for it. With data virtualization, this business:
- Shares information, with disparate data marts provided through virtual views and reports prepared using centrally defined business terms -- without duplication
- Collaborates, without fear of losing or corrupting data, since it's accessed through the virtualization layer and not from the source system directly
- Is data-driven, with trust established via the virtualization layer
Were I at a company struggling with trust issues and other data management challenges, data virtualization is a technology I'd like to check out. What about you?
— Beth Schultz, , Editor in Chief, AllAnalytics.com