We've just moved out of one generation of solution architectures and on to one that will define the enterprise computing trajectory for the next decade or more.
Earlier this year, I hit my 10-year anniversary of writing for the InformationWeek family of publications. I started writing for Intelligent Enterprise's and have since probably written more than 150 pieces for various pubs. That might not seem like a lot over 10 years, but it's certainly far more than I ever anticipated. Most of my writing focused on various aspects of information management, with some fun (and sometimes risky) tangents, like an occasional attempt at humor, a take on Sherlock Holmes, and a challenge of Nobel laureate.
But as I look back on these 10 years, I wonder, has anything really changed in information management? You bet. Here are six of the most compelling change agents in enterprise information management over this period.
Data Governance And Master Data Management
In terms of core data management, perhaps nothing has risen to prominence faster than the data governance and master data management combine. We have all been practicing data governance and master data management since ancient times--that is, for the last 30 or 40 years--but only in the last few years have they become household terms.
No longer consider merely buzz words, business and technology leaders and practitioners alike have come to accept the need for systematic management of business information driven by the users of that information and have recognized master data domains, including assets, customer, employee, product, as a framework that defines the very existence of a company. Whole institutions and national events have arisen with the purpose of facilitating data governance and master data management, in addition to their primary purpose, of course, to make money.
Despite such laser-like focus, I sense a continued ambiguity out there regarding what constitutes data governance, and how it's related to MDM. Think of it this way: All roads to MDM go through data governance, and all roads to data governance go through MDM. In other words, don't worry about which one comes first, get started with either, and you'll quickly find yourself defining your needs for the other. That's because you can't govern your overall data without first governing your master data, and you can't figure out a way to manage your master data without first laying down some basic (master) data governance principles and practices.
Why do you need either? Quite simply, because you won't survive without taming this two-headed beast one way or another.
Data Warehousing And Business Intelligence
Here's another compelling duopoly. The question here is must these two always go together? Definitely. Sure, there's lots of talk about “operational BI” and “real-time BI," and admittedly some action as well. But the fundamental mechanism for gathering your enterprise data from all these diverse sources, sprucing and lining it up, and making it interactive-analysis-friendly remains some kind of a data warehouse. (I'm counting data marts in this category--let's not get distracted by data architectural wars here.)
That said, there's been significant progress in several areas of data warehousing and BI. Notable among these are alternate database paradigms such as columnar databases, which at times look to be on a similar trajectory as say object and XML databases, but with the potential for greater resilience. A closely related area is data warehousing appliances.
Building a database “machine” has distinct advantages such as reduced deployment complexity and increased performance. (It also, unfortunately, has equally distinct drawbacks such as high cost and vendor lock-in.) Particularly exciting are the rise of real-time predictive analytics and complex events processing. By bringing together data from data warehouses with the data streaming in from real-time applications, companies can now look ahead--whether a few minutes or a few months--and take proactive measures to nurture their customers and protect and grow their businesses.
The Agile ArchiveWhen it comes to managing data, donít look at backup and archiving systems as burdens and cost centers. A well-designed archive can enhance data protection and restores, ease search and e-discovery efforts, and save money by intelligently moving data from expensive primary storage systems.
2014 Analytics, BI, and Information Management SurveyITís tried for years to simplify data analytics and business intelligence efforts. Have visual analysis tools and Hadoop and NoSQL databases helped? Respondents to our 2014 InformationWeek Analytics, Business Intelligence, and Information Management Survey have a mixed outlook.