IT Briefcase Exclusive Interview: Maximizing Data Management with Julie Lockner, InformaticaSeptember 24, 2012 No Comments
Although data management seems like a somewhat simple concept, achieving “optimal data management” can be a challenging task.
In the below article, Informatica’s Julie Lockner outlines ways in which applying lean data management best practices can help users maximize their output when moving data into the cloud.
- Q. What, in your opinion, are the biggest roadblocks to achieving optimal data management?
A. Optimal data management means that individuals have access to the information they need to meet its business or personal needs and get value from that information. It also means that IT is aligned in its ability to deliver that information in the most cost effective way throughout the information lifecycle. The biggest roadblock to achieving this nirvana is not knowing how data is going to be used or leveraged by the business. In order to have optimal conditions, both business and IT need to have an open feedback loop. If the business doesn’t provide clear requirements, IT may over or under provision. If IT doesn’t understand the business it is in, requirements may be misinterpreted. Organizations successful at running highly efficient data management practices have strong bi-directional communications between the business and IT from the C-level down.
- Q. How does the concept of “lean data management” fit into all of this?
A. Lean Data Management (LDM) is a strategy to implement processes, standards, technology and resources to best maximize IT operational efficiencies in an organization – and to ensure continuous improvements in the management of your data and applications. Organizations look to lean data management principals to either proactively prevent ‘data waste’ or – more likely than not – to fix an existing problem where there is an excess of data volumes and corresponding infrastructure that seems to have grown out of control. The first phase of implementing LDM is gaining an understanding of what data exists, its value to the business and how that value may change over time. Once this classification exists, an action plan can be put in place to either purge or delete data no longer needed, archive what needs to be kept to meet legal retention requirements, and optimize business data that may require more or less system resources to support the business appropriately. This can be applied to production applications and non-production copies, legacy systems, as well as data warehouses. Because it is a systematic process that is agnostic of where the data resides, LDM works for systems and data on premises and in the cloud.
- Q. What are the challenges with applying lean data management best practices when moving data into the cloud? Are these challenges limited to specific industries?
A. Anytime data is moved to the cloud, either as part of a new system deployment or a migration to a hosted application or infrastructure as a service, it is that much more important to incorporate sound data management strategies as part of the project plan. When data is ‘out of sight’ it also lends risk to ‘out of mind’. In fact, because the cost of storing data in the cloud is so astronomically cheaper, I see a lot of organizations not bothering to implement these best practices because the new economics make the problem less obvious. However, just because data is physically resident off premises does not mean the business is not responsible for adhering to legal data retention policies nor does it mean that data governance should not be extended to cloud-based data. What it does mean, however, is that existing data management processes need to be closely evaluated to ensure that data stewardship does not get overlooked.
- Q. How do you build a business case for lean data management especially in light of the economic value proposition of moving data into the cloud?
A. Many organizations will continue to manage business applications on premises. For these companies, the business justification for implementing lean data management best practices can be determined based on the amount of IT infrastructure that can be reduced, the amount of people’s time can be freed or reallocated to more strategic tasks, or the performance improvements that will directly impact the business.
A lot of times, it is just easier to move data in bulk to the cloud because IT doesn’t know what to do with it and it is cheaper. The problem is if some data is considered a record and is retained longer than the required retention period, the risk of falling out of compliance increases. Even though it may be less costly to store data in cloud infrastructure, if that data is relevant to a dispute, the time it takes for eDiscovery could introduce legal costs and potential fines that mitigate the initial storage savings.
- Q. How can Informatica’s Data Archive Portfolio help facilitate Lean Data Management?
A. Informatica Data Archive is a comprehensive platform designed to help organizations implement and automate processes in support of Lean Data Management strategies. With Informatica’s discovery tools that assist in data classification, IT can identify and quantify data that needs to be retained to support the business and legal retention requirements, data that is eligible for archiving, and data that can be simply purged. With this visibility, the software can then be used to implement appropriate action that eliminates data waste. The result is lowered infrastructure requirements through data archive and purge, improved system and application performance by freeing system and human resources to focus on mission critical tasks, and lowered risk by maintaining compliance. For those who are interested in learning how to build a Lean Data Management business case, ask your Informatica specialist for access to our free online Business Value Assessment tool.