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By Laurence James, Alliance & Solutions manager, NetApp
Moving to “the cloud” is no piece of cake.
So if your business wants to increase its cloud footprint, you need to think differently.
In moving data from your own premises to a public cloud, you should re-evaluate your business rules governing the location, security, movement and audit of data assets. Naturally, you must also comply with data-protection regulations and laws.
Moving data also amplifies the management challenges of good governance.
Most importantly, cloud migration may be perceived as a “technical” activity—but it isn’t. Data is an institutional resource, and the responsibility and accountability falls firmly within the realm of business process governance.
Yes, technology can help you stay compliant with policies set around your data, but it’s not the starting point.
So What Is The Starting Point?
The best way to begin is to recognize the need to revise your existing data governance and management policies. Laws and regulations that apply to data in a public cloud are very different from those for on-premises.
These policies underpin services levels, ownership and processes: Not only do they determine the lifecycle, activity and location of the data assets, but also their accessibility, consistency, accuracy, protection, performance and security.
And don’t forget all the foreign laws that apply to you when you have customers in other countries. For example, the EU’s new General Data Protection Regulation will be finalized later this year.
That’s not all. There are factors that determine the archival and backup requirements of the data, who has access, what the audit procedure looks like, and the impact of regulation on data. So you'll need well-defined rules.
What’s Next?
You can start building your plan. Here are three steps...
1. Understand The Impact Of Supply And Demand
Your IT resources will have a certain capacity and performance requirements profile. A closer look will tell you how much elasticity you need, to meet your business’s demands.
Let’s use retail as an example. I’m always reminded of the seasonal variations the industry sees. When the holidays come around, there’s a significant spike in sales, fueling a subsequent demand in IT resources.
Typically, businesses use forecasting models to influence budgeting and procurement. Holiday sales define their annual success or failure.
But the capital invested to meet a seasonal peak demand will provide returns for only a few months of the year. For the remainder, it’ll look like the organization is making ineffective investments.
Cloud provides a better solution: IT demand for the peak period can be satisfied by elastically expanding into a public cloud. Once the peak is past, it can be contracted. The calculations on returns—and shareholder value—look much more effective if based on the capacity plan from the months where the profile is more uniform.
Most industries see surges in demand, to a greater or lesser extent. The ability to buy IT resources when you need them—and give them back when you’re done—can be very useful.
2. Practice Mindful Change Control
A data governance and management plan will always be a “work in progress.” At some point, you’ll arrive at a version of the plan with appropriate mandatory and desirable requirements.
This is when you should determine a baseline model. It will provide the insight you need to plan, forecast and carry out the “what if” analysis.
Let’s look at retail again: Data is collected based on customer behavior at the point of sale and on usage of loyalty cards. This allows a retailer to understand buying patterns, effectiveness of product placement, marketing, etc.
Before a customer’s data is acquired at the store, the retailer will have to set down the rules around governance and stewardship, which should be in compliance with the regulatory framework. With processes around usage, quality, security, audit, etc., you can determine a baseline model.
Any changes in data governance must then be tested against the baseline. And any analysis or use of data for business benefit has to fit within the model.
3. Identify The Technologies (Yes, Now It’s Time)
When you arrive at a model that works for your business, it’s time to look at the technologies that drive the best flexibility, choice and cost, while also balancing risk.
They must be technologies that deliver a consistent method for managing data wherever it resides. Your data could be on-premises, off-premises, in or near a hyperscaler, but the management should be the same: You don’t want the cost and inflexibility of doing the same thing three different ways.
Your technology should allow you to move data rapidly into and out of the cloud. It should support different levels of performance for different types of work, and should allow easy movement between these performance tiers.
The Bottom Line
Data is challenging to manage and move. It has mass: It’s heavy, and activity is typically restricted by compliance and audit rules.
But any good data governance and management plan will follow the data regardless of location, application, server platform, network or storage. It will be adaptable and enforceable—wherever the data resides, and whatever cloud model you’ve adopted.
What's your take? Weigh in with a comment below, and connect with @lozdjames (Twitter).
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• Moving To The Cloud? 3 Steps To Good Data Governance ~ @lozdjames
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Image credit: Scheinwerfermann (public domain)
