New Year’s Resolutions 2019: Enterprise Goals for the Cloud

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People often set personal goals at the start of each new year, often called, “New Year’s Resolutions.” Setting goals is a great way to improve yourself, and the same logic applies to corporations. There are the obvious goals often set by a company’s leadership: revenue goals, product goals, diversity goals, and so on. But what about cloud goals?

2018 saw the largest growth of cloud computing use in the enterprise of all time. Now that we’re getting into 2019, what kind of goals is your enterprise IT setting for its cloud infrastructure? This blog post breaks down some of the goals enterprises may choose to pursue when it comes to the cloud and gives examples of some effective ways to meet them.

First Resolution: Reduce Data Center Operating Costs

Running an IT department isn’t cheap, especially with the cost of database licenses, hardware licenses, and services/support licenses shooting through the roof each year. Closing physical data centers and moving to the cloud is already a great way to reduce costs, but can we take it further? Cloud users often overlook things like networking, data transfer, and storage costs, and this leaves room for optimization. Many enterprises are hiring Cloud Cost Managers who do exactly as the name implies – they find ways to optimize cloud systems in order to reduce costs and save the enterprise money. There are also software options that help manage costs and shed light on areas that need improvement.

Second Resolution: Lock down data integration strategy

Many enterprises have moved to the cloud only to find that they struggle with data integration, and this issue is exacerbated by multiplatform data architecture (MDAs) and hybrid cloud configurations (despite their popularity). By the way, enterprises can succeed with MDAs by relying on best practices described in the following TDWI research report.

Multiplatform Data Architectures Best Practices: TDWI Research Report


The secret to getting the most out of your hybrid deployments is to develop comprehensive processes to decide what data sets need to sync across the cloud and on-premise environments, and how often that sync needs to occur. Certain data is more sensitive than others and may require special permissions and security – especially when sharing data with partners and customers.

Lackadaisical data integration will result in messy infrastructure, messy data sets, and privacy and security issues. Because of this, enterprises should make data integration strategy a top priority in 2019.

Third Resolution: Keep Increasing Complexity Under Control

As enterprises bring more data, software, and hardware into their stack, and IoT/Big Data applications promise to bring even more, the complexity of IT infrastructure at a given organization grows dramatically. It’s very important to document everything and create strong processes and workflows for data management. New software tools exist in this space to help manage complexity as well, such as cloud management platforms and IT infrastructure virtualization tools. By ensuring that the complexity of your data operations never escapes your control, you can keep your costs in check and your foundation sturdy.

Goals Lead to Growth

 Like many of us who vow to spend more time at the gym, get up earlier, spend less time on our phones, or swear off social media each year realize that setting goals is a lot easier than achieving them. As IT shifts from a cost center to a profit center by capitalizing on the power of big data, sticking to these resolutions becomes more and more important – enterprises are more dependent on IT for success than ever before!  By being disciplined and diligent when managing costs and complexity, and developing strong processes for data integration, enterprises can reach their goals and position their business to thrive in the new year.

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About Mike Waas


Mike Waas founded Datometry with the vision of redefining enterprise data management. In the past, Mike held key engineering positions at Microsoft, Amazon, Greenplum, EMC, and Pivotal. He earned an M.S. in Computer Science from the University of Passau, Germany, and a Ph.D. in Computer Science from the University of Amsterdam, The Netherlands. Mike has co-authored over 35 peer-reviewed publications and has 20+ patents on data management to his name.

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