Maintain Essential Operations When Demand Drops
Your business has dropped significantly. You’re still operating but with major downsizing or other changes. First, I’m so sorry that you and your employees are going through such extraordinary difficulties. Here are some things to keep in mind with respect to the role of data in your efforts to keep going.
This is the 3rd article in our series: 3 Scenarios for Adjusting Your Data Practice to Business During the Pandemic
Pinpoint where to cut, what to preserve with data.
Good, data-driven decision making is critical, so you can pinpoint the most valuable parts of the business to shore up, as well as the cuts needed to survive. What business lines are still seeing demand? What operational changes are working? Where can some additional costs be cut? Which vendor payments can be postponed or renegotiated? While this can be emotionally gut-wrenching, having access to the right data will help leaders make the tough calls.
From the outset, the goal of StreamSets has been to get data as fast as possible into the hands of those who need it. In times like these, hours matter. Dollars matter. Organizations who rely on the StreamSets DataOps Platform have been able to deliver big cost savings and big reductions in development time.
Slash your data infrastructure costs.
You’ve got to squeeze every penny out of your existing data infrastructure, consolidate as much as possible and wring out any waste. Depending on your situation, that may mean staying on legacy platforms and postponing modernization efforts. It also may mean the opposite. For example, if you have already started migrating to lower-cost, cloud-based platforms, accelerate the move so you can eliminate legacy systems and their costs.
StreamSets supports all the key data platforms, and the StreamSets DataOps platform is fundamentally architected to enable portability across them. So if your goal is to wring the most value possible out of existing infrastructure investments, StreamSets makes it easy to get new data sources into those platforms and transformed to be fit-for-purpose. If you’re moving to cloud data platforms as a way to lower cost and down-scale, StreamSets can accelerate the migration to reap those savings sooner.
Maximize productivity of whoever you still have.
If you’ve had to freeze or reduce headcount, you have to increase the productivity of those who remain, in spite of the emotional trauma they are likely experiencing. People who are shouldering more work than before require cross-training to cover areas and projects which have now fallen in their laps. Tools that can help data teams be more productive and cross over into new areas can be a game changer.
StreamSets’ easy-to-use, visual tools greatly increase the productivity of your developers and data engineers, regardless of which types of data pipelines they are building. Prebuilt support for a breadth of data patterns, from streaming to ETL to CDC, and data platforms, from Oracle to Hadoop to Databricks enables a single developer or engineer to handle all your different data workloads.
Turn on a dime.
You’ve got to make changes at lightning speed to pivot your business model, preserve cash, and protect whatever remaining jobs you can. Having full operational visibility into what is going on with your data can help you see where problems are, and make the call regarding which to fix and which to just let go. If you have resiliency and drift detection built into your data flows, you are less likely to experience data loss or outages even as you make overnight changes to your business processes or systems.
Pipelines built with the StreamSets DataOps Platform are fully instrumented, giving you real-time operational visibility into how your data is flowing. Our unique drift detection and handling capabilities minimize the risk of outages or data loss as you make cuts and other changes. That way you can move fast and minimize the risk of unexpected breakages.
How is your organization going into Temporary Hibernation? Share your coping story with us: #amazingdatastories.