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4 OKR examples for Data Engineering

What are Data Engineering OKRs?

The Objective and Key Results (OKR) framework is a simple goal-setting methodology that was introduced at Intel by Andy Grove in the 70s. It became popular after John Doerr introduced it to Google in the 90s, and it's now used by teams of all sizes to set and track ambitious goals at scale.

Formulating strong OKRs can be a complex endeavor, particularly for first-timers. Prioritizing outcomes over projects is crucial when developing your plans.

To aid you in setting your goals, we have compiled a collection of OKR examples customized for Data Engineering. Take a look at the templates below for inspiration and guidance.

If you want to learn more about the framework, you can read our OKR guide online.

Our Data Engineering OKRs examples

You'll find below a list of Objectives and Key Results templates for Data Engineering. We also included strategic projects for each template to make it easier to understand the difference between key results and projects.

Hope you'll find this helpful!

OKRs to enhance data engineering capabilities to drive software innovation

  • ObjectiveEnhance data engineering capabilities to drive software innovation
  • Key ResultImprove data quality by implementing automated data validation and monitoring processes
  • TaskImplement chosen data validation tool
  • TaskResearch various automated data validation tools
  • TaskRegularly monitor and assess data quality
  • Key ResultEnhance software scalability by optimizing data storage and retrieval mechanisms for large datasets
  • TaskOptimize SQL queries for faster data retrieval
  • TaskAdopt a scalable distributed storage system
  • TaskImplement a more efficient database indexing system
  • Key ResultIncrease data processing efficiency by optimizing data ingestion pipelines and reducing processing time
  • TaskDevelop optimization strategies for lagging pipelines
  • TaskImplement solutions to reduce data processing time
  • TaskAnalyze current data ingestion pipelines for efficiency gaps

OKRs to improve the quality of the data

  • ObjectiveSignificantly improve the quality of the data
  • Key ResultReduce the number of data capture errors by 30%
  • Key ResultReduce delay for data availability from 24h to 4h
  • Key ResultClose top 10 issues relating to data accuracy

OKRs to reduce the cost of integrating data sources

  • ObjectiveReduce the cost of data integration
  • Key ResultDecrease the time to integrate new data sources from 2 days to 4h
  • TaskMigrate data sources to Segment
  • TaskCreate a shared library to streamline integrations
  • Key ResultReduce the time to create new dashboards from 4 days to <1h
  • TaskAdopt BI tool to allow users to create their own dashboards
  • Key Result10 teams have used successfully a self-serve dashboard creation system

OKRs to deploy robust reporting platform

  • ObjectiveDeploy robust reporting platform
  • Key ResultIdentify and integrate relevant data sources into the platform by 50%
  • TaskMonitor and adjust integration to achieve 50% completion
  • TaskImplement data integration strategies for identified sources
  • TaskIdentify relevant sources of data for platform integration
  • Key ResultEnsure 95% of platform uptime with efficient maintenance and quick bug resolution
  • TaskDevelop fast and effective bug resolution processes
  • TaskImplement regular system checks and predictive maintenance
  • TaskMonitor platform uptime continuously for efficiency
  • Key ResultAchieve user satisfaction rate of 85% through user-friendly design
  • TaskCollect user feedback for necessary improvements
  • TaskImplement intuitive site navigation and user interface
  • TaskRegularly update design based on user feedback

Best practices for managing your Data Engineering OKRs

Generally speaking, your objectives should be ambitious yet achievable, and your key results should be measurable and time-bound (using the SMART framework can be helpful). It is also recommended to list strategic initiatives under your key results, as it'll help you avoid the common mistake of listing projects in your KRs.

Here are a couple of best practices extracted from our OKR implementation guide 👇

Tip #1: Limit the number of key results

Focus can only be achieve by limiting the number of competing priorities. It is crucial that you take the time to identify where you need to move the needle, and avoid adding business-as-usual activities to your OKRs.

We recommend having 3-4 objectives, and 3-4 key results per objective. A platform like Tability can run audits on your data to help you identify the plans that have too many goals.

Tability Insights DashboardTability's audit dashboard will highlight opportunities to improve OKRs

Tip #2: Commit to the weekly check-ins

Having good goals is only half the effort. You'll get significant more value from your OKRs if you commit to a weekly check-in process.

Being able to see trends for your key results will also keep yourself honest.

Tability Insights DashboardTability's check-ins will save you hours and increase transparency

Tip #3: No more than 2 yellow statuses in a row

Yes, this is another tip for goal-tracking instead of goal-setting (but you'll get plenty of OKR examples below). But, once you have your goals defined, it will be your ability to keep the right sense of urgency that will make the difference.

As a rule of thumb, it's best to avoid having more than 2 yellow/at risk statuses in a row.

Make a call on the 3rd update. You should be either back on track, or off track. This sounds harsh but it's the best way to signal risks early enough to fix things.

Building your own Data Engineering OKRs with AI

While we have some examples below, it's likely that you'll have specific scenarios that aren't covered here. There are 2 options available to you.

Best way to track your Data Engineering OKRs

The rules of OKRs are simple. Quarterly OKRs should be tracked weekly, and yearly OKRs should be tracked monthly. Reviewing progress periodically has several advantages:

  • It brings the goals back to the top of the mind
  • It will highlight poorly set OKRs
  • It will surface execution risks
  • It improves transparency and accountability

Most teams should start with a spreadsheet if they're using OKRs for the first time. Then, once you get comfortable you can graduate to a proper OKRs-tracking tool.

A strategy map in TabilityTability's Strategy Map makes it easy to see all your org's OKRs

If you're not yet set on a tool, you can check out the 5 best OKR tracking templates guide to find the best way to monitor progress during the quarter.

More Data Engineering OKR templates

We have more templates to help you draft your team goals and OKRs.

OKRs resources

Here are a list of resources to help you adopt the Objectives and Key Results framework.