How OKRs Complement Data Science

Objectives and Key Results (OKRs) is the fundamental aspect of goal-setting and revolves around improved focus, increased transparency, and better alignment. This is done by organizing employees and their work towards achieving common objectives.

Since Data Scientists constantly drift into enduring research or optimizing less-valuable metrics, which often leads to wasted time and resources thereby endangering a successful delivery, integrating OKRs enhances strategic guidance and measurability, which help you achieve your project goal.

The underlying factor behind Data Science is to fulfill a goal. The frame or yardstick of goals might vary from researching a new model to creating a prototype for improving an existing system.

Whatever might be the quest, accurate goal-setting is paramount for Data Science projects. Data Science projects are well-suited for accurate goal-setting because we can compare the key results of our model with the required metric.

It’s a common knowledge that product management is a science of experimentation and discovery, hence basing predictions solely about and on user behavior, may go wrong sometimes. It is a natural human instinct to assume that our rational assessment of a problem, based on our past experiences, will yield sound conclusions and represent the likely sentiment or behavior of others. But this is rarely true.

However, with Data Science, you can very effectively and with precision constitute segments based on a variety of factors such as consumption pattern, search history, purchase lifecycle, and social media behavior. This, in turn, creates for you an effective target audience.

Since Data science revolves around metrics like accuracy, precision, and recall, based on your target audience, you will know which of the metrics works for solving the business case. After defining the key metric, you are closer to defining your Data Science OKRs.

In many projects, it makes sense that the key metric is a part of the Objective. It shows where you want to go. Next, you need to define Key Results to display how to reach the goal. Each objective needs two to five key results.

For a seamless integration of data science and OKRs, the structure of OKRs programmes should be broken down into 4 levels. The levels are the brands’ Ultimate Goals ( over the years), Company OKRs ( yearly), Group OKRs ( quarterly), and Initiatives.

1. Ultimate Goal

It’s possible that your brand like most organizations has a mission and vision but you are finding it difficult to understand and can be confused with one and other. In a situation like this, you resort into turning your mission and vision into an Ultimate Goal.

Your Ultimate Goal defines your organization’s long-term purpose and acts as the cardinal or focal point to which all other OKRs align. An Ultimate Goal should aim for a point at a considerable distance in the future; 10, 15 even 25 years is reasonable.

An Ultimate goal helps provide the focus for your entire organization. An example of an Ultimate Goal is:


– Build factories in 15 more states in the U.S.

Key Results

– Cost the factors that will be necessary to build the factories.

– Source for funds through bank loans or Venture Capitalists.

– Acquire land to build the factories.

2. Company OKRs

Company level OKRs represent your strategy. These are the 3 or 4 things your organization decides it must achieve in the next 12 months. Most organizations review their strategy yearly and this sets the timeframe for Company OKRs.

The best procedure in deciding what can be achieved in a year and making it actionable is giving everybody in the organization an opportunity to have an input. A way of doing this is to initiate an OKR workshop where all key stakeholders responsible for company strategy first ask for and then gather input from employees on what they think top priorities should be.

Where employees are reluctant in freely expressing themselves, their inputs can be gathered anonymously through the use of suggestion boxes. These random inputs can then be collated, streamlined, homogenized, and then discussed in relation to existing company strategy and broken down into 3 to 5 OKRs.

This can be done using post-it notes, collaborative documents or even a whiteboard. The objective of the exercise is to come to an agreement on what the organization should have achieved by the beginning of the following year.

An example in this case is:


Acquisition of customers.

Key Results

– Set up a target audience.

– Embark on a marketing campaign.

– Homogenize potential customers.

3. Group OKRs

There should be an alignment between the group or team objectives with that of the brand, which all work to attaining the company’s Ultimate Goal. Once they are achieved, the impact must be visible.

Unlike the Ultimate Goal and Company Objectives, they must have a short time frame so that corrective measures can be taken once they are not beneficial to the brand.

The group key results must have high impact and reflect the big change on the organization when positive at least up to 70 percent. They must be focused, specific and have a clearly defined scope. They should be within your influence but not under your direct control.


– Increase the departmental output in a bottling company.

Key Results

– Ensure a high degree of punctuality.

– Reduce under-filled to less than 0.1 percent.

– Award best-performing employee weekly.

4. Initiatives

Initiatives are the things you will be doing to achieve these results. They’re basically your hypotheses for how to make it all happen. While progressing on your Initiatives, you need to keep an eye on your Key Results to see whether these Initiatives deliver the desired outcome.

This is what you will actually do to move the needle at you Key Result. It answers the question “What will I do to get there?”. You have full control over it. Being a hypothesis, it will not necessarily turn out to be successful.

As an employee who wants to get the weekly best performance award, you may have to go to bed early and wake up early to increase your chances of punctuality.

In conclusion, OKRs allow you to align and prioritize your work, set stretch-goals, and track your progress. This you can achieve by starting every Data Science project by setting the correct metric and OKRs and continuously measure your progress towards reaching them.

Every Data Science project is different. However, all projects start with setting a goal. Set your Data Science project up for success through using OKRs.

Photo Credit: fabola Flickr via Compfight cc

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About John Ejiofor

John Ejiofor is a curious life-researcher, whose quest to finding answers to life's pertinent questions has led to founding Nature Torch. This blog aims to debate and explore many questions about our earth -- including those a lot of people are uncomfortable with asking. He has been published on some of the internet's most respected websites, which you can find online.
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