I have used Apple Watch to monitor 3 key measures related to health activity on daily basis

• Move Goal: 350 Calories per day
• Stand Goal: 12 hrs per day
• Exercise Goal: 30 minutes per day​

Apple Watch enabled monitoring the data throughout the day and data is captured automatically instead of manual data entry. It was possible to monitor performance on a daily/weekly basis and stay motivated or take corrective actions to get back on track. It took 6 months of data before indicators are shown. 

Applying this example as a framework to a business (department or overall) scenario:

• Identify what data can be measured automatically on daily or weekly basis in an automated way based on how you operate(like website visits)
• Set a realistic goal (based on your business type, identify what is a realistic goal)
• Take actions to achieve goal on regular basis (run campaigns to attract visitors to the site)
• Monitor streak of achieving goals (how frequently are you meeting goals)

To achieve this in a strategic mode, identify one area at a time. 

Digital transformation has made it easy for any organization to measure their activity and convert that data into useful metrics. Just like fitness data is critical to your personal wellbeing, operational data of business is also important for business wellbeing. Recording data as business is happening will improve data quality and reduce errors when data is manually recorded after a delay. Automate this as much as possible to reduce gaming the data. 

As a data analyst you will be analyzing lot of datasets. All datasets cannot generate equal impact and value. 

If analysis focuses on data and metrics that may not generate any business value then effort spent in analysis is a wasted investment. Data analyzed should establish a baseline, set a target and enable you to track progress towards a target.

As an example, measuring monthly personal expenses without any action is not useful. 

- Based on measurement of expenses for a few months, average expenses can be set as a baseline

- A target should be set to reduce average expenses by 10-20% of baseline

- On a monthly basis progress can be tracked 

We recommend reading following articles for more examples.

Vanity Metrics

Definition of Vanity Metrics

Converting an organization that is not used to having a ‘data driven’ culture to become one takes considerable effort. Following are steps we recommend

1. Analyze data that is already captured: Start with what you have before starting new  data initiatives. Use the data to tell a story of what is happening

• How are we performing? – What is total of <metric>?
• Are we improving? – What trends are we seeing? Increase, decrease or no change?
• How can we look at the data in different ways?

2. Build ‘Data Democracy’:  Make data accessible to everyone. Everyone who is impacted by that data should be able to view the data. This establishes trust and transparency.

• What data is available?
• How is it collected?
• Can everyone come to same conclusion looking at the data?

3. Ask for data: Encourage team members to ask for data. Data should be asked by every team member as evidence whenever there is a shift in direction.

• What data is causing us to make this change?
• Are we sure? do we need to analyze the data further?

Are you doing any of the above? Is this proving effective? Share your thoughts.

In future articles, we will cover what steps can be taken to take this culture to next level.