Performance Measurement and Data Collection

Performance measurement is all about gathering data from different sources, turning that raw data into useful information that can be used to aid decision making and improve the overall performance of the business.

These days, with the advent of modern technologies, social media and a host of data sources, organizations are only a click away from consuming that breakthrough data their businesses need to outperform their peers.

Online communities such as Twitter, Facebook, LinkedIn, Digg, Blogs, Internet, Email, E-commerce and other online discussion forums are connecting like minded people to share vast amounts of data and information.

In other instances, you have to go out there, talk to people, observe, and listen to get the data you need.

Big Data is the big thing today. Now we talk of Petabytes and as such, organizations must have the capacity and capability to analyse these vast amounts of data in a timely and cost effective manner to get insights that aid decision making.

With the need to increase sales revenues, cut costs and increase bottom-line profits, it is not surprising that most of the time, a lot of resources are wasted gathering and gathering data, some of which is useless.

It should be noted that, data alone is not a driver of business. There is need for careful analysis and interpretation of that data to get information that will help answer your performance measurement related questions.

Actionable insights improve decision making and help drive business performance.

There is no doubt that data collection process is a very costly and time-consuming process if not handled properly. To avoid making the same mistake of collecting and storing data that is not serving its purpose, below are a few tips that I think businesses should consider:

• First define your performance measures; this will help you to clearly identify the data requirements for each measure.
• Find out who will be the users of the data you intend to collect and why obtaining that data is important to that user group.
• Design an appropriate data collection process and manage the integrity of the data.
• Randomly select your sample for data collection as this reduces chances of bias.
• Decide from the onset, how much in cost you are willing to spend and also whether that figure will be enough to get you all the data you need.
• Always involve the right people who are dedicated and are willing to own the data collection process.
• Be clear of how you are going to collect the data. Is it an automated process or manual process? What are the risks of using one or both methods?
• Understand that a high response rate is no guarantee to reliable data. Double check your figures and always pilot test your findings before “implementing them”. This will help you identify dark spots.
• When data collection involves key stakeholders of the business, it might be worth considering outsourcing the process for confidentiality purposes.
• Find out within your line of business or networks who else is collecting the same data that you need to avoid any duplication of efforts.
• If you are using questionnaires or forms, make sure that they are simple to understand and try to ask open-ended questions to allow the respondent to clearly express themselves.
• When storing data within your organization, always make sure that there is easier sharing. Avoid storing data in independent systems. However, a decision has to be made of who has access to data and who doesn’t for integrity purposes.
• If possible, automate your data entry for easier detection of errors. This is also important for data integrity preservation.
• Always be aware of the time frame that you store your data before making an analysis to avoid making decisions based on out-of-date data.

By collecting the right amount and quality of data and finding insights from this data, sound decisions can be made that are critical to the ongoing success of the business.