How to Measure What Matters

           Today’s world is filled with and driven by data. Data is an essential part of running and sustaining a successful business. It can be the leading cause of your success if you know what you’re looking at or your inevitable downfall if you jump in head first with no idea what’s going on. In Katie Paine’s book, “Measure What Matters,” she explains just how important it is to ease into data if you aren’t familiar because it can get very overwhelming very fast. This is where a lot of businesses go wrong and Paine does her best to explain to her audience just how to avoid getting overwhelmed by data. She gives us six steps to figure out what you should be measuring.

           

            Step one is understanding your background. Personally, I feel like this is an obvious step because you won’t even know where to begin if you don’t know what your background. With that being said, Paine offers some good points, saying that a business should list its target audience, key competitors and its main influencers. These are a few key things to get a solid place to stand when starting to measure data. Finding a target audience, I believe is the most important because that can help a business determine everything from product design, placement, and marketing strategies to use. All of which can be measured by data. Identifying key competitors allows you to generate a playing field and gives you the ability to create the competitive advantage that the business may need. And lastly, identifying influencers can mean easy targets if the business chooses the right people to support them and push their products and services.

            Step two is to assemble your team and ban the “jargon.” Good communication is the key to running a successful meeting and without that communication, even the best plans can begin to fail. Because data is such an intricate piece of the organization and basically the cog that keeps the business running smoothly, everyone needs to be on board and attentive during meetings, conferences and other learning opportunities because this can be vital to the process of data measuring.

           

            Step three is to ask them what they mean when they say they got their butts kicked, as Paine puts it. Asking questions is key to the communication process and keeping everything running smoothly. If a certain portion of the data is way worse than it normally is, ask questions, figure out why its lower than normal and find a solution. This is the point of data. It is hard evidence of everything going on and the source of problems can be very easily identified when using it.

            Step four is to “ask them what they mean when they say they kicked butt,” stated Paine. Numbers are up, stats are looking good and everything is running smoothly. This is when you start asking questions like “What went well this week?” and “How can we keep up this performance?”. And just like last time, start checking the data. This is the easiest way to know what is working so well for the business in the given period and these opportunities can be capitalized on when the data is interpreted correctly.

            Step five is to find the objectives of the employees. This is essential because a measurable objective determines where the business stands. It allows employers to get a grasp on how different employees operate and function and analyzing that along with the data being collected already can help goals be reached more efficiently.

            Step six is to prioritize. Take objectives listed by the employees and have a vote on them, Paine says. This allows employees to collaborate and work towards a goal that was agreed upon fairly and not just administered to them. This removes the stress and headache of data measuring because it is done in a reasonable manner and gets the employees involved in a plan to work more efficiently and create a better workplace.  

            Paine gives some good points on how to work your way into the data measuring process even though it can be very overwhelming at first. She explains the mains points of why it is a necessary process and for someone who knows very little about data, she makes the process seem very easy to understand. I am looking forward to see what the rest of her book has to offer.