Data Driven Decision Making
Done well, data analysis is an invaluable part of any decision-making process. But, with so much information now available, it can be hard to know exactly what to do with it all.
In my previous blog, I looked at the dangers of only considering a narrow range of metrics within your organization. In this blog, I'll outline how you can ensure that your data is actionable, rather than simply providing you with information.
How Is Data Used When Driving Decision Making?
Imagine you're on the train home after a long day at work. You check your cell phone and notice that it's on 8 percent battery. You were planning to respond to some emails and listen to music, but now it looks like you won't make it home with a working phone.
With that statistic (8 percent battery power) you have an actionable insight. You can shut down any active apps, turn on "low power mode," or search for a power outlet nearby. You can act upon the information to get the best performance from your phone.
In that moment, other data is irrelevant. There's no point opening your "settings" to find out which apps are using the most power, or the time of day when you were most active on your cell phone, or when it was last charged.
All you need to know is what you can do to conserve energy. In fact, digging deeper into the data will only drain your cell phone's battery more!
It's true, you should avoid becoming limited by your metrics. At work, that might mean looking beyond a single aspect of data, such as total sales, or the number of page views.
But, as well as broadening your data perspective, it's always important to ask what you can do with your information. Can you take any action based on the data? If not, what value is it serving?
Finding actionable insights should be the number one priority for any data report.
How Do You Create Data Driven Decisions?
Here are three ways to make sure that all your reports gather useful, actionable data:
1. Consider the cost-benefit ratio
In a world of "data worship," vanity projects are commonplace. Many people seem to be impressed by shiny numbers and intriguing stats, so it's easy for bad practices to go unchecked.
I once worked with a manager who was obsessed with marketing attribution statistics. We had to collect and compare large amounts of data on customer touchpoints. It was all very interesting, but nothing was actionable. I couldn't help but feel it was little more than a waste of time.
As in all other areas of the organization, it's important to think in terms of the costs vs. benefits of your data-led tasks. How many resources are you putting into a project? Is a data report worth what it will cost the company to produce?
It pays to have a small planning stage before undertaking a data project. That way, you can make sure that you're focused on harvesting actionable insights, and that the task is truly worthwhile.
2. Keep in mind desired outcomes
Try to tie your data projects to your original desired outcomes, and constantly measure your work against them.
For example, imagine you've been tasked with finding the overall numbers of employees accessing an L&D portal. You've discovered that only 55 percent of employees have ever logged in.
With that data point, you can take action to improve the login rate. You can send regular updates and reminders, encourage participation through rewards, and check that everyone has access to their accounts.
That all sounds straightforward enough. But it might be tempting to start checking which resources are most popular, what is the average time spent on each resource, what are the average completion rates, and so on. Before you know it, you can fall down the "data rabbit hole"!
Always remember that you're looking for insights that you can act on right now. Those other data points are interesting, and can be beneficial, but save them for a future project.
Stay focused on the wider strategy driving your data analysis, and you'll stop any project from getting out of control.
3. Stay commercially aware
To survive as an organization, commercial awareness is of vital importance. What are your competitors doing? What changes lie just around the corner for your industry?
This applies just as much to data analysis as to any other area of business. If you're spending time and money drilling down into irrelevant data, you're not only wasting valuable resources. You're also missing the crucial patterns that will determine your organization's future.
The cost of ignoring the bigger picture can be immense. Take the Japanese camera company Konica Minolta, for example.
Konica Minolta was one of the most successful businesses in the field of camera manufacturing. But, in the early 2000s, photographers and the general public started switching to digital cameras.
By the time Konica Minolta launched its first digital SLR camera, in 2004, competitors Nikon and Cannon had firmly established themselves in the digital market. By 2006, the company had sustained such heavy financial losses that it withdrew from the market, handing over much of its existing technology to Sony.
Your sales might be hitting targets, as may website views or conversion rates. But what lies just around the corner? Which data is truly valuable to ensure your organization's future?
Benchmarking companies can help you to keep in front of any changing trends. Companies such as PwC, Orbis and LNS Research (to name a few) will anonymously assess your performance and data against those of your competitors. The findings can provide you with clear areas for improvement, which you can then turn into an action plan safe in the knowledge that your data points are aligned with the rest of the industry.
How Do You Generate Insights from Data?
In the spirit of this blog post, I'll cut to the chase and give you my actionable insights into actionable insights:
- Stop those vanity projects in their tracks! Make sure that the benefits continue to outweigh the costs of any large piece of analysis.
- Don't get lost in the data. Measure your project against the original desired outcomes.
- Stay commercially aware. Who needs an analysis of old news?
With so much information available, cutting out the noise and staying focused is essential when using data to drive your organization forward. I hope that these techniques will help you in your next data project!
James' recommended resources
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