Right this very moment, you’re swimming data.
Your business has access to so many different numbers, statistics, and analytics that you don’t even know where to start. You know that you need to use these rich insights to make smarter decisions, but you’re overwhelmed by the thought of taking action.
Is there a key to being successful with data-driven decision making?
What is Data-Driven Decision Making?
In the simplest and most generic terms, data-driven decision making (DDDM) is the process by which you collect data based on key performance indicators (KPIs) and measurable goals. This data is then combed for insights and analyzed for patterns, which ultimately allow your organization to develop proactive strategies and make smart choices that benefit stakeholders.
In order for DDDM to work, you need clean and accurate data, as well as the right digital infrastructure to analyze trends and pull out relevant insights. Organizations do this through a combination of technology, experience, and strategic processes.
At the end of the day, the hope is that the right DDDM strategy leads to lower costs, higher profitability, better customer satisfaction, and improved resourcefulness – though precise goals will differ based on your plans and needs.
How to Improve Your Data-Driven Decision Processes
Whether you already have a rudimentary DDDM process in place, or you’re starting from scratch, here are some tips you can use to make the most out of your data-driven decision making:
- Understand Your Purpose
Make sure you’re starting with a purpose and vision. Your program needs to outline and identify precisely what you’re trying to gain and how it aligns with larger organizational goals. According to PinnacleART, which works closely with organizations to make informed reliability decisions, this step saves significant time, money, and resources.
- Begin With Clean Data
This step is absolutely imperative. If you miss the mark here, you’ll end up wasting your time and money. It could even lead to poor decision making that compromises the integrity of your organization and drives profits, reliability, and stakeholder relationships into the ground.
Good clean data is data that’s accurate and relevant. Clean data requires a reliable source. You also need to think about the sample size of the data to determine how sensitive your numbers are to isolated deviations.
When you have clean data, your people will have a greater tendency to trust the data they’re using to make strategic choices. This leads to more informed decision-making processes.
- Guard Against Your Biases
Most people think they’re fairly impartial in their thought processes and decision making, but all humans are affected by biases. It’s important that you implement proactive safeguards to prevent your team’s biases from corrupting your data.
The best plan of action is to include more than just one or two people in the process. By working with a team of people, you democratize the data and increase your chances of making informed decisions.
- Use Data Visualizations
Cultivating data-supported insights is important. However, they’re useless unless your team is able to use these insights to make smarter decisions.
One of the best ways to make your insights more applicable is to use data visualization tools to organize and display key takeaways. You can even find dashboards that allow you to display KPIs in real time.
- Run a Cost Analysis
While DDDM processes almost always end up saving organizations money, don’t take this for granted. It’s always a smart idea to run cost analyses to see if the costs and effort are justified.
Figuring out the cost of a DDDM program is pretty straightforward. The challenge is determining the tangible benefits. Do your best and keep an eye on the numbers over time. You should see a positive impact on the bottom line. If you aren’t, something in your plan needs to be tweaked.
Adding it All Up
Data-driven decision making is the way to go. But in order to make it worth your time, you need to be meticulous in your approach. The majority of the work is done on the front end – establishing the proper processes, using the correct data, and monitoring the right factors. If you’re intentional about these aspects, the rest will fall into place with relative effort and ease.
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