Organizations are pushing for more data. But it’s not the amount of information you collect but how you use data to solve and identify problems. Data itself doesn’t add value to your team. You must analyze content and feedback to define gaps and diagnose your pain points.
How to Make Data Work for You
While the first step to a solution-driven data process is assessing the problem, leveraging information to identify trends and patterns will help you reach your goals faster. For instance, if your numbers indicate that most employees perceive leadership as disconnected from the issues. You could respond by developing a calendar of engagement events. But further analysis of the data may reveal that most respondents who feel disconnected work remotely and are new to the team. The additional insight is pivotal to designing a responsive action plan that shows team members that you’re aware of the challenges and committed to positively shifting organizational culture.
Analyzing data is also a way to identify barriers to success. If there is a common pain point or person that is derailing progress, it won’t be able to hide behind the numbers and feedback. Look for areas of decline to predict both current and future challenges. With all that is going on in your business, you don’t have time to sweat the small stuff. But be sure to pay attention to outlier data as well. What starts as a minor issue can quickly grow into something bigger. Track data over time and compare reports against historical information for a comprehensive analysis.
Practical Steps to Simplify Data Analysis
Over 76% of data scientists admit that preparation is their least enjoyable task. Data prep and analysis may not be the sexiest job, but a proper plan can streamline processes.
- Define your goals – determine your objective for collecting data. Without direction, you may not make sense of the details or be able to identify influencing trends.
- Organize information – with access to a substantial amount of data; you can easily mix up feedback or miscalculate figures, resulting in inconsistent reporting and a false narrative. Add titles, delete unneeded figures, and sort data by category or response.
- Identify Your Tools – select an analysis tool that supports the type of data collected. For instance, organizations with smaller sample sizes and survey data may use excel spreadsheets, whereas teams that want automated processes may use AI technology.
Types of Data Analysis Methods to Guide Your Process
The desired level of detail, schedule and available resources will influence the data analysis method that works best for your organization. Still, it will likely fall within one of five categories.
- Descriptive Analysis – use raw data from various sources to determine what’s happened. It’s an ideal method for organizing data and leveraging information for invaluable insight.
- Exploratory Analysis – mine or explore data to define the connection between the details and variables, allowing you to hypothesize and identify solutions to your problems.
- Diagnostic Analysis – get answers to questions and gain a contextual understanding of why something happened to create a team-specific plan of action.
- Predictive Analysis – this type of data is most useful when coupled with other methods. With an understanding of why and what happened and the relationship between data points, you can make future sound predictions.
- Prescriptive Analysis – examine data patterns and trends to identify interventions. This analysis method is an essential part of informed decision-making and helps determine the future course of action.
With comprehensive assessments and in-depth data analysis, you will have the insight to prescribe a specific recommendation for your team’s problems.
To get refreshed on using evaluation tools to uncover the root cause, check out “Not Ready for Training? How Assessments Can Help You Navigate Workplace Challenges.”
“Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says,” Forbes. May 2016.
