Today’s business environment is competitive, so you should not overlook the value of the one crucial asset that every organization inherently possesses: data analysis. Being able to quickly distill complex data sets to executable actions can help businesses of every size make more informed decisions to reach that next level of performance and exploit competitive advantages.
And yet, so many organizations still brush off data as a nice-to-have-if-you-have-the-time. With so many pressures coming from every direction, there’s never enough time to properly clean up that data and set processes for managing and utilizing it…unless you make the conscious choice to prioritize it.
If your organization hasn’t been fully leveraging your data, now, more than ever, is the time to learn how it can drive business value.
Start with the Right Data Analysis Technique
Companies that value data typically use the following analysis techniques based on different needs:
- Descriptive
- Diagnostic
- Predictive
- Prescriptive
Descriptive Analysis
Descriptive data or, in other words, data trending, is vital when something needs to be “seen” as a narrative. It is used to quickly identify patterns and signals at an operational level. It is visceral and longitudinal, providing a story for how we got here from past performance to now, and how we need to craft strategies and tactics for the future.
Diagnostic Analysis
Diagnostic analysis goes one step further to determine what happened and why. It’s often used for root cause analysis, as it gets to the core of why specific trends or events occurred. This is especially useful when you want to move from constantly putting out fires to learning what’s causing issues so you can take proactive preventative actions.
Predictive Analysis
Predictive analysis looks at past data to predict what will happen next. You can employ machine learning to analyze patterns that can be applied to future actions.This type of analysis is extremely useful when looking at sales and marketing operations, or forecasting demand, supply, or overarching supply chain performance.
Prescriptive Analysis
Prescriptive analysis is quite possibly the most complex, but also the most useful type of analysis. By taking the predictive data you generated, you can design a decision tree about what your next action sequence should be. With this type of analysis, you are able to come up with various forking decision paths, as well as determine the implications of each and the associated confidence values for each.
Remember That More Data Is More Better
You likely have a wealth of untapped data at your fingertips. The key is knowing what is useful in achieving your goals. You should use multiple sources and types of data together in your analysis.
Don’t limit yourself to only your internal data, but also include engagement data such as market studies or customer feedback and third-party data such as financial, political, data broker, or competitor intelligence.
Often, data is used in silos within an organization, and that can hold you back from being able to fully leverage it. For example, the engineering staff may use machine performance data to improve output on a certain type of machine (descriptive data). However, marketing analysis data within the sales organization shows that the need for that type of machine is decreasing (predictive based on consumer purchasing trends). This type of lack of communication often costs organizations significant time and resources because they are focusing on the wrong tasks. The lesson here is: be sure that you have access to all data across departments and functions to ensure your organization is marching to the beat of the same drum.
The Missing Ingredient: Organizational Digital Acumen
The last key to successfully harnessing data value within an organization is developing the right culture. You need to bring focus to developing data science talent and fostering a culture of data stewardship that is rooted in understanding business processes, value, and metrics. These data champions can help translate your raw data into key insights for the enterprise.
Understanding and operationalizing your enterprise data can add significant benefits by turning numbers into actionable decisions that lead to better performance across the entire organization.
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