Business Analytics helps businesses make sense of their data by answering the what, when, why, and how questions. Yet, even with all the tools and technologies available, Business Analysts continue to make these five common analytics mistakes:
1. Using data to confirm your plan or confirmation bias.
It’s way too common to create a plan of action and then consult the data. Before any brainstorming, any plans, or any decisions, consult the data. Let it inform your strategies, creativity, and next steps rather than vice versa. In fact, keep well enough versed in the latest data so that you can see needed change ahead of time rather than be forced into a reactionary position in the future.
2. Over-relying on data.
Data is increasingly part of our decision-making, but over-reliance can paralyze human input. A real-world example of this is the coronavirus. Customer needs changed daily or even hourly, and waiting on data would mean missed opportunity for many businesses. Data models will break down in real-time, large-scale situations. The data needs to empower or augment human decision-making, not vice versa.
3. Not telling the story.
Don’t just share the data. Engage your stakeholders with a narrative. Communicate not only what the data says but the why and how. Make use of your dashboards and other visual tools. Narrative engages both the emotional and analytical sides of the brain. If you want buy-in, data storytelling is the key. The data may have a powerful message, but it needs the narrative to communicate it.
4. Only looking at averages.
Averages, by nature, are a compilation of data sets and do not reveal individual issues. An average can hide a marketing channel with unsustainable costs. One page with an incredibly slow load time can be hidden in an average. If you rely only on averages, problems will not rise to the surface until they are systemic and costly to correct. Check the individual data sets regularly.
5. Using the wrong visual.
Data visualization reveals patterns and relationships. But the wrong visual can communicate a misleading picture. Ask yourself what key message you are communicating through this data. Choose the visual based on that answer. Consider using color to highlight key findings or size to emphasize the importance. Experiment until you find the best representation of your data and its message.
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