5 Common Mistakes Companies Make When Implementing Data Science Strategies — and How to Avoid Them

While many business owners recognize the importance of data, few have unlocked its full potential. 

The challenge? 

It's not just about collecting data, but about streamlining it to generate actionable insights that drive decisions.

Did you know? Over 85% of data analytics projects fail due to misinterpretation of data. An analytics initiative with well-managed analytics can reap organization wealth. But make one of these mistakes, and you run the risk of disaster. 

So, what are those mistakes? 

1. Garbage In, Garbage Out (GIGO)

A study shows that 62% of businesses rely on marketing/prospect data that's up to 40% inaccurate, 94% suspect their data is inaccurate, and 40% fail based on inaccurate data. There is a simple lesson here - if you put garbage in, you get garbage out. 

How to avoid it?

• Check accuracy of all data
• Thoroughly clean  and screen the data

2. Lack Of Clear Objectives

How would you respond if your manager asked you to analyze a dataset? 

Many people overlook the business objective and jump directly into visualizing the data. This results in inconclusive results. A lack of clear goals can lead to a loss of focus and a failure to uncover the insights that are really important.

How to Avoid?

• Set clear objectives before starting the project
• Define key performance indicators (KPIs)
• Evaluate the success metrics based on business strategies

3. Cherry Picking

Cherry picking is when data is selected to support an argument or hypothesis rather than reporting all findings. If we cherry pick only the best insights and ignore or hide anything contrary, we will make bad business decisions based on confirmation bias. 

Approximately $3.1 trillion is lost annually by U.S. companies due to bad data, according to a report by IBM.

How to Avoid?

• Improve data quality
• Regularly clean and validate your data
• Work on practical projects to build a strong foundation by reading online resources

4. Using Unprocessed Data

Accuracy is not determined by the quantity of data, but by its quality. An analysis can be negatively impacted by raw data with errors and inconsistencies..

How to avoid it?

• Keep consistent data for accurate analysis
• Remove data duplicacy 
• Use the right sata analytics software

5. Poor Data Visualization and Presentation

Data visualization tools are often inaccessible to businesses, and they don't present data in a way that's straightforward. This may result in stakeholders failing to leverage the information, despite its accuracy. 

So what is the lesson? Visualizing data alone is not enough; you need to make sure that it is easy to digest and fast to process.

How to avoid it?

• Make sure the tool offers customization options, which should be easy to use
• Multi-visualization options should be provided
• Workflows and data sources should be seamlessly integrated

You can't fix your data science project with anxiety or impostor syndrome. Make sure you learn from your mistakes and  practice above advice for businesses to optimize the data science efforts.

Don’t Let These Data Mistakes Derail You!