Winners of the Tenth Adolf Horn Award

Because of our innovative focus on corporate culture and passion to help Small and Medium Enterprises, we won the Tenth Adolf Horn Award for the Young Entrepreneur of the Year.

3 Most Common Analytic Data Mistakes (and how to avoid them)

3 Most Common Analytic Data Mistakes (and how to avoid them)

We love data!

In this article we help you to become a data master because we are aware it´s not an easy task.

In our research we found this interesting article “3 Most Common Data Analytics Mistakes (and how to avoid them)” written by Boris Guennewig co-Founder and CTO of Data as a Service company named “smrtr” for sure he knows what he’s talking  about.

 

Let´s see the 3 most common mistakes pointed out by Boris.

 

Analysing  data incorrectly

He says “Each year we see countless academic studies that attribute one factor with one result. Whether that be ‘dog owners are happier’ or ‘children who play team sports develop better social skills’. These sorts of studies essentially only consider one set of variables.This is also a mistake that we see in data science. When we see a link between different data sets, it is easy for us to get carried away and confuse correlation with causation.”

How to avoid it: Peer review

Having a team to review the methodology and conclusions throughout the process goes a long way to improving quality control.

 

 

Underestimate quality and quantity of data

He mentions: “Businesses often underestimate the amount of data required to gain valuable insights. While a business might be rich in data, this might only be data that tells an inward-facing story and has limitations in terms of external use. This is not to say an extensive amount of data is required to build an effective model. Sensical models, for example are created by using around 20 different data points.”

How to avoid it: Data cleansing and enrichment

  1. Consider have this services offered by an external provider, will take stock of the data that is currently available and will provide clear guidelines for forming a data strategy in the future.
  2. Have a team to review the methodology and conclusions throughout the process goes a long way to improving quality control.

 

 

“Cherry picking”

“When it comes to data analytics, businesses might unwittingly cherry-pick certain data if they are looking to tell a specific story. This might happen when the business goes into the project expecting a certain answer. This will not only negatively impact the effectiveness of the data-led initiative, it can also raise ethical issues.” Says Boris

How to avoid it:  Identify, Avoid and Assign 

  1. Choose the correct data: Identify the correct and appropriate data set for any given project
  2. Avoid making any preconceptions and let the data tell the story
  3. Assign a team which includes at least one person that is not connected to the wider objective

 

 

Conclusions

Through the years we can tell that there are a lot of stories of business wasting huge amount of time, money and energy on a poorly executed data analytics projects.

Remember you are not alone in this journey, we are here to help you, contact us! 🙂

Dominic Ramírez
No Comments

Sorry, the comment form is closed at this time.