Customer Success Analytics

How do I Learn to Trust Data?

In today’s modern world where technology runs our society, you would expect data and analytics to be a business priority, right? Well, you are wrong. Many businesses distrust data. Here are some reasons why:

  • Inadequate understanding of how analytics are used in a company.
  • Past experiences with incorrect data predictions.
  • Fear of analytics taking over jobs.
  • Lack of confidence in the data gathered.
  • A tendency to make decisions based on intuition or emotions.

It is important for businesses to overcome the mental roadblock these reasons create in order to know more about your customers, streamline existing company operations, manage company risk and compliance, and make informed strategic decisions.

Without trust in data, you will be making inaccurate predictions that will quickly lose consumer trust and the confidence of executives who rely on such predictions to make decisions. You may be confident in your judgment, but data can point out useful information that you may not have known otherwise.

How to Build Trust in Data in Your Company

Quality, effectiveness, integrity and resilience, which are defined below, are the four anchors of trusted analytics.

  • Quality: Questions if the base of Data and Analytics is strong enough. Determines whether or not companies understand the importance of quality in making and managing tools, data and analytics.
  • Effectiveness: Tests if analytics work the way they should. Questions if companies can pinpoint the accuracy and utility of the outputs.
  • Integrity: Asks if the data and analytics are being misused or not. Tests how well an organization keeps up with regulations and ethical principles.
  • Resilience: Questions if the operations are optimized for long-term usage. Tests if a company is good at data governance and security.

According to the infographic on page 7 of the linked article, few companies even practice all four anchors. Moreover, most do not have confidence in the quality, privacy or accuracy of their data. However, there are steps you can take to meet these anchors in your company. These are as follows:

  1. Identify the data in your company that employees do not use due to mistrust.
  2. Inform others of the purpose for collecting data.
  3. Develop an understanding of data and analytics with business users and create a specialized team to collaborate with others.
  4. Make sure you have employees with experience in analytics quality assurance.
  5. Have data transparency — show where data is published and who owns the data.
  6. Find who funded the collection and analysis of data to check for data bias.
  7. Create cross-functional data analytics teams across departments in your company.
  8. Promote innovation and experimentation with data for employees so that they can try different methods without fear of failure (e.g. practice data).

How do Analytics Cycles Help you Trust Data?

Humans are naturally predisposed to making decisions on emotional hunches, but data can prove us wrong, and better still provide more useful information to help us adjust our strategies. For example, a clothing company could think that social media shopping is the most popular mode of shopping for their customers, but without data, they would have no way of knowing only the Instagram account is pulling in the most customers amongst their other social media accounts.

The Data Analytics Cycle (as shown below) can be used to maximize the influence and reassure the credibility of your data for business strategies

  1. Planning Analytics: Find what your plan is and what you are trying to accomplish.
  2. Descriptive Analytics: Know that you can trust your data visualization tools before making misleading assumptions.
  3. Diagnostic Analytics: Use data discovery and exploration to find relationships in your data. Data discovery shows you what happened for you to later find why it happened.
  4. Predictive Analytics: What will happen next based on data patterns.
  5. Prescriptive Analytics: Take the insights of predictive analytics and apply them to what outcomes the organization wants.

The knowledge from the fifth step feeds back into the planning phase for the cycle to start again.

Data Analytics Cycle is all about having the knowledge to analyze past business performance, uphold data credibility, and use that information to predict future performance results based on whether the strategy changes or not.

On the other hand, the Lean Analytics Cycle is a process for improving a sector of your company. It involves creating a hypothesis out of data you can test and instituting change in business from information you identify:

  1. Decide what metric you need to improve and look at your business model for ideas.
  2. Form a hypothesis by understanding your audience
  3. Create an experiment by answering who is the audience, what you want them to do, and why they should do it.
  4. Measure whether or not your experiment was a success, failure, or needs different variables. Using this information decide what your next steps will be with the company.

The experimental nature of the Lean Analytics Cycle gives you the opportunity to measure the credibility of your data based on the results of the cycle.

Conclusion

Using data in your business strategies could be the make or break factor to whether or not your company will continue to thrive. The four anchors of analytics, the Data Analytics Cycle, and the Lean Analytics Cycle are all ways to help you build confidence in the data you have. If you use the guides provided above to check your data, you will become more comfortable with the information you are gathering and sharing with the company.

 

Resources

  1. https://www.techrepublic.com/article/infographic-7-ways-to-build-trust-in-data-and-analytics-at-your-company/
  2. http://www.data-first.org/questions/how-can-i-know-if-a-data-source-is-trustworthy/
  3. https://hbr.org/2015/10/can-your-data-be-trusted
  4. http://www.thedrum.com/opinion/2017/07/03/learning-how-put-your-trust-data
  5. https://marketinghits.com/blog/the-lean-analytics-cycle-metrics-hypothesis-experiment-act/
  6. https://marketinghits.com/blog/the-lean-analytics-cycle-metrics-hypothesis-experiment-act/
  7. https://www.kaushik.net/avinash/lean-analytics-cycle-metrics-hypothesis-experiment-act/
  8. https://assets.kpmg.com/content/dam/kpmg/xx/pdf/2016/10/building-trust-in-analytics.pdf

About Prerna Kandasamy