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Watch Out for These Common Analytics Mistakes Featured

Watch Out for These Common Analytics Mistakes Milan De Clercq

Technology is advancing at a fast rate. The same can be said about the emergence of new devices and their data. With vast data emerging from different sources, new approaches are adopted to process data and gather insights. As such, data analysis has gained fame. While big data analytics is a good thing for your business, mistakes can be made in the data processing. As you seek to implement this technology in your organization, watch out for these common analytics mistakes in your operations.

  1. Use of data alone when confirming ideas

One of the biggest mistakes we can make in analytics is looking for data after we have sourced all our ideas. For example, in marketing, where creative minds brainstorm key ideas, campaigns and strategies in various channels, they may ignore data before consulting. Instead of ignoring data from some sources, we must build a new mindset where insights from data emerging from different sources help define ideas, strategies and the course of action. This, however, does not mean that we should follow whatever data is telling us blindly. Rather, our natural creativity should be complemented by analytics.

  1. Choosing the wrong visual

One of the advantages of business intelligence is the push toward data visualization. Visuals are easily understandable compared to tables and numbers. However, as we develop analytics, we must strike a balance and design insights from consumable and actionable data while ensuring that the data is understandable. While tables are good, there will be no progress if we continue presenting analytics in the form of raw data tables. This is even more complex when the people we are targeting are knowledge workers who may have knowledge of the insights but are not accustomed to the traditional analysts’ language.  

  1. Performing analytics without objectives

A lack of a properly defined set of objectives leads to struggling in data analytics. Therefore, before you can even think of diving into your data, ensure that you have gone through a structured process to identify the business objectives you need in your campaign to accomplish results. The business objectives need to be translated into a quantifiable set of primary key performance indicators that can clearly evaluate the successes and failures. Furthermore, you should ensure there is coverage across the entire customer journey and lifecycle. This will provide a framework that will align your analysis with the business outcomes.

  1. Over-reliance on data

Although data is crucial in modern businesses and decision-making, over-relying on it can be counterproductive in most cases. This is becoming a big concern to many professionals as they try to make informed decisions based on data. Accordingly, current data models are not always responsible for responsible. Therefore removing people from decision-making roles can have adverse effects. With the increasing customer expectations, insights have become critical in empowering humans and increasing their expertise in decision-making. However, this does not mean that you should only depend on data. Rather, you need to strike a balance between data and human knowledge.

  1. Focusing on the wrong insights

While data can be useful in making decisions, insights can sometimes mislead and can also distract, even if the data is accurate. This is caused by too much data that is generated from many sources. Therefore, focusing on the wrong insights can be detrimental to decision-making as the decision arrived at can be wrong. Therefore, there is more reason to focus on how people make decisions. While we may concentrate on improving metrics, we can sometimes lose sight of the impact of the metrics and what they were supposed to achieve.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

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