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10 Common Data Analytics Mistakes to Avoid 

Data analytics is a critical part of business processes in today’s world. However, mistakes can be costly and lead to losses. Here, we’ll discuss the ways to avoid common mistakes in data analytics. 

Data analytics is a part of the global industry, helping countless businesses derive and use actionable insights to make business decisions. More organizations now rely on big data analytics to detect patterns and trends in large datasets and uncover information not visible to human eyes. 

Statistics show that the big data analytics market will touch $103 billion in 2023, and around 97.2% of businesses are investing in big data and AI. However, quite a few barriers affect the adoption of data analytics in enterprises. Limited access to data, lack of training, not investing in the correct tools, wrong metrics, etc., are common issues that lead to incorrect insights or errors. 

In this blog, we’ll discuss data analytics and the common mistakes you should avoid when running data analytics. We will also discuss the importance of hiring a data analytics company to help businesses avoid these mistakes and achieve the desired results. 

What is Data Analytics? 

Data analytics is the process of collecting, transforming, and organizing data to derive actionable insights for decision-making. Raw data is used to arrive at meaningful conclusions that help optimize business performance and increase overall efficiency. 

It is a continuous step-by-step process that can be automated using AI tools. Analyzing and reporting data with artificial intelligence tools makes it easy for employees to access the insights in real time. SMBs and large enterprises work with offshore data analytical service providers to integrate different systems and streamline data flow. Employees at various verticals can use intuitive dashboards to access data visualizations and make faster decisions. 

Types of Data Analytics

Before investing in data analytics projects, you should know about the different types of analytics and how these help your business. 

  • Descriptive Analytics 

As the name suggests, descriptive analytics describes data patterns and trends to help find more information and insights. Data mining and data aggregation are used in descriptive analytics to draw conclusions from vast datasets. 

  • Predictive Analytics

Large datasets are mined using predictive models to forecast future outcomes for a business. It also uses descriptive analytics to define and understand the predictions. Historical and present data is processed to detect patterns that forecast future opportunities and risks. 

  • Diagnostic Analytics 

Diagnostic analytics describes the techniques used for processing data. It takes descriptive analytics one step ahead but identifies the reason for the results. Data mining, drill-down, and correlations are some techniques used for diagnostic analytics. 

  • Prescriptive Analytics 

Prescriptive analytics uses the above three types of data analytics and focuses on data monitoring to derive more actionable insights. It combines mathematics, science, descriptive models, and predictive models to provide the necessary analytics to the business. 

What are the Common Data Analytics Mistakes to Avoid? 

While the concept of data analytics looks simple, it is easy to make mistakes that affect your business in the short term and long term. That’s why several enterprises partner with data analytics consulting firms to use their expertise and experience to successfully avoid the mistakes others make. 

Here are the most common data analytical mistakes business organizations make and methods to avoid them effectively. 

1. Sampling Bias and Cherry Picking Data 

Data is the core of data analytics, and choosing incorrect or wrong sample data can lead to distorted insights. For example, sampling bias is one of the primary mistakes many organizations make. Sampling bias is when you choose non-representative samples.

If you want to know how people feel about your product, you should choose a sample with your customers and non-customers. If your sample includes only your loyal customer base, you will not know how others view your product and whether they are even aware of your brand. 

Similarly, cherry-picking is where you intentionally choose a sample that will align with your hypothesis. If a sales manager wants to prove that their campaign was successful, they might present only those reports that support their claim. 

In both instances, you will not be aware of the actual market condition. To avoid this, you should gather data from multiple internal and external sources. Get data from social media mentions, websites, emails, chats, surveys, customer feedback, etc., to include more representations in your sample and use it for analytics. 

2. Wrong Sample Size or Market 

Here’s another vital question to consider when collecting data for analysis. Does the sample market align with your business industry? Businesses use large datasets to derive insights because a smaller sample size can lead to inaccurate conclusions. 

However, you should also focus on where your data comes from. Demographics are important when finalizing the data sources. For example, a business selling hearing aids should focus on people with hearing difficulties to get their views and feedback. The sample market is highly specific in this case. 

To avoid these mistakes, you should first define your business vision, mission, and goals. Be clear about what you offer so that you can identify your target market and proceed to list out data sources. 

3. Not Standardizing Data 

Raw data comes in various formats, structures, and types. It is collected from different sources like the cloud, spreadsheets, SaaS applications, social media, etc. Some data can be in tabular format, while some could be in percentages, fractions, and more. You cannot directly run analytics using these datasets if you want accurate insights. Not setting up a definite ETL process is one of the administrative mistakes examples in data analytics. 

First, you should standardize the collected data. Establish ETL processes (Extract, Transform, and Load) to clean the data and format it uniformly. Label the datasets and add tags for easy categorization. Then, proceed to use analytical and business intelligence tools to derive insights. This also removes duplicate data and minimizes errors. 

4. Vague Goals and Objectives 

What is the purpose of running data analytics? Each department in your business has different goals and objectives. For example, the sales team needs analytics to understand market preferences and customer behavior. 

The HR team uses data analytics to identify talent gaps in your organization and find the right candidates for the jobs. The R&D team focuses on understanding the types of features customers want from your products. 

A common mistake many enterprises make is to work without a definite strategy or objective. Start with the end goal and create an objective-oriented plan to collect data and arrive at conclusions. Focus on your business KPIs and departmental KPIs to align the analytical processes with them. 

5. Searching for Data after Ideation 

While strategizing and defining objectives is important, it should not influence data collection to an extent where you end up with skewed results. Going to extremes on both ends is not recommended. Errors in data analytics can be avoided when analytics work seamlessly with human expertise and creativity. 

Achieve this balance by investing in artificial intelligence-based BI tools with interactive dashboards. Instead of relying on standalone dashboards connected to individual data silos, you need to create workflows that connect all systems in the enterprise and give real-time access to a centralized database to decision-makers. 

6. Incorrect or Mismatching Visualizations 

Data visualizations are graphical representations of data analytics and business intelligence reports. They make it easy to understand complex insights by presenting the details in graphs, charts, maps, etc. Self-servicing BI allows employees to create data visualizations and change them with a few clicks on the dashboard. 

However, the type of data visualization you choose can also affect how you understand the insights and make decisions. Always choose visualizations based on data instead of doing it the other way around. Wrong visualizations can result in the misrepresentation of data, even if the insights are accurate. Flashy charts may not be effective or provide in-depth information. 

The visualizations have to provide what you want from the data. For example, a time series or a bar graph can be most effective when you want to know the performance of a product over a given period. 

7. Excessively Relying on Data or Numbers 

The main intention in using data-driven models and analytics is to rely on data and concrete information rather than guesswork. However, being totally dependent on data and numbers can also adversely affect your business. Even with real-time insights, some disruptions cannot be predicted. The Covid-19 pandemic is the best example of this.

External factors can always throw in a spanner and change the situation in a couple of hours. That’s where human expertise is helpful. Data analysis is useful and important but only when combined with human knowledge. Asking employees to ignore their expertise and instincts to focus exclusively on data can lead to unexpected losses. 

Train employees or hire data analytics experts to blend their knowledge with advanced technologies and use both cohesively for decision-making. Don’t let your talent pool turn into robots. 

8. Confusing Between Correlation and Causation 

Another major mistake many organizations make when analyzing data is to confuse correlation and causation. Identifying a correlation between two variables might imply that one caused the other. However, this may not be the case. The correlation could be due to various reasons, such as: 

  • Variable A causes B or vice versa 
  • Variable C causes A and B 
  • Variable C causes A which causes B (or vice versa)
  • The correlation is a coincidence with no causation 

It’s vital to consider more factors and examine the variables and the events in detail instead of jumping to conclusions based on a single analytical report or visualization. Conduct in-depth research to arrive at accurate insights. This will prevent mistakes in making decisions for your business. 

9. Ignoring the Context of Analytics 

Deriving insights is just one part of the job. Even when using data visualizations, the reports will not make sense if the context is not provided. Analytics don’t stand out on their own. They reflect the objectives of the team, challenges faced, market conditions, performance, etc. 

Withholding contextual information or presenting only the insights can lead to the misalignment of KPIs and objectives. It can impact business decisions and cause top management to make incorrect decisions. Context makes interpretation easier. Prepare data analytical reports with care and make them detailed enough to include all aspects relevant to the project. 

10. Not Establishing Data-Driven Culture 

Adopting the data-driven model is not limited to revamping your IT infrastructure and investing in AI tools. It is about changing the work culture in your enterprise to empower every employee to understand and use analytics as a part of their day-to-day work. The easiest method is to integrate insights with existing software applications. It makes the transition smoother as employees will not feel daunted by the task of learning new technologies ever so often. 

However, even such changes take time and need continuous support from the top management. Creating awareness about the necessity of using data analytics and gradually introducing the changes. Involve employees in the process and provide training and support to help them grow with the enterprise. 

Why is Data Analytics So Difficult for Beginners?

Data analytics combines various subjects, technologies, tools, and processes. It involves people with different skills and knowledge to handle large datasets and arrive at meaningful conclusions or interpretations. Many organizations fail to correctly implement data analytics due to various reasons such as:

  • Lack of proper strategy 
  • Not understanding the business vision 
  • Ignoring industry trends and standards 
  • Blindly copying a competitor 
  • Not hiring experts
  • Investing in the wrong technology, etc. 

Partnering with a reputed data analytics consulting service provider can remove all obstacles and ensure you get the expected results by investing in the data-driven model. It is a cost-effective and time-saving option that also delivers higher ROI. 

What are the Most Common Big Data and Analytics Problems?

The success of your big data strategy determines your revenue and profits. Here are the most common problems enterprises face when investing in big data and analytics:

  • Issues with handling large amounts of data
  • Inability to find and fix all data errors 
  • Complexities in data integration 
  • Difficulties in scaling storage systems
  • Uncertainties in identifying the right technologies 
  • Choosing between in-house data teams and offshore service providers 
  • Increasing expenses and overhead 
  • Issues with data security and governance 

What are the Most Common Data Management Mistakes?

Managing vast amounts of data comes with its own set of challenges and roadblocks. You should take care not to make the following mistakes with data management:

  • Not having a data governance body for administration management 
  • Not paying enough attention to data architecture 
  • Not focusing on data quality 
  • Still relying on data silos for storage 
  • Ignoring data profiling and collecting unwanted data 
  • Not hiring outside experts and putting excess pressure on in-house IT teams 


Are you avoiding these common analytics mistakes? Data analytics gives you a competitive edge by providing in-depth insights to make the right decisions for your business. You can easily avoid mistakes by hiring a popular data analytics service provider to offer end-to-end implementation and support services. 

You can start by adopting the data-driven model in a single department or plan for company-wide adoption. The data analytics consulting provider will provide a robust strategy to ensure you don’t make mistakes when using analytics for your business. Talk to us to schedule an appointment with our experts. 

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