A decision is only as good as the data that informs it.
Data analysis is incredibly useful for any business, whether evaluating performance, launching a new product, or scaling a solution. As data analysts, we must aggregate disparate data, crunch the numbers, and report valuable insights that influence a company’s decision. It is easier said than done. The dark waters of data analysis always have traps lurking. Even the best analysts occasionally slide into these pitfalls.
If you make mistakes in analyzing data, you are not alone. Let us do you a solid and educate you on the seven deadly data analysis errors and how to avoid them. Strap up, and let’s dive in.
1. Not cleansing your data
Poor data quality is a thorn in the sides of many companies. As a data analyst, you must always assume the data you receive is imperfect. Some causes of low-quality data may be software glitches, inaccurate surveys, sampling bias, and incompatible data sources. To overcome this obstacle, use pro-rated data cleansing tools that seamlessly integrate with your SQL, Snowflake, and NetSuite data servers.
You may use more than one data filtering tool to improve the quality of your data. However, it is advisable to have a tool you consistently use to improve the quality of data you receive. Data cleansing goes beyond improving data quality. It also involves normalizing data and comparing data sets. You’ll learn more about this when you use a data cleansing tool. “Dirty data” can easily translate into the following major mistake.
2. Sampling bias
Does the data you have represent the information you are trying to get? Using no representative data leads many data analysts to commit the sampling bias mistake. To put this into context, consider an analysis of your state’s most popular cat breed. If you have data from only a few cities in the state, the bias will lead to not discovering the most popular cat breed. Many marketers and analysts know the more significant the sample size and the more extensive the timeframe, the easier it is to eliminate sampling bias.
3. Focusing on the wrong metrics
Most of the time, we see business intelligence dashboards bloated with all metrics onscreen. While analyzing all the metrics well is spot on, it can be derailing. Before analyzing data correctly, define the metrics of interest that are pivotal in evaluating your company’s KPIs. Of core interest should be the metrics that influence sales, customer service, and, in general, profitability.
4. Not accounting for seasonality
In an organization’s lifecycle, you will have moments where you crush the market and other times where the performance is abysmal. Using data cleansing tools to clean and analyze data is common in both cases. However, using such seasonal data makes your analysis prone to errors. For instance, if you are a soda company, you may have an influx of sales during summer. In your analysis, it is essential to factor in seasonality. The prevailing conditions will change in another season; you must have the correct data to guide you.
5. Improper outlier treatment
Just because a data point seems irrelevant doesn’t mean it is, or does it? Outliers in a dataset have a story behind them. Sometimes as an analyst, you may ignore an outlier and lead the company down a false path. Other times you may devote precious time trying to establish relevance on an outlier that doesn’t hold much significance. It is essential to analyze, interpret and treat outliers correctly. While in this context, avoid under fitting or overfitting data.
6. Undefined objectives
Just like medieval sailors looked to the Northern Star for direction, so should you look to your objectives when analyzing data? A costly mistake data analysts make is analyzing data that isn’t in line with the company’s goals. Before analyzing data define your objectives in line with the KPIs you intend to evaluate against. Just like the Northern Star, objectives are your guide. Without them, you are lost.
7. Not looking beyond the numbers
The essence of analysis is obtaining insightful, actionable information from data. The analysis mainly involves crunching the numbers and making sense of them. But it doesn’t stop there. Most marketers focus on the numbers and present a good report without considering the context. When using data cleansing tools, they efficiently filter the data from the noise. However, you need to add context such that the report you present is intelligible to your team. The best way to add context is to make note of data sources, causations, and correlations of the data.
Do you need help with some of these errors? Knowing the errors is the preliminary step in correcting them. Now that you know the errors, you can work around eliminating them in your analysis process. You can improve your analysis skills and provide valuable insights every time. Through using AI-powered data cleansing tools, prioritizing accuracy, and leveraging your skillset, you will steer clear of the seven deadly errors of data analysis.