As a founder at Innowise Group, Pavel has 20 years of experience in IT and loves helping businesses grow. Data analysis.
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Data analytics has become commonplace for both large corporations and local businesses looking to succeed in designing and promoting their products. It helps perform the magic of defining the needs and desires of a target audience, optimize production and distribution, and more. However, when used incorrectly, data analysis can lead to poor decisions based on incorrect assumptions.
Data analytics is a powerful tool, but several major mistakes businesses can make when using it can lead to serious failures rather than spectacular success. Here are nine things to keep in mind when making data-driven decisions.
1. Refuse to create data lakes
Data lakes are a type of storage used to store raw and unprocessed data. Maintaining such data allows businesses to build accurate and predictive models based on historical data. It also enables the use of original data with new processing and analysis tools. If raw data is not stored, businesses must rely on third-party data brokers who may share inappropriate data or necessary information at disproportionate costs.
2. Ignoring visual representations
Data represented in clear formats such as graphs and dashboards allows decision makers to draw conclusions quickly and efficiently without the need for assistance from data analysts. By using specialized dashboards, businesses can make decisions based on the data displayed, compared to other valuable insights that act as contextual indicators. This leads to faster and more successful decision making and significant advantages over competitors.
3. Forget about AI and ML
Machine learning (ML) and artificial intelligence (AI) are key modern tools for data analysis. You can process incoming data in real time at a speed not available to a team of professionals. What’s more, tools like these turn the tables on competitors by revealing subtle trends and insights that people might otherwise miss.
4. Lack of data quality control
Data quality control is the process of ensuring that you obtain representative and useful data suitable for further analysis. Businesses that do not monitor data quality often use unreliable data in their internal processes. This often leads to poor and ill-informed decisions, which can be disastrous. It is important to ensure that your data is accurate, relevant and representative before using it for any purpose.
5. Ignore data context
Certain events can cause dramatic changes in the data obtained. Even special events like Elon Musk’s tweets about DogeCoin can significantly increase attention and demand for certain products. Data must be used carefully and always contextualized to determine which events influence specific outcomes. Sometimes it is a good idea to cut such influential events out of the overall data analysis model and work with them separately.
6. Ignoring data security
Data security is another important aspect of data management and analytics. Maintaining information security means protecting business strategies and proprietary knowledge from being used by competitors. If businesses don’t take steps to protect their data, they’re wasting resources on what they provide to everyone around them.
7. Ignoring data ethics, privacy and legal concerns
Although it is a powerful tool, data analysis brings with it a fair amount of risks. All data collected must be obtained in an ethical, user-friendly form and in compliance with local and international regulations regarding data collection and analysis. Without it, a business can face damages like fines and loss of reputation or even be shut down.
8. Failure to control for confounding variables
Confounding variables are those that affect both the dependent and independent variables. When they occur, they can spoil the results of data analysis by bringing false correlations and results to the table. If such cases are not monitored and handled, the information obtained may be inaccurate, and decisions based on it may not be accurate.
9. Lack of clarity about data analysis and decision making
Data analysis and decision-making processes need to be transparent for several reasons. First, it shows how ethical and reliable data analysis procedures are. Second, if there is a flaw in the pipeline, employees and other stakeholders can suggest adjustments. Moreover, when making decisions, if there is a deficiency in data analysis, it can indicate and prevent a business from taking poorly planned actions.
Final thoughts
Although it is a powerful tool for decision making and planning, data and analysis tools should be approached very carefully. It can help a business collaborate with a team of experts who have the most experience in the field.
Today’s businesses need data analytics to help them gain a competitive advantage, but tomorrow, it could be the key point of survival. Take it as it is, but consider these considerations that may affect decisions.
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