Nine Mistakes Your Business Could Be Making

As co-founder of Innowise Group with 20 years of experience in IT, Pavel loves helping businesses grow. data analysis.

Data analytics has become commonplace for both large companies and local businesses who want to successfully design and promote their products. It works the magic of defining the needs and desires of your target audience and helps chart the path to optimized production and distribution. However, misuse of data analysis can prevent you from making good decisions based on faulty assumptions.

Data analytics is a powerful tool, but there are some big mistakes companies can make while using it that can lead to critical failures rather than overwhelming successes. Here are nine things to keep in mind when making informed decisions.

1. Refuse to create a data lake

A data lake is a type of storage used to hold raw, fully raw data. By retaining such information, companies can build accurate and retrospective forecast models based on historical data. You can also use the original data in new processing and analysis tools. Without raw data stored, businesses must rely on third-party data brokers who can share inappropriate or needed data at immeasurable cost.

2. Ignore visual presentation

Data presented in vivid formats, such as charts and dashboards, enables decision makers to reach conclusions quickly and effectively without the assistance of data analysts. Professional dashboards allow businesses to make decisions based on visualized data and compare it to other valuable insights that act like contextual indicators. This makes decisions faster and more successful, giving you a valuable advantage over your competitors.

3. Forget AI and ML

Machine learning (ML) and artificial intelligence (AI) are the leading modern tools for data analysis. They can automatically process incoming data in real time at a pace impossible for teams of experts. Moreover, such tools often turn the tables on competitors by revealing overlooked trends and insights that humans might miss.

4. Lack of data quality control

Data quality control is the process of ensuring representative and useful data suitable for further analysis. Businesses that do not track data quality typically end up using unreliable data in their internal processes. This often leads to poor and uninformed decisions that can be devastating. It is important to check

5. Ignoring data context

Certain events can lead to dramatic changes in the data captured. Certain events, even small ones like Elon Musk’s tweet about his DogeCoin, can dramatically increase the attention and demand for a particular product. Data should be used judiciously and always in conjunction with its context to determine which events influence a particular outcome. In some cases, it may be desirable to separate such impacting events from the general data analysis model and handle them separately.

6. Ignoring Data Security

Data security is another important aspect of data management and analytics. Keeping your data safe means preventing your business strategy and unique knowledge from being used by competitors. If companies don’t take steps to protect their data, it’s like spending resources on something to hand out to people around them.

7. Ignore data ethics, privacy, and legal concerns

Despite being a powerful tool, data analysis carries a fair amount of risk. All data collected must be obtained ethically, with user information kept in a depersonalized form and in compliance with local and global regulations on data collection and analysis. Without it, businesses can suffer damages such as fines, reputational damage, and even be shut down.

8. Do not control for confounding variables

Confounding variables are variables that influence both the dependent and independent variables. When they occur, they can bring spurious correlations and results to your table and ruin the results of your data analysis. If such cases are not tracked and managed, the resulting information may be inaccurate and the decisions on which they are based are less likely to be appropriate.

9. Lack of transparency in data analysis and decision making

Data analysis and decision-making processes should be transparent for several reasons. First, we demonstrate how the data analysis process is ethical and secure. Then, if the pipeline is flawed, employees and other stakeholders can suggest fixes. In addition, when making decisions, it is possible to point out deficiencies in data analysis, and prevent unplanned business actions.

final thoughts

Despite being powerful tools for decision-making and planning, data and analytical tools must be treated with extreme caution. We can help your business by working with a team of experts who have extensive experience in their field.

Today’s businesses need data analytics to gain a competitive advantage, but in the future, data analytics could become a key survival point. Leave it alone, but keep in mind the following considerations that may influence your decision-making:

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