We’re living in a data-rich era. Therefore, understanding how to manipulate and analyze data to extract actionable insights is one of the major drivers of business success.
Despite the large volumes of data generated daily, only a mere 0.5 percent of this data is analyzed and used for further discovery and improvement. Considering the digital era we’re in today, data analysis is crucial to boost business practices.
The term data analysis refers to the process of cleaning, analyzing, transforming, interpreting, and visualizing for further discovery of potential information that can be used to make better business decisions. The major reason why data analytics professionals need to know the tools is for extracting useful information and make decisions based on the analysis.
If you’re looking to grow your business, all you need to start doing is analyze your data. For instance, imagine your business isn’t functioning well, what would be your first initial step? Most probably you might look at the past business performance, acknowledge the mistakes you’ve made and build a better business strategy and ensure not to make the same mistake again. Even if your business is flourishing, you still need to make sure you improve your business. All you have to do is to analyze the data gathered and utilize it for business purposes.
Tools used in data science and analytics
Data analysis tools make it easier for data scientists and data analytics professionals to manipulate, analyze and process their data. These tools are also essential to identify trends and patterns for future prediction. Some of the most common tools are:
- Power BI
- Periscope Data
Once you have your data ready, you can select any tool for data analysis. The next question is, how do you find the right tool to use? Well, for this, you need to first study the company’s needs and learn who would be using these tools – a data scientist or a data analyst. Some platforms are technical while some focus on point and click analysis for non-technical users. Therefore, only after studying briefly about the company, you’ll be able to decide which data analysis tool will be perfect for your organization.
Techniques used in data analysis
Data analysis techniques are suitable for both quantitative and qualitative data. However, all you need is to first use multiple analysis methods based on the type of data you’re looking to analyze.
Below are some examples of the major data analysis techniques:
- Text Analysis: Also, referred to as data mining, text analysis is one major method used to analyze data from larger datasets using data mining tools.
- Predictive Analysis: This analysis demonstrates what’s likely to happen in the future by using the past data. For instance, last year you bought two new dresses according to the savings made and if this year you’re likely to receive an increment, you might as well purchase four dresses. Well, this is not always the case because you also need to think about certain circumstances like the prices of the clothing or perhaps you would like to purchase something else.
- Prescriptive Analysis: In this method, the analysis is done by collecting insight from previous analysis to understand what action to take in the current decision or problem. Many data-driven companies use prescriptive analysis to augment data performance.
- Statistical Analysis: This analysis demonstrates what happened with the help of past data. This method includes gathering, analysis, modeling, and interpretation of data.
- Diagnostic Analysis: As the name suggests, this analysis demonstrates why certain situations took place by detecting the cause from the insight obtained from statistical analysis. This method helps identify data patterns.
Data analysis is crucial to improve the organizations’ products or services. Analysis of structured and unstructured data helps discover potential insights about customers and also understand their requirements.