Businesses need every advantage and edge available. But, thanks to challenges such as economic uncertainty, market demands, shifting political landscape, finicky customer attitude, and global pandemic, businesses today are working with zero tolerance for error.
Companies make choices and better informed- decisions with the information available, which is possible through the availability of data.
Data Analysis
Data analysis is the process of cleaning, changing, and raw processing data and extracting actionable, relevant information that helps businesses make informed decisions.
Importance of Data Analysis
- Better customer targeting: enables the company to focus its resources, time, and promotional activities on the target.
- Appreciate Target Customer Better: data analysis tracks how well products and campaigns perform within target demographics. We also get a better idea of the target audience’s spending habits, disposable income and areas of interest. It helps in setting prices, length of the advertisement and projection of the number of goods needed.
- Reduces operational costs: it shows areas in the business that needs more resources and money, aspects of the company not producing and thus should be scaled back or eliminated outrightly.
- Better problem-solving methods: data analysis helps the business to make the right choices and avoid pitfalls. Informed decisions are more likely to be successful decisions.
- Accurate data: data analysis helps businesses acquire relevant, precise information suitable for developing future marketing strategies and business plans.
Types of Data Analysis
- Predictive analysis: answers the question, “What is most likely to happen?”Analysts use data found in older data and current events to predict future events.
- Diagnostic analysis: answers the question, “Why did this happen?” Using Insight gained from statistical analysis, analysts used diagnostic analysis to identify patterns in data.
- Prescriptive: mix all Insight gained from the other data analysis types.
- Statistical: answers the question, “What happened?” This analysis covers data collection, analysis, remodelling, interpretation and presentation using the dashboard.
The statistical analysis can be broken down into two sub-categories descriptive and Inferential.
Data Analysis Methods
- Qualitative: drives data via words, symbols, pictures and observations. Qualitative methods include;
- Content Analysis – for analyzing behavioural and verbal data.
- Narrative: used for working with data culled from interviews and diaries.
- Grounded Theory for developing simple explanations of a given content.
- Quantitative Data Analysis: statistical data analysis is used to collect raw data and process it into numerical data.
Quantitative data includes
- Hypothesis testing: for assessing the truth of a given hypothesis or Theory for a data set or demographic.
- Mean, or average determines a subject’s overall trend by dividing the sum of a list of numbers
- Sample size determination: uses a small sample taken from a larger group of people for analysis. The result gives a representative of the entire body.
Data Analytic Techniques
Regression analysis: is used to model or estimate the relationship between a set of variables. It does not tell about cause and effect.
Factor analysis: helps data analysts uncover the underlying variables that drive people’s behaviour and choices.
Cohort analysis: A cohort is a group of users who have specific characteristics in common within a specified period.
Cluster: it segments the data into groups that are internally homogenous and externally heterogeneous.
Time series: A sequence of data points that measure the variable at a different point in time.
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