There is hardly a single day in the modern world where data is not utilised, therefore actual and practical skills in data analysis are vital requirements. Firms, companies, and scholars utilise data analysis to interpret and capitalize on the trends, behaviours and results, as obtained from data in organisations. This article aims at advancing the knowledge of methods, strategies, and tools that enable analysts and professionals to reveal the maximum potential of data.
Data analysis is the process of cleaning, modifying and organising data to reveal meaningful information, resolve problems and support decision-making. Ideally, it processes several phases, such as data gathering, using statistical methods and techniques, as well as results interpretation. To summarise, data analysis is the process of converting collected raw data into useful information that can be acted on.
Following are the steps involved in data analysis process:
Define the Objectives: Start with a clear understanding of what goals you have when engaging in data analysis. This step describes and states the objectives of steps that follow.
Collect Data: Collect information from different sources of information. Depending on the type of analysis required, it can be surveys, databases or web analytics etc.
Clean and Process Data: Measurement data in its original form may contain inaccuracies, discrepancies, or missing entries. Data cleaning is basically the process of deleting inaccuracies, dealing with incomplete values and making certain that all data is accurate.
Analyse Data: Select proper data analysis techniques and methods for analysing the data collected. Statistical models and tools play an important role in the analysis tasks here.
Interpret and Communicate Results: Last, convert the results into easy-to-understand format, more often by utilising graphical or reported formats for decision-making.
Selecting the type of data analysis technique basically depends on the type of data, purpose of using the analysis and result expected from the analysis. Here are some of the most widely used data analysis techniques:
Initial step in the data analysis process, descriptive analysis provides converted data that display infrequent patterns, trends, and distributions. It answers the question, "What happened?" and serves as a starting point for subsequent analysis. Companies need this for tracking their historical results, for instance, the monthly sales figures or a customer satisfaction rating.
Taking this one step further, diagnostic analysis wants to know why a particular result was obtained. As for trends, it assists organisations to identify reasons for successes or failures, while investigating causes.
For example, when there is a drop in sales, diagnostic analysis can identify possibilities, which might be seasonal patterns, shifts in consumer behaviour or actions by competitors.
Predictive analysis uses vast historical data when predicting something in the future. This technique primarily uses statistical modelling, machine learning and Artificial intelligence technologies to predict future events.
For instance, the supply chain planners would employ predictive analysis to estimate customer demand for products within the retail shops and avoid stock deficiency or overstocking.
Prescriptive analysis is the most accurate type of analysis for a recommendation of which action should be taken based on the discovery. It answers, “What should be done?” by applying findings from the other types of analysis, including optimisation algorithms or simulation models. This is particularly useful in logistics, where prescriptive models are used to prescribe delivery routes, or in healthcare, where the models are used to prescribe treatment options.
These techniques assist the analyst to have a vision on what action to take based on data trends and patterns. If you are dealing with marketing, healthcare, financial problems, or whatever field you are in, these data analysis methodologies will help improve decisions.
A good data analysis strategy is important to make sense of big data analysis and indeed gain valuable insights. Here are some popular data analysis strategies:
Quantitative analysis targets numerical data as the name suggests. It employs the use of statistical as well as mathematical models to standardize and analyse tangible factors. For instance, financial analysts utilise quantitative analysis to gauge an investment prospect or a risk level.
While quantitative analysis is about numbers, qualitative analysis is the process that handles non-numerical data including texts, voice or video. It is often used in subjects such as social sciences where data may comprise of interview notes, transcripts, survey responses and observation journals. Quantitative analysis is interested in the attitudes, behaviour or feeling associated with the information.
Using both quantitative and qualitative data is useful for the all-sided view, when both types of data are accessible. For example, the organisation bringing a new product into the market may employ quantitative tools to assess sales effects, meanwhile, qualitative tools for customer feedback.
Where the hypothesis is not known in advance, exploratory analysis allows analysts to find patterns, associations or other insights in data without a specific result in mind. This approach is particularly useful in initial stages that involve analysis of the data structure or variables which are involved in the analysis.
These data analysis strategies provide a different perspective to data and data analysis and the use of one or the other depends on the data and questions being addressed. In this way, by choosing the suitable strategy, analysts can get rid of inaccuracies and get much more meaningful results.
Modern technologies offer a great variety of data analysis tools that may facilitate the process and increase the level of result accuracy. Some of the popular tools include:
Excel: It is a kind of all-inclusive medium that can be used as a primary data analytic and graphical computation utility.
Python: Statistical analysis and most of the machine learning algorithms are coded in python environments such as powerful libraries like Pandas, NumPy, and Matplotlib.
R: A mathematical programme used mainly for computing, data analysis and especially for data visualization.
Tableau: Tableau, famous for its dashboard designs, aids in creating presentations of the data making it aesthetic.
SQL: Very important for handling and processing archive data stored on relational databases.
The use of such tools enables the analysts to manage complex data hence making it easier to come up with actionable implications on the overall data analysis strategy.
Whether you’re a beginner in data analysis, or want to sharpen your skills, it is necessary to master the approach, methods, and tools of data analysis. Step by step movement from descriptive and predictive analysis to using other advanced data analysis tools such as Python and Tableau can help get one closer to a real discovery. Actively developing the data analysis strategy is crucial to organisations to remain relevant, make reasonable decisions and, therefore, succeed in todays’ world full of data.
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What is an example of a data analysis?
An example of data analysis would be an analyst who reads different finance data such as stock performance for the purpose of forecasting. It means that they will be able to analyse historical variations of the stock prices and give recommendations to clients.
What are the 4 types of data analysis?
They include descriptive analysis, which seeks to summarise past data, diagnostic analysis, which aims at determining causes, predictive analysis, which makes forecasts as well as prescriptive analysis which offers recommendations. Each of them has its function of achieving an accurate interpretation and usage of data.