We hear so many good things about data and its importance to big tech firms. It is in fact true that the modern era belongs to data. However, data in its raw form is not very useful to any organization. One needs to clean, structure, process, interpret and present the data into something useful that helps the organization in achieving defined objectives. The process by which professional converts raw data into some meaningful insight to help in decision-making or problem-solving is known as the data analysis process. Data analysis is extremely important to businesses without which data is meaningless.
Data analysis is an integral function or defined role in many organizations usually performed by professionals commonly known as data analysts. It is not limited to just crunching numbers and presenting to management. It is a long process involving several steps, mastering various tools and requiring excellent skills. The modern workplace is fast-paced and tech-driven, data analysts play an important role in business and are valued across organizations. In fact, many firms have started offering data analytics programs to train their employees in this field.
Each analysis is different and depends on the type of business problem. However, a generic process can be defined which can help what a data analyst needs to do to convert the data. Present articles mostly focus on the steps followed by a data analyst to transform raw data.
Table of Contents
Key Steps of the Data Analysis Process
Data analysis is a long and extensive process. It needs a good amount of time and study. Over the years several professionals working on data have developed plenty of resources and wisdom to achieve this prolonged procedure in a short span of time. The overall data analysis can be broadly divided into six steps.
- Understand Business Requirement
- Data Collection and Storage
- Data Processing and Filtering.
- Data Exploration and Analysis.
- Sharing, Monitoring, and Feedback
Understand Business Requirement
Everything that a professional does is to achieve a definite business objective or goal. Data analysis is not different and always starts after defining end objectives to achieve. Data experts work with various stakeholders to clearly understand the objective of the analysis. Some examples of objectives are listed below
- Improve product quality.
- Enhance customer satisfaction.
- Prevent losses.
- Explore new opportunities.
- Increase sales or business revenues.
Objective definitions initially are generic in nature and can be further broken down or simplified to technical requirements. This can be achieved by discussing with customers to understand the requirement in more detail. For example “improve product quality” can be further broken down to add certain features or amend certain features in a product. The Objective defined has to be specific, actionable, and measurable.
Data Collection and Storage
Once business requirements are clearly stated and understood, the next step is to collect the data from various sources. One has to find the data which is in line with the business objective defined and help in arriving at an innovative solution. Data can be gathered from multiple sources in multiple formats such as querying existing databases, data loggers, old files, interviews, feedbacks, questionnaires, surveys, and so on. Sometimes If data is already available, this step only involves receiving the existing data sources and adding new ones if research suggests so. Data gathering sometimes requires a lot of effort and time. Collected data can be stored in files in the form of spreadsheets in databases such as relational tables. The choice will depend on the size of the data, smaller datasets can be stored in files while larger datasets may require databases.
Data Filtering and Cleaning
The next step is to review the data gathered. This is a very important and crucial step in the data analysis process. Data incorrect format and without any error is of paramount importance to data experts for further analysis. The data collected at the earlier stage is clean and filtered to remove any errors, specifically NAN, missing data, outliers, unwanted data. It is not an easy step and requires tremendous effort from data experts. One may need to carefully analyze the data at hand and may need to prepare a statistical summary, plots, charts to review the quality of data. As data collected is unique for each requirement, errors in data may also be unique and have to be handled case by a case basis.
Data Exploration and Analysis
The next step in the analysis process is data exploration in which the filtered and processed data is carefully inspected to figure out any logical hidden relations between important parameters. Various statistical techniques or mathematical formulas can be employed to get better insights and hidden patterns. Advanced level analysis requires the use of Python, R, or other programming languages. Extensive libraries with ample pre-built functions or methods are available for quick analysis of data.
It is one of the most important steps of the data analysis process. Visualization is representing data in the form of trendy charts, plots, and tables. Visualization helps key stakeholders (including non – technical) to see patterns or trends in the data easily. Analysts can communicate their findings in the form of stories with good and attractive reports. One can create a visualization in the static form such as pdf or in the dynamic form such as dashboards to provide additional flexibility to stakeholders. Tableau, PowerBI, and several other user-friendly tools are available in the market which enables analysts to create appealing visualizations. Programming languages Python and R provide powerful modules to create trendy reports.
Sharing, Monitoring, and Feedback
The final step of the process is to share the reports with all stakeholders. If it is a static report it can be shared as a PDF or if it is a dynamic report, a dashboard can be made available on the server. After reviewing the results stakeholders can give feedback on the level of acceptance and helpfulness of reports. If analysis meets the requirement, a solution is implemented. The report generated is monitored continuously for any anomalies or change requests. If the analysis does not meet the requirement, stakeholders may suggest finding alternative solutions. In such cases, one has to review all the steps and see the possibility of improvement.
Developing a process for achieving any business objective is key to success. A carefully drafted process guides one in the right direction and helps achieve the objective within a defined timeline. The steps defined for the data analysis process are applicable in general to each possible requirement. However, some requirements may need a few steps to follow and the rest can be ignored. One has to master all the steps for being a successful data analyst. Furthermore, data analysis steps give one opportunity for improvement and automation.
The steps of data analysis are similar to golden arrows to succeed in any organization. The steps always work and will give one desired result if followed properly. Apart from following the steps, one needs to learn a lot of tools, technologies and practice them for being a successful data expert. Opting for online training will help one to practice and make good command of the tools. Training also helps one to be aware of the latest trends in the field along with the essential skills required. One should not think deeply and wait if interested in data-related fields, it is highly recommended to start learning and dive into the space immediately.