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Like any academic and scientific discipline, data analysis follows a rigorous process comprising several stages. Each step and stage requires you to learn different skills and develop know-how of various scientific mechanisms. Understanding the process as a whole is vital for efficiently analysing the data for your dissertation or research paper. An efficient underlying framework is necessary for producing results that stand the tough scrutiny required for the analysis process.
In this post, we will explore in detail the vital steps and stages of data analysis. It will cover the ways to identify your goal, methods of data collection and the techniques to carry out an analysis. Let us start the article without wasting time any further!
What Are The Steps Involved In The Data Analysis?
There are six basic steps involved in the data analysis process. The names of those steps are the following:
- Defining the Research Question
- Data Collection
- Filtering Out the Required Data
- Analysing the Data
- Sharing the Results of the Analysis
- Accepting the Failure (If Any)
We will describe each step in great detail in the article below. At the end of this post, you will also be acquainted with the basic understanding of these steps and stages.
Defining the Research Question
The first step in data analysis is identifying and defining your research aim and objective. In the jargon used in data analytics, it is often termed the "problem statement".
Presenting the Problem Statement:
To present the problem statement, you must formulate a hypothesis and determine how to test it. You can start this by asking yourself the following question,
"What research problem am I trying to solve?"
While it may sound easy at first, it is important to clarify that it is not as easy as it may sound. For example, your organisation's senior management may present an issue that may seem trickier to solve. They might ask why their company lost the customers – now, this question will not get you to the core of the problem. As a data analyst, your job will be to understand the company's business and goals in depth so you can frame the problem immediately. It will help you carry out the remaining process in the right direction.
A Practical Example:
Let us suppose you work in a company called Digital Stars, which manufactures custom training software for clients on demand. The company is excellent at gaining new customers but has a comparatively lower return rate of customers. The question to be analysed in this scenario might be the following:
"Which factors are responsible for negatively affecting the customer experience?"
Or
"How can the company Digital Stars boost the customer retention rate while minimising the costs to be borne in the process?"
Formulating the Hypothesis:
Now that you have identified a problem and formulated the problem statement, you will need to determine which data can help you the best in solving the said issue. At this point, you will use your business acumen to figure out the possible causes of the issue in front of you. For example, you might notice that the sales process for new customers is efficient, but the company's production team is inefficient. From this scenario, you can hypothesise that the sales process is good, but the subsequent customer experience is poor. After figuring it out, you will ponder the sources that can help you find an answer to your problem.
Which Tools Can be Used to Assist You in Defining Your Objectives?
Defining the objectives usually involves applying your soft skills, lateral thinking and business knowledge. You will also need to keep track of KPIs, i.e. Key Performance Indicators and business metrics while pondering your research problem. Monthly reports of the company's performance can assist you in tracking the problem points in the organisation's business.
Examples of Helpful Software:
Some KPI dashboards can be used as paid versions, such as Databox and DashThis, while others are open-source software, such as Grafana, Dashbuilder and Freeboard. These are great for producing simpler dashboards, which can prove helpful for you both at the start and the end of the data analysis process.
Data Collection
Once you have clearly stated your problem statement and established your objective, you will need to devise a strategy for aggregating and collecting the relevant and appropriate data for the analysis.
The first step will be to determine the type of data you need for analysis. The required data might be quantitative, such as sales figures or qualitative, such as customer reviews about the products sold by the company. Quantitative data is numeric, while qualitative data is descriptive. You can classify the required data broadly into three categories, which are the following:
- First Party Data
- Second Party Data
- Third-Party Data
Let us explore each category in detail.
First Party Data
First-party data is the one you or your company collect directly from the customers. It might come to you in the form of the transactional tracking data available in the records of a company or from the CRM (Customer Relationship Management) system of your organisation.
This data type is usually organised and structured in a clear and defined way, no matter its source. The other sources of getting first-party data include interviews, customer satisfaction surveys, focus groups and direct observation.
Second Party Data
To conduct the data analysis better, you might want to collect secondary data per the requirements of the analysis. Second-party data is the first-party data of other organisations. It might come directly from the company itself or through a private marketplace.
The primary benefit of getting secondary data for the analysis is; it is usually structured. Although it may be less relevant to you than the first-party data, it can be reliable. Some examples of second-party data include websites, and social media activities, such as shipping data or online purchase histories.
Third-Party Data
Third-party data is the sort of data you collect and aggregate from several sources by a third-party organisation. Often it comprises many unstructured data points that can be remotely relevant to your analysis process. Many companies use this data to make industry reports or conduct market research.
Which Tools Can Prove Helpful In Data Collection?
After devising a data strategy (identifying the required data and figuring out the best way to collect it), you can use many online tools to collect data per your needs. In most cases, you will need to use a DMP (Data Management Platform). A DMP is a piece of software which allows you to identify and collect data from several sources per your needs.
Filtering Out the Required Data
Once you have collected the data, the next step you will have to take is to make the data ready for analysis. It implies cleaning or filtering the data because it ensures that you employ only high-quality data for the analysis. Key tasks while trying to clean the data include the following steps:
Removing the Errors
In this stage, you will remove all the errors, outliers and duplicates from your data. Such errors are inevitable problems when aggregating data from various sources.
Removing Unnecessary Data Points
In this step, you will extract unwanted and unnecessary data points and observations that no longer bring any meaning or value to your intended analysis.
Structuring the Data
This step entails the process of fixing the layout issues or all sorts of typos in the collected data. It will help you map and use your aggregated data more easily.
Filing in the Gaps
As you structure the data, you may notice missing a significant part. Once you identify such gaps, you can work on filing them and curate the required data per the needs of your analysis process.
A good data analyst processes the data with extreme care and caution and avoids using wrong data points, which might negatively impact the analysis results. You should spend a great deal of time cleaning the data to save you from the trouble of analysing erroneous data.
Which Tools Can Be Used For Filtering The Data?
Manually cleaning a larger amount of data can prove a daunting experience. Fortunately, several online tools are available to make the process as smooth and streamlined as possible. Open-source tools, such as OpenRefine, can prove excellent for primary data cleaning and high-level exploration. Free tools usually provide limited functionality for large databases. Python libraries and some packages of R software are better suited for the process of large data scrubbing. Some enterprise tools, such as Data Ladder, can also assist you better in data cleaning and scrubbing.
Analysing the Data
After cleaning the data, you will get to the gist of the process – analysing it!
The type of data analysis you choose to carry out depends greatly on your goal. Broadly, you can classify the data analysis types into these four categories:
Descriptive Analysis
The descriptive analysis describes the events that have already happened. Companies perform such sorts of analyses before proceeding with much deeper explorations. The company may not conclude such insights, but summarising and describing the data will assist them in determining how to proceed further.
Diagnostic Analysis
The diagnostic analysis focuses on understanding the reasons behind the happening of certain events. It is synonymous with diagnosing a problem, just like a doctor who uses a patient's symptoms to diagnose a disease.
Predictive Analysis
Predictive analysis helps you identify future trends based on existing historical data. In businesses, such analysis is usually carried out to forecast future growth. For example, we can consider the insurance industry to illustrate this better. Insurance providers usually use past data to predict what customer groups will likely get into accidents. Then they hike up the customer insurance premiums for such groups and benefit themselves from the hike.
Prescriptive Analysis
Prescriptive analysis helps you make recommendations for the future. It is the final step in the process's analytics part and a complex one. It incorporates the effects of all other analyses described above in this post. Such analysis helps the companies decide on new products to be launched and the areas of the business to invest in.
Sharing the Results of the Analysis
After finishing the analysis, you will reach the final step of your data analysis process. At this point, you will have your insights; the last step will be to share these insights with the outer world. This step involves interpreting the results you get and presenting them in a digestible manner for all audiences. Your data analysis results should be 100% unambiguous.
It is also important to shed light on any gaps in the data or highlight any such interpretations that might be open to further research. You should cover everything clearly and concisely, which will suggest that the results you got are scientifically sound and factually correct.
Accepting the Failure (If Any)
Data analytics is a messy domain; the process you follow will be different and might be novel at several stages. For example, while cleaning the data, you might encounter something that sparks many questions. You might also find that the results of your analyses are erroneous or misleading, and it will send you back to square one.
While these pitfalls can feel like worse failures, you must not get disheartened if they happen. As we said earlier, data analytics is inherently messy and chaotic, and mistakes keep occurring. What's important is to hone your capabilities and rectify the errors.
Conclusion:
In this article, we have narrated the core steps of the data analysis process. These steps can be re-ordered, amended or re-used per your preferences, but they underpin the work of almost every data analyst. You can get creative with these steps and figure out what works best. Just do not forget to follow the core principles – this way, you will be able to create a tailored and custom technique which works for you!
