The data analysis process is a collection of steps required to make sense of the available data. Identifying the critical stages in a data analysis process is a no-brainer. However, each step is equally important to ensure that the data is analyzed correctly and provides valuable and actionable information. Let's take a look at the five essential steps that make up a data analysis process flow.
Step 1: Define why you need data analysis
Before getting into the nitty-gritty of data analysis, a business must first define why it requires a well-founded process in the first place. The first step in a data analysis process is determining why you need data analysis. This need typically stems from a business problem or question, such as:
- How can we reduce production costs without sacrificing quality?
- What are some ways to increase sales opportunities with our current resources?
- Do customers see our brand positively?
In addition to finding a purpose, consider which metrics to track along the way. Also, be sure to identify sources of data when it’s time to collect.
This process can be long and arduous, so building a roadmap will greatly prepare your data team for all the following steps.
Step 2: Gather Data
Once a goal has been established, it is time to start gathering the data required for analysis. The type of the sources used to acquire the data will dictate how in-depth the analysis is, therefore this phase is crucial.
Primary sources, sometimes referred to as internal sources, are where data collection begins. Typically, this is structured data that has been obtained from applications like CRM, ERP, marketing automation, and others. These sources include data about customers, finances, sales gaps, and other topics.
Next are secondary sources, also referred to as outside sources. This data, which can be acquired from a variety of sources, is both structured and unstructured.
Step 3: Clean unnecessary data
Your data team will be tasked with cleaning and sorting through the data once it has been gathered from all the required sources. Because not all data is good data, data cleaning is crucial during the data analysis process.
To produce accurate results, data scientists must find and eliminate duplicate data, anomalous data, and other irregularities that could distort the analysis.
Step 4: Perform data analysis
Analyzing and altering the data is one of the final processes in the data analysis process. There are numerous ways to accomplish this. Data mining, which is referred to as "knowledge discovery within databases," is one method. Data mining methods such as clustering analysis, anomaly detection, association rule mining, and others may reveal previously undetectable underlying patterns in the data.
Additionally, there is software for data visualization and business intelligence, both of which are designed with decision-makers and business users in mind. These options produce reports, dashboards, scorecards, and graphics that are simple to interpret.
Predictive analytics, one of the four types of data analytics now in use (descriptive, diagnostic, predictive, and prescriptive), can also be utilized by data scientists.
Step 5: Interpret the results
The analysis of the data analysis's findings is the last phase. Because it's how a firm will actually benefit from the first four processes, this step is crucial.
Even if the outcomes of your data analysis are not entirely conclusive, they should nevertheless support the reasons you undertook it. To cut production costs without compromising quality, for instance, "options A and B can be studied and tested."
During this phase, analysts and business users should try to work together. Additionally, take into account any difficulties or restrictions that might not have been reflected in the data when analyzing the results. This will only increase your confidence as you proceed.