Data analysis steps

Living in the digital age comes with many pros, and one of them is definitely the accessibility of data. With an abundance of data available at human  fingertips, data analysts have more opportunities than ever before to help businesses drive growth and identify new opportunities that they can use to their advantage to drive sales and achieve their goals.

With more and more businesses relying on statistical data to help drive decision-making, getting acquainted with data analysis will help you become a valuable asset to any organisation and will help you make better-informed decisions.

What is data analysis?

Data analysis is the process of collecting, cleaning, examining, and modelling data to derive useful information and insights that will help decision-makers make informed data-driven decisions and identify opportunities and weaknesses within an organisation.

Like any scientific discipline, data analysis follows a step-by-step process, with each stage requiring a different set of skills and know-how. To get meaningful and relevant insights, it’s important to understand the process as a whole in order to be able to leverage the insights to your advantage.

The data analysis process

Businesses have more access to data than ever before, but in order to leverage it to their advantage, they’ll need to be able to interpret it in meaningful and relevant ways. The data analysis process ensures that the data that is collected, cleaned, and analysed will help decision-makers make more informed data-driven decisions by only looking at the relevant data that will help businesses achieve their goals. Data analysis entails a 5-stages step-by-step process which is outlined below.

1. Defining the question
2. Collecting the data
3. Cleaning the data
4. Analysing the data
5. Sharing your results

Let’s take a look at what is required in each stage of the process below.

Defining the question

The first step in the data analysis process is defining your objective. In data analytics jargon, this is called the ‘problem statement’. Defining your objective means that you need to come up with a hypothesis and figure out how to test it. Start by asking, “What business problem are we trying to solve?” The problem statement will be the business problem you are trying to solve.

Defining the business’s objective relies on your soft skills, business understanding, and lateral thinking capabilities. This is because it will require a thorough understanding of the business in order to pinpoint the pain points, and it will require creative thinking in order to define an objective that will help solve the problem.

Once you’ve identified the problem, you will need to determine which sources of data will best help you understand and solve it. This is why it’s important that data analysts fully understand the business and its goals in order to ask the right questions and collect the appropriate data.

Collecting the data

Once the business objective has been determined, you will ’ll need to create a strategy for collecting and aggregating the necessary data. The key part of this stage is to determine which data you will need to collect and aggregate. It may be quantitative, such as sales figures, or qualitative, such as customer reviews. You may also consider which type of data will benefit you the most, such as First-party data, Second-party data, or Third-party data.

Once you’ve identified which data you need and how best to collect it, you will  have a data strategy that you can start tinkering with. One thing to note is that you will require a data management platform, DMP, to help you collect and aggregate this information all in one place.

Cleaning the data

Once you have collected and aggregated the data, the next step is to get it ready for analysis. This will require some data cleaning or scrubbing. A critical step in the data analysis process, data cleaning is crucial to ensure that you’re only working with high-quality and relevant data that will help you solve the problem and not leave you feeling more lost than before with an abundance of irrelevant data.

Data analysts spend around 70-90% of their time cleaning data which just goes to show you the importance of it. The key data-cleaning tasks that need to be done in this stage are:

• Removing major errors, duplicates, and
• Removing irrelevant data
• Structuring your data to map and manipulate it more
• Filling in major gaps of

Analysing the data

Once you have defined your problem statement, collected the data, and cleaned the data, it is finally time to analyse it. The type of data analysis you conduct largely depends on what your goal is; you may choose to utilise the univariate or bivariate analysis technique or time-series analysis, or maybe even a regression analysis. But more important than the different types of analysis you choose to use is the application of them. How you apply them depends on what insights you’re hoping to gain. Broadly speaking, all types of data analysis fall under four categories which are listed below.

• Descriptive analysis
• Diagnostic analysis
• Predictive analysis
• Prescriptive analysis

Sharing your results

The final step in the data analysis process is the sharing of the insights with the relevant parties, such as your organisation’s stakeholders. It may sound simple but simply presenting your findings in their raw form is not enough. A data analyst will have to interpret the outcomes and present them in a manner that is easily understood by all types of audiences. Since you’ll be presenting your findings to decision-makers, it’s important that your insights are presented in a clear and unambiguous way. For this reason, data analysts make use of reports, dashboards, and interactive visualisations to support their findings.

Final words

With more and more businesses relying on data analysis to inform them of insights that can help them solve their problems and help drive their decision-making, the need for skilled data analysts is at an all-time high. The ability to extract meaningful insights from data has become a highly valued skill, as it can help businesses thrive in this ever-changing and fast-paced world.

Data analysts play a vital role in helping business leaders make informed decisions and distinguish between effective and ineffective practices within their organisation and help them to solve key business problems. Sign up for an online data analysis short course and learn the fundamentals of data analysis and unlock your analytics potential, regardless of the industry you’re in.