Data Analysis Process: Understanding Its Intricacies

What does it mean to make data-driven decisions? it means to make strategic decisions based on data analysis, and interpretation. A?data-driven?approach enables companies, and individuals to examine and organize their?data?with the goal of better serving their customers, and consumers.

Being data-driven means using facts, metrics, and data to guide strategic business decisions that align with your goals, objectives, and initiatives. When organizations realize the full value of their data, that means everyone is empowered to make better decisions with data, every day. However, this is not achieved by simply choosing the appropriate analytics technology to identify the next strategic opportunity.

In order to learn how to make data-driven decisions, you need to understand how to analyze data. In this article, we will traverse the world of data analysis to further understand its intricacies.

What is Data?

Data is an abstraction of real life. I.e. it represents people, places, and things. It is a snapshot in time that represents or captures something that moves and changes.

What is Data analysis?

Data analysis?is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Data analysis is a lucrative, and in-demand career and you don’t need to be a mathematician to get a grasp of it .

How is data analysis used in business?

Data analysis is important for businesses today, because data-driven choices are the only way to be truly confident in business decisions. Some successful businesses may be created on a hunch, but almost?all successful business choices are data-based.

Data analysis provide you with more insights into your customers, allowing you to tailor customer service to their needs, provide more personalization and build stronger relationships with them.

Data analysis can help you streamline your processes, save money and boost your bottom line. When you have an improved understanding of what your audience wants, you waste less time on creating ads and content that don’t match your audience’s interests.

Why does data analysis matter?

Data Analysis is one of the most important processes that businesses can leverage to make the right decisions. There is a virtual business in this information age that does not need analysis of their data to be able to make the right decisions.

Effective data analysis is a skill that can be applied to finance, retail business, medicine, and healthcare, and even in the world of sports. For example, Liverpool FC has consistently churned-out world-class performances in the last two years which is why they have been?dominating the Premier League , the secret is data analysis. It’s a universal language and it’s more important now than ever before.

Types of data analysis

There are different types of data analysis depending on what you are trying to achieve. let’s take a quick glance;

• Descriptive Analysis:?This is about looking at past data and drawing insights from it. This is the most common type of data analysis. This is often used when tracking Key Performance Indicators (KPIs), revenue, sales leads, and more.
• Diagnostic Analysis:?This has to do with aiming to determine why something happened. Once your descriptive analysis shows that something negative or positive happened, diagnostic analysis can be done to figure out the reason. A business may see that leads increased in the month of October and use diagnostic analysis to determine which marketing efforts contributed the most.
• Predictive Analysis:?This is all about predicting what is likely to happen in the future. In this type of research, trends are derived from past data which are then used to form predictions about the future. For example, What happened to sales the last time we had a hot summer? How many weather models predicted a hot summer this year?
• Prescriptive Analysis:?This entails combining the information found from the previous 3 types of data analysis and forming a plan of action for the organization to face the issue or decision. This is where data-driven choices are made.

Now, let’s take a look at the process used in analyzing data.

The process of data analysis

The following are the guidelines to making an effective analysis of your data. They are;

1. Identify your questions

In your organizational or business data analysis, you must begin with the right question(s). Questions should be measurable, clear, and concise. Design your questions to either qualify or disqualify potential solutions to your specific problem or opportunity.

2. Data collection

With your question clearly defined, now it’s time to collect your data. As you collect and organize your data, remember to keep these important points in mind, to keep your collected data organized in a good naming convention, and appropriate storage system.

3. Data Cleaning

Once processed and organized, the data may be incomplete, contain duplicates, or contain errors. The need for?data cleaning?will arise from problems in the way that the datum are entered and stored. Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, deduplication, and column segmentation. Such data problems can also be identified through a variety of analytical techniques.

4. Analyze the data

After you’ve cleaned the data, it’s time for deeper data analysis. Begin by manipulating your data in a number of different ways, such as plotting it out and finding correlations or by creating a pivot table in Excel. A pivot table lets you sort and filter data by different variables and lets you calculate the mean, maximum, minimum, and standard deviation of your data. As you manipulate data, you may find you have the exact data you need, but more likely, you might need to revise your original question or collect more data. Either way, this initial analysis of trends, correlations, variations, and outliers helps you focus your data analysis on better answering your questions and any objections others might have.

5. Get Insights

After analyzing your data and possibly conducting further research, it’s finally time to interpret your results. As you interpret your analysis, keep in mind that you cannot ever prove a hypothesis true: rather, Meaning that no matter how much data you collect, chance could always interfere with your results

As you interpret the results of your data, ask yourself these key questions:

• What questions will provide actionable insight?
• Will the answers to my questions matter?
• Are the answers to my questions correct?

If your interpretation of the data holds up under all of these questions and considerations, then you likely have come to a productive conclusion. The only remaining step is to use the results of your data analysis process to decide your best course of action

The answer to each question depends on the answers that come before, and it’s common to jump back and forth between questions.

As shown in the image below, it’s an iterative process. For example, if your dataset is only a handful of observations, this limits what you can find in your data and what visualization methods are useful, and you won’t see much.

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