7 Data Analysis Steps You Should Know by@techofknowledge

7 Data Analysis Steps You Should Know

Smart businesses rely on quality data to stay on top of their game. The average business may be disadvantaged without access to data and data analytics. To analyze data adequately requires practical knowledge of the different forms of data analysis, including text, statistical, predictive, prescriptive, and diagnostic analysis, as well as the right order of steps to follow in doing so. Here, we give a rundown of seven core data analysis steps below: Establish the objective of the data analysis process and lay a strategy to collect and aggregate the data.
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Smitha

Content Writer with an experience of Ten years in data science, machine learning, cybersecurity and more.

Whether it is to make decisions, launch new revenue streams, or reinvent business operations to align with future trends, smart businesses rely on quality data to stay on top of their game. This means that it is possible to have access to overwhelmingly vast volumes of data yet not make the most out of it. Data collection alone does not yield actionable insight unless it is analyzed and interpreted adequately based on the requirements of the business.


Today, the average business may be utterly disadvantaged without access to data and data analytics, not forgetting qualified professionals who have acquired practical knowledge and experience.

Seven Data Analysis steps

To analyze data adequately requires practical knowledge of the different forms of data analysis, including text, statistical, predictive, prescriptive, and diagnostic analysis, as well as the right order of steps to follow in doing so.


Below, we give a rundown of seven core data analysis steps.

1. Establish the objective of the data analysis process

First things first, you need to ask yourself the following questions before


Why do I need to perform data analysis?


What issue do I intend to address?


What question(s) am I trying to answer?


What outcomes do I envision after performing data analysis and implementing its results?


Answering these questions beforehand helps you not just to set a clear objective for data analysis but also provide a roadmap for you to do so. In addition, defining your objective helps you come up with a problem statement.


It is important, at this juncture, to note that your questions need to be clear, measurable, and concise. Secondly, as you define your objectives, you should also have an idea of the metrics you will use to measure how effective the data analysis results will be in addressing the problem raised.


For instance, a problem or question could be,


“Why is there high customer churn for XX service?”


Or


“How can we reduce production cost for product Y?”

2. Determine the data sources

Apart from providing a clear direction, a roadmap helps the data analyst to organize his/her team for the entire data analysis process, and this will also include determining which sources of data will best provide relevant data to solve the problem being addressed. How relevant a data source is will determine the depth of analysis of the data and insights that will come out of it.


Data collection has basic criteria that can be adopted for successful analysis. First off, the collection starts from primary or internal sources. A business’s internal database, including a business CRM, ERP, marketing tools, offers the most relevant data for the business. Such data is typically structured and directly relating to the business as it carries customer, financial, sales, and other business information.


Next, the data analyst explores external or secondary data sources such as social media or market research data by third-party agencies. External data is typically both structured and unstructured thus demands much input in terms of preprocessing and cleaning.

3. Gathering data

Having identified your data sources, lay a strategy that you will use to collect and aggregate the data. Which specific data do you need for the business problem you are trying to address?


For instance, if you are addressing customer churn, you may consider collecting sales figures and customer opinions.

4. Data preprocessing

Once data has been gathered from different sources, it cannot be used in its raw state. It has to be prepared and converted into a format that can be analyzed effectively to produce accurate results. Data preprocessing happens in four stages, namely:


  • Data cleaning.

    From the point of view that not all data is good data, the data cleaning process becomes absolutely necessary. This involves identifying and removing inconsistencies in a dataset such as duplicate data, anomalous data, outliers, errors, unwanted data points, and missing data.

  • Data integration.

    Data integration is the process by which data from various sources is consolidated into one dataset to provide a single unified view of the data while also enabling consistent delivery, easy access, and analysis of the data by users.

  • Data transformation.

    The process of converting the structure and format of raw data into the structure and format of the destination system to enable efficient analytics.

  • Data reduction.

    This is the process by which certain aspects of data, such as its volume and dimensionality, are reduced usually to reduce the capacity required to store the data, reduce computational and disc access costs, as well as to increase the database system efficiency. Thus storage capacity is usually referred to as either raw (before reduction) or effective (after reduction), and it should be possible to revert to the original data format if need be.


The aim of these processes is to ensure that the data you are analyzing is high quality. For this reason, data analysts will spend up to 90% of their time preparing and cleaning data.

5. Data visualization

Once you have your high-quality data compressed into an effective dataset, you may opt to visualize the data. Visualization refers to the visual representation of data in the forms of charts, graphs, maps, infographics, dashboards, and many other forms. Visualizing structured and unstructured databases using tools like Tableau enables the data analyst or data scientist to view trends and patterns and gain insights at a glance. This helps to develop insights or hypotheses upon which data analytics will be based.


Alternatively, data visualization can be done after data analysis to display the results and facilitate their interpretation.

6. Analyzing data

Data visualization in itself does not provide a complete picture of the insights available in datasets. A step further to analyze and manipulate data will provide accurate results that can be depended upon to make crucial decisions. Four broad categories of data analysis techniques can be applied depending on the goal of the analysis. These techniques are:


  • Descriptive analysis. A commonly used statistical analysis technique, descriptive analysis quantitatively summarises the features of a dataset.
  • Diagnostic analysis. Get down to the bottom of why things happened the way they did.
  • Predictive analytics. Attempt to forecast future trends and outcomes based on historical data.
  • Prescriptive analysis Makes recommendations on the appropriate course of action for the future based on past and present data.

7. Interpreting and presenting analysis results

What do you do with the results you get from data analysis?


Insights from data are not helpful unless they are actionable and applicable to the problem or question identified at the outset. Interpreting data analysis results accords the business the actual value it requires out of the whole exercise. This is because it will determine the next course of action to be taken.


At this point, it is important to consider the challenges and limitations of the data analysis process. Also, the presentation should be done in such a way that the interpretation of the results is objective, unbiased, and understandable by the stakeholders involved.


Data analysis is invaluable


Whether for a small business, large enterprise, government agency, or multinational, the problem isn't really the lack of access to data. Rather, the ability to identify business needs, analyze relevant data, make the right interpretation, and ultimately appropriate data-driven decisions.

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