Data analytics

How to Analyze Data: 5 Strategic Steps to Master

The volume of data you can capture from different sources determines the insights you can gain about how effectively your business processes are working. It can also position your team to collaborate in alignment with future trends. 

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Mile Zivkovic

Apr 01 2020 6 min read


Table of Contents

    Nevertheless, data collection does not produce any meaningful and actionable results without adequately analyzing it. You will only end up with numbers and figures with no basis.

    However, there's no rule of thumb for analyzing data. Data analysis is based on your requirements and the form of data you wish to source. These factors will determine the methods you will adopt for your data analytics. That's the more you need to understand the form of data and the best outcomes.

    What is data analysis?

    Data analysis is the process of fine-tuning, converting, and modeling data to generate meaningful and actionable insights that inform sound business decisions. Each stage of data analysis requires different skills and know-how. Data analysis aims at extracting vital information from data and implementing decisions leveraging the data analyzed.

    This collection of data can come in different forms, such as in-house or outside sources, surveys, interviews, questionnaires, focus groups, etc.

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    You need to prioritize data analysis of your business data and processes according to metrics and KPIs.  Once you collect data, this is the next step that follows.

    What are the different types of data analysis?

    We have 5 different forms of data analysis based on technology and business.

    They are as follows:

    1. Text analysis
    2. Statistical analysis
    3. Diagnostic analysis
    4. Predictive analysis
    5. Prescriptive analysis

    Let us cover each of these in detail and explain what they mean and how they are done.

    1. Text analysis

    Another name for text analysis is called data mining. This method is deployed to discover any pattern in large data sets utilizing data mining tools and databases. Text analysis is leveraged to change raw data into business data.

    There are many business intelligence tools available in the market, and they can be used to make informed decisions. Most importantly, they provide a means of generating and examining data and discovering patterns before interpretation is made. 

    2. Statistical analysis

    Statistical analysis asks, 'What happened?' by relying on previous data in the dashboard forms. This form of statistical analysis incorporates data collection, analysis, interpretation, presentation, and data modeling. It analyzes data samples or a set of data from your focus groups. There are two forms of statistical analysis - descriptive and inferential analysis.

    Descriptive analysis

    This analyzes a sample of summarized numerical data sets or complete data. It depicts the mean and deviation for serial data while it shows the percentage and frequency for categorical data.

    Inferential analysis

    In this form of data analysis, you can cull several conclusions from the same data sets by picking various samples.

    3. Diagnostic analysis

    The diagnostic analysis seeks to answer, 'Why did it occur?' by discovering the cause from insights in statistical analysis. Diagnostic Analysis is vital in identifying the behavioral patterns of big data in applications such as machine learning. This data analysis method often resorts to AI and machine learning to help teams automatically analyze large sets of unstructured data. If a new issue surfaces in your business operation, then you can delve into this Analysis to discover related patterns of that issue. That way, you can utilize the same prescriptions for that new issue.

    4. Predictive analysis

    This type of Analysis asks 'What's likely to occur' by relying on previous data. A good example is if last month, we spent $500 to generate 1,000 likes on Facebook. If our ad spend increased to $1,000, we can conclude that we should generate 2,000 likes this month. However, it's not as easy as that; there's also a need to factor in other events such as Changes or Updates to Facebook Advertising or other factors and data points.

    Therefore, we can assert that Predictive Analysis forecasts future outcomes based on previous or current data. The forecast accuracy relies on how detailed the information you have sourced is and how much you researched it. 

    You don’t have to be a data analyst or some other type of data science expert to do this type of job. However, having some data visualization tools helps, especially if you have a multitude of data sources.

    5. Prescriptive analysis

    This form of Analysis leverages insight from all data to decide on an action plan or resolve an issue. A lot of data-driven enterprises use Prescriptive analysis as descriptive and predictive are not enough. It would help if you had more than Analysis to enhance data performance. The prescriptive analysis uses current problems and events to analyze data and arrive at a decision. Prescriptive analysis can yield powerful sets of visual data that can improve a number of key areas such as marketing, sales, customer experience, HR, fulfillment, finance, etc. 

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    Data visualization

    Having analyzed your data, it is highly essential to represent the data in a graph, chart, and other visual formats. This is where Data visualization comes in. It uncovers the relationships of the analyzed data using images, whether it’s quantitative data or qualitative data. With data visualization, you can easily see trends and patterns.

    Whatagraph has more than 40 integrations with the most popular data analysis tools and allows you to create beautiful visual reports in minutes. Pick a report template (there are over 100 in our library) and populate your presentation with bar charts, graphs, widgets, and other visual information.

    Once you create a report, you can change the details you want, including logos, colors of the elements and even white-label the report to remove Whatagraph branding.

    So how do you hone your data analysis skills and enhance your decision making?

    5 steps for doing effective data analysis 


    1. Begin with the right questions

    In your data analysis, there's a need to start with the appropriate survey questions that are measurable, clear, as well as concise. Tailor those questions so it can annul or disannul likely solutions to the specific issues or opportunities.

    For instance, a PPC agency is experiencing rising costs, and it's finding it challenging to tender competitive contract proposals. One possible question to resolve this issue might incorporate: Can the agency downsize without compromising quality?

    2. Establish clear measurement priorities

    You can break this step into two sub-categories

    A. Conclude what you intend to measure (e.g. customer satisfaction from social media)

    B. Decide the strategy to adopt in measuring it.

    Let's examine those two sub-categories.

    Conclude on what you intend to measure

    Going by the analogy of that PPC agency, you might need to examine the types of data required to answer salient questions. In this situation, you'd need to understand the number of employees and freelancers working with you. Their cost, as well as the percentage of duration they spend in the business operations - all of this is important for choosing the right data analysis methods.

    This question alone will generate several sub-questions such as:

    Are we maximizing our workforce?

    If not, what agile and process improvement can we leverage?

    Lastly, when you are ready to measure, ensure you factor in any reasonable objections your team might have. For instance, how will the company cope with an increase in demand if the agency reduces staff?

    Decide the strategy to adopt in measuring it

    It is highly important to ponder how you will measure your data before the data collection phase. This is because your measuring procedure either enhances or jeopardizes your analysis later—some salient questions.

    What's the time frame - annual or quarterly expenses?

    What's the unit of measurement - USD or Euro?

    What parameters should be incorporated?  Annual salary or an annual salary, coupled with the cost of employee benefits?

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    3. Source data

    Having defined the question and established your measurement priorities, you will need to keep these vital points in mind:

    Before sourcing for data, decide on the data to be sourced from existing databases.

    • Source for this data first.
    • Decide on a file storing and labeling framework ahead of time to enable all members saddled to collaborate. That way, you can save time and prevent double sourcing.
    • In case you need to adopt an interview or observations, then design a template for the interview beforehand to guarantee consistency and save time.
    • Organize the data you've sourced along with sourced dates and include any source notes as you progress. This action will validate your findings as you progress.

    4. Analyze data

    After you've sourced the correct data to resolve your question in the first step, the next stage is to go deeper into data analysis.

    Start by converting your data in several ways, like plotting it on a graph, examining the correlations, or creating a pivot table in your Excel.

    What is a pivot table?

    This table enables you to sort and filter data over different variables to calculate your mean, minimum, maximum, and standard deviation of that data. This is also where data cleaning happens.

    Data analysis software and tools are highly important during this stage. You can try apps and analytics tools such as Stata, Visio, Minitab, and Microsoft Excel or find more data analysis tools.

    5. Interpret results

    As soon as you've analyzed your data, go ahead, and interpret the results. While interpreting your analysis, bear in mind you can't prove the validity of a hypothesis. Instead, you can only accept it. This means that regardless of how much you source data, your results can be interfered with by unforeseen circumstances.

    So you need to ask yourself as you proceed:

    • Does this data provide a solution to the first question? How?
    • Does the data empower you to safeguard against any objections? How?
    • Are there limitations on the findings or any perspectives to be considered?

    If the interpretation holds against all these questions and factors, you have possibly reached a productive conclusion. The only stage is to utilize the data analysis process results to make informed decisions in your business.


    By adopting these 5 data analysis steps, you will make better and more informed decisions for your business or digital marketing agency. Your choices will be backed by data that you've carefully sourced and analyzed.

    Published on Apr 01 2020

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    Mile Zivkovic

    Mile is the head of content at Whatagraph in charge of all content and communications for Whatagraph’s data platform. A marketing heavy with almost a decade of SaaS industry experience, Mile has managed multiple content marketing teams without losing an ounce of his writing passion. The author behind some of the most-read pieces on our blog.

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