How to Make the Most of Data Analysis

How to Make the Most of Data Analysis

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A common factor behind most leading organizations today is their dependence on efficient usage of data-driven analytics. Understanding and deriving valuable information from colossal volumes of data are what these organizations seek. Putting the derived data to its practical use is key to setting an organization on the path to eventual success.

In the past, organizations wished to employ data-driven strategies but lacked the required volumes of data to conduct thorough analyses. However, this has shifted, and in today’s data-rich age, companies instead now struggle with handling vast volumes of data. Read on to discover how companies can benefit from data and derive meaning and value out of it.

Where to begin? Have a Clear Hypothesis or a Question.

Before conducting data analysis, it is the organization’s responsibility and the decision-makers to set clear, measurable, and concise objectives regarding what they expect from the process. The goals should be framed as a question that requires an answer. Data analysis will help in finding solutions to those questions. Without setting clear objectives, it will become complicated and confusing for analysts to derive valuable information from a sea of data.

However, the effectiveness will be dependent upon the quality of data utilized by the organization. The involvement of all stakeholders at this stage is critical because setting the right vision will further influence the campaigns and strategic goals that the company wishes to achieve through the process. For those willing to accelerate their careers in the field of data analytics, it would be ideal to enroll in a masters in data science online to step into the ever-expanding area of big data.

Data 101: Understanding the Types of Data Available

Qualitative data refers to the type of data that is more subjective and open to interpretation. Instead of focusing on numbers, qualitative data is generated from surveys, reviews, questionnaires, interviews, and other subjective methods.

Quantitative data, on the other hand, is self-explanatory. It involves the collection of raw data in the form of numbers, statistics, and characters. Examples of quantitative data include metadata, quarterly sales figures, payroll data, click-through rates, product refund data, etc. Big data is a mix of both qualitative and quantitative data. AI learning and algorithms are thus applied to this big data to conduct analyses and to make practical information, and generate insights through the collected data.

Where to Start on the Journey of Data-Driven Insights?

The majority of companies these days are already involved in the process of accumulating large volumes of commercially relevant data at an ever-increasing rate, especially for companies engaged in digital products, services, and e-commerce. Clicks per page, page views, shopping cart data, trending products, metadata regarding the time, location, price of goods sold, etc., are all examples of data that companies are involved in collecting regularly.

How should you measure data and set clear harvesting priorities?

Quantitative tools assist analysts in interpreting hard facts and creating meaningful information. There are three methods generally employed by companies to accurately measure data for future predictions and analysis: Hypothesis testing, regression studies, and Monte Carlo Simulations.

These quantitative methods allow analysts to forecast the impact of new decisions on their organizations by comparing two variables and finding their correlation. For instance, hypothesis testing can see the correlation between productivity and longer working hours. On the other hand, regression studies can predict the impact of changing trends and factors on an organization’s sales figures. Monte Carlo Simulations can help analysts understand and predict the possible effects of random variables on proposed projects.

How to extract value and insights from raw data?

It is essential to understand that data analysis methods are not fool-proof and do require qualified interpretation. For example, hypothesis testing can only assist in either rejecting a hypothesis altogether or being unable to reject an idea. The key here is to note that the hypothesis testing method cannot prove a hypothesis true.

Therefore, at the results interpretation stage, analysts should ask themselves three questions; Firstly, does their analysis successfully answer the original objective? Secondly, are there any objections that can be made to the method of data analysis? Third, are there any limitations on the information or conclusions you’ve arrived at? Or have you missed out on any other angles of approaching the original objective?

Final Words

So are you ready to enter the corporate world and willing to make the most of big data? By following the above techniques, one can start making the most data analysis and the subsequent strategies to collect, organize, test, and evaluate data. While all the derived insights may not be helpful or relevant, the constant evaluation and re-evaluation process will strengthen the conclusions. However, additional data is required to be added to the mix constantly to generate more reliable and valid conclusions.