6 Key Elements of Data Analytics You Need to Focus on

All businesses, big or small, know the power of data. Based on the data your business produces or collects from various sources, the management can make better decisions on how to improve various processes for higher revenue making. 

To manage and process data efficiently, it’s important to have an organized data analytics strategy in place. Let’s take a look at some of the most significant elements of data analytics. 

Defining the Question

To get started, you need to define your objective, which is often termed a ‘problem statement.’ You should know what business problem you are trying to solve. Based on the issues your business is facing, you might be looking to solve one or multiple problems.

Once you’re ready with the problem statement, determine the source of data that can help you solve the persisting problem. For example, if the problem is ‘how to boost customer retention while reducing costs, you should know what source helps you get the answer. 

You can get this data via Apple Mail on your MacBook or Gmail on a Windows computer. The large volume of data files may lead to an unresponsive inbox, though. As a data analyst, you ought to know how to refresh Apple Mail or Gmail to proceed further. 

Collecting the Data

After you have established your goals, the next step is to create a strategy that helps you collect and aggregate data. You need to determine the data you require to fulfill your organization’s core objective. Examples of data include customer reviews or sales figures.

The data you collect must fit one of the three major categories – first-party, second-party, and third-party. First-party data is one that you collect from a company’s transaction database or the customer management system (CRM). 

The Second-party data is the information that you collect from another company or a marketplace. This data is the first-party data of the organization you get it from. Subsequently, third-party data is the information you get from multiple data sources. 

Cleaning the Data 

Once you are ready with the data, the next challenge is to get it ready for thorough analysis. Clean and scrub data to ensure you are left with high-quality data only. The cleaning process involves multiple stages, such as:

Eliminating major errors: At this stage, you review all the contents of the data to remove errors and duplicates. Data impurity is probably the most common problem you face when aggregating it from multiple sources. 

Removing unwanted data points: When cleaning data, you should have a purpose and clear requirements about the data you need and the data you can dump. So, extract any immaterial observations that have no solid base for analysis. 

Structuring data for better use: The data you collect from different sources are raw information in an unorganized structure. You need to organize it by defining a layout or eliminating typos so that you can map and manipulate data easily. 

Carry Out Exploratory Analysis

Exploratory analysis is an important and unavoidable step in data analysis as it helps identify characteristics and trends. It is used by data scientists to investigate large data sets to determine main attributes that can simplify decision-making. 

This practice makes it easier for data professionals to discover patterns, check assumptions, test hypotheses, identify anomalies, and more. It also helps determine if the statistical techniques and standard practices you are implementing are appropriate. 

Analyze Your Data

So, you have finished the most complex part – cleaning data. Now you need to analyze it thoroughly. The techniques you use for data analysis depend largely on what you want to achieve, such as the type of business problem you are looking to solve. 

Based on your business problems and data analysis goals, you need to select one among the Time-series analysis, Regression analysis, and Univariate or bivariate analysis. The most important thing is how you apply these techniques to the data.

All these different types of data analysis fall under four major categories, including descriptive analysis, predictive analysis, diagnostic analysis, and prescriptive analysis. Again, the data analysis category you select depends on your business problems and the purpose of the analysis. 

• Descriptive analysis helps identify something that has already happened
• Predictive analysis helps identify future trends on the basis of historical data
• Diagnostic analysis enables an understanding of the core reasons behind the problem
• Prescriptive analysis allows preparing future recommendations

Sharing the Outcome 

Now you have finished carrying out analysis on the given data; you are ready with the insights. It’s time to share the analysis result with the concerned authorities. Not to mention, it is a more complex process that needs some other considerations. 

Keep in mind; you are sharing the outcome of your data analysis with the decision-makers. Check that everything has been covered in a clear and concise manner. Make sure the reports you prepare using Power BI or other tools are data-backed, based on facts, and scientifically sound. 

So, these are some of the main steps of your data analysis strategy. Based on your business goals, you can reorder or re-use the steps as you see fit.