What’s the Difference Between Data Analytics and Data Analysis?

Today businesses are relying increasingly on data to gain quantified insights that can lead to positive business outcomes. Data takes the ‘guesswork’ out of a business approach and replaces that with hard facts that can make decision-making more efficient. Considering that people are generating more than 2.5 Quintillion bytes of data every day, it is hardly a surprise that ‘data’ is what everyone talks about. According to IDC, this data has grown ten-fold and crossed 44 zettabytes! The IDC report also revealed that the amount of useful data has increased from 22% in 2013 to 35% in 2020. The data explosion is only going to increase more as the number of smartphone users around the globe increase. We can see over 50 billion connected smartphones since the last five years. While we continue to marvel at this data explosion, it has to be stated that data is just data and nothing more unless it is made to work. It is only when data is made to work does it provide the business advantage it promises.

Data Analysis and Data Analytics are two terms that are frequently used interchangeably. With the help of analysis and analytics, raw data is converted into actionable insights that deliver business value. These terms might sound similar but are quite different. While both analysis and analytics enable insight and evidence-based decision making by uncovering patterns and opportunities lying within the data, the main difference between the two lies in their approach to data. To put it simply, one looks towards the past and the other towards the future.    

Data Analysis

Here is how data analysis helps organisations –

• It assesses the requirements of the business and sees how functions and processes can be used to improve performance and outcomes.
• It is done to identify relevant data sets and use these data sets to derive meaningful insights to improve business performance and improve decision-making.
• Data analysis helps in breaking down the macro picture into a micro picture to rule out human bias with the help of statistical analysis.

Data analysis comes into play when organisations want to create strong business architectures, prepare solid business cases, conduct risk assessments, identify market dynamics, gauge the effectiveness of business processes, or assess product performance, etc. Taking a look at historical data uncovers insights of what worked, what did not, and what is possibly expected out of a product and service.

It involves expert hands-on data exploration, drawing up conclusions based on those evaluations, uncovering of multiple micro views, and gaining deeper and significant insights.

Data Analytics

Data Analytics is more expansive in its scope and includes data analysis as a sub-component.

Data analytics helps organisations utilise the potential of their data to identify new opportunities and helps businesses outline the way forward. Be it how to reduce costs and improve decision making or understand customer sentiment and come up with new products or services, data analytics unlocks the right information that is needed for business growth.

• With data analytics, organisations can measure business results and make business changes that can lead to better outcomes.
• Through exploratory, confirmatory, or qualitative data analysis, data analytics analyses raw data to draw conclusions that can enable better business decisions.
• It uses the relevant tools, techniques, and proficiencies to assess past performance and gain insights for future performance.

Data Analytics, thus, is an exhaustive and detailed business practice that starts with identifying which data to analyse, collecting the right data, and then organising that data into the right data sets using the right algorithms and statistical techniques. Data analytics also involves a certain amount of data cleaning to deliver the right analysis based on which, the right decisions can be taken. Once the data is transformed into a useful form, some mechanical or algorithmic process has to be applied to it in the form of a Machine Learning algorithm or statistical process to derive insights. This is done by comparing the different data sets to gain answers to the problems the data is being used to solve. Once this is done, the data analyst has to represent the data in a form that can be understood for business benefit.

Data analytics also involves creating quantitative models by taking into account different variables to create predictive models for business opportunities and give them opportunities to compete in a crowded marketplace.

Both data analytics and data analysis aim to make the data work for an organisation and in doing so, these might seem quite similar, much like two sides of the same coin. However, to say that they are one and the same thing will not be quite right.