Data Analysis 101: Why do data analytics and analysis?

Data analytics and data analysis are some of the must-haves when running a business in this fast-paced digital world. In this article, the first of our article series, we will learn what the difference is between data analysis and data analytics and how they are closely related, why data analysis is important, and some ways businesses take advantage of the power of data analysis.

Data analytics or data analysis?

Before we start this primer on data analysis, let us first learn the difference between data analysis and data analytics, for as they sound so similar and tackle similar things even Google had some difficulty sorting the search results sometimes.

What is data analysis? Here are some of the definitions of data analysis that you can get from a Google search:

Wikipedia: Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
Office of Research Integrity, U.S. Dept of Health and Human Services: Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data.
Hackr.io: The systematic application of statistical and logical techniques to: describe the data scope, modularize the data structure, condense the data representation, illustrate via images, tables, and graphs, and evaluate statistical inclinations and probability data to derive meaningful conclusions is known as Data Analysis.

Clearly, data analysis is an important step of data analytics. To further highlight this distinction, we will define the term as follows:

Data analysis is the process of using mathematical and statistical methods to extract patterns from existing data.

Data analysis is often associated with the natural sciences, but any field where you can systematically gather data can benefit from data analysis.

How about data analytics? Here are some of the definitions of data analytics that you can get from a Google search:

Informatica: Data analytics is the pursuit of extracting meaning from raw data using specialized computer systems. These systems transform, organize, and model the data to draw conclusions and identify patterns.
Investopedia: Data analytics is the science of analyzing raw data in order to make conclusions about that information.
Wikipedia: Analytics is the systematic computational analysis of data or statistics.[1] It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns towards effective decision making.

You can see similar definitions on other websites. We will define the term then as follows:

Data analytics is a broad field that involves data collection, data processing and analysis, and data presentation to help in decision-making.

The discipline has gone a long way since various sectors and industries began adopting its methods. According to SAS, several governments and industries such as healthcare, manufacturing, and retail have embraced it to improve their services and achieve their goals. Clearly, an investment in data analytics is worth it.

There is another shorthand way of defining the difference between data analysis and data analytics, and it often invokes the concept of past and future. In data analysis, you analyze existing data, which means you see the past state of what you are analyzing. Data analytics, on the other hand, uses these insights from data analysis to help in decision-making, which means you look to what may happen in the future. Of course, we look at past data to know what to expect of the future, and that is the main reason for the existence of data analytics.

*We look at the past data to hopefully see the future.*

Besides data analysis and data analytics, let us define other related terms:

Big data: Gartner defines big data as high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation. The immediate takeaway here is the implied criteria for a dataset to qualify as big data:

High-volume: it is huge. It could be gigabytes, terabytes, or petabytes of data.
High-velocity: that huge volume of data arrives real time at that amount per unit time. Such amounts of data generated per second happens in the real world, especially in the largest sites such as Google, Facebook, and Amazon.
High-variety: the data often arrives unstructured, yet to be preprocessed before analysis can be done.

Oracle further defines value and veracity as equally important to big data:

Value: while data has intrinsic value, processing and analysis can yield more value from it.
Veracity: truthful data gives the biggest value and is most useful while false data will yield false conclusions.

Data science: A new field has emerged recently to answer the question of processing big data. Northeastern University defines data science as involving design and construction of new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis. Therefore, data science involves making sense of oceans of data that don't seem to make sense on first look. It’s a tall order! But for our primer, we will focus on data analysis – one of the aspects of data science.

There are more terms related to data analysis, but we do not wish to zoom out and get lost for now. We will deal with them when needed.

Why is data analysis important?

*Data analysis should help in making correct decisions.*

The Harvard Business School Online lists three main reasons why data analysis is important to businesses:

More informed decision-making - Questions are asked all the time to make important strategic decisions solving problems. Problems can range from a decrease in sales to aiming at a further increase in sales. The top brass can make an important decision or two based on the answer presented by data analysis.

Improved operational efficiency - Data analysis may not be targeted to achieve more lofty goals but to further fine-tune the existing system. This can even include anticipation of future problems and issues.

Greater revenue - more informed decision-making and improved operational efficiency serve to bring greater revenue and/or reduce costs.

How can you take advantage of data analysis?

*Fully take advantage of data analysis by using it to different areas of your business.*

There are several areas of your business where you can apply data analysis. Here are some of them:

Customer acquisition and retention - From the first visit to your website and/or online store to the checkout of a shopping cart, there is potential for a large amount of data to be collected and analyzed to understand customer behavior during these interactions. The data collected and analyzed are then processed via data analysis techniques to be condensed into so-called metrics – quantities that describe an aspect of performance of a business. Metrics not only condense and summarize a potentially vast amount of diverse data but can also illuminate patterns not obvious but useful in decision-making.

This information is used to improve all the steps of the so-called marketing funnel, as we have briefly discussed in our article for creating a marketing budget. In fact, this is one of the most important applications of data analysis in businesses. Customers (or other businesses) are the ultimate source of revenue for a business, and acquiring and then retaining them is the most competitive aspect of running a business.

Our app Lido can help you in that aspect. With its integrations with several e-Commerce and marketing services/platforms, Lido can also filter the data and give you the most relevant metrics you need to acquire and retain customers. Learn more about the Lido app here.

Focused and targeted campaigns - The image you used when you established your business cannot be used forever to sell products and/or services. Furthermore, the nature of competition in the market today means that you need to keep thinking of ways to make your offerings as fresh as possible to the eyes of your target customers. This fact is not lost to businesses both local and multinational. Marketing campaigns are, therefore, an essential part of running a successful business.

A successful marketing campaign, however, rests on the awareness of your target customer group. This is only possible if you can gather data on your target demographics. The existing customer data that we have must be augmented by the so-called market research. Hubspot defines market research as the process of gathering information about your business's buyer personas, target audience, and customers to determine how viable and successful your product or service would be and/or is among these people. The data gathered through market research has to undergo rigorous and comprehensive data analysis so that valuable information can be extracted. This information can be used for planning and executing marketing campaigns, increasing the probability for their success.

Identification and mitigation of risk and fraud - Whatever product or service your business sells, there is always potential for fraud. Real-time data analysis from multiple channels of data will give you oversight and protection from fraud and the ability to respond to risks that threaten the operation of your business.

Real-time big data is not the only source of information for the potential risks. Past big data can be processed via targeted data analysis to analyze previously-unknown potential risks. This is the playing field of actuarial science, a field specialized in analyzing the financial consequences of risk. According to Purdue University, actuaries, the term for those who conduct actuarial science, are often employed by insurance and pension companies. Consulting firms, government, hospitals, banks, and investment firms also employ them.

Product innovation - customer data and market research can also be used not only to give fresh and new looks to existing products and services but also to conceptualize new products and services, as well as to offer product customizations even before the need arises. Every business should be aware of the changing customer needs and wants, and that includes the need to innovate new products that better fit your customer base. Even businesses that sell a product or service that serves a physiological need such as water and food still need to innovate their products one way or another to stay relevant.

Depending on your industry, there are more ways you can take advantage of data analysis, and it’s up to you to discover them!