Learn what data analytics and data analysis are, as well as how they can benefit your business. Realize the importance of these tools when applied to different aspects of businesses.
1. Data analytics or data analysis?
2. Why is data analysis important?
3. How can you take advantage of data 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.
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:
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:
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.
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:
Oracle further defines value and veracity as equally important to big data:
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.
The Harvard Business School Online lists three main reasons why data analysis is important to businesses:
There are several areas of your business where you can apply data analysis. Here are some of them:
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.
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.
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.
Depending on your industry, there are more ways you can take advantage of data analysis, and it’s up to you to discover them!
All the sources used, cited or not in the article, are listed below.
Data Analytics Vs. Data Analysis: What's the Difference?
Data Analytics vs Data Analysis: What’s The Difference?
Data Analytics vs. Analysis – What's The Difference?
What is the Difference Between Analysis and Analytics?
Data Analytics vs. Data Science: A Breakdown
Business Analytics: What It Is & Why It's Important | HBS Online
Why Is Data Important for Your Business?
Five Benefits Of Big Data Analytics And How Companies Can Get Started
5 Benefits of Data Analytics for Positive Business Outcomes
What is an Actuary? - Department of Mathematics, Purdue University