What is a good definition of data analytics? Think about it like this: Every company has to collect and analyze data relating to finance, marketing, operations, production, IT, etc.
They do that to make decisions on many levels. That’s why companies need data analysis within these areas to ensure success.
Data analytics analyzes raw data to draw conclusions about it to help business leaders make informed decisions.
Once analyzed, data is used to optimize procedures and systems to improve various areas, such as operations, manufacturing, accounting, investing, and marketing strategies.
But there’s so much more to “what is data analytics?” than a simple or even more involved data analytics definition. To fully understand what data analytics is, let’s look at the process, the history of data analytics, and why it plays such an important role in business strategies.
The Data Analytics Process
The data analytics process starts with a question (or a series of questions):
- “How can my business cut production costs?”
- “What’s the best way to forecast sales to ensure needed cash flow?”
- “Where can my company focus on investments for potential growth?”
- “What demographic should my marketing campaign focus on?”
- “Can my company reduce staff yet maintain current production output?”
Once a question or questions have been established, it’s time to gather the data needed to come up with an answer and, subsequently, a strategy or decision to move forward.
So, in the case of cutting production costs: an analyst might assemble data such as current production output, materials and labor costs, product demand, marketability of the product, and other associated production factors.
This data can be derived from computer files, observations, interviews, or combinations of these methods. Then, data is arranged according to many different variables, such as trends, staffing, or set vs. infrequent costs.
Following is the act of data cleansing: removing any errors or duplicated information. This is an important step before moving on to analyzing collected data.
Does the data analysis show that cutting costs will benefit the company with losing revenue? Do the data analysis results provide a viable solution to cutting costs that will not harm the company’s bottom line?
Once the data has been gathered, cleaned, analyzed, and interpreted, the last step is data visualization. Using charts, maps, graphs, and other visual aids, data analysts will display their insights to others to help them better understand how the data was used to arrive at a proposed solution or decision.
This process can be applied in multiple business areas to help executives make decisions about such various things as developing new products, starting a new marketing campaign, or investing in new computer systems.
The History of Data Analytics
Data analytics has its beginnings in statistics: the assembly and analysis of numerical data. The birth of statistics is often credited to John Graunt and William Petty, who in 1662 used statistical methods in early demographics.
Businesses would soon adopt statistical analysis practices to help determine patterns and trends that would assist managers in making decisions.
This would lead to early data analytics practices; in the late 1800s, Frederick W. Taylor used production technique analyses and studies of laborers’ body movements to create his System of Scientific Management that increased industrial production. This is believed to be the first use of business analytics in a formalized manner.
Another early example of data analytics in business would be Henry Ford analyzing data to determine automobile assembly line speeds.
Even early U.S. Census methods used archaic data analytics methods; inquiries were sent by mail addressing product values and social issues. It took many months to gather such information and years to tabulate it and organize it for publication.
Today, data analytics allows for improved quality assurance, quicker responses, and faster evaluation of information.
As data warehouses became able to hold and store larger amounts of data relating to market trends and pricing throughout the 1980s and 1990s, businesses became more adept at analyzing data to better their operations.
Tools such as Microsoft Excel (1985) and Google Analytics (2005) soon helped users analyze data in a more organized way. They were the predecessors of full-service data analytics software and platforms that allow for real-time analytics, big data analysis, and automated systems that require no manual input of information.
Different Kinds of Data Analytics
Now that we’ve answered, “what is data analytics?” and provided a data analytics definition and a brief history, we can now look at the different kinds of data analytics and how data analytics is used today.
One kind of data analyst is a business intelligence analyst. In this kind of role, professionals gather data relating to market trends, competitor practices, production processes, product pricing, and other information to help company leaders make decisions about manufacturing, staffing, marketing campaign development, and project management, to name a few areas.
Data analysis can be even more specific. Within a business, there are marketing data analysts. This position is responsible for assessing marketing trends, conducting market research studies, measuring marketing campaign effectiveness, and making suggestions to maximize marketing strategy results.
A business systems analyst or IT data analyst would apply data analytics processes to assess and measure IT projects, use data analysis software to produce reports, gather information about systems requirements, perform testing on hardware and software systems, and summarize analyses to make recommendations for systems re-designs or upgrades.
An operations analyst would use data analytics to evaluate internal reporting systems, product manufacturing, distribution systems, and documentation processes to improve or streamline these practices.
Cyber data analysts would collect information about computer networks, software, and security systems to analyze upgrades and improvements. Project managers use data analytics to track progress, create reports, oversee productivity, and control costs.
Other, perhaps lesser-known, areas of analytics include fraud analytics (the monitoring and analysis of data fraud), credit analytics (lending analysis including risk, approvals, and credit reporting), and social media data analysis (analyzing data used to build and advance social media technologies).
Why is Data Analytics Important?
A good data analyst will take the guesswork out of decision-making. Instead, they rely on clean and accurate data to guide hypotheses about future behavior or outcomes.
In an article in the Harvard Business Review, appropriately titled, “What Great Data Analysts Do — And Why Every Organization Needs Them,” the author writes, “While statistical skills are required to test hypotheses, analysts are your best bet for coming up with those hypotheses in the first place. For instance, they might say something like ‘It’s only a correlation, but I suspect it could be driven by …’ and then explain why they think that. This takes strong intuition about what might be going on beyond the data and the communication skills to convey the options to the decision-maker, who typically calls the shots on which hypotheses (of many) are important enough to warrant a statistician’s effort. As analysts mature, they’ll begin to get the hang of judging what’s important and what’s interesting, allowing decision-makers to step away from the middleman role.”
It’s easy to see that companies will continue to rely on data collection and analyses to make decisions in nearly every facet of business operations. As computer systems and analytics software continue to evolve, it is not inconceivable that job opportunities in data analytics will rise in demand.
Analysts will play a vital role in business strategy development, planning strategies, decision-making processes, systems improvements, and growth and expansion considerations.
Frequently Asked Questions
Data analytics involves examining datasets to draw conclusions and identify patterns. It’s used in various industries to enable more informed decision-making, optimize processes, and predict future trends.
The main types include Descriptive Analytics (what happened), Diagnostic Analytics (why it happened), Predictive Analytics (what could happen), and Prescriptive Analytics (what should be done).
Essential skills include statistical analysis, data visualization, data cleaning, proficiency in analytics tools like Python or R, and a strong understanding of the business context.
Data analytics focuses more on finding actionable insights from sets of data and is often less technical, whereas data science involves more complex algorithms and predictive models.
A data analyst collects, processes, and performs statistical analyses on large datasets. They translate numbers and data into plain English to help organizations make better business decisions.
Companies use it for various purposes, including market analysis, customer segmentation, fraud detection, cost reduction, and performance improvement.