The term data analytics can be quite nebulous and ambiguous, which makes it difficult to know what it actually means and how to apply it to your business. Fortunately, data analytics encompasses a broad range of concepts that can be applied to pretty much any area of industry or business, from marketing to accounting to HR. Here’s an overview of what data analytics means, why you need it, and some examples of how companies have successfully implemented data analytics in their own businesses.
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| introduction to data analytics |
What Is Data Analytics?
Analytics provides a framework for evaluating an organization's past performance. In other words, data analytics helps identify the best methods for achieving the desired results in future circumstances. This process relies on developing a statistically significant body of information that characterizes existing conditions and projects their likelihoods of occurrence in the future. Data analysts work closely with all areas of business including finance, engineering, marketing, human resources, sales, customer service and support teams. Analytics can be thought of as the GPS for running a company because once they're built they're never changing so they always know where they are going (i.e., where your business is today) and where your business needs to go (i.e., long-term strategy).
Data Analytics can help an organization determine how likely or unlikely a particular result will be based on historical outcomes. They can help identify patterns in customer demand, manufacturing defects, competitor behaviour and employee performance that may not have been evident without them. Additionally, predictive models can allow organizations to assess potential changes within their business environment by simulating possible scenarios - such as fluctuating commodity prices, changing consumer trends or new government regulations - in order to prepare for them before they become realities.
Data Analytics - Third Paragraph: A well-designed analytics program allows an organization’s management team to make informed decisions regarding day-to-day operations, long-term strategy development, financial planning and risk mitigation.
What are the tools of data analytics?
Data analysis has a multitude of tools that allow for people to figure out the story hidden in their data. The three most well-known include the scatterplot, histogram, and bar graph. Scatterplots are line graphs with two variables shown on either axis. Histograms are graphs that represent a distribution of numerical values. Lastly, bar graphs show individual or categorical values as heights on a horizontal axis plotted against frequencies along a vertical axis (hence its alternative name as a 'bar chart').
Another important tool is power analysis. Power analysis can help someone determine if they have enough statistical power to be able to detect differences in groups using statistical tests.
What can I use it for?
There are many different use cases for data analytics. One of the most popular reasons to use this type of software is lead generation. It's not just about collecting a list of people who may be interested in your company, but using the information to actually convert that list into people who have purchased your product or service. Data from an online survey can be used as an example: the respondents will be emailed a newsletter containing news relevant to their interests.
Data analytics can also be used for general marketing purposes. For example, a company could test different offers and discounts to see which ones make people more likely to buy their product or service.
How do I get started?
As a business owner, it's no secret that you're going to have questions that arise from time to time. Luckily, the world has moved online in the past decade or so, and we now have access to an abundance of information at our fingertips. One of the best tools we can use when there's an issue that needs answering - more often than not, a statistical analysis question - is data analytics. But what is it? And why do I need it? Data analysis involves finding patterns in large sets of raw data by utilizing specific formulas or models (also known as algorithms). These formulas produce insights into factors such as marketing effectiveness, risk levels and customer segmentation. The term data analytics stems from the common usage of algorithms; they take raw numbers and turn them into information.
What does my first step look like?
Figuring out where the company's problems lie can be a complex process. One way of finding them is by analyzing your company's data. And there are a few ways of getting this done. Your first step will be figuring out which method suits your company best - whether that be through self-analysis or getting professional help. If you decide on the latter, here are some steps you'll need to take before engaging with a vendor.
The next step would be considering how the software will interact with the various departments at your company, as well as how well they mesh with your employees' abilities and needs.
What about pitfalls?
The first mistake one could make when thinking about Big Data Analytics is relying on old-fashioned methods, or drawing inaccurate conclusions. It's very important for the individual or company that wants to use this form of analytics in order to be successful, that they approach the opportunity with an open mind. They should not be afraid of change and willing to embrace new things in order for their goals to be met. The other potential problem that one may have with Big Data Analytics is not having the time or resources needed in order make an impactful decision based off the information they've collected. If you do not have enough time or resources, then it will result in your decisions coming back being skewed, which can ultimately affect the final outcome of your project.
Summary
Data analytics has been around for decades, but only recently has its popularity soared due to exponential increases in available information. What once was a field limited to scientists, mathematicians, and select businesses with big budgets has now become something that companies of all sizes have the ability to use thanks in large part to the abundance of free tools available on the internet. Data analytics allow companies of all sizes track customer trends such as buying patterns, demographic information, behavioral patterns, etc. Data analysts can help these companies understand their customers better and provide them with more personalized services which can lead to increased profitability.
