Data Analytics

Data analytics has been thrown around a lot, especially in industries and organizations. It can improve advertising campaigns, sales, reach, and brand identity in different ways. Learn what data analytics is and why you need it for your business!

What is Data Analytics?

Data analytics is a field that refers to analyzing data to create conclusions and make decisions. It’s a science that deals with the processes and strategies in an automated way through algorithms and mechanical procedures. 

Many still do not understand the definition of data analytics because it includes various topics and sub-disciplines. And you can subject all types of information to data analytics techniques for insights. But it’s simple. Just investigate the datasets and draw objective generalizations from them.

Most data analytics techniques utilize specialized systems and software for businesses and research. You’ll finde four kinds of data analytics:

1. Descriptive analytics examine what occurred at a specific time. Some guide questions to include are: have the views gone up? Are sales higher this week than last?
2. Diagnostic analytics is more about why a situation happened. The data you need to input here are more diverse and troubleshooting. For example, is the weather linked to the sales? Did the current campaign increase sales?
3. Predictive analysis is all about what is about to happen in the future. One question includes: what is the amount of weather models foreseeing the hot summer?
4. Prescriptive analytics is about making an action. For example, is it better to add a weather station for better prediction or improve the old one?

Why Use Data Analytics?

Data analytics is essential for companies and other organizations who want to optimize their performance. Every organization must implement data analytics into its business model to reduce costs and produce more profit.

A company also employs data analytics to create better decisions and investigate customer trends and satisfaction. It will lead to better products. You will better understand the current market scene and change it completely. You will also know how to trigger a demand for new development of a product.

Data analytics also helps with producing targeted content. Before creating a customer-oriented marketing campaign, you can identify what your viewers or subscribers want. It lets you customize your video, post, picture, or story according to their needs and interests. 

With data analytics, you may also determine which client segment responds best. It will save more money on the cost of pursuing new customers or maintaining existing clients. 

Lastly, data analytics promotes operational efficiency. You can determine potential opportunities that will kickstart your operations and create more profit as a company. You may also choose potential threats and resolve them right away. There’s no more need to wait for the problems to occur because you can take action to prevent them. That said, the organization can see which operations have produced the best results in certain conditions. They can also see which facets are prone to error and which ones need improvement. 

What does Big Data Mean?

It’s exactly how it sounds. Big data refers to the vast datasets that are so extensive that they need specialized software. Big data also requires those massive computers for processing. YouTube, Facebook, Google, and other big companies produce big data every day, which they store in massive warehouses. These locations usually contain various hard drives to keep everything.

Your company probably doesn’t produce big data, but you could learn from these vast entities on analyzing them.

First, big data teaches us to measure the trends instead of the exact figures. You can’t analyze your progress based on one number alone. The greater the amount of data you gather, the more intelligent your results and conclusions are likely to be.

Second, focus on the patterns. The patterns will become more evident as you gather more data over time. You can consider monthly, seasonal, and even yearly trends from weekly analytics.

Last but not least, examine the anomalies. Find out why your expected patterns suddenly change. Once you have done the predictive analysis, use this information to create actions from now on. 

What is Data Mining?

Data mining refers to looking for hidden patterns in massive databases and figures during data analytics. Instead of having someone as a human analyst, an automated program processes the information. It pulls apart the data and pairs it with known patterns. You’ll find many astonishing results when you’re data mining.

What is Data Visualization?

Data visualization includes setting the data in a more visual version for better examination. It makes the information more understandable, readable, and easier to analyze. You can visualize data through histograms, graphs, bar charts, and pie charts.

Other Data

Remember that there are multiple data sources when you’re doing data analytics. It can make the process more challenging but worthwhile when delivering results. Web-based analytics can only do so much, so get data from these sources:

• Online data does not only come from your website. You also have to look at other sources to connect with your audience. It may include social media, email, forums, and more. These socmed definitely possess a data analytics platform of their own, such as Facebook Insights.
• Databases can keep important information about the customer. These details include their CRM information, contact number, and loyalty programs. You’ll also find that they support anonymous data through tangible demographic insights.
• Software data is simply data from specific software that catch information usage. Some variables here include user habits, crashes, and more. Use these data for your next application or web update. 
• App store data or analytics lets organizations track and analyze how their customers or clients download their apps. How do they pay for the app? How do they use it? Google Play Store and Apple App Store provide these valuable pieces of information through in-built analytics.
• Offline data are those you can’t find on the internet. Some off-the-web details to look for include customer service logs and in-person surveys. Even informal, unstructured interviews can also be sources of data. You may also refer to point-of-sale records and in-store foot traffic. 

© Image credits to Steve Johnson