We can define data analytics as to the process of collecting information and then analyzing it to confirm various hypotheses. Data analytics is a way to tell a story. We use the collected data to clearly and concisely convey the state of the world to the people around us. In simple terms, data analytics is the use of information to make and influence decisions. We have always used data analytics as a way to move through life. The only difference today is that we use data analysis more digitally.
Data analytics begins by defining a problem. And, then you create your hypotheses. To test the said hypotheses, you have to gather data. To clean that same data and then to analyze it.
Data Analytics Vs Data Analysis
Many people confuse data analytics with data analysis. Although these two terms are quite similar, there is one subtle difference between them.
Data analysis, unlike data analytics, can be done without numbers or data, such as business analysis, psychoanalysis, etc. Some people even state that Analysis works with data that has a history. And, analytics works more with future predictions.
Types of Data Analytics
1. Diagnostic Analytics - This type of analytics works to answer the question “Why did something happen?”. When we use diagnostic analytics, we use previous data and previous performances to look at why something occurred. There are several techniques we use to solve the mystery. These techniques are data discovery, drill-down, data mining, and correlations. For example, we use this type of analytics to find why some
2. Descriptive Analytics - This is the simplest type of them all. It uses collected data to discover why changes happen in a business. It gives a summary of facts and numbers. And it does that in a relatively simple format. To report these changes, irregularities, or fluctuations, it uses only two methods: data mining and aggression.
3. Predictive Analytics - This kind of data analytics provides answers for future occurrences. It uses historical data to predict when future trends will show up. This type is flawed, though. It can’t tell whether an event will occur or not. It merely gives a probable estimate if there is a chance for the said event to happen.
4. Prescriptive Analytics - This is a type of analytics that can suggest some outcomes to an action or to suggest actions that will result in the desired outcome. Prescriptive analytics uses a strong feedback system. This system never stops learning from past behaviors and updates itself. For example, Google’s self-driving car is based on this type. The vehicle uses previous data and past experiences to update its navigation system. It also predicts traffic and turns.
These types of data are a crucial key to a business’s success. And, to maximize one’s performance many business owners carry out these types simultaneously into their business model.
Where is Data Analytics used in real life?
We’ve mentioned several examples of real-life applications. But is data analytics only linked to business and vehicles?
Of course not. Data Analytics can be and is used everywhere. Here are two more cases where this is used:
Many healthcare systems rely on data to maximize their hospital’s capacity. They also use data to cut costs on patients. It helps predict which patients have an inclination towards chronic illnesses and give early intervention. These systems use analytics to see which patients of a certain demographic or socio-economic class are prone to kinds of medical issues. To accurately predict these things, the system uses medical history and medical profiles. Not only this benefits the patients, but it also helps the hospitals to allocate their resources to the right places.
Data Analytics is crucial for successful web design. Game companies have to use analytics to keep their users engaged. Developers must know how much time their users are spending playing the game, when, and why do they leave the game. This is important as it helps manufacturers improve their game and make it more engaging. Analytics collects data from millions of users in real-time. With this, developers don’t have to wait for feedback from the players. They can see their habits and behaviors to take action.
The applications of data analytics are endless. When we understand how these data models work, we can direct the flow of our work to perform efficiently and to solve complex problems in no time.
The more data is collected, the better the opportunities people get. From business to science, it won’t be surprising to see data analytics a part of everyday life.