Different types of data analytics amount of data that is being collected are increasing every second in the world that we are a part of. It becomes really important to have tools that will help us with the amount of data that is being generated. If data is in the raw format, it is unstructured and often not very useful to anyone.There is a lot of significant information that can be derived by structuring raw data that is where data analysts come into the picture. This is also where different types of data analytics come into the picture. Businesses can drive new initiatives when they have insights available that are driven by data.
4 Different types of data analytics.
- Descriptive analytics
- Prescriptive analytics
- Diagnostic analytics
- Predictive analytics
Let us try Different types of data analytics to understand each of these ones by one and when exactly these are employed.
Simply put and understood by the name itself, descriptive analytics is a process where raw data extracted from various sources is converted into a summarized form that can be understood by humans easily. The process results in describing an event from the past in great detail.
Descriptive analytics can help derive patterns from past events and also help draw interpretations from those events eventually helping an organization to frame and create better strategies for the future. It is the most commonly employed analytics across most organizations. Measures and key metrics can be revealed with the help of descriptive analytics in almost any kind of business.
The process of breaking down data step by step in a given situation is what is known as prescriptive analytics. For example, consider that you have booked a cab on Uber. The Uber driver is on his way to pick you up but the regular route has a lot of traffic on it.
He then gets an alternate route shown to him on Google Maps. This is a part of prescriptive analytics. Google Maps analyzed the current situation and suggested an alternate route to the Uber driver so that he reaches you for a pick up as soon as possible and time is not wasted. This leads to a better customer experience as well.
Diagnostic analytics is known as the successor to descriptive analytics. With the help of diagnostic analytics, data scientists are able to dig deeper into a problem and eventually reach the source of that problem. The tools used for descriptive analytics and diagnostics analytics usually go hand in hand in any business environment.
It is very important for a business to have foresight and vision if it wants to succeed. Predictive analytics helps businesses to forecast patterns and trends by analyzing present-day events. From predicting the probability of events that might take place in the future or even trying to estimate the exact time that the event will take place, it can all be forecasted using predictive analytics.
Predictive analytics makes use of variables that are co-dependent to create a pattern and understand the ongoing trend. For example, if you look at the healthcare domain, based on an individual’s current lifestyle which consists of his/her eating habits, exercise, travel time, etc. you can predict the kind of illnesses they are likely to contract in the future. Therefore, it can be said that predictive analytic models are the most important as they can be employed across all fields of life. learn python if you want to be Data science.
Data Science Techniques
There are many types of analysis that are available to any business, which can be used to extract and retrieve data. The result or the outcome of every project that is based on data science will be different and will be varied.
What kind of data science technique is to be used depends on what kind of business you are applying it to and what kind of business problem you are trying to solve? There are various data science techniques that could be employed for a business and therefore, the outcome of each of these techniques could be different resulting in different insights for the given business.
This could be confusing but what one needs to understand as a data scientist is that they need to observe information that is relevant to the business which can be figured out easily by recognizing patterns in datasets that are huge. Let’s go through the most common techniques that are practiced in data science for businesses today.
When you go through a dataset that is exhibiting an expected pattern, but then all of a sudden there is some part in it that doesn’t fit the expected pattern, it is termed as an anomaly, and the process to find this anomaly is called anomaly detection.
Anomalies are also known by other terms such as outliers, exceptions, contaminants, or surprises and their presence often offers valuable insight into the data. Outliers are odd objects that deviate from the standard of a given set of data or deviate from the general average pattern of a dataset. With respect to numbers, an outlier suggests that it is different from all the other data in the dataset, which leads to an understanding that there is something wrong or incorrect about it and requires more analysis.
Anomaly detection is of great interest to data analysts and data scientists as it helps them to understand if there is any kind of fraud or risk involved in a process, which also helps them decide if there is any kind of advanced analysis that would be required on the available data. Thus, the process of anomaly detection helps a business identify if there is any flaw in their process, fraud, or areas of business where the existing strategies are failing.
As a data scientist, it is important to accept that a small set of anomalies are possible when you are dealing with huge datasets. Anomalies show deviation from standard data but it can also be caused by something that is very random or it may also end up being something that is very interesting statistically. More analysis is needed when such situations arise.
The process of identifying data sets that exhibit attributes that are similar in nature and understanding their similarities and differences is known as clustering analysis. Clusters display specific traits that have common attributes, which if materialized on, can help optimize algorithms that can result in better targeting.
For example, consider clusters of data that show the purchasing behavior of customers; this information can be used to target a specific set of customers with products that could fall in their purchasing power and lead to a better rate of conversion. One of the many outcomes of clustering analysis is customer persona development.
This basically refers to fictional characters created to represent types of customers within a demographic region. A particular customer persona defines various attributes of a customer such as their purchasing power, their salary range, their regular purchases, etc.
which helps all customers who exhibit the same attributes to be clubbed together and eventually helps a business to target them with the right products. We have learned about software platforms earlier that can be used by the given business to integrate with their cluster analysis.
If there is a large-scale database at hand, a business can identify and understand the relevant association of various sets of data and its variables with the help of association analysis. Using the technique of association analysis, data scientists can find valuable information in a dataset that is often covert in nature.
This will help to detect hidden variables inside a dataset and also let us know if there are co-occurrences happening for variables in a dataset that exists at frequencies that are different. The technique of association of analysis is helpful to find patterns inside datasets from a point of sales view, and therefore, is used extensively by retails stores.
Using this technique, retail stores can recommend new products to customers based on their history of the purchase of previous products or the kind of products a customer usually bundles together on a monthly basis. If used efficiently, association analysis can help a business grow and multiply its conversion rates. Let’s look at an example.
Using data mining techniques in 2005, Walmart studied historic data of customers buying products from their store and learned that every time a hurricane was approaching, the sales of strawberry pops would increase seven times than regular sales. To capitalize on this, every time a hurricane was expected to strike a particular area, Walmart strategically placed strawberry pops at the checkout counter to increase their sales even further.
If you want to learn about the dependency between attributes of a dataset, regression analysis is what will help you achieve it. It is assumed that one attribute has an effect that is single-way in nature on the response of another attribute. Attributes that are independent could be affected by each other’s presence.
This does not mean that the dependency is mutual between them. Regrets Regression analysis helps a business detect if one variable in a dataset is dependent on another variable but not vice versa. Regression analysis can also be used by a business to understand client satisfaction levels and if customer loyalty can be affected by an attribute and if it may end up affecting the service levels as well, for instance, the current weather.
Another great application of regression analysis is dating websites and dating apps which use regression analysis to improve the services offered to the users. Regression analysis checks the attributes of users of a dating application and tries to match two users based on those attributes to create a match that is best for the users who are participating.
Data science helps achieve businesses focus on information that is important and relevant from the point of view of growth for the business. Therefore, eventually, data science helps establish business models that can help a business that can predict the behavior of its customers and helps the business get better conversion rates. Gathering more information would help to build better models which can be used effectively by applying processes of data science to the information, which will increase the value of the business gradually.
The approach to gathering the information that is relevant and crucial about data in a systematic manner is known as classification analysis. When you have a lot of data, classification analysis techniques help a business identify which data can be used for further and deeper analysis.
Given that classification of data is usually a prerequisite before you start clustering data, classification analysis goes hand in hand with cluster analysis. The biggest users of classification analysis are Email providers. A user receives a lot of emails on a daily basis, some of which is useful and the rest is spam.
Email providers have algorithms in place that help classify the email as genuine or spam. This is done based on the metadata of the Email that is contained in the headers of the Email such as from address, reply-to address, etc., or the content that is in the actual body of the email message.