Descriptive and Predictive Analytics

Data analytics is the method of exploring raw data sets in order to find trends and draw conclusions about the information they contain. Across the globe companies are considering various analytic solutions to discover what will allow them to get the most out of their information. Let’s see the two main types of Data Analytics methods; Descriptive and Predictive Analytics.

DESCRIPTIVE ANALYTICS

As name suggests Descriptive Analytics takes raw data and describes that data into human understandable language. Descriptive analytics is the easiest form of analytics that mainly uses simple descriptive statistics, data visualization techniques, and business-related queries to understand past data. One of the primary objectives of descriptive analytics is amazing ways of data summarization.

A most common example of Descriptive Analytics is business reports that simply provide a historic review of an organization’s operations, sales, financials, customers, and stakeholders.

Let see this by example in our day to day life –

Below figure shows visualization of relationship break-ups reported in Facebook.

visualization of relationship break-ups reported in Facebook.
Image taken from https://informationisbeautiful.net/

You can see that spike in breakups occurred during spring break and in December before Christmas. HAHAHA!!!!! There could be many reasons for increase in breakups during December.

Many believe that since December is a holiday season in foreign country, couples get a lot of time to talk to each other, probably that is where the problem starts.

However, descriptive analytics is not about why a pattern exists, but about what the pattern means for a business. How it can help a business to grow.  

The fact that there is an observable increase in breakups during December can analyze from following data:

1. Data from online dating sites.

2. Data from relationship counsellors and lawyers.

3. Data from the brand of alcohol individual drink. 

4. Data from cafés
.

.

.

These types of data can be combined and can used to do visualization to connect some dots.

The more in tune a business is with its historical data, the more effective they can be in adapting their reporting and future strategies for data optimization. 

So, as I said earlier Descriptive Analytics using visualization identifies trends in the data and connects the dots to gain insights about associated businesses. In addition to visualization, Descriptive Analytics uses descriptive statistics and queries to gain insights from the data.

PREDICTIVE ANALYTICS
Descriptive and Predictive Analytics

In the analytics capability maturity model (ACMM), predictive analytics comes after descriptive analytics and is the most important analytics capability. It is nothing but predicting the future events such as forecasting demand for products/services, customer churn, employee attrition,  loan defaults, fraudulent transactions, insurance claim, and stock market fluctuations.

Descriptive analytics is used for finding what has happened in the past, predictive analytics is used for predicting what is likely to happen in the future.

Anecdotal evidence suggests that predictive analytics is the most frequently used type of analytics across several industries. The reason for this is that almost every organization would like to forecast the demand for the products that they sell, prices of the materials used by them, and so on.

Irrespective of the type of business, organizations would like to forecast the demand for their products or services and understand the causes of demand fluctuations. The use of predictive analytics can reveal relationships that were previously unknown and are not intuitive.

Let’s see Predictive Analytics by real time examples

Netflix – Predicts which movie their customer is likely to watch next. 75% of what customer watch at Netflix is from product recommendations. Recommendations can be based on watch history or based on user rating.

Amazon – Uses predictive analytics to recommend products to their customers. It is reported that 35% of Amazon’s sales is achieved through their recommender system

Moneyball – “Moneyball,” the Oakland Athletics baseball team used analytics and evidence-based data to assemble a competitive team.

The examples shown in above represent a tiny fraction of the predictive analytics applications used in the industry.

Companies such as Procter & Gamble use analytics as a competitive strategy every critical management decision is made using analytics. If one were to search for the reasons behind highly successful companies, one would usually find analytics being deployed as the competitive strategy.

Google also developed accurate prediction models that could predict events such as the outcome of political elections, the launch date of a product, or action(s) taken by competitors.

Do you know

Across globe the most widely used predictive modeling techniques are decision trees, regression and neural networks.

What do you need to get started using predictive analytics in your project?
Descriptive and Predictive Analytics
  • First Step to use predictive analytics is you need to find problem to solve. What do you want to understand and predict?
  • Second step is find the appropriate data. Without data you cannot predict future. Data selection is considered one of the most time-consuming aspects of the analysis process. So be prepared for that.
  • Third step is pre processing of the data. In order to get accurate result, your data must be clean and should contain minimum outliers. The data can have many irrelevant and missing parts. To handle this part, data cleaning is done. It involves handling of missing data, noisy data etc.
  • Fourth step is model building. That means developing the models to work on your chosen data – and that’s where you get your results. In 2020 it happens very few time that you need to build model from scratch in Machine Learning. N-numbers of model are already available in the market. You just have to import them and tune the parameters.

However, the advancements in predictive analytics will surely pave the way for its development. Hope this article gave you a better understanding of the analytics spectrum.

Written by –

<strong>Dhanashree Fale</strong>
Dhanashree Fale

Electrical Engineer and Machine Learning Practitioner

3 thoughts on “Descriptive and Predictive Analytics

Leave a Reply

UP↑