Data Analytics
Data analytics is the science of analyzing raw data to make conclusions about that information. Data analytics help a business optimize its performance, perform more efficiently, maximize profit, or make more strategically-guided decisions - Investopedia
Go directly to what Data Analytics can do - Data Analytics Applications
Four Primary Types of Data Analytics
Descriptive analytics answer questions about the past performance and what has happened. This process requires the collection of relevant data, data processing, analysis and data visualization. The outcomes of this analysis will enable organization to come up with the improvement plan
Diagnostic analytics helps answer questions about why things happened. These techniques takes the finding in descriptive analytics and dig deeper to find the cause. The process identifies the anomalies and collect the relevant data to these anomalies. Some statistical techniques will be used to find the correlations and trends in these anomalies.
Predictive analytics answer questions about and predict what will happen in the future. These techniques use historical data to identify trends and the likelihood that a specific thing will occur. Prediction methods include various statistical and machine learning techniques.
Prescriptive analytics delivers what should be done. By using actionable findings from predictive analytics, data-driven and informed decisions can be generated. Since it involves large data sets, prescriptive analytics techniques rely on machine learning algorithm. By analyzing past decisions and events, the likelihood of different outcomes can be estimated.
SAMPLES OF WHAT DATA ANALYTICS APPLICATIONS
Broadband Internet Users Profiling in Internet Service Provider
ISP can utilize the usage data per household in certain period in a day to put the user in certain Quality of Service group. To maintain the service level in a fixed bandwidth resources, it is in the interest of ISP to have a good balance of heavy, medium and light users in each group.
Outcomes: the decision to put a user in a specific group which is suitable with the service level agreement and to optimize the bandwidth resources. The system must be fed with the usage observation over certain period of time
Branch Bandwidth Allocation
Data analytics will be used to analyze the bandwidth consumption of every branch in an organization over certain period of time, peak or operating hours for example. This data, combined with the number of customers lining up to get services and the type of computer applications concurrently running, will predict how big the communication network pipe should be allocated
Outcomes: network bandwidth in every branch
Critical Illness Prediction
The fitness data, dietary info, genetic factor, blood pressure, resting heart rate, positive and negative habit, medical test result, existing illness, and others for large number of people will be used as raw data of machine learning. The system will predict the likelihood that an individual will get certain critical illness.
Outcomes: after entering some health data, the likelihood that a person will get critical illness
WHAT WE DO
Using the field raw data you have collected, we will help you to use that data to describe, diagnose, predict, and prescribe what should be done to improve your business efficiency and optimize your business operation. We will present you with visual information and build the application software to deliver the objective of your data analysis. Our application software will be based on various machine learning algorithm suitable with the task at hands. The algorithms will be trained with the raw data and tested against the existing data. The objective is to come up with as accurate prediction as possible and actionable data.