Data Analytics

What is Data Analytics?

Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialised systems and software.

Data analytics is the science of analysing raw data in order to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into processes and algorithms that work over raw data for human consumption.

Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimise processes to increase the overall efficiency of a business or system.

Data analytics is broken down into four basic types.

Descriptive analytics describes what has happened over a given period of time. Have the number of views gone up in the last quarter? Are sales stronger this month than last? Were there more car crashes this year?
Diagnostic analytics focuses more on why something happened. This involves more diverse data inputs and a bit of hypothesising. Did the weather affect beer sales? Did that latest marketing campaign impact sales?
Predictive analytics moves to what is likely going to happen in the near term. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year?
Prescriptive analytics suggests a course of action. If the likelihood of a hot summer is measured as an average of these five weather models is above 58%, we should add an evening shift to the brewery and rent an additional tank to increase output.

Surveys - Who viewed and who didn't view the survey? Why did they respond, how can we make respondents want to respond?
Business sales - What are people buying? Why are they buying it? Whats the relationship between who is buying and when? What type of people are purchasing? Is there a trend with the combination of products being bought?
Lap Timing - Who had the fastest lap? What is the average lap? How long were there pit stops? Why were there pit stops faster then another team? Is there a pattern that you can see in different sectors?
Traffic - What times of the day are the busiest? How can this be solved? What types of traffic? Where are people going? Is there an alternative route? Why are people driving on this road?
House Sales - What is the median house price? Have sales increased? Have prices increased over time? When is the best time to buy? What month is the most popular suburb?Most expensive Suburb, cheapest suburb?

Availability
- How much data is currently available?
- Do you have a big enough set or sets of data?
Quality of data
- Garbage in = Garbage out.
- Is the data set complete and include valid data that we can use meaningfully?

A data source is simply a source of data contained in a file, database, DBMS or even a live data feed. It may located on one or more devices.

Data Sets are tables of data separated into columns and rows.
A table is composed of records and fields that hold data.
A field stores one single piece of data about a record. Examples could include, first name, last name, phone, time etc.
A record is a row of information about one particular thing - person, place, incident, item etc. Multiple fields make up one record.

Data Cleaning

Data Sets can be massive and have millions or records and many fields.
It is important to check your data before you start working with the data to ensure that you get true, accurate and authentic information.

Currency

Authenticity

Relevance

Accuracy

Outlier

Are there any records within the data that are an abnormal distance from other values?
Is this an error or just abnormal?
How much impact will this have on calculations?
Can outliers be discounted or should they be included in your analysis?

Report abuse