Data Analytics definition
Data analytics is the process of reviewing and analyzing raw sets of data to find trends and make deductions based on the information gotten. Data analytics enables companies and organizations to solve certain problems, perform efficiently, maximize profits and make better business decisions.
In data analytics, data is gathered and analyzed from various sources (could be structured, semi-structured or unstructured data), and then it is worked on and converted into intelligible information humans can interpret and use to make decisions. This analysis process involves various tools and frameworks, and the results are usually represented visually using charts or graphs for better interpretation.
A Data analyst is the professional responsible for gathering, processing and analyzing large data sets. He/she analyzes the data and comes up with ways to better improve the business needs using the information. Some types of data they analyze include customer feedback, logistics, accounts, marketing research, etc. Data analysts should be well-versed in communication to enable them to properly convey their results to those who will make the decisions.
The major types of data analytics
1. Predictive data analytics
Predictive data analytics is a type of data analytics used by companies and businesses to forecast future outcomes and performances. It is the most used form of data analytics which involves the use of historical data, statistics, probability theory and modelling techniques to predict possible future events. Predictive data analytics can be broken down into predictive modelling and statistical modelling.
Predictive analytics help companies take the most appropriate decision in certain situations. It reduces risks and boosts operating efficiency through a series of techniques such as data mining, artificial intelligence (AI), statistics and machine learning. This type of data analytics can be used for purposes like weather forecasting, sales forecasting, and developing marketing strategies and investment portfolios.
2. Prescriptive data analytics
After predictive analytics, prescriptive analytics helps in deciding the actions and conclusions that should be taken from the outcomes that have been predicted based on past decisions and events. Prescriptive analytics involves the use of machine learning, algorithms and computational modelling procedures. This type of data analytics has a huge influence on the company’s or organization’s decision-making.
It enables businesses to make informed, effective and data-driven decisions in certain situations using insights from predictive analytics. This form of data analytics can be divided into optimization and random testing.
3. Diagnostic data analytics
This type of data analytics investigates the cause of events and the ‘why’ in the data analysis. Diagnostic analytics identifies the abnormalities in large sets of data that cannot be explained. For example, diagnostic analytics is responsible for figuring out the cause of an unexpected drop in sales for February in a business.
After recognizing the anomaly in the data insight, a discovery phase is undergone by the data analyst to figure out any additional data sources that might explain more about the cause of the anomaly. Then similar events corresponding to the same events detected are uncovered and used as references in taking further actions. Techniques deployed include data discovery, drill down, data mining and correlation.
4. Descriptive data analytics
Descriptive analytics involves expressing and summarizing historical data to get insights into events that occurred in the past. Data is gathered, organized, analyzed and presented using a method that helps to understand the trends and patterns. This is frequently used in various organizations such as finance, healthcare, business, etc to analyze sales trends, operational efficiency, customer behavior and several other key performance indicators.
Descriptive analytics is a type of analytics that is based on past events. The two techniques involved in descriptive analytics include data aggregation and data mining. The data is first accumulated in a summarized format and mined to discover the patterns. Then the data is presented in a way that it can be assimilated by a general audience.
Data analysis software tools
A data analyst depends on various tools for an effective operation. Some of these include;
- Spreadsheets e.g., Excel
- Data mining programs
- Open-source programs e.g., Apache Spark, Apache Hadoop, etc.
- Data visualization and reporting tools e.g., Tableau
- Business analytics services e.g., Power BI
- High-level programming languages e.g., Python
- Web analytics services e.g., Google Analytics
- Statistical analysis software e.g., SPSS
