Data Science and Analytics - Glossary

1.What is Data Science?

Data Science refers to methods used to analyze large sets of data (big data) and identify patterns, trends, and correlations that predict future events or outcomes. 

Data Science also offers a means of using information and insights to innovate, engage customers, improve operational processes, and drive competitive advantage within an enterprise.

2.What is Data Discovery?

The term Data Discovery is used to describe the approach to understanding the complete universe of available data within an organization. 

This process requires the development of an organization’s analytic plan through hands-on workshops to customize the best analytic solutions based on the organization’s needs and data. The goal of Data Discovery is to identify what data an organization has available to them before they begin analyzing it.

3.What are Big Data Services?

Big Data Services includes consulting, implementation, infrastructure support, and ongoing maintenance. These services enable an organization to use specialized expertise to provide strategic insights from its structured, unstructured, and semi-structured data sets.

4.What is Data Migration?

Data Migration refers to the selection, extraction, preparation, and cleansing of data that is being migrated from one system to another. There are various types of Data Migrations such as 

  • Cloud
  • Database, and 
  • Enterprise Data Migrations

 

5.What is Data Visualization?

Data Visualization allows organizations to turn their complex analysis results into user-friendly visual formats via interactive applications and dashboard designs, so business stakeholders can quickly determine what data metrics are being measured and tracked for strategic analysis and development.

Data Visualization allows business stakeholders to use data without having any technical knowledge of the particular information.

6.What are Descriptive Analytics?

Descriptive analytics uses historical data to gain an understanding of how things occurred at a certain point of time, which lays the groundwork for very basic business intelligence. Descriptive analytics aids businesses in employing metrics to monitor how they have performed over time.

7.What are Predictive Analytics?

Predictive analytics utilizes historical patterns and data to predict what will continue to happen in the future. Also it allows organizations to anticipate potential outcomes of future events and make proactive choices.

8.What are Prescriptive Analytics?

Prescriptive analytics utilizes both predictive analytics and descriptive analytics to give users suggestions/recommendations of what they should do. By using prescriptive analytics, organizations can make more informed decisions regarding the actions they take.

9.What are Real-Time Analytics?

Real-time analytics are the instant insights of a user. They utilize the event information to provide instant insight into customer interactions, the resolution of issues, and the ability to make immediate changes in business operations.

10.What is Customer Profiling?

Customer profiling is a technique to help businesses determine which customers are at greatest risk of abandoning a product or service. By identifying these customers, businesses can implement targeted approaches to help create a sense of loyalty in their customers. 

Customer profiling also enables businesses to identify the specific issues that need addressing through targeted engagement strategies.

11.What is Data Governance?

The concept of data governance establishes the framework within which strict standards are set for managing data. This includes areas such as 

  • Encryption
  • Access control
  • Compliance audits, and 
  • Clauses in contracts

Additionally, it serves to protect sensitive and confidential data to ensure compliance with regulations.

12.What is Model Drift?

Model Drift is the degradation of the performance of a data science model over time due to fluctuations in business conditions. Model Drift can be mitigated through regular monitoring and retraining of the model based on the latest data, as well as ensuring that the model stays in sync with the current conditions of the business.

13.What is Analytics Optimization?

Analytics Optimization is the process of utilizing current analytics to take actions for maximizing efficiency. Analytics Optimization uses more sophisticated technologies to guarantee that all analytical capabilities available to an organization are being utilized.

14.What is Data Quality Management?

Data Quality Management is a way for organizations to ensure that the data they collect is clear, accessible, true, and reliable through the use of implemented data verification methods. 

Data Quality Management enables organizations to create data-driven decisions, increase the return on technology and AI investments, and improve the amount of data produced.

15.What is Analytics Outsourcing?

Analytics Outsourcing is the process of providing companies with access to a large, highly skilled workforce of analytics professionals and innovative technologies while not making a long-term financial commitment. 

Analytics Outsourcing will allow organizations to accelerate their analytics initiatives and provide opportunities for internal resources to continue focusing on their core business activities while also making logical and data-driven decisions.

16.What is Data-Driven Decision Making?

Data-Driven Decision-Making (DDDM) is a form of decision-making based on the data collected. It allows organizations to think systematically and logically when making decisions rather than only relying on logical conclusions based on their interpretation of the data, and therefore leads to a more predictable and successful outcome compared to making decisions based on intuitive or observational interpretation only.

17.What is Interactive Dashboard Design?

Interactive Dashboard Design is the creation of a dynamic and user-friendly interface that displays KPI’s (Key Performance Indicators) in real-time. 

Users can explore their data by using various means including filtering, drilling down, and customizing their views. This allows users to quickly comprehend large amounts of complex information, as well as enables stakeholders to effectively monitor the performance of the company they are working for.

18.What is Data Analytics Infrastructure?

Data Analytics Infrastructure is the technology used in the collection, storage, processing, and analysis of data. 

All hardware, software, networks, and cloud platforms used to collect, store, process, and analyze data, along with any analytic applications that run on those technologies.

19.What is Churn Prediction?

Churn prediction determines the likelihood of customers opting out of using a company’s product offerings through analytics or ML (Machine Learning). Through an understanding of the customers’ behavior and engagement metrics, it enables organizations to proactively deploy retention methods.

20.What is Data Modernization?

Data Modernization defines a company’s transition from legacy hardware (existing technologies) to develop a state-of-the-art methodology to maximize the return on investment associated with the company’s existing data analytics technology, while allowing the organization the flexibility to address possible future growth needs related to the analytics space by utilising Cloud platforms.