Actionable intelligence
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Business Intelligence
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Cloud Computing
The cloud, or cloud computing, is a type of storage that uses a network of physical locations to store software, hardware and services that run on the internet rather than on your device. The cloud is an all-encompassing term that includes Read more
Cognitive Analytics
Cognitive Analytics is intelligent technology that covers multiple analytical techniques to analyze large data sets and give structure to the unstructured data. To put it simply, a cognitive analytics system searches through the data that exists in its knowledge base Read more
Collaboration
Collaboration is the action of working with others to achieve a certain goal. With analytics, this can refer to two or more people using the same data set, working on the same report, or creating separate reports that relate to Read more
Dashboard
A Visualization Tool to Monitor KPIs and Metrics A dashboard is a visualization tool that tracks, analyzes and exhibits the status of key performance indicators (KPIs). Dashboards are a one-stop location that combines and displays various performance metrics and numbers Read more
Data Aggregation
Data aggregation is the process where data is collected and presented in a summarized format for statistical analysis and to effectively achieve business objectives. Data aggregation is vital to data warehousing as it helps to make decisions based on vast Read more
Data Exploration
Data exploration is the process of describing the main characteristics of a set of data. This is often the first step people take when performing data analysis. During this process, the analyst decides which questions the data is going to Read more
Data Governance
In today’s fast-paced and highly competitive business environment, data governance is more important than ever. Businesses have the chance to capture large amounts of data from various sources, but they also need a way to maximize the value, manage the Read more
Data Lake
A data lake is a centralized repository that offers storage for both structured and unstructured data, regardless of scale, and allows multiple types of analytics to guide business processes. Data lakes contain raw-form data from all data sources, and they Read more
Data Mashing
A process of combining heterogeneous data from multiple sources. Data Mashing is the process of integrating business-related heterogeneous and application data from numerous sources to give a more unified view from a “big picture” perspective. Usually, mashing is a process Read more
Data Mining
Data Mining is the process of uncovering hidden information or patterns from large data sets. Data is extracted from large databases using statistics, artificial intelligence, decision trees, machine learning, etc. The primary goal of the data mining process is to Read more
Data Modeling & Management
Build the Reports That You Need When You Need Them Data Modeling & Management Orbit’s data modeling functionality achieves highly-tuned queries by identifying the objects needed from multidimensional data relationships. You can build reports as per your business requirements to Read more
Data Scientist
Data science uses scientific methods, algorithms, processes and systems to gain insights from data. The end result of their methods are usually charts or other visualizations that reveal trends and produce insights to inform the decision-making process and thus help Read more
Data Security
Data security means protecting data in a database, personal computer or network from unauthorized users. In general, data can range from personal files to any corporate analytics information. Though some information is intended for general use, there might be some Read more
Data Visualization Dashboards
Simple, Intuitive, and Powerful Dashboards Data Visualization: Dashboards Orbit Reporting and Analytics brings all of your data together in real-time and interactive dashboards, so you can gain a clear view of your business – at a glance. View Data from Read more
Data Warehouse
A data warehouse is loaded with data from numerous heterogeneous sources into a central repository for analytics. Data warehouses store multidimensional information in a way that allows analytics to be efficiently performed. Data warehousing helps organizations in effective decision-making, and Read more
Descriptive Analytics
An initial stage of business analytics to summarize historic data. Descriptive Analytics, a preliminary stage of data processing, exemplifies retrospective analysis that drills through the past behaviors and provides an insight into the future outcomes. Descriptive Analytics, famously known as Read more
Edge Analytics
Edge analytics is a method of data collection and analysis that uses an automated analytical data computation that is performed at a sensor or other device. This is performed before the data is sent to a centralized store. This process Read more
Financial Statements
Financial statements are reports on companies’ spending and fiscal positions. These statements can be audited by the government to prevent tax fraud and other illegal activities. Financial analysts use these statements to analyze a company’s performance, then use that information Read more
Hierarchies
These are the methods which use parent-child relationship to drill up/down the data. Hierarchies use parent-child relationships within the data, so that a user can go deeper in order to gain a better insight into the information they are responsible Read more
Hybrid Cloud
Hybrid cloud infrastructure is a combination of at least two cloud infrastructures, such as private, public or community, that remain individual but are connected by standardized technologies that allow data portability. On its most basic level, this means that hybrid Read more
JD Edwards
JD Edwards is a suite of enterprise resource planning (ERP) software from Oracle. Focused on a modern and simplified user experience, the purpose-built applications are aligned to create a seamless experience for end users that’s integrated with digital technologies. JD Read more
Key Performance Indicator (KPI)
A measure of key business objectives of an organization. A Key Performance Indicator (KPI) is a measure that determines how effectively, or ineffectively, organizations, projects or individuals achieve their key business objectives compared to their strategic objectives and targets. With Read more
Metadata
Metadata is data that contains information about other data. Metadata, in general, is used almost everywhere. The most common example is the use of meta tags in a web page. Search engines use these meta tags to identify a web Read more
Mobile BI
Mobile Business Intelligence (Mobile BI) is software that allows BI data to be viewed on a mobile device. Mobile BI has just recently become popular among BI users, and is helpful for remote workers that need information but do not Read more
Multi-tenant
Multi-tenant architecture uses a single instance of a software application to serve multiple customer organizations, called tenants. Tenants are a group of users who share a common access point to the software. The systems that operate this way share the Read more
OLAP
Online Analytical Processing, also known as OLAP, is an approach to view and analyze multidimensional data from multiple perspectives. Unlike OLTP (online transaction processing), which is useful in transaction processing (involving less complex querying), OLAP deals with business intelligence and Read more
OLTP
OLTP is an initialism for Online Transaction Processing. This is a type of software program that is designed to support transaction-oriented application processing. Online transaction processing systems are used in business for handling processes like financial transactions, order entries, customer Read more
On-Premises
On-premises architecture is comprised of data, software and applications that are installed and operated from an in-house server and computing infrastructure. It utilizes the organization’s native computing resources. It is the traditional method for storing data and running enterprise applications, Read more
Pivot Table/Crosstab
Pivot tables and crosstabs are ways to display and analyze sets of data. Both are similar to each other, with pivot tables having just a few added features. Pivot tables and crosstabs present data in tabular format, with rows and Read more
Pixel Perfect (Printable Reports)
Pixel perfect describes reports where the user can manipulate the size and layout with precision. This includes allowing the user to change the size of the report, the size of the printed page, and the position of the different elements Read more
Predictive Analytics
A part of advanced analytics to make future forecasts. Predictive analytics are a part of advanced analytics that provide a probable picture of what might happen in the future. Considering the previous behaviors from the descriptive analytics, predictive analytics might Read more
Prescriptive Analytics
It provides a best course of action taking a clue from predictive and descriptive analytics. Prescriptive Analytics is the final stage of Business Analytics (BA) that takes insights from descriptive and predictive analytics to identify the best course action for Read more
Query
A database query is a means for extracting data from a database and formatting it in a readable form. When you’re searching for data from a database, you can submit a query to request specific information. However, a query needs Read more
SAP HANA
SAP HANA (short for “high-performance analytic appliance”) is an in-memory relational database management system that allows the rapid processing of large sets of data in real time. Having computational power in-memory gives HANA the ability to process the data stored Read more
Scheduling
An automated process of refreshing the jobs or reports on a regular basis. Scheduling is a process of planning any event or a job at a required time. Similarly, on the technical front, scheduling helps in automating the required procedure Read more
Subject Area Modeling
An initial level of Enterprise Data Model (EDM), which provides a structure for organizing EDM by Subject Areas. A Subject Area Model, together with a Conceptual Model and a Conceptual Entity Model forms the complete structure of the Enterprise Data Read more
Tabular Data
The term “tabular” refers to data that is displayed in columns or tables, which can be created by most BI tools. These tools find relationships between data entries in one or more database, then use those relationships to display the Read more
Taleo
The Oracle Talent Acquisition Cloud (Taleo) is a data-enriched talent management software suite that helps organizations find, develop and retain prime talent. Taleo History Oracle acquired this cloud-based talent management software in 2012, though it was founded in 1999 by Read more
Home | Glossary

Data Wrangling

Data wrangling is a method of gathering, choosing and transforming data to answer an analytical question. Often referred to as “munging” or data cleaning, data wrangling makes up approximately 80 percent of a data scientist’s time, with the rest devoted to modeling or exploration.

Why Is Data Wrangling Necessary?

There is going to be a wide range in quality between different data sets. Some will be big data streams that contain unstructured data. Others will be structured (eg data fields are clear and consistent) but will include duplicate or irrelevant data. Other datasets may be in good condition, but so large as to require metrics which have been rolled up in a data warehouse or star or snowflake schema to allow analytic queries.

Steps to Data Wrangling

  • Gather data from sources inside and outside the organization.
  • Document sources and limitations.
  • Clean the blanks, nulls, duplicates and other errors.
  • Combine data into a single table.
  • Create new data sets by calculating fields and categorizing.
  • Eliminate outliers and illogical results by visually plotting the data.

The Challenges of Data Wrangling

Data wrangling is something of the unspoken grunt work of data science. It takes time to clean data to the point that it can be used for analytics. These are some of the challenges you will face when data wrangling:

  • Obtaining access to data: A data scientist should have permission to access data. If they don’t, they must provide instructions for scrubbed data and hope the request is granted.
  • Clarifying the use case: Data is dependent upon the question you’re looking to answer, so the use case must be clarified to choose the right data sets.
  • Understanding the data: You need to understand what fields are required or are unnecessary or incomplete. You should use some basic queries to determine if the data makes sense, or if bad or missing data will skew your queries.
  • Identifying data relationships to determine how entities are related to one another via keys.
  • Avoiding selection bias: Selection bias is a problem that occurs in data science. Selection bias remediation can be difficult, but it’s important to be sure that the sample data is representative of the implementation sample.

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