ISYS Exam 3 Ch. 17 ABCC – Flashcards

Refers to an assortment of software applications to analyze an organization's raw data.

The answer is a resounding "no!"

are computer-based systems that support an organization's decision-making activities.

Data would also include smartphone metadata, Internet usage records, social media activity, computer usage records, and countless other data sources, so it could be sifted for patterns and trends

Structured Data, Unstructured Data, Semi-structured Data.

This data is typically well labeled and often with traditional fields and records of common data tables.

Disorganized data that cannot be easily read or processed by a computer because it is not stored in rows and columns like traditional data tables.

Unstructured data and can possibly be converted into structured data, but not without a lot of work.

Volume, Velocity, Variety, Veracity.

- Refers to the amount of data collected by an organization. How much data does your business need, and further, where do you keep it once you've collected it?

- How fast can you collect data, and more importantly, how quickly can you analyze it?

- You may have identified what data you wish to collect, but is it Structured, Semi-structured, or Unstructured? Very likely it is a combination of all three, which could potentially throw a wrench into your data collection gears.

- Is the data your organization collected any good? Just because some other business amasses data you may want, doesn't necessarily make it trustable or valuable. Lots of data sources have data that is not "clean," meaning it may be too fragmented to be valuable or usable, or that it was simply collected poorly in the first place.

Customer Relationship Management System. Contains information about an organization's sales, marketing, customer service records, and much more.

Remember that most of this data is unstructured, 80% of it.

Where an organization stores and consolidatea disparate data in a central location.

Limited in complexity of databases, and smaller than a Data Warehouse, but are cheaper to implement than a full warehouse.

Extract, Transform, and Load. Used to standardize data across systems, that allow it to be queried.

an acronym for Extract, Transform, and Load,

a tool that lets you ask your data questions that in turn lead to answers, and eventually decisions.

Once you've determined where your data resides, you can start extracting it, often from Customer Relationship Management (CRM) or Enterprise Resource Planning (ERP) software.

to centralize an organization's data so that it ends up being a wealth of data with value across the organization.

Once you've extracted data, it needs to become normalized. Data is no good to you unless it's organized.

that your data is typically organized into the fields and records of a relational database.

the standard data format required to analyze data.

Once data is transformed and normalized, it's ready to be finally transferred into the data warehouse or datamart.

the massive amounts of time, money, bandwidth and storage space required for ETL.

an infrastructure for storing and processing large sets of data across multiple servers.

is the processing arm, or engine of Hadoop.

basically applying statistics and logic techniques to define, illustrate, and evaluate data.

attempts to make sense of an organization's collected data and turn it into useful information and validate its future decisions, like what product to sell or whom the organization should hire.

The examination of huge sets of data to find patterns and connections, and identify outliers.

Catalogs phrases of an organization's customer feedback into relevant topics.

Searches through unstructured text data to look for useful patterns.

Attempts to make connections between data so organization's can try to predict future trends that may give them a competitive advantage.

Descriptive Analytics, Predictive Analytics, Decision Analytics

Defines an organization's past data that can be grouped into significant pieces.

attempts to reveal future patterns in a marketplace, essentially trying to predict the future by looking for data correlations between one thing, and any other things that pertain to it.

builds on Predictive Analysis to make decisions about future industries and marketplaces.

Graphic display of the results of data mining, analytics and BI in general, typically in real time.

Easy-to-use graphical interfaces that characterize specific data analysis through visualization.

Volume

Dashboards

Hadoop

Datamart

Structured

Predictive Analytics

BI

Big Data

1950's

It's too expensive

Internet speeds

Customer Relationship Management

Decision Support System

Velocity

Veracity

Transmission

Data Discovery

Variety

80%

Data Warehouse

Text-Mining

Unstructured

Dashboard

Normalization

Hadoop