session 6: data knowledge mgmt

Question 1

database

Answer 1

is a collection of related data files or tables that contain data

Question 2

difficulties in managing data (10)

Answer 2

1) data increase exponentially with time
2) data are scattered throughout org.
3) multiple sources of data
4) data become outdated
5) data media rots
6) data security/quality/integrity may be compromised
7) new sources of data
8) legal requirements need to be met with appropriate data-storage methods
9) lefacy IT systems/functional requirement may results in redudancy or inconsistency
10) high volumes of big data + variety of data collected increase in complexity

Question 3

sources of data

Answer 3

internal sources: corporate database, company docs…
personal sources: personal thoughts, opinions…
external sources: commercial database, gov. reports, coprorate website…
new sources: blogs, podcats, tweets etx

Question 4

clickstream data

Answer 4

data that visitors and customers produce when they visit a website and click on hyperlinks

Question 5

Data governance (subset of IT governance)

Answer 5

an approach to managing info across an entire organization

Question 6

data governance objective

Answer 6

enable available, transparent, useful data => single version of the truth

Question 7

data governance involves…

Answer 7

provides a planned approach to data mgmt for all types of data
includes a formal set of business processes for data handling
requires well-defined unambiguous rules +> which address creating, collecting, handling, protecting data

Question 8

master data mgmt

Answer 8

process that spans all of an organization’s businsess processes and applications

Question 9

master data mgmt goal

Answer 9

goal : effecitvely store, maintain, exchange and synchronize master data
provide consistency, accuracy, timeliness, up-to-date master data

Question 10

master data def

Answer 10

set of core data such as customer, product employee, vendor etc
stored in a master file or as tables as part of the database

Question 11

transactional data def

Answer 11

generated and captured by operational systems describe the business’s activities
represents activtiies or events (payroll cheques, customer invoice etc)
stored in transaction files or as table in the database

Question 12

big data def

Answer 12

collection of data that is so large and complex that it is difficult to manage using traditional database mgmt systems

Question 13

characteristics of big data

Answer 13

exhibit variety
include unstructured/structured/ semi-structured data
generated at high velocity with an uncertain pattern
do not fit neatly into traditional, structured, relational databases
can be captured, processed, transformed and analyzed in a reasonable amount of time

Question 14

sources of big data

Answer 14

traditional enterprise data (customer info, web sotre transactions…)
machine-generated/sensor data (smart meters, manufacturing sensors…)
social data (feedback comments…)
images captured by billions of devices

Question 15

big data 3V

Answer 15

volume
velocity
variety

Question 16

Issues with big data

Answer 16

come from untrusted sources
big data is dirty (innacurate, incomplete, incorrect etc)
changes

Question 17

data warehouses def

Answer 17

repository of historical data organized by subject to support decision makers

Question 18

data mart def

Answer 18

low cost, scaled down versions of data warehouse designed for end-users needs in a startegic business unit

Question 19

Query by example (QBE)

Answer 19

method of creating database queries that allows users to search for doc based on an example in the form of a selected string of text

Question 20

characteristics of data warehouses and data marts (6)

Answer 20

organized by business dimension or subject
use online analytical processing
integrated
time variant
nonvolatile
mutlidimensional

Question 21

ETL

Answer 21

extract, transform, load

Question 22

generic data warehouse environment

Answer 22

source systems : provide data to the warehouse or mart
data integration technology and processes:: prepare data for use
storing data: handled by variety of architectures
metadata: data about data
data quality issues; data cleansing needs to be used to ensure data meets user’s needs
BI: establishing ppl, comittees/processes to maintain data warehouses
users: business value for users rises when data can be accessed quickly

Question 23

data lakes def

Answer 23

central repository that stores all of an organization’s data, regardless of its source or format

Question 24

information silo def

Answer 24

an info system that does not communicate with other related info systems in an organization

Question 25

how companies can use big data to gain a competitive advantage

Answer 25

strategies:

make big data available
use big data to conduct experiments
micro-segmentation of customers
creating new business models

use in functional areas of org. :
human resources (employee benefits, hiring..)
product development (capture customer preferences…)
operations (analyze data to make operations more efficient)
marketing (better understanding customers…)
gov operations

Question 26

architectures for data mart and data warehouses

Answer 26

one central enterprise data warehouse (without data marts)
independent data marts: data marts store data for a single application or a few
hub and spoke: contains a central data warehouse that stores the data plus multiple independent data marts that source their data from the central repository

Question 27

benefits of data warehousing

Answer 27

• end user can access needed data quickly and easily through web browsers because these data are located in one place
• end users can conduct extensive analysis with data in ways that were not previously possible
• end users can obtain consolidated view of organizational data

Question 28

data warehouse and data lakes differences

Answer 28

data:
warehouse:
relational from transactional systems, operational databases and the lines of business apps
lakes:
non-relational and relational from IoT devices, websites, mobile apps, social media and corporate applications

schema:
warehouse:
designed propr to the DW implementation (schema-on-write)
lake:
written at the time of analysis (schema-on-read)

price/performance
warehouse:
fastest query results using higher cost storage
lake:
query results getting faster using low-cost storage

data quality:
warehouse:
highly curated data that serves as the central version of the truth
lakes:
any data that may or may not be curated

users:
warehouse:
business analysts
lakes:
data scientists, data developers, business analysts

analytics:
warehouse: batch reporting, BI and visualizations
lake: machine learning, predictive analytics, data discovery and profiling