Economic data or economic statistics are data (quantitative measures) describing an actual economy, past or present. These are typically found in time-series form, that is, covering more than one time period (say the monthly unemployment rate for the last five years) or in cross-sectional data in one time period (say for consumption and income levels for sample households). Data may also be collected from surveys of for example individuals and firms[1] or aggregated to sectors and industries of a single economy or for the international economy. A collection of such data in table form comprises a data set. Methodological economic and statistical elements of the subject include measurement, collection, analysis, and publication of data.[2] 'Economic statistics' may also refer to a subtopic of official statistics produced by official organizations (e.g. statistical institutes, intergovernmental organizations such as United Nations, European Union or OECD, central banks, ministries, etc.). Economic data provide an empirical basis for economic research, whether descriptive or econometric. Data archives are also a key input for assessing the replicability of empirical findings[3] and for use in decision making as to economic policy.

1. POWER OF PREDICTIVE ANALYTICS
LOW
H
IGH
H
IGH
CO
M
PLEXITY
BU
SIN
ESS
VALU
E
Technologies used SURVEY OF BUSINESS AND IT EXECUTIVES
BUSINESS INTELLIGENCE TECHNOLOGIES
EACH DISCIPLINE BUILDS ON THE ONE BELOW IT – THESE ARE ADDITIVE, NOT EXCLUSIVE, IN PRACTICE
Source: SAS 2016 Source: TDWI Research 2014
REPORTING
WHAT HAPPENED?
ANALYSIS
TOP 10 DRIVERS FOR
PREDICTIVE ANALYTICS
WHY DID IT HAPPEN?
MONITORING
WHAT’S HAPPENING NOW?
PREDICTION
WHAT MIGHT HAPPEN?
Predictive analytics
Dashboards, scorecards
Query, reporting
& search tools
OLAP and visualisation tools
PREDICT TRENDS01
UNDERSTAND CUSTOMERS02
PREDICT BEHAVIOUR05
IMPROVE PRODUCTIVITY09
IDENTIFY NEW BUSINESS
OPPORTUNITIES08
IMPROVE BUSINESS
PERFORMANCE03
DRIVE STRATEGIC
DECISION-MAKING04
DRIVER OPERATIONAL
EFFICIENCY06
IDENTIFY RISKS10
PROVIDE TARGETED
PRODUCTS/SERVICES07
01 02 03
04 05 06
Identify data
that is relevant
Integrate and erich
the data into an
analytical data set
Run statistical and
machine-learning
algorithms to find
the model
Test the model
to make sure it
will work
Use the model
in applications
Measure the
effectiveness of the
model in the real
world
SIX STEPS OF PREDICTIVE ANALYTICS
MONITORDEPLOYEVALUATE
MODELPREPARE DATAUNDERSTAND
DATA
Based on a graphic in the July 2016 Forrester report, Predictive Analytics Can Liberate I&O
Pros From The Tyranny Of Firefighting
HIGH MEDIUM
LEVEL OF IMPORTANCE
LOW
Data availability/quality/
governance/privacy
Accuracy/fit of
predictive models
Executive buy-in/
approval/acceptance
Business case
content/formulation
Internal people’s
skills/expertise
Budget availability/size
Technologies/
tools/infrastructure
GLOBAL SURVEY OF C-SUITE EXECUTIVES
TOP FACTORS ENABLING PREDICTIVE ANALYTICS CAPABILITIES
79%
79%
73%
73%
71%
70%
69%
13%
19%
18%
20%
21%
23%
11%
10%
8%
8%
9%
9%
9%
8%
Marketing/
market analysis
Sales Executive
management
Customer
service and
support
Finance Operations
management
IT, network
or computer
managememt
Engineering/
scientific
research
Online
presence/
social media
Product
development/
life-cycle
management
Engineering/
scientific
research
GLOBAL SURVEY OF C-SUITE EXECUTIVES
SECTORS WHERE PREDICTIVE ANALYSIS IS USED
64%
54% 49% 46%
39% 37%
30%
29%
26%
25%
19%
Source: TDWI Research 2014
Using today and
will keep using
Will use within
three years
No plans Not applicable/
don’t know
Structured data
from tables/records
Demographic data
Time series data
Web log data
Geospatial data
Clickstream data
from websites
Real-time event data
Internal text data
from e-mails/calls
External social
media text data
Machine-generated data
2
98
77 11 6
11
21
16
25
20
18
25
38 21
10
6
9
14
12
9
10
6
65 14
37 33
35 37
32 29
31 31
31 45
21 44
19 22
GLOBAL SURVEY OF C-SUITE EXECUTIVES
DATA USED FOR PREDICTIVE ANALYTICS (%)
Using today and
will keep using
Will use within
three years
No plans Not applicable/
don’t know
Direct marketing
Cross-sell/up-sell/propensity to spend
Retention analysis
Portfolio
analysis/prediction
Optimisation
Risk analysis
Econometric forecasting
Fraud detection
Quality assurance
Scientific investigation
Loan default
58
55 21
55 17
47 23
46 31
43 26
34 31
30 19
24 28
20 16
15 9
13 20
16 8
17 11
18 12
15
17
21
32 19
27 21
35 29
45 31
14
14
8
9
WHERE PREDICTIVE ANALYTICS ARE BEING USED (%)
GLOBAL SURVEY OF C-SUITE EXECUTIVES
Source: TDWI Research 2014
Source: TDWI Research 2014