Background
Statistics
Sample
Statistics (%) | |
---|---|
1. Scope | |
Business scope | |
Fixed and mobile | 54 |
Mobile | 31 |
Fixed | 15 |
Geographic scope | |
Mono-country | 57 |
Multi-country | 43 |
HQ location | |
Europe | 47 |
Americas | 24 |
Africa | 5 |
Asia/Australia | 24 |
Customer scope | |
B2C | 79 |
B2B | 71 |
Own assets scope | |
Network coverage | 78 |
Distribution coverage | 71 |
IT coverage | 55 |
Call center/install coverage | 45 |
Cont(./.) | |
2. Financial performance | |
Revenue (USD) | |
Less than 1 billion | 29 |
Less than 5 billion | 22 |
Less than 10 billion | 12 |
More than 10 billion | 37 |
Revenue growth (%) | |
Negative | 18 |
<4 % | 41 |
>4 % | 41 |
Cash flow (%) | |
Negative | 17 |
<10 % | 44 |
>10 % | 39 |
3. Business priorities | |
(% Implementing) | |
New innovation | 67 |
Lean cost technique | 53 |
Enterprise process digitization | 46 |
Outside telecom diversification | 38 |
Digital Capabilities upgrade | 31 |
Big data | 30 |
M&A | 26 |
Business model re-definition | 25 |
Statistics (%) | |
---|---|
1. Big data projects | |
Big data priority | |
Doing it | 30 |
Considering | 45 |
Interested | 23 |
Big data domains when invested | |
Sales/marketing | 77 |
Customer care | 57 |
Competition | 41 |
Network | 36 |
Supply chain | 30 |
Big data architecture investments made | |
Applications | 53 |
Database | 44 |
Megaservers | 22 |
Big data capabilities in house | |
Analytics | 78 |
IT architects | 78 |
Telecom digitization projects | |
Digitization priority | |
Doing it | 46 |
Considering | 32 |
Interested | 19 |
Digitization domains when launched | |
Sales/marketing | 76 |
Customer care | 62 |
Competition | 22 |
IT/network | 55 |
Supply chain | 60 |
Processes digitized | |
Admin | 44 |
Book to bill | 39 |
IT as service | 44 |
R&D | 42 |
Network | 38 |
Install | 20 |
Digital investments | |
Applications | 68 |
Database | 26 |
Servers | 21 |
Big data adoption in telecom
Big data relevance and adoption
Big data archetypes
Big data pain points
Constraints | Dimensions | Percent encountered (%) |
---|---|---|
Data | Lack of quality | 60 |
Lack of permission to use data | 37 | |
Lack of sufficient data | 23 | |
Talent | Limited pool to recruit | 29 |
Limited intelligence as to where and how to apply big data | 15 | |
Organization | Big data org too low in organization | 57 |
Big data IT not synchronized with function | 47 |
Big data returns
Big data as a portion of telecom investment
Big data contribution to telecom profit
The distribution of profit impact of big data
Explaining drivers of big data returns
Theory and hypotheses
Adoption factors
Internal firm effects
Variables | Average (%) | Standard deviation (%) | Coeff variation (%) |
---|---|---|---|
DIG | 46.0 | 38.9 | 84.6 |
BD | 30.0 | 28.0 | 93.3 |
ARCH | 42.3 | 20.0 | 47.3 |
ORG | 63.5 | 31.0 | 48.8 |
BDIT | 0.9 | 1.2 | 133.3 |
BDHT | 1.1 | 1.0 | 90.9 |
CASE | 38.3 | 31.0 | 89.8 |
The econometric model
Controlling for possible biases
Final regression system
Results
Variables (t-stat) | Use cases | ||||
---|---|---|---|---|---|
Sales and marketing | Customer care | Network | Supply chain | Competitive intelligence | |
Constant | 8.1 % | −0.6 % | −0.8 % | 0.4 % | −3.7 % |
(2.23) | (−3.11) | (−1.03), NS | (0.98), NS | (−2.92) | |
C1 | 2.6 % | 1.2 % | 4.7 % | 1.9 % | −2.2 % |
(3.21) | (1.03), NS | (3.17) | (1.97) | (−2.35) | |
C2 | 1.4 % | 2.3 % | −2.0 % | 0.6 % | 0.3 % |
(4.56) | (2.02) | (−1.56), NS | (2.23) | (2.90) | |
DIG | 20.2 % | 24.0 % | 14.4 % | 10.7 % | 21.2 % |
(3.78) | (4.02) | (2.18) | (2.84) | (1.97) | |
SPILLOVER | 23.4 % | 32.0 % | 41.7 % | 33.4 % | 44.0 % |
(4.55) | (2.79) | (3.71) | (1.99) | (2.49) | |
Pseudo R2 | 45.5 % | 37.60 % | 29.20 % | 46 % | 51 % |
Variables (t-stat) | Use cases | ||||
---|---|---|---|---|---|
Sales and marketing | Customer care | Network | Supply chain | Competitive intelligence | |
Constant | −1.0 % | −0.2 % | −0.9 % | −1.7 % | −1.3 % |
(−4.06) | (−1.49), NS | (−3.84) | (0.98), NS | (−2.2) | |
C1 | 1.1 % | 2.4 % | 3.2 % | 0.9 % | −0.2 % |
(2.17) | (2.45), NS | (1.99) | (1.97) | (−1.36), NS | |
C2 | −0.2 % | 0.4 % | −1.7 % | −0.6 % | 0.3 % |
(−0.55), NS | (3.61) | (−1.63), NS | (2.23) | (2.90) | |
CBD | 1.0 % | 0.7 % | 0.6 % | 1.4 % | 0.8 % |
(3.00) | (2.24) | (2.67) | (2.84) | (1.97) | |
ARCH | 5.9 % | 4.7 % | 4.0 % | 2.7 % | 1.9 % |
(2.12) | (1.96) | (3.71) | (3.07) | (2.06) | |
BDIT | 23.3 % | 19.60 % | 35.20 % | 31 % | 45 % |
(4.06) | (4.14) | (2.29) | (2.15) | (1.99) | |
BDHT | 34.2 % | 41.2 % | 45.2 % | 12.8 % | 35.8 % |
(3.78) | (2.79) | (2.18) | (2.84) | (1.97) | |
BDIT*BDHT | 197.20 | 112.80 | 188.90 | 97.70 | 67.60 |
(4.55) | (2.32) | (1.97) | (3.04) | (0.78), NS | |
CASE | 1.9 % | 1.3 % | 1.9 % | 3.0 % | 4.7 % |
(3.78) | (1.02), NS | (2.01) | (2.45) | (3.01) | |
ORG | −3.2 % | −2.1 % | −4.0 % | −1.1 % | −4.5 % |
(−4.55) | (−2.65) | (3.14) | (−2. 34) | (−2.98) | |
R2
| 55 % | 61 % | 49 % | 62 % | 44 % |
Big data adoption probit estimates
Big data contribution to profit regression estimates
Gap | Use cases | |||||
---|---|---|---|---|---|---|
Top 25 %/bottom 25 % | Sales and marketing (%) | Customer care (%) | Network (%) | Supply chain (%) | Competitive intelligence (%) | Weighted average (%) |
Gap of which | 2.7 | 2.8 | 2.5 | 1.6 | 2.9 | 2.6 |
C1, C2 | 0.2 | 0.9 | −0.1 | −0.1 | 0.1 | 0.3 |
ARCH | 0.7 | 0.6 | 0.5 | 0.3 | 0.2 | 0.5 |
BDIT, BDHT | 0.8 | 0.7 | 1.0 | 0.6 | 0.8 | 0.8 |
CASE | 0.4 | 0.3 | 0.4 | 0.6 | 1.0 | 0.5 |
ORG | 0.5 | 0.35 | 0.66 | 0.2 | 0.7 | 0.5 |