Supporting water sanitation

and hygiene services for life

31 October 2018

Use of monitoring data for

evidence-based decision

making:

A factor analysis

Marieke Adank

UNC Water and Health Conference

Source: mowie.gov.et

Setting the scene

Setting the scene

However, is this data used for informing decision-making processes?

How can this be improved?

What are the factors than enable or hamper use of

monitoring data in evidence-based decision making?

Use of monitoring data for decision making

• For improvement

• For accountability

• Data, information and

knowledge

• Continuous collection

and analysis -> trends

• Instrumental

• Conceptual

• Symbolic

• Operations and

corrective actions

• Planning

• Resource allocation

• Regulation

• Policy making

3 Models

Linear model

• Influence and dependencies

(MICMAC)

• Centrality

• Causal loops

Data useWell-packaged

information

Data use

Data producer

Data user

Relationship model

Systems model

Data

useActors and factors

CapacitiesData characteristics

Factors

Data use

Data Relevance

Data

Accessibility

Data

Timeliness

Motivations

Data Quality

Data Quantity

Incentives

Interest

Culture

Individual capacity

Organisational capacity: Financial and logistical resources

Organisational capacity: Data and information systems

Institutional capacity

Relationships between factors(Matrix of Direct Influence)

Data characteristics factors Capacity factors Motivation factors

Outcome

factor

Influenced

Influencing

Data

relevanc

e

Data

quality

Data

quantity

Data

accessibility

Data

timeliness

Individual

capacity

Organisational

capacity -

Financial and

logistical resources

Organisational

capacity -

Data and

information

Institutional

capacity Incentives Interest Culture

Use of

data

Data relevance 0 0 0 0 0 1 0 0 1 0 2 0 2

Data quality 0 0 0 0 0 1 0 0 1 0 2 0 2

Data quantity 0 0 0 0 0 1 0 0 1 0 1 0 2

Data accessibility 0 0 0 0 0 1 0 0 2 0 2 0 2

Data timeliness 0 0 0 0 0 1 0 0 1 0 2 0 2

Individual capacity 2 2 1 0 2 0 0 1 1 2 2 1 2

Organisational

capacity -

Financial and

logistical resources 1 1 1 1 1 1 0 2 0 2 1 1 2

Organisational

capacity -

Data and information

systems 1 2 1 2 2 0 0 0 1 0 1 1 2

Institutional capacity 2 1 2 2 1 1 1 2 0 1 1 2 1

Incentives 2 2 2 1 2 1 2 2 0 0 2 2 2

Interest 2 2 2 1 2 1 2 2 0 2 0 2 2

Culture 2 1 1 1 1 1 1 2 2 2 2 0 1

Use of data 0 0 0 0 0 2 0 0 2 2 2 1 0

Factor dependency and influence(MICMAC)

Dependence

Infl

uen

ce

Leverage points

Low significance

Potentially volatile

Highly sensitive

Potentially volatile

Data characteristics

Dependence

Infl

uen

ce

Leverage points

Low significance Highly sensitive

Motivations

Capacities

Factor dependency and influence(MICMAC)

Centrality

Label

weighted indegree

(influenced)

weighted outdegree

(influencing)

betweenness centrality

(bridging)

Interest 21 21 15.9

Incentives 12 21 1.7

Institutional capacity 13 18 15.9

Individual capacity 13 17 10.2

Culture 11 17 2.6

Organisational capacity - Financial and logistical resources 7 15 0.1

Organisational capacity - Data and information systems 13 13 1.5

Use of data 22 12 4.4

Data accessibility 10 8 0.5

Data relevance 13 7 0.5

Data quality 12 7 0.5

Data timeliness 12 7 0.5

Data quantity 11 6 0.5

Using Gephi 0.9.2 (https://gephi.org/)

Generated using Gephi 0.9.2 (https://gephi.org/)

Centrality

Causal loopsData relevance

Data quality

Data quantity

Data accesability

Data timeliness

Individual capacity

Finacial and logistical

arrangements

Presence of data and

information systems

Institutional

capacity

Incentives to use

data

Interest to use data

Culture

Use of data

Causal loopsData relevance

Data quality

Data quantity

Data accesability

Data timeliness

Individual capacity

Finacial and logistical

arrangements

Presence of data and

information systems

Institutional

capacity

Incentives to use

data

Interest to use data

Culture

Use of data

Causal loopsData relevance

Data quality

Data quantity

Data accesability

Data timeliness

Individual capacity

Finacial and logistical

arrangements

Presence of data and

information systems

Institutional

capacity

Incentives to use

data

Interest to use data

Culture

Use of data

Conclusions

Focussing improving data characteristics through focussing on data-

collection, processing and storage systems will not ensure improved

evidence-based decision making.

There is especially a need to also address:

- Culture and incentive structures (including regulatory frameworks);

- Individual capacities related to collection, analysis and use of

monitoring data;

- Organisational capacity related to financial and logistical resources

for data collection, analysis and use;

- Institutional capacities, including relationships and trust.

Visiting address

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The Netherlands

Postal address

P.O. BOX 82327

2508 EH The Hague

The Netherlands

T +31 70 3044000

info@ircwash.org

www.ircwash.org

Supporting water sanitation

and hygiene services for life

Thank you!

Blog (and paper) forthcoming on

https://www.ircwash.org/blog

For (further) comments, suggestions, inputs and feedback,

please contact me: Marieke Adank: adank@ircwash.org