Various methods of scoring were deliberated and the final selection was based on the ease of interpretation of the scores for all stakeholders.
A number with discrete values for each answer would yield an overall average score across sectors and sections of the survey (the number was used to categorise and also to rank).
This score could be interpreted as percentage values. While the results of this method would be more quantitative and it would be easier to make comparisons, it was difficult
to tie the score back to the stages of the model at the level of understanding of all stakeholders in the process.
An ordinal scale of 1 to 4 was decided upon, with 1 being the lowest score representing the “Seedling” stage and 4 the highest score representing the “Fruitful” stage since it was easier to standardise according to the properties of each stage of data maturity. Every answer to each of the questions was designated a score on a scale of 1 to 4 to match the four stages of data maturity described above according to the model. The example in Table 4 below shows the score assigned to each answer for data quality and availability question 14: “What format is the data available in?”
Data quality, availability answer scoring.
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The Data is Not Available
1. The first interview revealed that our questionnaire was too lengthy for an exercise with no tangible incentives and some indicators were redundant or unclear. We developed a second version of the tool from this feedback which we used for the remaining department in that county, and for subsequent interviews in the other counties.
2. The survey respondents were not incentivised to participate as we felt it may affect the quality and candour of responses. We reviewed the tool and came up with a shorter version that captured as much information as possible and took less time to complete.
3. Each county has different priorities when it comes to sectoral indicators and hence it was difficult to compare responses from different counties.
4. Some of the interviews had to be conducted virtually or filled in by the officials due to connectivity issues and/or logistical and scheduling challenges.
Information and communication technologies (ICT) offer great opportunities for counties to enhance the effectiveness and efficiency of their internal and external operations. Internally, ICT enables counties to adapt to changes in governance policies and processes.
Externally, it provides rich capabilities that facilitate service convergence and citizen participation. However, providing seamless services and reliable information requires more than just scalable and secure technology capabilities. Also required are information strategies that emphasise management, quality, and governance of data, and organisational practises that enfold a strong compliance program, ongoing training, and data sharing practises. Thus, achieving effectiveness and efficiency is not just a matter of capitalising on ICT capabilities but achieving higher levels of maturity in the management of technology, information, and organisational processes.
The growing importance of data management in counties cannot be overstated. As with any organisation,
data is a critical asset to counties. Government organisations collect and maintain different types
of data related to citizens. Data ascribes meaning to the information that county governments wish
to share. Data drives the information discovery needs of citizens and counties as agencies of the national government. It enables information exchange among public agencies and external partners.
Government agencies integrate and reconcile data from different sources according to the functions
and services they provide. Thus, data management is central to achieving operational effectiveness,
reducing costs, and improving efficiencies of government services
