Data capability framework guide

For those seeking to lift their organisation's data capability, this guide provides expanded information about the Data Capability Framework, including the levels of capability, descriptions of use, and example use cases.

Data capability framework guide [PDF, 6MB]

Purpose

The framework is a tool designed to help define and develop data & analytical capabilities.

Data has the power to change lives and create better outcomes for New Zealanders, by informing government policy and decision-making. The value of data can be maximised by those who have suitable data and analytical capabilities to use it, and decision-makers who understand how the data can be used in their decision-making processes.

The importance of improving these capabilities is not new in the data system. However, it is becoming more relevant to a wider range of roles as more data becomes available and its value is more widely recognised.

The benefits of using the framework are:

identifying current capability gaps and strengths of individuals and teams
talent pool identifcation for teams and organisations
matching training opportunities to capability-building needs
identifying future capability needs for individuals and teams
career planning for roles within an organisation or across the government sector
identifying secondment opportunities within and between organisations.

Who is the framework for?

The framework is for managing capability at an individual, team, or potentially organisational level. Its intended users are everyone in central government agencies who needs to use data in their work. It may also be useful to people working with data in other organisations, such as local government, NGOs, iwi, or Māori organisations.

It has been deliberately created in a “broad brush” way so that it can appeal to and be used by as wide a data-using community as possible. It is not designed specifically for statistics and data professionals.

Framework structure

The framework has been constructed according to the data lifecycle:

Plan		The processes and resources are mapped out for the lifecycle of the data. The project’s goals are stated, and a full data management plan is created.
Collect		Data is gathered or generated by the individuals/ organisation wanting to use it.
Describe		The data is accurately described using the appropriate metadata standards.
Store		The data is stored in a digital repository, is made secure and reusable. This often very quickly follows collection.
Analyse		The data is analysed (that is, explored and interpreted).
Use		The data is used for the purpose for which it was collected or generated, and reused for additional value.
Save or Destroy		Actions are taken to safeguard the long-term viability and availability of the data.

What is in the framework?

The framework defines 25 capabilities that are associated with data use. They are grouped into the 7 categories of the data lifecycle.

Many of the capabilities naturally fall into more than one category. Depending on how you want to use the Framework, you can look at all the capabilities together in no particular order or look at each category and the capabilities that they contain.

Each capability is also described with 3 levels of skill or knowledge: “New”, “Proficient” and “Expert”.

The following example shows one capability with all the categories it is in, and the levels of skill or knowledge that can be reached:

Employ data coding and classification principles

Categories: Describe, Store, Use, and Save/Destroy

New	Is aware of relevant data classifications and coding protocols, and their proper application to data in general. Knows who to consult for expert knowledge.
Proficient	Has a comprehensive knowledge of data classifications and coding protocols. Knows where to obtain expert advice about coding and classifications as needed.
Expert	Is consulted regularly by others about data classifications and coding protocols. Can employ conceptual frameworks in support of data classification and coding.

How to use the framework

The following describes possible positive impacts the framework could have for an individual, a team or an organisation. Three particular contexts are identified with the possible impacts included in bulleted form, followed by a more detailed example involving an organisation.

In a strategic and future planning context

While exploring or confirming a strategic direction, data capability needs and gaps can be identified using the framework.
The framework can be used to create data talent pools and help earmark individuals to be thought or technical leaders.
Existing organisational capability frameworks can be aligned with the framework to create an expanded view.
Key data roles can be identified and succession planning processes enhanced using the framework.

In a performance and development context

Once individuals have used the questionnaire to assess their capability levels and their managers are in agreement, there are a number of ways the framework can enhance performance and development:

The framework capabilities can be incorporated into performance and development plans as training needs for “New” or “Potential” individuals or as mentoring goals for “Experts”.
Training can be targeted to improve specific capabilities for individuals or teams.
Changes can be made in day-to-day responsibilities to ensure an individual can practise an identified capability.
Career planning can use the framework capabilities to chart a particular direction, either so an individual can become more rounded in their data capabilities or so they can specialise.
Secondment opportunities could be identified within the organisation and potentially wider, as roles are assessed against the framework.

In a recruitment and onboarding context

Once an organisation or team has clarified what capabilities it needs and at what levels, recruitment can be more targeted and informative:

Role descriptions and advertisements can be more focussed and consistent in their language.
Advertisements can refer to specific capabilities that are required in particular roles.
Capabilities can be assessed against during the selection process, with questions focussed on drawing them out.
The framework could be introduced during induction, as appropriate, to create a common language and potentially a common set of expectations.

Case study / example of use

The following is a summary of how an organisation might use the framework to strengthen its data and information use.

Need

The organisation identifies that it is not making the best use of its data, in terms of its decision-making and impact. It recognises that its current data capability levels or future data capability needs remain unclear, which represents a definite risk.

Approach

The organisation decides to take a workshop-based approach, after first using role descriptions to confirm who should be in attendance (i.e. all of those who work with data and analytics, plus a small number of individuals in key policy and leadership roles).

The first workshop focuses on introducing the data capability framework to the workshop attendees via a series of activities. Attendees are encouraged to start thinking in terms of these capabilities when assessing their organisation.
The second workshop concentrates on three aspects of the data capability within the organisation:
a. How data capability should be used in the future, in 1-year and 2-year terms
b. How data capability is currently used that should stop
c. What barriers are in the way of achieving the desired future.

Desired state after one year

The resulting conversation reveals that, within one year, participants think the organisation should be clear on the existence, level, and location (i.e., who has them) of capabilities. Participants also agree that the organisation should be clear on how individuals/teams can develop capabilities, either from scratch or by increasing the current level.

It is recognised that the way data is handled needs to change. It is felt that data should be more readily available (while still staying secure) within the organisation, and that there needs to be better communication between those who create and gather the data and those who analyse and use it.

The final desired change is in how data is visualised. Workshop participants want an increase in this capability to improve how data is communicated and understood.

Desired state after two years

Participants are keen for the organisation to have a reputation for using data well. They want the appropriate technology and tools to enable data producers and analysts to collaborate fully and maintain best practice. They also feel it is important that all data users, not only analysts, are upskilled enough to ask good data questions and reach useful answers.
The third workshop focuses on the barriers and ways to overcome them. Perceived barriers include:
a. A lack of a technology roadmap that could support the need for better data technology and tools
b. Not enough capacity for the data storage needs of the organisation
c. Inadequate standardised rules for metadata and for data collection
d. Data illiteracy among the Policy decision-makers
e. No training/upskilling plan for everyone who works with data
f. No understanding or consistent practice of data stewardship by all those working with data.

Capabilities that are identified as vital in helping overcome some of these barriers include:
a. Describe and summarise data
b. Use data quality assurance methods
c. Improve data processes/systems/products
d. Value organisational data as assets
e. Visualise data.
Participants are brought back together in order to complete the framework questionnaire. This is in order to reach the desired state of knowing the organisation's current capabilities. It is acknowledged that many people may have capabilities that are not known or certainly under-utilised.

Results indicate that there are specific capabilities, already noted as vital to the future success of the organisation, that are lacking. The results also highlight that there are a small number of individuals who rate themselves as “Expert” in vital capabilities; this leaves the organisation potentially vulnerable should those experts leave.

Actions

It is agreed that managers will sit down with staff members who filled in the questionnaire to compare their views of the staff members self-rating. This is to reduce the risk of under- or over-estimating capability levels.
The development of a technology roadmap that includes data capability needs is approved as urgent.
Mentoring partnerships are set up so that knowledge and skill are shared, and risk reduced.
A plan is begun to look at how data can be made more available within the organisation, while staying secure.
Data storage needs are compared to current and future capacity.
Workshops are set up for both data custodians and data analysts to talk about improved communication.
Data capability development plans are created, and training providers sought to address gaps (e.g., data visualisation).
It is decided that data stewardship will be introduced as a discussion topic to the next round of quarterly organisational forums. Attendees will be asked to debate what role they think they play in data stewardship, in order to extend the understanding beyond those who immediately produce, gather, or analyse data.

Results

The organisation found that using the framework as a tool for exploration and identification was vital. The framework was able to surface gaps, strengths and needs in a way that was easily translatable in to action.

Glossary

Please refer below for the terms and definitions used in the framework:

Term		Definition
Administrative data		Data which is derived from the operation of administrative systems (e.g. data collected by government agencies for the purposes of registration, transaction and record keeping, which is then used for statistical purposes).
Classification		A way to group a set of related categories in a meaningful, systematic, and standard format, e.g., country or region.
Data assets		Data collected and/or sourced and stored by an organisation.
Data governance		Data governance is a collection of practices and processes, which help to ensure the formal management of data assets within an organisation.
Data sources		A place or system or service where data is obtained.
Exploratory data analysis		The analysis of datasets to describe their main characteristics, e.g., the distribution of variables.
Information management principles		Gathering data and then analysing, categorising, contextualising, and archiving (and in some cases, deleting) it, in order to support a business's needs.
Output		Analytical outputs may be graphs, charts, infographics, or reports with analytical content.
Processing Methodology		Statistical procedures used to deal with intermediate data and statistical outputs, e.g., weighting schemes, statistical adjustment, or methods for imputing missing values or source data.
Time series Forecasting		Use of statistical methods to predict future behaviour based on historical data.

Contact us

If you’d like more information, have a question, or want to provide feedback, please email datalead@stats.govt.nz.

Content last reviewed 18 September 2020.