SECTION A: Data maturity and whether it is positioned as IT or strategic business imperatives
We want to understand how mature public sector thinking is around data and whether it is still seen as an IT imperative or business strategy. Including which areas are being developed / being established. "
1. Do you have a policy/strategy on how you use your data? (Outside of data protection and data privacy covered under general legislation) "(select one)
a) We have no strategy around data
b) Our data strategy is covered by the IT strategy
c) Our data strategy is an essential part of our IT strategy and clearly supporting our organization’s goals
d) Our data strategy encompasses all of our departments and is aligned to our organization’s goals
e) The data strategy is a key part of business model & company strategy
f) Our data policy is covered by the trust or wider departmental organisation we belong to"
2. (If yes) What considerations are included in your data policy/strategy? "(scaled based on maturity - from Well established, Working on, No intent to include)
Data sovereignty
Data interoperability
Data security
Data skills
Data availability
Data ethics and responsibility
Inter-departmental/agency collaboration
Citizen outcomes / Tax payer value
Data retention"
3. Do you have a dedicated resource aligned to your data initiatives (excluding data protection)? "(select one)
a) There is no specific budget for data initiatives available
b) Some data initiatives are covered by IT budgets
c) Budgets for specific data initiatives are managed in the IT department with contributions from other departments
d) There is a cross-departmental budget for our data strategy which is provided centrally
e) The executive office decides on the data strategy for our whole organization, including strategic budget allocation
"
4. Do you have these roles within your organisation? "(options - Yes, No, Partially, Plan to hire)
a) Data analyst
b) Data scientist
c) Data engineer
d) Machine Learning engineer"
5. Do you outsource these roles? "(options - Yes, No, Partially, Plan to outsource)
a) Data analyst
b) Data scientist
c) Data engineer
d) Machine Learning engineer"
6. Who is responsible for setting your data strategy? (Understanding how senior the ownership is within the organisation) "(select one)
a) Level 1: CEO
b) Level 2: CDIO, CIO, CTO, CxO
c) Level 3: Senior Manager (Director/VP)
d) Level 4: Delegated to departmental/line managers without organization-wide responsibility
e) Level 5: Implicit throughout the organization"
"SECTION B: Understanding of location of data
Understanding the maturity of data storage – level of hybrid consideration. Also what considerations are made from where and why data should be stored"
7. What percentage of your data is stored: "(scaling percentage 0%, 0-10%, 10-25%, 25-50%, 50-75%, 75-100%, NA)
a) On-premise
b) Private Cloud
c) Public Cloud"
"8. What percentage of your data is stored:
" "(scaling percentage 0%, 0-10%, 10-25%, 25-50%, 50-75%, 75-100%, NA)
a) With UK providers
b) With USA providers
c) With European providers
d) Other"
9. Post brexit will your organisation store more data in the UK or less data in the UK? "(select one)
a) More
b) No change
c) Less"
10. When developing your data storage strategies what are your main considerations? "(Scale on importance)
a) Data sovereignty
b) Data gravity
c) Data security
d) Storage latency
"
11. What is the maximum period you would hold data for? (If there are varying period for different types of data, please provide more information) (free text)
12. What is the process for identifying obsolete data in your organisation? (free text)
"SECTION C: Using data
How embedded data is into decision making, level of maturity. Is data still seen as IT or wider business strategy? How much value is currently being derived from data? "
13. How mature is your architecture in managing your data end-to-end, from ingestion / generation to final consumption? (**Need a descriptor to define mature) "(select one)
a) Our data is only available in the respective application
b) We capture and share data from multiple applications through data warehouses
c) A central data lake holds all of our data at rest
d) A central data hub connects data producers and consumers for both analytics and operational use cases, and holds both real-time data and data at rest
e) We have a central data hub which also includes external data sources
f) We put everything into a data lake and then classify into data warehouses"
14. At what level does data add value to your organisation? "(select one)
a) Application
b) Team
c) Departmental
d) Organisational
e) Cross-organisational"
15. What level of analytics methodologies do you master and support? "(two boxed answer – currently use, currently master)
a) Spreadsheet-based analytics
b) Business intelligence/canned reporting
c) Statistical analytics and machine/deep learning on big data at rest
d) Statistical analytics and machine/deep learning with real-time data
e) Formal methods such as CRISP-DM, KDD or SEMMA"
16. How far does your analytics architecture span across your organization? "(select one)
a) Our analytics remains local and/or within application boundaries
b) Our analytics use cases can include data from multiple applications
c) Our analytics use cases can consume data sources from all relevant applications
d) In addition to consuming data sources from all relevant applications, our analytics use cases also consume real-time data
e) Our analytics use cases consume data from data warehouses/data lakes,
f) In addition to consuming real-time data, our analytics use cases also leverage external data "
17. What are the biggest obstacles in your organization to creating value from data and implementing a data-centric strategy? "(select all that apply)
a) Our top management does not see data value creation as a strategic priority
b) There are cultural barriers to adopting a data-centric approach
c) We don’t have enough budget
d) We don’t have the required skills
e) There is no clear accountability for data strategy, governance and processes
f) We don’t have access to the required technologies, tools and platforms
g) We struggle to access external data in an efficient and trustful way
h) Regulatory uncertainties with regards to data ownership and data privacy
i) We don’t have enough data
j) Our data quality is too low
k) Our data is buried in silos
l) We have no operating model for managing AI solutions in production"
SECTION D: Open Data/Data Transparency
18. Do you have a policy for open data or transparency data "(select one)
a) Only an open data strategy
b) Only a transparency data policy
c) Both
d) Neither - do not have any
e) Under an existing IT or data strategy
e) In the implementation process or drawing up a strategy"
19. Do you outsource your open data service? "(select all that apply)
a) We outsource our open data service
b) We use data.gov.uk
c) We use a local company to host the data
d) We use internal software"
SECTION E: Data Standardisation
22. Do you have definitive protocols for managing your data in terms of… "(select all that apply)
a) Lineage
b) Provenance
c) Heritage d) Not sure