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New Zealand - Data use and frameworks.

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New Zealand - Data use and frameworks.

  1. 1. Data and users. North Macedonia CRF
  2. 2. What our internal customers were saying 12 months ago 2 “I frequently have to chase the team up over email and phone to get an answer” “There are frequent delays caused by miscommunication of requirements as we do not have a common language to talk about the data” “Reports frequently contain more data than requested, which requires more effort to amend and format before providing to the customer” “It would be incredibly helpful to have self service access to a wider set of data so I can be more independent” “The application form asks for the same information multiple times” “Trying to get reports amended sends them back into a death spiral of ‘waiting for action’, ‘follow up’, ‘review’, ‘follow up” “It would be great to have a joined up view over different registers” “I cannot say with 100% confidence that any of the reports tell us everything we’re expecting” “We have requests dating back to June 2017 that we’re still trying to get resolved” “Reports get sent directly to the customer without me knowing”
  3. 3. Our data enablement journey 3 Our Data Strategy identified a number of data initiatives to address the identified gaps in capabilities that were preventing us achieving our mission. Collectively these initiatives have helped us address key business risks and become more customer centric. Current State IM Assessment Data Inventory Customer Demand Analysis Customer Engagement & Service Analysis Data Strategy Insight Driven Organisation Lab Data Privacy Framework Data Governance Data Quality Data Concierge Data Services Website Establishment Data Visualisation Capability Establishment Use Case Definition Use case PoC Development Completed In-flight
  4. 4. Data Inventory 4 To structure the Data Inventory and create a repeatable framework with definitions of data objects that can help us talk about our data consistently we have created Data Domains & Sub-Domains. Data Domains are mutually exclusive, and collectively exhaustive high level objects of data that we possess. The Sub-Domains represent the Key Data Elements of data that make up each Data Domain. Collectively they provide a silo and data source independent view of the data that is digestible by customers and business users to help them engage with our data. Data Domain Definition Registered Entity A unique object registered within a Business Register. Role Information related to a function or position within the entity. Event A historic record of transactions, filings and events that have occurred since the entity was added to the register. Authority Information about the individual or organisation that has been granted authority to maintain an entity. Internal Data Data created or captured to facilitate, support or manage registry processes. Product Information about the products consumed by internal and external clients. Customer Information about the customers consuming our products. Asset Asset based information related to a registered object. Registered Object Domain Sub Domain Definition Example Registered Object A unique object registered within a Business Register. NZ Limited Company, Financial Service Provider, Sole Trader. Unique ID The register ID Company Number Name The registered name visible on the public register. This may also include any trading names. Example Company Limited NZBN New Zealand Business Number 9439123456789 Status The status type of the registered object on the register. Registered, In-Liquidation, Removed Contact Details The primary contact details for the registered object. Address for Communication, Email Address The specific address of the chosen address type. Registered Office Address: 135 Albert Street, Auckland, 1010, NZ Address Type The type of address. Different address types serve different functions for the registered object. Registered Office Address, Address for Service Type The type of registered object prescribed within the legislation. NZ Limited Company Compliance Information Information related to compliance or filing duties. Annual Return filing month Registration Date The date that the object was registered on the register. Incorporation Date Deregistration Date The date that the object was removed from the register. Removed Date Other Agency DataData supplied by and/or belonging to Other Government Agencies. IR Data collected upon company incorporation
  5. 5. Data Privacy The Data Privacy framework classifies the privacy of the data in the recently created inventory. This classification, in conjunction with the customer segment, identifies the privacy agreements/conditions that must be in place/satisfied in order to obtain the associated data. 5 Privacy Classification Privacy Agreement/Condition Internal (MBIE) Public Govt. Agency A: Public Data No restriction ✔ ✔ ✔ B: Restricted Data1 OIA ✘ ✔ ✘ Privacy Act ✘ ✔ ✔ Statutory Powers to obtain Information ✘ ✘ ✔ MOU ✘ ✘ ✔ AISA ✘ ✘ ✔ Authorised user ✘ ✔ ✘ Business Decision (Internal Users) ✔ ✘ ✘ C: Confidential Data2 Directors : - Date of Birth - Place of Birth Limited Partners: - Names - Residential addresses - Date of Birth - Place of Birth OIA ✘ ✘ ✘ Privacy Act ✘ ✔ ✔ Statutory Powers to obtain information ✘ ✘ ✔ MOU ✘ ✘ ✔ AISA ✘ ✘ ✔ Authorised user ✘ ✘ ✘ Business Decision (Internal Users) ✔ ✘ ✘ 1 Restricted data means data which is collected or generated/captured by the Companies Office in relation to the registry process for an entity but is not viewable on the public register 2 Confidential information means director date and place of birth which is collected under the Companies Act 1993 and Limited Partner names, residential addresses, date and place of birth which is collected under the Limited Partnerships Act 2008 and the release of which is subject to particular statutory restrictions.
  6. 6. Data Quality 6 The quality of our data assets has a direct impact on the value customers are able to derive. Currently we are reactive in way we address Data Quality issues and recognise that this approach has the potential to undermine our mission. To address this challenge we are in the process of implementing a systematic and repeatable process that is supported by a right sized Data Governance function. This will provide us with the capability to increase the value of our data and build the confidence of our key stakeholders. Remediation • The capability and processes to facilitate the remediation of data assets to the agreed standards Governance • The function to facilitate organisational decision making on the options, standards and priority to remediate issues • The capability provide ongoing monitoring and drive performance against agreed data quality standards over time Identification • Provide the channels for stakeholders to raise data quality related issues for investigation • Provide the technology to help us profile the data and better understand the issues raised by the business. Triage • Establish the capability to analyse and evaluate issues raised • Create the frameworks to assess the business impact and priority for remediation • Processes to identify and communicate the options to remediate
  7. 7. Data Governance 7 Managing our data as an asset is paramount to delivering our mission and supporting the wider organisational vision. Currently our operational data related decision making can be ad-hoc, inconsistent and untimely – which does not enable the outcomes we are looking for. To address this we are in the process of establishing a right sized Operational Data Governance function. This will provide a forum to facilitate project and BAU data related decision making in coordinated and structured way to maker higher quality decisions around the management of data and organisational resources. Cadence • The monthly operating rhythm and phased introduction of the function to stakeholders • Training and education on the purpose and role of the function Charter • Defining the mandate, focus, guidelines and resources supporting the Operational Data Governance function • Principles to guide the establishment of the function and group decision making • Model to define the interactions between the governance roles Artefacts • The templates, frameworks and registers to manage the processes and outcomes of the function Processes • The organisational processes to support the governance function including the ongoing monitoring of data quality
  8. 8. www.companiesoffice.govt.nz
  9. 9. Our process for turning ideas into action 9 A high engaging, business value focussed and repeatable process has been established to turn ideas into repeatable industrialised insight provisioning solutions. The process is focussed on the identification of high value uses cases where registry data can support the customers mission. Once identified a model is then collaboratively and iteratively built, tested and refined to meet the success criteria of the use case and then industrialised. Identify Key Business Questions Prioritize Proof of Concept Canvas Delivery Action Plan Build, Test, Refine Deploy
  10. 10. IDO IET use case a) Users can filter by FSP ‘change’ type. c) Ability to zoom in on ‘hot spots’ of interest using geo- location. b) Key statistics change dynamically to show the number of associated Companies, Directors and Authorities. This dashboard provides IET with a comprehensive view of investigation candidates that would normally fall under the radar.
  11. 11. What they are saying now! 11 “Being able to reuse previously created reports has saved me time” “The request form has a cleaner look and is more intuitive to complete” “The privacy classifications have prompted me to second guess whether it is appropriate to request certain data on behalf of external clients” “The functionality to view all the data elements available has enabled me to request additional data that I didn’t know existed which would be useful for my reports” “The data concierge not only provides a simple and quick method to raise reporting requests, it more importantly nails ‘transparency’ which was a real problem before” “To me it feels like somebody has finally turned on the lights….. Before the data concierge tool making a request was like firing a shot into the dark and hoping for the best” “It promotes transparency of both the data we have, that I may not have considered, as well as ‘transparency’ of where the delivery of my inflight reporting request is currently at”

Notes de l'éditeur

  • My name is Karla Flood and I manage the data and applications team within the companies office. I want to talk to you about our reusable frameworks that we have created to allow us to better understand our data assets and make our data more accessible and transparent.
  • The companies office is a custodian of a large wealth of data assets that play an important role in supporting economic growth and market fairness. As an organisation we wanted to understand our data better and make it more available and transparent. We wanted to create one place where people can access our data and understand what data assets we are responsible for.


    We started out journey by talking to our customers at times were experiencing a lot of frustration around the services we were providing.
    A lot of this was to do with the lack of transparency of our data combined with ineffective of non existent formal processes
  • HIGH
    Our journey of enablement has involved a number of data related initiatives.
    These initiatives have in turn enabled others – for example creating a data inventory has enabled a number of downstream initiatives such as the privacy framework, data concierge platform and data governance.
    Collectively these initiatives have markedly increased our maturity and capability to not only better manage our data, but to turn it into insights and value for our customers.
  • MEDIUM

    At the start of our journey we did not have visibility of all our data assets and were unable to promote greater transparency of our data, and identify opportunities to further enrich the value of the data and products to customers.

    To structure the data inventory and create repeatable frameworks with definitions that can help us talk about our data consistently we have created data domains and sub domains. Essentially these are high level categories that describe our data. Within each domain are a number of sub domains that describe the key data elements associated with each domain.

    This has enabled us to logically group large volumes of data across the registers so we have a common language and structure around our data. This has enabled us to classify our data in terms of privacy, data quality and priority which has been fundamental in enabling our data concierge platform, data quality and other analytical initiatives.
  • MEDIUM
    Ensuring the privacy of our most sensitive data whilst promoting open and transparent sharing is central to our mission. This framework has helped manage the complexity of data privacy in a really simple way that helps guide the decision making of the team. In addition to the data categories, we identified the exhaustive list of avenues that each customer group has to obtain each category of our data

    This has reduced business risk around the sharing of data assets
    Confidence to leverage process automation and self service to meet customer demand.
    Visiblitly over what we can share openly or not.

  • MEDIUM
    We have just started our journey to building the capability to proactively manage the quality of our data.

    We have established the technology and capability to identify business rules, profile and report on the quality of data.

    We have stood up an operational data governance function and supporting processes to provide the ongoing focus and decision making required to uplift the quality of our data over time.
  • HIGH
    The operational data governance function that provides the forum for us to more effectively manage change, data related issues and decision making in a consistent, timely and focused manner.

    We have created a charter that defines the mandate, focus, guidelines and roles & responsibilities of team in supporting the function. And option papers where our stakeholders can submit to the group any data quality issues

    We are now in the process of finalising the supporting processes, artefacts (options papers, issue/decision registers and monthly cadence etc).
  • Now that we understand our data we are able to use our data to support stakeholders problems.

    This process comes from the Deloitte global Insight Driven Organisation (IDO) framework.
    The ability to industrialise the process of taking an idea and transitioning it to an enterprise product whilst ensuring real value is achievable by our customers will enable us to become real partners – we can transition from being suppliers of data extracts to providers of data insights!
  • The problem statement that the Companies Office Integrity team asked us to help solve or draw insights from our data.

    It is currently very time consuming to identify Financial Service Providers that have been registered as a result of false or misleading information for example a Financial Service Provider registered appearing to be a low risk entity, then the entity amends their registration post registration which would categorise them as a high risk entity.

    Joining the Companies Data with the Financial Services providers data allows the Integrity team to view any other associated companies of directors.

  • And finally one year down the track we have checked back in with our customers to ensure that the voice of the customer was driving our data initiatives
    It is still early days but the feedback we are receiving from our customer is overwhelmingly positive
    We still have more work to do, but it great to hear that the work we have been doing to date is having a real impact on our customers!

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