Over the past several months, I have spent much of my time working with individuals, government agencies and companies, trying, primarily, to understand the requirements published in the THE NDIS PROVIDER TOOLKIT to successfully transfer from the current model to the new NDIS, mainly in the process to assess Records and Data Management, Data Collection and Storage, Data Reporting and Use and Safeguarding, Quality Management and Improvement. These titles can be in the found in the NDIS PROVIDER TOOLKIT under INFORMATION AND KNOWLEDGE MANAGEMENT and SAFEGUARDING, QUALITY MANAGEMENT & IMPROVEMENT.
The Toolkit is structured around seven business domains that are encouraged to use in the approach to positioning organisation for the NDIS. It is geared to organisations whose primary business is disability and is also applicable to multi-service providers. In this series of 3 our first post main focus is the Records and Data Management followed next week Data Reporting and Use.
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Data Management is the vast array of tasks that begin before data collection and continue throughout the lifecycle of the data. From data program conception through the lifecycle of the data, good data management requires consistent, well thought-out and universally supported procedures and guidelines for its collection, maintenance and dissemination.
Why we care about data management
- Need for a sustainable, efficient system for registration and follow up on individual clients.
- Need for better data gathering system and analysis for evidence based policy making.
- To assist the Government bodies in monitoring the quality of social services.
- Improve the data on clients and services.
- Improve the monitoring system and data analysis at the central level.
- To analyse trends on certain social issues.
- To better monitor the workload of the NDIS or any other program.
- Better implementation of laws, regulations and standards.
Data Management Includes Data Resource Development
The data resource is the centralised data repository used for storage and access of company data.
Data management tasks for developing and maintaining the data resource include:
Identification of data to be included in the data resource
Designing the data models
Designing data element naming/format standards
Complying with hardware/software standards
Performing routine and disaster recovery system administration
For the purposes of compliance with ethics and data storage policies, ‘data’ means ‘original information which is collected, stored, accessed, used or disposed’.
Data management includes all aspects of data design and collection…
Identifying any additional information that will be required to make good use of the data
Developing clear, concise manuals and instructions for data collection
Designing user-friendly paper and electronic forms for data capture and entry
Building data validation schemes in data entry applications
Maintaining data set documentation and history
Data is the basis of Information, Knowledge, and Wisdom.
Data Management Includes Data Maintenance
Data Maintenance is an ongoing process that ensures the highest quality data are available at all times.
Data management tasks for developing and maintaining the data resource include:
Storing data in the centralised data resource
Detecting and reporting errors via data monitoring applications
Maintaining a history of when and by whom data are added, modified or deleted
Periodically auditing data by tracking data from collection to dissemination via the data path
Developing and following formal change control procedures
Developing and documenting data processing procedures
Developing and testing applications before release
Data collection and management aims to maximise efficiency of staff and resources, ensure the collection of accurate and reliable data, and focus on careful management of data once they have been collected.
Data Management Includes Data Dissemination
Providing user support via training and problem resolution
Complying with data accessibility issues
Ensuring data availability and ease of integration with other data sets, as needed
Establishing data security
Complying with data publication formats and deadlines
Managing your data …
- Ensuring physical integrity of files and helping to preserve them.
- Ensuring safety of content (data protection, ethics, morality, etc.).
- Describing the data (via metadata) and recording its history (provenance).
- Providing or enabling appropriate access at the right time, or restricting access, as appropriate.
- Transferring custody at some point, and possibly destroying.
How Is Good Data Management Achieved?
The actual methods for fulfilling the data management policy are defined in the Data Administration Guidelines. These guidelines define how you are going to implement the Data Management Policy. Where the Data Management policy might not change for a number of years, the Data Administration Guidelines will most likely be revised and refined on a regular basis.
Data Management Policy
Policy for the management and protection of agency data.
Set of broad, high-level principles forming a framework in which data management can operate efficiently and effectively.
Data management policy is a short, clearly written statement or outline of the organisation’s philosophy, vision and goals for management of data. This policy applies to the entire organisation, not just the IT/IM role groups and as such must be written in clear non-technical language. It should be inspiring, not threatening. The policy defines what you want everyone in the enterprise to accomplish.
Introduce a Data Management Plan
There are several ways to think about a data management plan:
A document that is created to manage the data in your company. This is a ‘living document’ that is designed to evolve over time. It would cover the following topics: Data source(s); Data collection, creation and analysis; Data administration; Data sharing; Archiving; Data documentation and metadata; and Budget. A typical data management plan, or handbook, might be as large as 50 pages. It would serve as a resource for all members, and could be used for training new members.
A document that is created which describes the data to be collected, probable sizes and formats, collection and analysis methods and tools, software, instruments, processes, workflows, and storage and sharing options.
A Draft Data Management Policy
Programs that generate data will adhere to data management policies and guidelines.
Data and metadata will be managed and stored in a centralised data resource.
The data resource will be safeguarded and protected.
It is important to note that in today’s world, each one of these tasks cannot be executed in isolation from the rest. A data form cannot be designed without input from the manager or even a operational manager. The data steward cannot decide to add additional fields to the data set without consulting with the person training the data collectors and the person writing the data collection manuals and probably every role group down the data chain.
Meet Datanova’s FlowLogic a cloud based Data Management Solution for Service Providers
Good data management is fundamental to our success. For some, it will require a new way of thinking: Enterprise Thinking.
Datanova has designed and created an extremely flexible product from the ground up. A cloud based solution that works with you, communicating with the power of collaboration. Every unique process within your business can be supported by FlowLogic’s powerful workflow engine. FlowLogic also gives you powerful reporting and analytics that ensure that you can measure the efficiency and productivity of all of your clients interactions whilst ensuring Government compliance.
About Datanova: Over the last 12 years Datanova has gained a wealth of experience working with various social service providers. All have varying requirements and localised approaches to executing and delivering care. Our collaborative approach to developing our cloud based data systems in conjunction with our end user community means we always evolve our systems with the direct input of the industry. Creators of FlowLogic a Case Management Solution for Social Services CRM.
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In Part Two of this series of articles we take a look at Safeguarding, Quality Management and Improvement and encourage readers to return to explore more information to a safe and sound transfer or…SAFEGUARDING, QUALITY MANAGEMENT & IMPROVEMENT.
Brohan, M., 2001, The Need for a Formal Data Management Policy, DM Review, v. May. http://www.dmreview.com/.
Flanagan, T., et al, 1998, A Practical Guide to Achieving Enterprise Data Quality. http://www.techquide.com.
Imhoff, C., 1998, Ensuring Data Quality Through Data Stewardship, DM Review, v. Apr. http://www.dmreview.com/.
Imhoff, C., 1997, Data Stewardship: Finally a Process for Achieving Data Integrity, The Data Administration Newsletter. http://www.tdan.com.
Intra-governmental Group on Geographic Information, 2000, The Principles of Good Data Management. http://www.detr.gov.uk/.
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