Asset information workshop. Julian Schwarzenbach Project Lead

Transcription

1 Asset information workshop Julian Schwarzenbach Project Lead

2 Agenda Structure of the Handbook Data quality attributes and how to measure them Data quality solutions Data governance Participative session Successes Horror stories

3 Structure Policy Assess current position 4 Solution identification Option selection 5 6 Monitoring Governance Information strategy Deliver improvements 7 Sustain improvements 8

4 Data quality attributes Accuracy The record is correct in all details and is a true record of the entity it represents Completeness The record having all or the necessary attribute values relative to its intended purpose. Additionally, all entities of a particular class or type are recorded Validity Data conforms to all standards expected Consistency An entity that is represented in more than one data store can easily be matched Uniqueness A single record exists for each physical entity Accessibility Data is easily accessed when required

5 Assessing current position Current state analysis Information availability Data quality requirements Managing data quality at the correct point Data confidence grades Toolbox (see next slides)

6 Accuracy Is the existence of the asset correctly recorded? Are any assets recorded which do not physically exist? Is the location of the asset correct? Are attributes of the asset correctly recorded? Are relationships to other assets correctly recorded? Is the asset history correctly recorded? Is financial information about the asset correctly recorded?

7 Completeness Have I got a record of the asset? Is its existence recorded? This aspect of completeness is inherently harder to quantify Have I got data for all the relevant asset attributes? Have I got a record of past values of the data for this asset? Do have I have records of time varying values e.g. hours run or current temperature? Do I have past history of asset data when an asset has been relocated?

8 Validity Does data match business and database rules? Data profiling can quickly assess large volumes of data Two example checks that could be undertaken are: Postcode data would be expected to conform to one of six recognised formats, such as the format aann naa A customer reference number has a set pattern of aaa-nnnnn-an, you would check that all records follow this pattern

9 Consistency Check that entity identifiers are consistent from the master system to other systems Check that the entity is clearly represented in any common crossreferencing tables, data mastering or MDM systems Check key attributes are consistent between systems

10 Uniqueness Check that there is only one record for each entity or asset Check that the same entity is not recorded more than once using different names and identifiers Checks for assets that are not recorded should be identified in completeness checking Check that the same identifier is not used in different systems to refer to different assets Check classification and coding systems to ensure that identical or similar codes for the same thing are used Checking for unofficial data stores which may duplicate corporate systems

11 Accessibility Can the data be found when sought? When the data is found, can it be made available to users in acceptable timescales? Do all users have access to the data required to do their jobs? Do all potential users know of the existence of the data? Do all systems requiring access to the data have access in acceptable timescales?

12 Solution options Data quality framework Do Nothing is a valid option Developing and improving standards for asset information ensures there is clarity of information requirements Site surveys can be used to gather missing data or to validate data Expensive activities Time consuming Plan and execute carefully Improvements to data validation ensure that invalid data is not entered Data infill techniques can fill gaps in data without asset surveys It is important that data gathered in this way is clearly marked as such! Technology and system changes can improve the management and use of asset data Organisation changes to manage data update activities better Changing the culture and behaviours towards data Can require ongoing and sustained effort to achieve

13 Option selection There is no silver bullet! Benefits of data improvement activities are typically diffuse and fragmented Time needs to be spent to identify and quantify all relevant benefits The costs of data improvement should be estimated as accurately as possible and should include: Software costs Costs of improvement Training costs Ongoing costs to sustain quality Business cases should express the costs and benefits of improving data quality including assessment of payback periods and rate of return

14 Monitoring Ongoing monitoring of data quality and processes is essential Agree the key measures to monitor Targets should be set to encourage improvements in performance Targets should be SMART, i.e. Specific, Measurable, Achievable, Realistic and Timely Assurance methods should be developed to encourage compliance Presentation of measures should allow key messages to be easily identified Benchmarking of other organisations can provide an awareness of areas of strength and weakness Suitable audits of both data quality and data related processes will help identify any deviations from planned approaches

15 Data governance Data Architecture Management Data Quality Management Data Development Metadata Management DATA GOVERNANCE Database Operations Management Document and Content Management Data Warehouse & Business Intelligence Management Reference and Master Data Management

16 Data governance Data Governance essential for managing the quality of asset data Board-level sponsorship is key Strategy vision of where the organisation wants to get to and how Legal and business requirements some data quality requirements may be externally specified Roles and Responsibilities defined, staffed and appropriately motivated/incentivised Training/competence Written policies, plans and procedures Knowledge Management Governance processes to drive data quality forwards and prevent any fallback Continuous improvement incorporating lessons learnt

17 Information strategies Information strategies and enterprise architectures will not directly improve data quality Will provide greater awareness of the scale and nature of problems and optimum solutions Information architectures provide data models, data dictionaries and metadata Assess data as an input, output and enabler of a process Greater awareness of quality requirements can be developed Develop a clear strategy for the sources of master data All other systems using this data should replicate this key data Master Data Management (MDM) can be utilised where data mastering strategies are more complex IT project requirements help ensure they don t degrade data and provide enablers for ongoing data improvement

18 Successes and horror stories Over to you

19 Thank you for your time