It is well known that high quality data is essential for effective policy and decision making, underpinning your strategic outcomes. Utilising poor quality data, including data that is inaccurate, incomplete, contradictory, or out of date, is using data that is not fit for purpose. This can not only result in operational inefficiencies, leading to reputational damage but ultimately may attract the attention of the regulator and the prospect of fines and penalties. Trustworthy and high-quality data will improve efficiency, help mitigate risk, foster innovation and enable you to have confidence in your decisions.
Numerous organisations in the sector routinely use data that doesn’t integrate, whilst inconsistencies in how data should be formatted, especially in open text fields, introduces more potential for inaccurate data to be recorded. Furthermore, data is always changing, and even over a relatively short time much of it will have altered.
However, a solution to address these wide-ranging poor-quality data issues is readily available from 3C Consultants Ltd, allowing you once again to place trust in the data on which you will base your decisions moving forward and help alleviate your compliance anxiety.
Snapshot Data Audit
We will use our own software, 3C Data Logic, to conduct a snapshot data audit on one business area (property data is used in the example below). The business area you choose can be any one from: properties; customers; tenancies; property attributes/components (stock condition etc); compliance data (FLAGEL); repairs and maintenance; rents or service charges.
Stage 1
We will conduct a 2-hour workshop with your subject matter experts for property data to determine:
Subject matter experts – these are your colleagues that are primary users of property data, for example colleagues from asset management, compliance, repairs and maintenance, and customer services to identify the data sets to be reviewed and to identify any areas of concern with data quality.
We will now need you to provide csv files containing your property and asset data. We will need this for each asset data source identified during the workshop with subject matter experts.
Stage 2
Using the outputs from the workshops and our existing knowledge of common asset data quality issues, we will configure the validation and data rules required within our own product 3C Data Logic. We will assess your data against the data quality dimensions of:
Accuracy - how well does a piece of information reflect reality? For example, a postcode may be in a valid format but is not the correct postcode for the address, or do you have properties with 11 bathrooms?
Completeness - does the data fulfil your expectations of what is comprehensive? For example, you may require each address to have the county entered.
Consistency – does information stored in one place match relevant data stored elsewhere? For example, if the number of bedrooms in a property is stored both in your housing management system and your asset management system, are these values the same. Are there garages with bedrooms?
Timeliness - is your information available when it is needed? For example, are new properties available on systems when colleagues need them?
Uniqueness - Is this the only instance in which this information appears in data sources? For example, are there duplicate addresses in your property information?
Validity - Is information in a specific format, does it follow business rules? For example, do particular property types have a valid entry for number of bathrooms? Are postcodes valid? Are property inspection dates after build dates?
Accessibility – Is the information that colleagues need easily available to them when needed? For example, data may only be held in a spreadsheet, rather than within the master data source of a housing management system, and therefore is not accessible to all colleagues when needed.
Relevance – is the information you are collecting of relevance to your business? For example, do you have disposed property information being held for longer than your data retention policy stipulates?
If asset data is held in more than one place, we will also include cross-system validation.
Stage 3
A 2-hour workshop with subject-matter experts, data owners, data analysts, to interactively demonstrate the data quality issues that have been found in 3C Data Logic and enabling you to look at any other issues you suspect may be present. This may result in us configuring additional data quality rules for you.
This will then be followed by a 2-hour basic training session to enable you to access and explore your data yourselves.
We will produce a data quality assessment report addressing data quality management arrangements, the existing asset data quality and detailed information as to the asset data errors. This report will also include the improvements required and the recommended actions.
Cost
Prices for this snapshot data quality audit start from £2,975 plus VAT.