This article provides some good tests to assess the data used in your organisation for decision making and highlights introducing Data Management as a discrete activity into the void space and using pre-built tools to automate the journey.
Data Management Void
Optimistically in 2006, Humby coined the phrase “Data is the new oil”. … (Oil) has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so, data must be broken down and analysed for it to have value.”
By 2020 Accenture were advising us to “think of data as water, water is everywhere……we are often drowning in data because we have yet to figure out how to harness the vast sea of data effectively… (and) we treat data as an accessory to decision-making.”
The first quotation seems empirically true and the second quotation implies there is something missing from the process of controlling or dealing with this thing called data. So whether it is a data lake or a data swamp, is there a Data Management Void in housing?
| Integrity of data is fundamental and permits good board decision-making. Failure to manage data integrity risk is indicative of a poor control framework….. |
| The Regulator for Social Housing, England, 2020 |
| RSLs should consider phasing out any data storage/compliance systems that are easily corruptible and can result in inaccurate data, for example spreadsheets. |
| Landlord Health and Safety, October 2018, Registered Social Landlords in Wales |
| The governing body bases its decisions on good quality information. The governing body and senior officers have the skills and knowledge they need to be effective. |
| Standards 4 and 6, Scottish Housing Regulator, 2019 |
Why are all the Regulators on about data management and how do they know there is a problem?
If governance grading is under review, is there a problem with the source of the data, how it has been transformed or how it is being interpreted. Or probably a combination of all the above.
There is a lot of talk about digital in housing and lot of new sources of data from Apps, from tenants and increasingly from devices. Our lives are becoming richer with data and organisations are optimistic about how this data can be used to improve the services to tenants, do more with less and target investment to where it is needed.
However, the 2019 Public Accounts Committee Report (Challenges in using data across government September 2019) reported that data has not been treated as an asset, and that it has become normal to ‘work around’ poor-quality, disorganised data. Does employing work-arounds suggest that we could do better at Data Management?
For example, the data fields in existing core systems might not have contemplated some of these new data sources, new requirements or staff in different departments might have a different understanding of definitions e.g. do we all mean the same thing by 100% compliant or indeed “void property.”
The first Data Management recourse when the Board needs data urgently or new questions need answering is, still, typically a spreadsheet which provides the operator their own private “single version of the truth.” Unfortunately, this data is almost never automatically reconciled against the other at least 12,000 data points circulating in the organisation.
Reconciliation might happen in preparation for Board KPIs or the Quarterly Survey, Financial Forecast Return or Statistic Data Returns and can start to highlight issues when the “single version of the truth” appears to have more than one version. This is the same source of data that is used for operational decision making.
The next recourse when the limitations of current systems are revealed by the quantity of VLOOKUPs in the organisation is to look at new operating systems that reflect the increased complexity, new linkages between systems (which also increases complexity) or new reporting systems.
These are typically significant undertakings that take a lot of time, expenditure and project management. In the meantime the business problems keep piling up: customers want new things, data keeps getting entered, transformed, deleted and those IOT projects really are getting closer to harvesting vast quantities of data.
But do these solutions, dependent in some cases on data migration solve the “data management void” or just create new voids.
To bridge the data management void, I think we should be talking more about Data Management as a discrete activity.
The Data Quality Framework which was launched in December 2020 by the Government Data Quality Hub is pushing a similar agenda across government:
“Getting data quality right means a lot more than data cleansing. It means knowing the quality of data, sharing that information with others and taking the right action to address problems.”
It also includes some good tests to assess the data used in your organisation for decision making. Remember as an organisation there might be 12,000 individual data points that are being managed across the swathe of activities in Social Housing all of which will be manipulated and processed to provide the regulatory reports and Board KPIs.
Just for fun, ask about the following Data Quality framework dimensions of any report or data that is presented:
• Completeness – does it contain all the expected records or are any missing e.g. are we 100% compliant about the cases we know about but there are some we don’t know about, will empty fields mean asset management plans don’t reflect investment over the next 30 years.
• Uniqueness – How often is the same piece of data being used or stored elsewhere in the organisation e.g. do different departments have different lists of properties.
• Consistency – The extent to which data is stored in the same format e.g. can I compare the list of properties in finance to that of operations or are the formats incompatible on the different systems.
• Validity – Is the data in the expected format (e.g. are dates DD/MM/YY or DD/MM/YYYY or worse MM/DD/YY).
• Accuracy – often the human factor in the problem. E.g. Do we have any tenants living at 1 Street Name in a house that was built in 1900 etc
• Timeliness – How long has this data been held, when was it last updated or checked.
The framework seems like a reasonable manifesto for Data Management as an activity rather than a void space in Housing. The first step is often to understand the As Is or the current level of Data Maturity in the organisation i.e. if you can measure it you can improve it.
As a quick test have a look at the figure below to see if you are off the first step of your journey or not – there are 5 levels to ascend…:
| Maturity Level 1: Unacceptable |
| We do not consider data quality when creating data or commissioning new IT. |
| We do not measure or report on data quality. |
| We have no plan to improve data quality. |
| Key staff do not have the right skills to monitor and improve data quality. |
| We do not consider the confidence that we have in our data and information. |
Source: Environment Agency data integrity maturity model.
Obviously the first step in any journey is knowing where you are. Fortunately for the second and subsequent steps in that journey there are ways of following fast. There are organisations who are already down this route. They are introducing Data Management as a discrete activity into the void space and using pre-built tools to automate the journey.