Enterprise software specialist AMT-Sybex, part of Capita plc, has flagged up six key risks for water quality data in the run-up to the opening of the competitive marketplace in 2017.

According to AMT-Sybex, as the deadline looms ever larger, the release of the latest version of the Market Architecture Plan (MAP)by Open Water at the end of last year was a positive step. Crucially, MAP2 includes a first draft catalogue of the data flows that water companies will need to be able to support by the “shadow launch” of the Open Water market in October 2016.

The software specialist says that although the water companies still cannot count on the final requirements being identical to those listed in MAP2, at least the catalogue provides a rough benchmark that they can measure their existing data against.

Six key risks for data quality

The firm is already advising several leading UK water suppliers on how to perform comprehensive appraisals of their data-sets, using a methodology that assesses six key risk factors and provides a score for the likely accuracy of the data. AMT-Sybex has now set out the following six key risk factors explaing why they are important when deciding how to address data quality issues and the questions water suppliers need to ask when assessing the data:

1. Source suitability

Is each item of data being taken from the system where its master record resides? Or is it coming from a downstream system, where errors may have been introduced? If the latter, the risk of low data quality is much higher.

2. Architectural strength

How did the data get into your systems in the first place? Is the ecosystem engineered to maintain the integrity of data? Or does it depend on ‘leaps of faith’ – for example, relying on the data captured by the technician who installed the meter, without cross-referencing it against the meter manufacturer’s own data-sets The latter is a warning sign that this data may need to be examined more closely.

3. Integrity of controls

How complete is the data-set, and how much duplication does it contain? How well does it conform to a valid set of values? If it doesn’t, that’s another red flag for data quality problems.

4. Unity of purpose

Has a given data field always been used for the same purpose, or has it spent different periods being used to capture different things? Users will often pick a rarely used field, and use it to store information for a special project or temporary requirement. This overwrites the existing data, so it’s a potential problem if that particular field needs to be used in an Open Water dataflow.

5. Ease of correction

If errors in a particular field are easy for users to correct, it’s more likely that users will have fixed them already. On the other hand, if a piece of data can only be verified and updated by visiting a site and checking it manually, it’s more likely that the error will go unnoticed and uncorrected – which means more errors are likely to build up over time.

6. Ease of transformation

Some types of data, such as dates and postcodes, are likely to be in the right format already, or at least should be easy to convert into whatever format Open Water finally specifies. But for other types, the transformation process itself is likely to be more difficult and error-prone. For example, the location of a meter on a site may need to be expressed as a code; whereas in many current data-sets, meter location is described in a free text field. Converting the text into codes could perhaps be achieved using automated text analytics – but the results won’t be perfect, and the error rate is likely to be high.

AMT-Sybex is now working with water suppliers and feeding the factors into a model can generate a set of risk scores that predict which areas of the data are likely to need the most attention.

An assessment can then be made of which of the areas are most critical for Open Water compliance, and whether they will be required from day one of Open Water or can be phased in later.

Critical data quality issues should be remedied well ahead of Open Water deadline

The firm says that by identifying the data-sets that are most critical and have the highest risk of data quality issues, it can then advise its water clients on how to proceed with data improvement initiatives. According to AMT-Sybex, this enables them to create a practical, prioritised programme of work to remedy critical data quality issues well ahead of the Open Water deadlines.

AMT-Sybex concludes:

“A lot of the water industry’s concerns about Open Water’s data requirements relate to the sheer scale of the problem. A very steep rise in data quality seems likely to be required, and there is an ocean of data to deal with.”

“The beauty of our approach is that you don’t have to boil that ocean. By assessing the risk of poor data quality in each data-set, you can understand where to focus and prioritise.”

 The firm is recommending that water suppliers should not waste their efforts on improving things that don’t matter, or that don’t need to be done by day one, suggesting that instead they plot a "smooth, simple course" for Open Water compliance by charting the biggest data risks before they start.