By Gary Clark
Over the last decade, registered providers have increasingly become independent social businesses. The sector has engaged with complex funding markets and some providers have become commercial developers too.
The sector is no longer entirely reliant on government grants and overly prescriptive regulation that seeks to tell providers what to do and how to do it. In fact, the current regulatory framework has overhauled the approach of previous regimes and replaced it with a ‘co regulatory’ working environment. Accountability now sits firmly with boards.
This shift of power and responsibility has led to many providers wanting to ensure that their business decisions are evidence based, that their business is ‘data-driven’. This requires access to skills and knowledge that have rarely been called for before but are now essential for any organisation to survive and thrive.
Any business wanting to base its decisions on the insight provided by its data, needs three things:
Because data in its raw form tends to be hidden from view, it can easily be forgotten about and when converted into charts, graphs and reports, its validity, integrity and provenance can be difficult to establish.
Data quality can be measured by multiple dimensions, including validity, accuracy, completeness, timeliness, relevance, consistency and accessibility. Few organisations monitor data against many of these dimensions, if any. Indeed, many organisations cannot accurately state where their data is stored, most will accept that business processes often involve the collection of data into spreadsheets, but often the spreadsheets that hold the ‘truth’ are hidden from general access, in individual team member’s drives.
If you want to ensure data quality, it is important first to clean the data and then to put in place a full data quality framework that will provide the policies, procedures, standards and governance to maintain the data in the future. In order to clean the data, you need to know which data represents the ‘truth’ by mapping the dataflows, identifying all the spreadsheets, bits of paper and people which hold data used in business processing.
Once the data is cleansed the data quality framework will assist with maintaining data quality in the future. However, it is not uncommon to find that data quality can deteriorate, which is where data quality management processes that can monitor data, wherever it is held, come into the equation. And alongside these activities, master data management solutions can collate data held across the organisation’s information architecture to deliver a single version of the truth.
Sounds too good to be true doesn’t it? The holy grail of data is actually achievable. Provided that, of course, the data is correct in the first place.
Then it’s purely a case of employing the right skills, analysts and story tellers, before presenting the data to executive team members, enabling them to draw conclusions from the data, make sound decisions and take the necessary action.