By guest author, Credit Risk Specialist Andrew Tierney

“You say you want a revolution” sang John Lennon. “Well you know, we all want to change the world”. The Beatles may have been singing about the political protests of the 1960s but their song was echoed in some of the reactions to the High Level Panel’s call. No one is against the need for more – and more reliable – data to inform development analysis, monitoring and policy making. Indeed, the call for better data has been a mantra for many years. But a number of voices asked what a revolution would look like and to what purpose? They noted that, in some countries at least, plenty of data is collected but not used. The challenge is also about creating a “data liberation” and stimulating a greater demand for – and capacity to use – data.

Source:  ‘YOU SAY YOU WANT A REVOLUTION’ by UN Data Revolution, October 8, 2014

Big Data has been much hyped over the last few years.  Many people have commented on the size of the data, or the breadth of the data, or even the huge insights that will be gained from the data. What hasn’t been discussed is the preparation and path to get from ‘here’ to ‘there’.

Like the Industrial Revolution, many can see the benefits of Open Data, Comprehensive Credit Reporting and the wider mountain of data that is building up over time.  But to be positioned to take advantage of this mountain takes some strategic thought, a pragmatic approach, and the ability to pierce the noise that surrounds this topical conundrum.

Understanding Data

Like any other product, data is useless if it cannot be commoditised.  Unstructured or ‘dusty’ data is not and cannot be structured, does not make any sense, and cannot be properly analysed.

When thinking strategically about data and flow, observe the following key points for your Data Revolution journey:

  • Start sooner rather than later – while you may have a large database, if it cannot be extracted in a usable format, it will not be able to provide much insight. A data management project will assist in starting you on the journey of creating a coded, structured and formatted data set for analysis.
  • Format for control – standard formatting of data is paramount for analytics. This needs to be driven right from the start and to be protected from the input stage through the storage and final output of the data.  Integrity will play a significant role in this stage.
  • Code, Code, Code – codifying the data and creating a flexible and easy to use code structure will make the extraction component easier. Make sure that it is compatible with the appropriate extraction tools (think SQL, SAS, etc) and can be accessed easily.
  • Monitor and Maintain – coding and the format of the data will require maintenance. How you maintain and improve the data over time will determine when and how much additional insight you can obtain.

A systemised approach to Manage Data

Whilst there will be many data sources, a data warehouse should be considered in the wider context of storing and extracting data.  The insights that could be gleaned from a considered and consistent data warehouse would be powerful.

Approaching the management of data and the ability to extract into a usable form will need to be considered within context of the following:

  • Size and complexity of data – this will determine the way you would need to store data and the power behind the data; the motor, or speed and grunt of your data engine.
  • Structure of the data blocks – data will more than likely flow from different core systems or sources. The structural building blocks for the incoming data will need to be determined; and thought around the purpose(s) of the data, the extraction requirements, and the output view required (eg. Business reporting, data modelling, etc)
  • Protection of the data – as with all data, particularly Personal, there will need to be security considerations. The consideration includes hacking and/or loss of data, Personal Data security, and control of the data in context of internal use.
  • Extraction tools – good quality extraction programs that are widely utilised will provide a basis for on-going MIS, custom data extraction/analysis, and bespoke projects. This will ensure that you are gaining the most out of the data.

Data analysis and the Golden Nuggets

This is probably the hardest part of the Data Revolution to answer.  Data is evolving and therefore the nuggets of analysis output are also evolving.  There will be standard analytical outputs, standard reporting, and finally there will be custom or ad-hoc analysis for R&D purposes.

Data Management and Reporting will play a significant role in shaping the future of the business.  From the identification and review of functional output, through to the development of new Products and Processes.  Following are areas of analysis to consider:

  • Standard analytical outputs – this will provide a backdrop for quality of data, audit and data compliance. Standard company or industry metrics will underpin the evolving landscape in which a company will be asked to compete. 
  • Standard Reporting – MIS (Management Information Systems) reporting will provide assurance and detail around how the company is tracking. This could be Approve/Decline rates for Financiers, Churn rate for Telco’s or Error Rate for Manufacturers. 
  • Ad-Hoc Research & Development – as the market landscape changes, then re-defined or bespoke analysis can lead to the identification of threats, challenges and opportunities as, or before, they arise.

Managing a ‘whole of business’ data transformation Project can be daunting. However, with the right focus, people and plan, a solution can be found. 

‘Preparing for the Data Revolution’ is written by Andrew Tierney.  Andrew is a Credit Risk specialist at Balance Risk Consulting with over 20 years in the FinTech and credit data sector, most recently completing 8 years with Veda/Equifax in an Advisory and Analytics capacity.   He now advises firms on the strategies they can employ to improve origination quality, credit risk and collections performance.

A final note on Data Rights


A Rights-based Revolution?


UN Data Revolution October 14, 2014

As data becomes more central to sustainable development, the immense scope for data to empower people is becoming apparent.  But alongside this opportunity are clear risks, as people around the world question the accessibility and privacy implications of the new world of data.  At the heart of issues like these—both the potential and the risks— are rights.  Some members of the panel have been asking: what would a rights-based approach to the data revolution entail?

  • Right to an identity (right to be counted)
  • Right to privacy (in Europe: right to be forgotten)
  • Right to participation
  • Freedom of expression/ speech
  • Ownership: right to own your personal data
  • Right to access data about you (re-use, sale of data)
  • Principles of consent
  • Right to due process (how data is used, ie. how to regulate the algorithm)
  • Protection from discriminatory uses of data
  • Right to non-discrimination and equality (how data hides or shows inequalities among subgroups of the population)