Data! Data! Data! From line managers to executives – everyone is asking: What are we doing about our data journey? Can we trust this data? What insights are you pulling from the data? And of course, can we make a dashboard with this data…?

Data is certainly the new gold and can help decision-making, give insight into a business or product, help understand customers’ needs and wants better, change behaviour, the list goes on. But… data has a credibility problem it turns out. Data is the commodity, but data quality (“DQ”) can be its downfall.

So, we sat down and chatted to Brent Solomon from CIEL Data Services (CDS), and we chatted about DQ and how he and his team tackle these inherent problems with data. The key he says is to “find the truth in the mess - companies really need the ability to trust their data”.

He is correct of course, as a Harvard Business Review study showed that “on average, 47% of newly-created data records had at least one critical error, and only 3% of the DQ scores in the study can be rated “acceptable” using the loosest-possible standard.” This is alarming to say the least, but he assures me that through a proper discovery process, the holy grail of “trusted data insights” can be reached.

So, what is a discovery process? He says it is defined as “the initial process of understanding the data structure and the context of its use”. It involves a progression of investigation, consultation, and various technical methods to analyse, define, measure, and to ultimately gain trusted and relevant insights.

The aim Brent says is to “build trusted insight” and CDS has been extensively collaborating within the CIEL Group with BankOne, BNI Madagascar and in the Healthcare cluster on projects that range from automating reporting (functional, compliance, audit, operational) through to strategic insight and analysis, all the way to the complex world of artificial intelligence where machine learning allows computers to learn from the trusted data that has been curated.

And how do they choose these projects?

Behind the data science and a lean engineering philosophy based on agile principles, are their fundamental pillars guiding who they choose to work with. They need to connect on a vision with their chosen partners, they collaborate and ensure that change and cultural adjustments are made for long-term success, and they create through design and implementing data driven solutions that give a competitive advantage.

Seems Brent and his team are in the right business with the right approach at the right time. And underpinning all of this is there grounding in the Ubuntu philosophy from the Zulu Nation in South Africa – “together we are stronger”. Perhaps that is the real gold!

Reference: https://hbr.org/2017/09/only-3-of-companies-data-meets-basic-quality-standards