Peter Jackson appears on screen from the far end of a large boardroom. A dozen empty chairs surround a heavy wooden table behind him. With a quick adjustment to the camera mounted above, he zooms the frame slightly to bring himself into focus before the conversation begins.
It is a small moment, but it neatly reflects the way Peter approaches data itself: bring the signal into view and remove the noise.
Today he sits at the centre of three roles. He is Chief Data and Technology Officer at Outra, a 100-person data provider delivering insight across 30 million UK households. He is also a co-founder of Carruthers and Jackson, and a tutor and course leader at the Carruthers and Jackson Data Leaders Summer School.
Few people sit quite so directly at the intersection of data leadership, commercial practice and education. It gives Peter a rare vantage point on how the discipline has evolved, and where it is heading next.
The Early Days of Data
Looking back to the early stages of his career in the 1980s, Peter describes a profession that looked completely different to today’s data landscape. The role of Chief Data Officer simply did not exist, and what we now recognise as modern data operations had yet to emerge.
At that time, the focus was almost entirely on digital transformation. Peter’s early career centred on advanced web development, where working with cookies and SQL databases felt genuinely cutting edge. As technology progressed, the ecosystem began to evolve quickly. Organisations started adopting NoSQL databases and experimenting with new platforms entering the market, including tools such as Alteryx, Snowflake, Exasol and ThoughtSpot.
Despite these technological leaps, Peter notes that the work remained largely digital-focused until around a decade ago, when organisations began to recognise data as a strategic capability rather than simply a technical function.
Writing the First Playbook for Data Leaders
Peter’s move into publishing came from a realisation that many senior data professionals were operating without a clear blueprint.
In 2017, while working at Southern Water, he met Caroline Carruthers. Both were navigating senior data leadership roles and quickly discovered something surprising: there was no practical guide for Chief Data Officers.
As Peter explains, they found it perplexing that in a world filled with professional guides, the domain of data strategy remained largely undocumented. That realisation led them to write The Chief Data Officer’s Playbook, which went on to become a number one Amazon bestseller.
The aim of the book was to reach what Peter calls “our tribe”. It was designed to help people in similar roles build the foundations of data literacy, data maturity and data operations that ultimately deliver customer value.
However, they soon realised another audience needed the same clarity: executive leadership teams.
Many organisations were talking about data, but few leaders fully understood how to embed it into business strategy. That insight led Peter and Caroline to write their second book, Data-Driven Business Transformation, which was published in 2019.
Aligning Data Strategy with Business Strategy
Across Peter’s work, one message appears consistently. Data strategy must align directly with business strategy.
He emphasises that organisations often make the mistake of focusing on technology first. A data strategy cannot be built around adopting the latest tools or producing impressive dashboards. Instead, it needs to clearly explain the role data plays in delivering value to customers and identifying opportunities for the business.
A successful strategy also requires organisational adoption. Peter stresses the importance of a strong data literacy plan that educates employees about the value of data and how it influences their work. Without this level of understanding, many data initiatives fail before they have the chance to deliver results.
Solving a “Literal Shit Storm”
Peter has seen the practical impact of data throughout his career, often in unexpected places.
At Southern Water, he faced a challenge he summarises succinctly:
“Navigating a literal shit storm.”
Sewage blockages, often caused by so-called fatbergs, were creating serious operational problems. These incidents could lead to sewage bursts that flooded streets and caused significant damage, not to mention the cost of fixing them.
To address the issue, Peter and his team analysed hundreds of data points to understand where blockages were most likely to occur. The analysis revealed four key factors that consistently appeared together.
Areas with higher populations of younger families often saw nappies and non-degradable wipes flushed down toilets. Locations with large numbers of fast-food outlets contributed fat and grease to the drainage system. Many incidents occurred within older Victorian brick sewer systems, and areas with heavy tree coverage also increased the likelihood of blockages.
By identifying these patterns, the team could predict where problems were most likely to happen, even if they could not determine exactly when. This insight allowed Southern Water to dramatically reduce the number of sensors required across the network. Instead of covering the entire system, sensors could be placed in high-risk areas.
The result was a reduction of roughly 80 percent in sensor deployment, saving millions of pounds.
Finding Hidden Risks in Pension Data
Another example came during Peter’s time at The Pensions Regulator. The organisation was analysing pension scheme failures against the backdrop of high-profile events such as the Philip Green saga and the BHS pension scandal.
The team analysed roughly 45,000 data points across hundreds of pension schemes to identify the factors that led to pension defaults.
Using machine learning techniques, they eventually discovered that the most significant predictor was surprisingly simple: the absence or malfunction of trustee email addresses.
The insight may have seemed straightforward once identified, but within such a vast dataset it was extremely difficult to detect. Once discovered, however, it allowed the regulator to focus its efforts on addressing a clear and measurable risk.
Mapping Data Across 30 Million Households
Today, Peter’s work at Outra operates at a completely different scale.
The company’s mission is to map data across all 30 million households in the United Kingdom. The scale of the dataset is enormous, and new technologies play a crucial role in managing it.
Image recognition tools allow the team to transform publicly available information into structured data. For example, analysing property listings on platforms such as Rightmove can reveal detailed insights about the housing market.
Artificial intelligence is also used to fill gaps where data is missing. If property details such as price or layout are unavailable, models can infer those values based on surrounding properties and comparable datasets.
This approach enables Outra to make increasingly sophisticated predictions, including anticipating when properties may enter the market.
The company was also among the first in the UK to adopt Snowflake’s latest technology. Combined with a strong focus on data accuracy, this allows Outra to compete directly with established providers such as Experian and Equifax in delivering market insights.
Skills for the Next Generation of Data Leaders
For professionals entering the field today, Peter’s advice remains grounded in the fundamentals.
Success still depends on aligning data initiatives with business objectives. Technical capability alone is not enough if the work is disconnected from the organisation’s strategic goals.
He also emphasises the importance of peer-to-peer learning. Networking with other data leaders and sharing experiences can provide insights that formal training alone cannot deliver.
Education remains central to Peter’s own work. Through the Carruthers and Jackson Data Leaders Summer School, he helps run a free 10-week programme featuring weekly sessions designed to deepen participants’ understanding of data management and leadership.
Conclusion
Peter Jackson’s career mirrors the evolution of the data profession itself. From early work in web development and databases to large-scale AI-driven analysis of millions of households, the role of data within organisations has expanded dramatically.
Yet the core principle that runs through Peter’s work remains unchanged: data only creates value when it is connected to real business problems and understood by the people who use it.
