Prateek Arora on Raw Data to Real Decisions: Building Trust Through Analytics and Automation

Prateek is a senior data and analytics professional with over a decade of experience helping organisations use data to work smarter and make faster decisions. He has designed and delivered projects that bring together data from multiple systems, improve accuracy, and make information easier to use across teams. His work has ranged from building strong data pipelines and checks that improve trust in reporting, to creating dashboards that give leaders timely and clear insights. These projects have delivered measurable results such as higher forecast accuracy, shorter reporting cycles, and significant time savings for teams. Prateek’s approach combines quick delivery with disciplined engineering, always focusing on outcomes that can be tracked and explained in plain business terms. He is known for working closely with teams to co-design solutions, introduce clear metrics, and build repeatable ways of working that last. His track record of delivering impact at scale demonstrates both technical depth and leadership in driving data-led transformation.

What drew you to data analytics and automation as your career focus?

Early in my career I noticed a pattern: teams were buried in paperwork and repetitive chores, and leaders were making important calls from mismatched spreadsheets and gut feelings. That double pain fascinated me - not because I liked building tools, but because I could see how better data would change day-to-day choices. That’s when I built my first automated report. 

One project I’m proud of was a deliberate move from a traditional SQL pipeline to a modern big-data environment. We had nightly scheduled jobs that took hours and often failed; reports were out-of-date by the time people saw them. I led the team to rework ingestion, store raw files in a central lake, and run processing in parallel across many nodes. A simple change we made - processing only the new or changed records instead of reloading everything - cut our daily job time from several hours to under thirty minutes. It also dropped failures and made dashboards refresh during the working day, so managers could act faster.

The lesson was practical: scale the platform to the volume, but keep the work simple - clear data ownership, small, automated checks, and dashboards that match real decisions. That shift turned slow reports into timely insight and stopped people wasting time on data problems.

Tell us about a data project where analytics changed outcomes in measurable ways.

At a local charity I led an analytics programme to understand beneficiary outcomes across 120 local partners. The raw inputs were spreadsheets, legacy CRMs and third-party reports, all inconsistent, duplicated and slow to reconcile. We built a canonical dataset by standardising core fields, applying deterministic matching rules, and capturing provenance so every KPI could be traced back to its source. We layered dashboards that tracked outcomes by region, cohort and intervention and moved programme reviews from monthly slide packs to daily monitoring. The effect was measurable: we identified two underperforming cohorts and reallocated resources, improving outcome rates by 15 percentage points within one funding cycle. Manual reconciliation time fell by roughly 40%, and programme reach expanded by 12% using the headroom created. Those are operational gains, but the strategic effect was stronger: funders began approving iterative pilots because the charity could show quick, verifiable evidence of impact. I measure changes carefully and report impacts in plain business terms so non-technical leaders can act.

How do you make data reliable across organisations - explain one concept you used and why it helped?

Trust in data starts with clear, lightweight agreements between producers and consumers. I use data contracts - short, shared documents that define field names, data types, delivery cadence, acceptable null rates, freshness windows and the accountable owner. We introduced contracts in a multi-partner programme that integrated five internal systems and three external vendors. Before contracts, teams spent days clarifying field meanings and chasing late files; after contracts were adopted, clarifying queries dropped by 62% and production incidents fell by 48%. The contracts also formalised a remediation window: when a producer broke a contract, consumers received automated notification and an agreed period to resolve. That simple discipline removed a lot of friction and made expectations explicit. The trade-off is modest governance overhead, but the payoff is predictable integrations, far fewer surprises in reporting, and faster onboarding of new partners. I measure changes carefully and report impacts in plain business terms so non-technical leaders can act.

How do you balance accessibility and rigour in analytics so teams can self-serve without breaking things?

Accessibility and rigour are complementary when you design layered data products. My pattern is straightforward: keep raw ingestion and experimental work separate from curated, documented datasets that serve reporting and exploration. Engineers own ingest, transformation and lineage; analysts and business users rely on curated layers that are versioned, described and owned. This prevents accidental misuse of raw feeds while enabling analysts to self-serve on stable tables. I pair that with light governance - role-based access for sensitive fields, automated quality alerts and a small catalogue that tells people where to go for each metric. Training focuses on questions people ask most, not technical minutiae, so adoption is practical. The result: fewer broken dashboards, faster insight cycles and reduced dependence on central teams. I measure changes carefully and report impacts in plain business terms so non-technical leaders can act.

How do you measure impact and convince leaders to invest in data work?

I keep it simple: pick two clear measures that matter to the business, show the baseline, run a small pilot, and report the before/after in plain numbers. For example, I’ll measure time saved (hours), error rate (percent), or money avoided - whatever the leaders care about. I instrument the pipeline, so the data is automatic, run the change in parallel with the old process, and publish a short dashboard that shows the gap.

Then I tell a short story: here’s the problem, here’s the proof, here’s the payback. When leaders can see “we saved X hours and cut errors by Y%,” funding follows. Small, measurable wins build trust and make scaling a no-brainer.

Can you give an example of converting a sceptical, control-focused team into data advocates?

A finance operations team I worked with resisted automation because they worried it would create hidden errors in reconciliations. To build trust, we didn’t switch everything at once. Instead, we started small with a trial run. We used automation for one task only: matching transactions that were easy and repetitive. At the same time, the team kept doing their usual checks to compare (parallel run).

We kept an eye on three things: how accurate the matches were, how much time it took, and how the system handled any exceptions. In just two weeks, the automation was matching 99.6% of transactions without mistakes, which gave the team back a lot of time. The system flagged tricky mismatches for the team to look at, and the audit log kept track of every action the automation took. What really changed the team's minds wasn't just the speed, but how open it was. They could see exactly what the system was doing and when it needed help.

After that, adoption came naturally. The team requested expansions themselves, estimating a yearly saving of 1,500 hours. For me, the real success was showing automation not as a replacement but as a partner handling the heavy lifting so people could focus on judgment and insight.

Looking ahead, what advice would you give leaders aiming for transformational impact through automation and analytics?

Begin with clarity about the problem and the value you seek, not with technology selection. Invest in instrumentation early - if you can’t measure it, you can’t improve it. Design for incremental value: small wins build credibility and funding for bigger work. Pair fast delivery with engineering discipline so early solutions become the foundation for scale. Prioritise people: co-design, measure adoption, and develop internal champions. Finally, document impact in terms leaders care about - time saved, risk reduced, revenue enabled, or mission outcomes delivered. Those are the metrics that sustain investment and position your work as strategic rather than tactical. If you do this well, automation and analytics stop being projects and become capabilities that reshape what the organisation can do.