For an engineering group, projects lived in one system, billing in another, and sales in a third. Leaders wanted a single source of truth, so decisions on purchase orders, invoicing, receivables, and payables were made from the same facts. We used Salesforce as the spine, integrated Mission Control for delivery and Accounting Seed for finance, and streamlined the data flows so key fields and definitions aligned.
The proper use of Data — data about team performance, data about customers, or data about the competition, can be a sort of force multiplier. It has the potential to dramatically help a business to scale. But sadly, many businesses have data but don’t know how how to properly leverage it. What exactly is useful data? How can you properly utilize data? How can data help a business grow? To address this, we are talking to business leaders who can share stories from their experience about “How To Effectively Leverage Data To Take Your Company To The Next Level”. As part of this series, we had the pleasure of interviewing Satish Thiagarajan.
Satish Thiagarajan is the founder of Brysa, a Salesforce and data consultancy based in the UK. His company advises media, industrial and services clients on using Data Cloud and Agentforce to turn signals into action. His work focuses on closing the loop between insight and execution in sales, marketing and service.
Thank you so much for joining us in this interview series. Before we dive in, our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?
I’m Satish, founder and CEO of Brysa, a Salesforce consulting and implementation firm. I studied engineering, business, and culture, but have spent most of my career in the media industry, with stints in banking and engineering. I come from a modest background, which shaped the values I try to live by and build into Brysa: trust, commitment, and service.
Starting Brysa was an evolution rather than a leap. I had implemented and run Salesforce on the client side for a UK media company. During a period of M&A I saw first-hand how services could be delivered with more rigour, clearer ownership, and better change management. A mantra from my basketball days stayed with me: when you see an opportunity, take the shot. I took it, set up Brysa in London, and built a team that focuses on outcomes rather than activity.
We exist to help clients succeed because only if they succeed do we succeed. That means understanding their business problems, not just their tech stack, and tailoring Salesforce to remove friction in sales, service and operations. Three years in, I’m proud of the team we’re building and the learning culture around Data Cloud, Agentforce, and applied AI. The impact shows up in client results, from faster decision cycles to concrete time savings, such as the 2,000 hours we saved for National Family Mediation.
It has been said that sometimes our mistakes can be our greatest teachers. Can you share a story about a humorous mistake you made when you were first starting and the lesson you learned from that?
What separates comedy from tragedy is how it ends. When we were implementing a very complex integration between our client’s Salesforce instance and Google Maps API, the team was palpably excited. It suffices to say that the solution we were implementing hadn’t been tried before, and the client stood to gain a lot if this were successfully done. In the process, one of the developers forgot to switch off a functionality in the development environment. This oversight hit home when I got a call on a Saturday morning, saying that the client was hit with a bill worth many thousands of pounds for API calls consumption. Brysa was ready to take the hit, as it was out mistake, despite the fact that it would make the project unviable commercially. I reached out to the Google technical team and explained the situation while sharing the algorithm and where the mistake had been made. After much deliberation, they agreed to waive the fees. I still remember the bashfulness mixed with relief I could see in our team. All’s well that ends well, I suppose.
Leadership often entails making difficult decisions or hard choices between two apparently good paths. Can you share a story with us about a hard decision or choice you had to make as a leader?
Although the decision to focus on the technology makes sense in retrospect, it was a very difficult choice to make. In the early stages of our business, specialising in Salesforce and Data Cloud felt like narrowing our options for growth. There are other technologies that promise similarities to Salesforce, at least on the face of it. However, thanks to some good advice, data analysis of the market potential, and no small amount of following our instinct, we made the right choice of specialising and excelling in Salesforce. It’s turned out to be the most innovative data and CRM platform in the world. And that has made all the difference!
Are you working on any new, exciting projects now? How do you think that might help people?
We’re working on a number of new and exciting projects.
Firstly, we’re working on a data-backed, Agentforce Marketing Cloud AI implementation. Briefly, the project will bring together many streams of real-time data, both structured and unstructured, and in conjunction with the Salesforce Marketing Cloud campaign agent, propose data-driven campaign options to the marketer. As part of the execution, the agent will assist with in-flight analysis and tweaking of the campaign while it’s still on. Post campaign, the agent will assist in developing the next campaign hypothesis based on performance data from the campaign, market, and intent signals from the data cloud. We are very excited by the potential of this.
Another AI project we are working on involves the Media Publisher sales teams, helping them to cut their admin times by 90% in one fell swoop. The challenge for the sales team has always been accessing the right data, at the right time, with little or no effort. The Agentforce agent we have built brings all the relevant data to the Slack window of the sales exec rather than them having to go fish for it on multiple applications. So, what used to be complex and time-consuming processes, like availability checking, proposal creation, opening and closing of deals, can all be done within the chat window, thus traversing multiple applications with ease never seen before.
Our list won’t be complete without mentioning our Agentforce for Customer Service implementation that we are about to roll out. This self-service agent will dynamically solve user problems from within the application and by using the ever-refining knowledge base, thus enabling them to solve 98% of the queries themselves and with very little effort. This is based on Resource Augmented Generation (RAG) or what is called “grounding” of the data to the client-specific use cases. This means the user gets their problems solved quicker and the client can provide better, more relationship-focused service while delegating commoditised services to an AI agent.
You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you share a story or example for each?
Success is the net effect of sustained effort and, as the cliche goes, is a journey and not a destination.
Three things that have helped me so far — and I pray will help in the future — are: client-focus, learning culture, servant-leadership.
Easier said than done, we’ve invested in our clients’ success, strongly believing that only if they succeed does Brysa succeed. This has meant that we had to suggest options that prevented more work accruing to us, as we’re very aware that in the long term, client trust and relationships will win out. We get closely involved in clients’ businesses, often at our own expense. By walking in their shoes, we can provide better, impactful solutions that will transform their businesses.
With the advent of Google, knowing facts lost value quickly, and those with insights prevailed. With AI, intelligence and insights are following the factual knowledge’s journey. We know that the clients can just ask GPT to find answers to their domain or product-related questions. This means to stay ahead of the curve, we are learning to provide value that LLMs can provide only up to a point. This includes deep, domain-specific knowledge of Salesforce clouds. Though AI can help navigate in broad brushstrokes, we find that our experience and expertise win out while architecting solutions that suit unique client needs. This includes taking people along in the transformation journey. So, we continue to invest in learning more about Agentforce, Revenue Cloud, and de-risking transformations.
I see it as my primary duty to serve: the clients and my team. There are four ways to get work done with a team: motivation, exemplification, exhortation, and pressure. All these are needed in different circumstances with different team members. However, serving the team by clearing and holding the space for them to thrive is the most effective, I’ve found. All my team members are way smarter, more intelligent, and motivated than me, which makes me want to help them succeed more urgently.
Thank you for all that. Let’s now turn to the main focus of our discussion about empowering organizations to be more “data-driven.” For the benefit of our readers, can you help explain what it looks like to use data to make decisions?
Let’s start with the obverse: what it looks like to not use data to make decisions. It will resemble the proverbial data-blind fishes in the pond that went dry. When a pond begins to go dry, the smart-fishes move out as the first reliable data emerges; the follower-fishes left when the conclusions that data showed were indisputable; the data-blind fishes relied on pure ‘instinct’ that they could still swim, only to turn up dead when the pond dried up on a hot day. The macabre story tells us a truth: those businesses that are not data-driven will die out.
To put simply, when your business uses data to make decisions –and this could be marketing data, sales data, operations data, finance data, or economic data — your business will be more predictable, manageable, and successful.
Let’s look at a couple of illustrations from our clients’ experiences. A big media house based in the North of England saw diminishing returns on investment on their Sales teams’ resources. They ploughed money into hiring more salespeople, providing them with more sales support, only to see lower sales. We consulted them on their adoption of Salesforce-driven processes and discovered that while the system is being used, there is no closed-loop feedback mechanism to help the sales team focus on the right activities. The team would waste a lot of time communicating with agencies and brands that wouldn’t result in proportionate business and vice versa. We had the data fed back to the team, their focus sharpened, and they concentrated more on activities that resulted in business rather than on dead-end alleys.
Another client of ours managed a £20 million revenue business on very poor financial data. Their month-end closing would take anywhere between one and two weeks, leaving the leadership team to work with imperfect information to make critical business decisions that would impact cash flow and forward investments. The key challenge was reconciling the sales pipeline and deal-closing data with the receivables and cash receipts management. We implemented a Single Source of Truth system where the sales and the finance team would work on the same data; this eliminated needless duplication and led to the monthly books being closed in two hours rather than two weeks. The CFO not only got real-time, reliable data, but the business could also make robust data-based decisions to grow the business.
Based on your experience, which companies can most benefit from tools that empower data collaboration?
Companies of all sizes and maturity can benefit from better data practices. That said, a few factors, if thought about and invested in ahead of the implementation of tools, will go a long way in helping the companies succeed. They are: data vision, culture of leadership, right tools, right partner.
Data vision: Without a coherent vision of what business problems are to be solved or what business goals need to be achieved with the tools of data collaboration, the project becomes a non-starter. One doesn’t need to have every detail plotted; however, it is important to have clarity of vision and measures of success established to make the effort have purpose and direction.
Culture of leadership: When implementing tools that empower data collaboration, there ought to be hard challenges along the way. While some of them can be solved through technology, most critical ones tend to be around the culture of adoption, teamwork, and collaboration. Right leadership needs to be provided to every team to help them make sense of how their interaction with the tools and the data fits in the broader picture and works for the greater good. And without leadership, teams tend to fall into silos, thus jeopardising the effective use of data’s power.
Right tools: The importance of selecting and implementing the right tools cannot be overstated. There are more than 7000 SaaS and AI tools in the market as of September 2025, and this is only going to grow. Choosing tools with a proven track record and enabling data collaboration across teams and business functions is important. And this selection must be done with the specific business circumstances in mind. A good approach is to set a hypothesis and invest in a bounded proof-of-concept to test that hypothesis. It is worth taking time on this step because tech stack architecture is a long-term decision that cannot be undone easily.
Right partner: With the vision, leadership, and tools, any company can give itself a good chance to succeed in putting data to best use; however, with the right partner, the business can derisk the programme. A good partner can bring an outsider’s perspective and yet ground it in the reality of the particular business. They can also help break the implementation down to manageable chunks, each with its own success metrics and learning, thus helping the company experience positive results early on. This can act as positive reinforcement, providing crucial momentum to the transformation.
Can you share some examples of how data analytics and data collaboration can help to improve operations, processes, and customer experiences? We’d love to hear some stories if possible.
Data is only useful if it changes the work. Give teams one version of the truth, surface the next step inside Salesforce or Slack, and track what improves. The examples that follow are plain, practical and close to the work.
A mid-market SaaS company was guessing at collections. Sales lived in Salesforce, invoices in their finance system and support issues in another queue, so the AR team called whoever shouted loudest. We joined the basics — contract terms, invoice ageing, usage and open tickets — and built a simple propensity-to-pay score. The work showed up in Salesforce as a prioritised task list with the right cadence and message for each account. Within a quarter, days’ sales outstanding improved by about a wee,k and write-offs edged down because we were fixing product issues before asking for cash.
An equipment services firm scheduled engineers by gut feel, then discovered parts were missing when the van reached the site. We linked work orders, parts availability and technician skills, and pushed a daily plan into Slack with changes reflected in Salesforce Field Service. Engineers got the right jobs with the right spares, customers received realistic windows, and managers saw the bottlenecks. First-time-fix rates increased, travel time decreased, and SLA breaches dropped because the plan was driven by facts rather than hope.
From your vantage point, has the shift toward becoming more data-driven been challenging for some teams or organizations? What are the challenges? How can organizations solve these challenges?
Yes. The shift is hard, and for predictable reasons. Here are the recurrent failure modes I see, with the fixes that actually work.
Vague intent, data theatre instead of decisions: Teams spin up dashboards and models with no explicit decision they will change. Businesses get activity, not outcomes. We have helped by suggesting they start with a decision backlog. For each decision, the owner, the cadence, the metric it influences, and the acceptable error — all these must be stated. If a dataset or model does not support a named decision, it will not be considered.
Weak data foundations: Siloed systems, no common customer or product key, conflicting definitions of revenue and pipeline. Identity resolution degenerates into manual matching. We solved these problems by establishing a canonical data model and master keys early. This helped treat the customer 360, product catalogue and chart of accounts as shared products with owners rather than working in silos.
Lack of accountability: Late, missing or duplicated records derail forecasting and reporting. Everyone blames ‘the data,’ and no one owns remediation. We helped organisations to define quality SLAs per critical table or feed: completeness, timeliness, uniqueness, and validity thresholds. The businesses were made to monitor them, log and remedy breaches, and keep records for known failures. This led to substantial data improvements.
Ok. Thank you. Here is the primary question of our discussion. Based on your experience and success, what are “Five Ways a Company Can Effectively Leverage Data to Take It To The Next Level”? Please share a story or an example for each.
1 . For a national media owner, key accounts were being managed informally. Senior leaders had no clear view of relationship health or service levels. The sales team used Salesforce for accounts, contacts, and activities, and relied on email too, but the signals were scattered and could not be rolled up into a decision.
We defined the minimum data points that mattered for relationship health, then streamlined how they were captured in Salesforce and collated alongside email. That gave management an immediate view of which accounts were well serviced and which were at risk, so time and support could be redirected. Within three months, relationship-based sales increased by 100 per cent.
2 . For a top media seller, the sales team were burning time on low-value accounts. Because activity data was patchy, no one could see which actions contributed to revenue. We reset the process to be data-led: clear cadences, disciplined activity logging and consistent data collation, with sharing settings, reports, and dashboards that made performance visible.
Slack became the frontline for nudges and updates, with Salesforce as the system of record. Automations captured time spent by each rep and correlated it with booked revenue so managers and sellers could see where effort actually paid off. The team redirected time to high-potential accounts, grew top-account sales, and intentionally attrited low-value relationships, cutting wasted motion.
3 . For a national retail media owner, store performance was managed with blunt averages. EPOS showed what sold and where, ANPR counted cars and day parts, but the signals never met. Sales teams priced and planned on heuristics, which made it hard to prove value to brands or to tune campaigns once they were live.
We joined EPOS with ANPR at site and day-part level, created store and time-of-day scores for propensity and uplift, then surfaced them in Salesforce for planning and in Slack for in-flight adjustments. Planners could target the right stores and windows, switch out low-yield slots, and evidence outcomes with post-campaign readouts tied to promoted SKUs.
Budgets shifted towards high-return locations and times, underperforming placements were retired, fill rates and effective yield improved, and advertisers renewed with greater confidence.
4 . A client held ESG data but had no way to organise it or present it to customers in a form they could use. The goal was clear: collate the data and make it consumable so buyers could factor supplier ESG into procurement decisions.
We implemented a warehouse in Amazon Redshift, built Airflow ETL for ingestion and cleansing, and added calculator models so users could interrogate the data intelligently. A React front end and an API made the outputs easy to access for end clients. The result was adoption. For the first time, buyers used supplier ESG metrics in live procurement choices.
5 . For an engineering group, projects lived in one system, billing in another, and sales in a third. Leaders wanted a single source of truth, so decisions on purchase orders, invoicing, receivables, and payables were made from the same facts. We used Salesforce as the spine, integrated Mission Control for delivery and Accounting Seed for finance, and streamlined the data flows so key fields and definitions aligned.
Once adoption was in place, sales, project, and finance teams worked on the same records. Project stages triggered the right financial actions, invoices were raised based on approved milestones, and receivables matched what had actually been delivered. Errors fell away and productivity improved because people stopped reconciling spreadsheets and started running the business from one dataset.
Based on your experience, how do you think the needs for data might evolve and change over the next five years?
Five years is a substantial timeline. Let’s break it down into smaller chunks. The current epoch is one of big data: where data warehouses house trillions of rows of structured data and petabytes of unstructured data. The next period, over one to two years, would be one of getting enhanced insights on that data through the application of AI. Current tools are limited to working best with structured data, and the crucial difference AI would bring is to derive insights from unstructured data and unifying it with insights from structured data.
There are many directions of possible progress from there. I predict that in three to four years, the focus will shift to the quality and impact of the data; I don’t mean simply from a data hygiene perspective, but more radical questions like: what data points are worth collecting and processing. That should lead to a world where, in five years, businesses and organisations will focus on quality over quantity, meaningful impact over narratives so far as data is concerned. Let’s wait and watch.
Thank you for your great insights, We are nearly done. You are a person of significant influence. If you could inspire a movement that would bring the most amount of good to the most amount of people, what would that be?
The tool at our disposal to affect people at scale is politics. If I were to achieve that noble goal of bringing the most amount of good to the most amount of people, I would like to create a movement that will hold public statements to account through data. What if social media platforms are demanded — by the users — to use data to substantiate all posts, and where opinions are expressed, it is clearly labelled. Much of the polarisation in our current politics stems from opinions being presented as facts without substantiation. In a nutshell, my movement’s slogan would be: make data accessible to all!
How can our readers further follow your work?
On our website: www.brysa.co.uk and through my Linkedin: https://www.linkedin.com/in/satisht/
Originally published on Authority Magazine:
Check out this interview at: https://medium.com/authority-magazine/data-driven-satish-thiagarajan-of-brysa-on-how-to-leverage-data-to-take-your-company-to-the-next-ccea0dfc3fe4