Pramata’s CEO Praful Saklani is off to CFO Forum in New York City in May to talk about taming data chaos. I was curious and interested to hear about the "taming" part, so I recently sat down with Praful to talk about this interesting subject.
Before I dive into the interview, know that while every enterprise understands the need to manage their onslaught of data, surprisingly, most companies don’t know where to start. Praful understands this after years of serving big enterprises with the Pramata Customer Digitization Platform. Pramata provides a solution that helps find and use customer relationship data to grow revenue, improve customer relationships and mitigate compliance risk.
Back to the data conversation. Despite investing heavily in technology in the time of digital transformation, 54% of executives surveyed by KPMG say the top barrier to success is identifying which data to collect, and 85% say they don't understand how to analyze it. CFOs have historically faced challenges like regulatory costs and consolidating finances, yet now they are looking for ways to access and gather meaningful (actionable) data.
What’s odd is that many enterprises aren’t aware that gaining a holistic view of their complex customer data can have big, positive impacts on revenue and operations. For example, a Harvard Business Review-led study which resulted in a recent whitepaper “Driving Revenue and Profit from Customers in the Digital Age," found that companies making data-driven decisions, on average were 5% more productive and 6% more profitable than their competitors.
Below are Praful’s insights on why taming data chaos will result in improved business performance.
Accurate data as a business issue
What are your thoughts on data chaos?
Praful: Before we get into how to tame data chaos, I'd like to look at today’s business competitiveness. If you want your company to outperform in your category—whether it's regarding revenue per customer, profitability, retention, or growth—you must ask “What is your competitive advantage?” And if you’re honest, some of it is your people, some of it is your processes, but a lot of comes down to your data.
Ask yourself how well you understand your customers or your ability to service them or maximize your profitability from each transaction with them? How well do you understand the risks in your customer base? These things, which are a percentage point here or there, can be the difference between average performance and best-in-class performance.
Everyone talks about data, but do you think that companies truly understand that their profitability and compliance issues lie within their data?
Praful: I don't think people understand that so many aspects of a successful business depend on good data.
So much so that it may not be obvious?
Praful: It may be one of those death-from-1,000-cuts issues. Or it may be a case of not understanding the scale of the value associated with using data to solve their problems. So, I think if you asked, “Do you think data integrity is your issue?” they would never say “Yes, that’s it.”
But then if we ask, "Well do you think it’s a problem that manifests itself in 3, 4, 5, or 6% of your revenue? The response is, "Well, I know I have a data issue related to a compliance problem," or, "I know I have a data issue that’s making my forecast inefficient.”
Okay, what I hear you saying is they know they have specific data issues, but the connection to bigger business impact isn’t always obvious.
Praful: I think B2C companies have some understanding of this, like Amazon has more data than anybody, so they know. But with B2B companies, I think people misinterpret the data that helps you understand your transactions and your actual current customer relationships.
The four key data characteristics: Complete, accurate, up-to-date and actionable
So, now let me ask you, what’s driving data chaos?
Praful: Well again, before we get to data chaos, let's talk just data. It should always be complete, accurate, up-to-date and actionable to have integrity. All four of those characteristics must be there. Look, most companies have a good handle on their data, but only in portions of their business. For example, maybe they have a little army of people trying to keep their arms around the data for four key customers. It's easy to understand the customers you throw an army at and the simplest ones, but all of those in the middle are complicated. Although not big enough that you can dedicate a 50-person team to keeping track of their data. This is where an enterprise can start to leak serious value.
There’s no getting around that you need all four characteristics for data integrity, because if you're missing any of them, you're not going to be able to take the right actions with your customer at the right time. It's almost like being a Buddhist and following the four-fold paths of Buddhism, it won’t work if you remove one of the paths.
It falls apart?
Praful: It totally falls apart. And so, there's more wisdom in those four critical data characteristics than meets the eye. It's important to talk about why these four characteristics are so fundamental to the integrity of data.
What drives data chaos
Okay, I understand why data and data integrity are so important. Now we dive into data chaos.
Praful: Yes. The complexity of business is increasing while the pace at which business needs to evolve to innovate is also increasing. Maybe in the old days your company sold hardware, but now you sell hardware, software and maybe some services, too. Some of this is sold as licenses, some as a subscription, some with third-party features bundled in.
But wait, there’s more! Maybe you want to negotiate volume discounts or certain pricing triggers or geography-based discounting. In other words, you have all kinds of special promotions you must track. Each of those things is great for business, but the complexity of all of it needs to be understood, tracked and documented. These days you also see M&A happen at a much faster pace than in the past. Where companies are required to gather more and more assets and products from other places. See the complexity here? Then inevitably the question becomes, "Can you tell me everything all of our acquired customers ever bought?" Good luck with that.
Plus, many companies have multiple billing systems, different CRMs and other core business systems. So even simple questions like, “Can you tell me every valid contract we have with a customer?" or “Can you tell me every single active product they have?” become almost impossible to answer.
Indicators of data chaos
How does data chaos show up in an organization?
Praful: If you sell one product, it's easy. If you sell ten products, but the pricing model is relatively simple, it's still easy. But if you sell let’s say 300 products that change every year and have different pricing models for six of them. Or suppose 50 of them were acquired from other companies, good luck piecing all of that together. And what happens when customers need to renew? You ask, “Which customers are coming up for renewal, and what's the billing amount associated with those renewals?” Then, “What's the documentation associated with these renewals?” It’s the number of questions you can’t answer that indicate your business’s level of data chaos.
How to solve the data chaos in your company
How can organizations begin to truly see the whole data chaos issue?
Praful: A good framework to understand how data chaos plays out in an organization is understanding the lifecycle of your customer relationship or your partner relationship. It shows you all the steps, all the different people and systems involved and where opportunities fall through the cracks. Or as we often say at Pramata, map the gaps.
So, the customer lifecycle acts as a framework for understanding a company’s data gaps?
Praful: Yes. Regardless of what kind of company you are, you must perform for your customers so you can grow your relationships or renew them over time. This is any company's customer lifecycle, so it doesn't matter whether you have 10,000 or 3,000,000 customers. If you're a B2B company, you invariably have some version of all the steps along the customer lifecycle.
Where are they failing in the customer lifecycle?
Praful: We find companies failing simply by not understanding the history of their customers. Basic stuff, like what they’ve bought from you before, what's active now, where all the documentation is or what terms were negotiated with them before. And it goes from there, like were there any unique operating terms? Any one-time SLAs? Any one-off pricing terms? Billing terms? You need to be aware of all of that.
It gets worse when they need to make sure what was in the contract or the purchase order matches what's in the billing system. And it just goes on and on from there, in many different use cases.
Addressing data chaos
How do you start addressing data chaos?
Praful: The two organizational teams that we work with most closely are finance and sales operations. And the first question we always ask is, “As you look at these lifecycle gaps, where are you leaking the most value?” The answers can vary. They may feel like they are churning a lot of customers or they may say they are losing out on tens of millions of dollars of profitability because they are unable to manage price changes correctly. Some are in a highly-regulated industry with a lot of compliance issues such as government contracting, and they don’t think they’re fulfilling their obligations. We’ve heard answers like, "I don't have enough price control to make sure I'm compliant with what I've agreed to."
For each organization, the first thing I would say is, figure out where your biggest problems lie. Plus, we found the 80/20 rule applies, that’s where 20% of the transactions represent 80% of the revenue, and they 20% tend to be a lot more complex. So maybe you zero on your 20%, so you don't have to boil the ocean across 100,000 customer relationships. Maybe you start off with 20,000 to fill the data gaps you need to drive revenue, profitability or pressing compliance objectives.
Okay, so now a company says, "Alright, we’ve got big pricing problems in this sector, and we know we're in trouble.” Then what?
Praful: One of our fundamental “aha” moments in working with our customers at Pramata has been that you need a stable foundation for understanding the customer to be able to generate and curate actionable information. And that stable foundation, especially when it comes to these complex B2B relationships, starts with the customer contracts. And when we say contracts, we're not just talking about a master agreement; we're talking about statements of work, order forms, and project documentation associated with the contract. All those things tell you what you agreed upon. They tell you what the customer bought, and what the terms, pricing, and the delivery date were.
Then, there is a lot of transactional information that you can get out of your ERP system and sales information that you can glean from a CRM. But all that needs to be validated against what was originally in the contracts for you to have a foundation of full customer understanding. The informational foundation you need can start with contracts you have in place. Then after that, you should gather information from other systems to be able to fully drive a business objective.
Tell me about how Pramata addresses the data gaps.
Praful: For each gap in the customer lifecycle we have a specific solution. One that allows you to digitize the information needed to achieve your stated objective. For example, we have a renewal manager solution that will allow you to decrease your churn and increase the effectiveness of your renewals process by giving you accurate, complete, up-to-date and actionable information. Same thing with price changes. Same thing with compliance. We have a variety of different solutions that address specific gaps in the customer lifecycle.
There’s great value in solving multiple data gap issues. For instance, one of our customers is a $10 billion (with a B) technology company that delivers hardware, software and services. We annualized the value of addressing their data gaps, and it ended up exceeding over $247M a year. That’s a pretty solid ROI. Plus, once Pramata is fully implemented, and we get people up and running in 90 days, there is a great longer-term value associated with filling data gaps.