Driving Digital Transformation with Clean Customer Data
- DATAMASTERS on Demand
- Driving Digital Transformation with Clean Customer Data
Customer data underpins the digital journey that many companies are undertaking. In this session, Avid will share their digital transformation story and touch on topics like why customer data is key to this initiative, how they continually clean and curate this data and what business outcomes organizations can achieve when they have 360-degree customer views.
Speaker 1: Data Masters Summit 2021. Presented to you by Tamr.
Larry Simmons: Hi everyone. I’m Larry Simmons, Chief Customer Officer at Tamr. Joining me is Dinny Mathew from Avid Technology. Dinny is Avid’s Senior Director of Enterprise Architecture, Data and Analytics. He leads the company’s digital transformation efforts and helps align them to AVID’s business goals. Dinny, thanks for being here.
Dinny Mathew: Thanks Larry. I’m very excited to be here today. Thanks for having me here today.
Larry Simmons: Excellent. To start, can you tell me a little bit about Avid and who your customers are?
Dinny Mathew: Absolutely. To better understand our customers, imagine all the way you consume media today. From streaming subscriptions to television news, sports, in movie theater and a live concert. Avid’s technology is enabling creation of the content for all those experiences. Our customers span from very large global media companies to Hollywood studios, to individual creatives, freelancers, students who create and edit audio and video content.
Larry Simmons: That was great. Could you expand on the challenges you’re facing with your data and your vision for digital transformations?
Dinny Mathew: Well, there are several challenges. First and foremost is the lack of data quality and data governance with the customer data. So how do you define customer data quality? To me, there are a couple of metrics to make sure the data quality. First, consolidated and enriched customer data across the cross-functional applications, preventing duplicates and data integrity issues. Second, standardized customer attributes to drive business processes and analytics.
In today’s system landscape, customer data is generated in various applications, such as CRM, ERP, e-commerce, marketing automation, professional services and learning management systems, resulting in multiple versions of the same customer.
The next challenge is defining and modeling a clean customer hierarchy to meet the needs of the various business functions like finance, customer service, sales, marketing, operations, and more. So once you cleanse and standardize the customer data, the maintenance of the data quality and preventing bad data at inception in source system becomes a major challenge. Besides this, operationalizing data governance policies and procedures, change management and building trust in the data is an enormous task.
Larry Simmons: So I think it’s been interesting to see how you take lots of disparate data and then you put it into an engine and you clean it and then you have purpose and that purpose is varied. Can you talk a little bit about the business case for having that final 360 degree view of a customer and when it comes out the other end?
Dinny Mathew: Absolutely. It’s a great question. I’ll put the business cases in two perspectives, an outside in view, and an inside out view. From an outside in perspective, the primary business case for customer 360 is to deliver a personalized transcendent customer experience throughout the customer journey. From an inside out perspective, the primary business case is to drive innovation, improve operational efficiency, define and build standardized and trustworthy KPIs and enable AI and ML based advanced analytics.
Larry Simmons: Yeah, I think it’s always this interesting balancing act between innovating and thinking and then operating and data’s imperative to both. How does clean data fit into digital transformation and issues, and really show the bit business value from that initiative?
Dinny Mathew: Great. So for us digital transformation has two main area focus. First, transforming the business process to enable cloud and SaaS based business model. Second, digitizing the business processes with modern technology platform. So digital transformation starts with customer in mind and how to build a seamless end to end experience at every customer touchpoint. So for a successful digital transformation, the fundamental need is clean customer data, which will help understanding customer’s expectations, choices, and changing behavior in how and why they interact with us.
A customer centric, digital transformation strategy powered by a clean customer data can show several business values. Number one, by delighting customers with the value driven services, product and experience. Number two, digitizing the business process with the modern technology stack and providing a self-service can really excel in customer experience as well as improving the operational efficiencies. Digitization of business processes benefit from having a clean and accurate enterprise data for trustworthy analytics and insight, enabling better decision making. Our expansive digital transformation investment, and a new CTO with the cloud background, enabling our organization to explore a lot of emerging technologies, making it an exciting time for on team Avid.
Larry Simmons: It. Yeah, it feels exciting. The thought of taking such a dynamic space and not only delivering clean data, but then improving a customer experience because anymore that’s what customers expect. They expect across channel, across interaction, to feel like you’re understood and known because that’s really a great customer interaction. From a technical perspective, what methods work when it comes to developing accurate customer records? And are there any you might shy away from?
Dinny Mathew: Great question. So there are many technical solutions and architecture options available to develop accurate customer records. One solution is around tools that provide offline and one time data cleansing. The offline approach cleans the existing data in a single system. However, the moment it is done, bad data could trick in again because the solution may not prevent the creation of duplicate in source systems.
The second type of solution is the traditional rule based MDM solutions. The challenge with the rule based solution is that we need to know all the rules in advance to identify duplicates. It takes massive even amount of time and effort to analyze, design and implement and to test the rule based solution. The third type of solution is more modern, artificial diligence and machine learning based solutions where the tool learn from the data rather than implementing the rules. In my experience an AI ML based solution has an order of magnet to advantage over a rule based system in terms of faster time to market and higher data quality.
We use Tamr as our MDM tool and I’m amazed by the speed at which Tamr can dedupe customer data using its human guided AI ML based solution. The time to market was in weeks, rather than months. From an architecture standpoint, the solution needs to consolidate and sync customer data in source systems periodically, maybe as daily jobs. The architecture should also enable a search before create capability in source systems to check against MDM for duplicates before creating any new customer data. For example, Tamr has a low latency match API to integrate with source systems.
Larry Simmons: And I think if you tie that together for a minute, if you think. When you look at solutions that take very long periods of time to implement, your ability to automate them and continually run them and continue to prevent the bad data that will always enter as you said, can be almost an impossible task. But when you do it in weeks, you have the ability to iterate really quickly, run and then re-run on a continuous basis. So more or less you can remove that bad input format or the never ending ingest of bad data. So we’re glad to hear it’s working.
Dinny Mathew: Absolutely, you’re right.
Larry Simmons: Yeah. Companies have tons of different data sets, and you sound even more so than most because you have lots of inputs and lots of different diverse outputs. Why should they start with customer data as the pin underneath it all?
Dinny Mathew: Well, absence of clean customer data has a significant impact on customer experience, business operations, KPI, and metrics. So let me share a few examples to explain the impact on customer experience and business operations. Imagine a customer buying a product through e-commerce that triggers multiple workflows across multiple systems. If the customer data is not synced and linked in e-commerce, billing and fulfillment systems, it would cause delayed customer onboarding, resulting in diminish the customer experience. Take another example. Imagine the internal sales team creating a code in CRM and pushing to ERP for order process. If the customer data is not synced and linked between CRM and ERP, it could result in incorrect pricing, discounting and credit holds causing higher lead time to service the customer and poor operational efficiency. Bad customer data still impacts analysis in many ways, including unreliable reports, lack of trust in data, manual and silod cleansing of data for reporting. Therefore, it is critical to lead digital transformation with clean customer data.
Larry Simmons: Yeah, that’s great. It’s like once you get that, then you can look at all the other entities because without the customer being the starting point, it’s difficult to think how the others would feed. Ultimately someone or lots of someone’s at Avid are going to use this data. Who’s absolutely the happiest about having good customer data?
Dinny Mathew: That’s a tough question. I think the answer is pretty straightforward. All functions across Avid will benefit from having a clean customer data one way or other. Just imagine the marketing team, they can send personalized marketing campaigns. Sales can have relevant and focused conversation with the customers on adding value. Finance can now create trustworthy and reliable reports. Customer experience and success management, they have all the relevant information about the customer. So when they call for help, they can help and resolve issues quickly. They can also proactively engage with the customers. A clean and standardized customer address benefits supply chain and order management in wrong shipments. And think about the legal departments. They benefit from enabling data privacy policies and regulations, including GDPR and CCP.
Larry Simmons: Yeah. More and more of those regulations are almost becoming expected and understood as opposed to what do we need to do to get there. So I agree with you. It’s a really important topic on top of all the business value.
Dinny Mathew: Right.
Larry Simmons: What are some of the outcomes you hope to achieve by having better sense of who your customers are?
Dinny Mathew: Very good question. A company needs to know who its customers are. So without knowing the customers, it is improbable to provide a differentiated customer experience by persona. Else, all customer personals will go through a standard customer journey. The primary business [inaudible] to delight our customers by creating an exceptional and highly relevant customer experience by persona. Knowing your customers or understanding the various customer segments can enable the creation of business processes and customer journeys relevant to those customer segments.
So we have our customers through various channels. B2B, B2C and channel partners. We want to give a differentiated and optimized experience to those customers based on their expectations. Think about a customer conducting the customer service. Having all information about the customer, including products they own, their past interaction with the company. With all the information available to the support agent, they can help them to provide a personalized experience and quicker resolution, keeping customers happy. Imagine sales rep meeting with the customer. Knowing the customer and their recent experience within the company, knowing if they currently have support issue will help them to engage in a way that the conversation becomes more effective and successful.
Larry Simmons: Yeah, that was great. I don’t know. Is there anything else you might add? I mean, I think this has been a good discussion. It sounds like you’re very forward thinking and executing at a really top notch clip there. Are there other thoughts that you might want to add?
Dinny Mathew: Absolutely. So if you are starting out on the digital transformation journey, I want to emphasize the need to start with the business outcome. Driven digital transformation partner with the businesses and have a business lead on your side on every project or initiative. Visualize the big picture and keep your end state in mind. You need that north star architecture, however, do design thinking and agile delivery.
Larry Simmons: Absolutely.
Dinny Mathew: Taking this approach will set you on a successful digital transformation path.
Larry Simmons: Yeah, you think the goal is to deliver value at every step and show the business the outcomes and the true value of the data that you deliver so you can add the next entity. I think it’s always most powerful when you can deliver value as you go as opposed to waiting for a major milestone.
Dinny Mathew: Absolutely.
Larry Simmons: This has been great. Thank you so much for your time. We can’t thank you enough for you being customer, but also for taking the time to talk to us and other customers and prospects of Tamr. So thank you so much.
Dinny Mathew: Thank you. Thank you for having me. It was a pleasure talking to you.