datamaster summit 2020

Putting Customer Data in Shape for Maximum Impact

 

James Kobielus, Marc Alvarez, Louise Baldwin

James Kobielus, Senior Research Director at TDWI
Marc Alvarez, Consultant
Louise Baldwin, Solutions Director at Tamr

Join TDWI’s senior research director James Kobielus on this webinar to explore how enterprises are boosting retention, up-sell, personalization, and other monetization outcomes with machine learning-driven approaches to delivering customer insights. He will discuss how ML can be leveraged to:

  • Discover the best available customer data from a wide range of internal and external sources
  • Reduce the cost of consolidating, transforming, cleansing, and enriching customer data
  • Deliver relevant, curated data-driven insights into every customer decision scenario

Transcript

00:01 – 00:13

Brenda Woodbridge

Hello, everyone. Welcome to the TDWI webinar program. I’m Brenda Woodbridge and I’ll be your moderator for today’s program. We’re going to talk about putting customer data in shape for maximum impact.

00:14 – 01:07

Brenda Woodbridge

Our sponsor today is Tamr. For our presentations today well, first hear from James Carville is with TBWI. After June six, we will have a roundtable discussion between Louise Baldwin and Taylor and Marc Alvarez. But before I turn the time over to our speakers, I’d like to go over a few basics. Today’s webinar will be about an hour long. At the end of the presentation our speakers will host a question and answer period if at any time during these presentations you’d like to submit a question, just use the ask a question area on your screen to type in your question. If you have any technical difficulties during the webinar, click on the health area located below the slide window and you’ll receive technical assistance if you’d like to discuss this webinar on Twitter with fellow attendees, just include the hashtag TDWI in your tweet.

01:08 – 01:31

Brenda Woodbridge

Finally, if you’d like a copy of today’s presentation, you can click here for a PDF lying there on the left middle of your console. We are recording today’s event and will be emailing your link to an archive version so you can view the presentation again later if you’d like or feel free to share it with a colleague. So again, today we’re going to talk about putting customer data in shape for maximum impact.

01:32 – 02:22

Brenda Woodbridge

Our first speaker is James Kobielus. Jim is a veteran thought leader, industry analyst, consultant, author and speaker from analytics and data management. Over the past three decades, Jim has held analyst positions at Future on Research, Forrester Research. Current analysis and the Burton Group. He also served as a senior program director, product marketing for Big Data Analytics for IBM, where he was both a subject matter expert and a strategist on thought leadership and content marketing programs targeted at the data science community. at TDWI Jim focuses on data management, which encompasses database platform, data governance, data integration, master data management, data ops pipelines and more. Jim, I’ll turn things over to you now.

15:51 – 16:07

Brenda Woodbridge

Great, thank you very much, Jim. And just a quick reminder to our audience, if you have any questions at any time during today’s event, please feel free to enter those into the Ask the Question window and we’ll be answering those questions in the final portion of our program. Yes.

16:07 – 16:59

Brenda Woodbridge

Now, as Jim mentioned, it’s my pleasure to now introduce our two guest speakers for today’s roundtable discussion. First is Louise Baldwin from Tamr. Louise is a solution director at Tamr, where she focuses on ensuring business impact and value generation for customers through master data. Before joining Tamr, she worked as an investment professional at Goldman Sachs and IFC World Bank Group. And joining her today is Mark Alvarez. Mark has built, delivered and managed sophisticated online information and analytics services for financial services firms and global information providers, including Thomson Reuters and the Mizuho Securities. He’s passionate about helping organizations leverage their customer data to drive digital transformation. Welcome, ladies and mark.

58:02 – 58:22

Brenda Woodbridge

Great. Thanks. Thank you so much, Louise and Mark. A lot of great information there. Yes, as you said, let’s go ahead and quickly jump into some of these audience questions. I’ll start with this one for you, Louise. Somebody is asking, does your ML technology totally remove people from the mastering process?

59:09 – 59:22

Brenda Woodbridge

Great, thanks. Here’s one for Mark. Someone is asking, what are your thoughts on build versus buy? What are the benefits of buying an advanced MBM tool instead of developing that in-house?

02:24 – 02:29

James Kobielus

Well, thank you, Brenda. Hello, everybody. Have a good having a good day. Yeah.

02:29 – 03:15

James Kobielus

So really the topic is cutting customer data in shape, putting your customer and data in shape for maximum impact and really maximum impact is all about making the customer happy, but also very much you profiting from engaging effectively and smoothly with the customer through every touchpoint. Where the customer data, of course, is the core tool that you need to be able to know your customer well, comprehensively and intimately in order to maximize the their satisfaction. Hold on to those customers, keep them from churning. But also you need that customer data to be able to serve them as efficiently as you as you can.

03:16 – 03:37

James Kobielus

And that’s quite a challenge, especially considering that many organizations have tens, hundreds, thousands, even millions of customers. How can you scale up your management of customer data and the necessary analytics needed to serve the customer across all your touchpoints all the time through customer data is the key for delivering maximum impact.

03:37 – 04:15

James Kobielus

Really, knowing your customer and the way you can achieve that promise is by using machine learning in line to your customer data pipeline. Use machine learning to accelerate, automate and augment more of the things that you do to know and to server and to manage the customer relationship really to to deliver data driven insights both of to human beings at those touch points, sales and marketing and so forth, but also to automate more of the functions that need to be automated in order to keep your customers happy.

04:15 – 04:40

James Kobielus

And the data needs to be trusted and be accurate, needs to be comprehensive, needs to be conformed, needs to be current and relevant to every decision that’s made by both the customer themselves directly through self-service interfaces, but also needs to be relevant to every decision that your personnel on the front line and also in the back end need to make to keep the customer happy.

04:42 – 05:41

James Kobielus

So we’ll talk about today is really the centrality of high quality customer data analytics for keeping the customer happy, holding on to them, upselling and cross-selling and target marketing and everything else you need to do. That relies on high quality customer data, the need for you to automate every step that’s feasible in the customer data pipeline because you’ve got more customers and more data and more touchpoints all the time. Where this data is critically relevant and the only way you can possibly keep up is by automating the entire pipeline from the discovery and ingestion and transformation of that data, putting it in shape to the cleansing in Richmond and also analysis of the customer data to drive derive necessary insights to make the best decisions. And also, I’ll touch on the need to leverage machine learning to automate, augment and accelerate the pipeline at every touchpoint.

05:41 – 06:09

James Kobielus

Machine learning is the key to automation, and automation is the key to managing the customer relationship. Twenty four by seven across every touchpoint in a world where data is exploding and where customers themselves have more choice than ever. So customer loyalty is something you cannot take for granted, and you’ll need to continue to improve your ability to manage customer data in order to earn their loyalty ongoing.

06:09 – 06:42

James Kobielus

So really, when you look at the impact the business impact of customer data, clearly it’s on the bottom line of really, how do you manage the entire customer relationship management lifecycle across all front end and back in business processes across all marketing campaigns, touchpoints, customer channels, be they online with a brick and mortar and whatnot? How do you manage that? The customer relationship across diverse transactions and interactions across mobile environments and across all stakeholders?

06:42 – 07:15

James Kobielus

Well, you know what you need to do. First of all, in terms of the KPIs, the metrics you need to maximize. Of course, as a business, your your goal is to maximize the customer’s potential monetary worth you through throughout the course of their relationship with your business, in which the KPI is to really maximize the net present value of that revenue. Lift your yield from managing the customer relationship and also very important maximize customer satisfaction and loyalty so you can boost renewal rates and really customer growth.

07:16 – 07:49

James Kobielus

And also what you need to do focus on in terms of the business impact is minimized customer churn and also minimize the net. Present value of the costs for managing that entire customer relationship. That clearly depends on greater automation and efficiencies in every and everything you do to manage the customer relationship. Some so boosting customer lifetime value, really, that’s maximizing the net monetary worth and minimizing the net present value of the cost and serving the customer throughout that lifecycle.

07:49 – 08:26

James Kobielus

And really the key to that is machine learning and other in-line data analytics. People are accelerating, automate and augment every one of these functions because fundamentally, businesses, I mean, there are lots of qualitative metrics one needs to set up really. In a quantitative sense, businesses exist to catch, keep and grow customer relationships, which really depends on knowing customers intimately. And in doing so, you’re going to be boosting both your your bottom line, but also, in many ways, boosting your brand equity. You know, the aura of great customer service, great value that keeps customers coming back.

08:27 – 08:50

James Kobielus

And so knowing your customer intimately depends on a holistic analytics throughout customer life, keeping all customer data at your fingertips on their what they bought for you from you. You know your interactions with them through the portal, through, you know, in person, through every channel you want up to date profiles of every one of your customers that include their preferences and propensities and interests.

08:50 – 09:21

James Kobielus

All that clearly, what your analytics, namely you to do if you’re using them effectively, is to unify all of the time horizons in the customer journey so you can have historical data on what you’re cutting, what customers have done with. You can have current data and what say what customer is doing on your website right now or on your mobile app, but also have predictive analysis able to boost the the scale of the lifetime value with an eye towards all aspects of the customer time horizon.

09:22 – 10:01

James Kobielus

So what you need to do, of course, is aggregate, correlate, contextualize, analyze historical, current and current data all the time. Keep it fresh in your data lake, your data warehouse, your lake house, whatever your platform is, and use the full power predictive analysis, whatever analysis, trending and modeling and forecasting to be able to predict how the customer might react under various circumstances. So you can get ahead of the game and make sure that you’re serving them as effectively as possible, considering what is likely to happen in the future from the customer’s point of view, really boost that customer experience under all circumstances.

10:02 – 10:32

James Kobielus

So knowing your customer and having the right analytics under your fingertips it will do is build more powerful machine learning, deep learning and other statistical analysis to be able to drive all of these decisions up upsell, cross-sell, customer retention, customer segmentation, personalization, targeting of offers and so forth and so on. You need the data. And so success as a business depends clearly on high quality customer data analytics customer data insights.

10:33 – 11:32

James Kobielus

A recent survey of data analytics professionals, we asked them from a technical perspective what can be improved in your customers analytics efforts to make it more successful? It really with an eye towards customer data analytics and what they told us was essential that they require advanced analytics to distill customer intelligence from disparate data within repeatable pipelines that came up front and center in terms of the feedback on really what they need to be successful as a business focusing on repeatable pipelines for customer data analytics sets automation. First and foremost. So automation is a key component, and machine learning is a key component in automating more of the data ops processes and also, in many ways, automating the ML processes, also known as model ops, to be able to have the best fit models in line to all applications to drive all these customer interactions.

11:32 – 12:22

James Kobielus

So really, in that in that in that server, we also asked what is the primary motivation for evolving your organization’s data strategy? Know supporting modern analytics was first and foremost, but also reducing costs. Having the best analytics enables you to boost customer lifetime value, but also reducing costs is very much part of that equation. So customers are the respondents clearly stated it’s the customer impact, but also very much the efficiencies gained from putting machine learning in line to your customer relationship management lifecycle. It really ML is a key business acid that is produced within a well orchestrated data ops pipeline, but also increasingly is is an asset embedded within that pipeline to be able to drive greater amounts of automation and contextualization across.

12:23 – 13:01

James Kobielus

All decision points. So we’re going to automation spans the entire data of the pipeline, and in a 2020 survey, we asked the professionals, what analytics tooling would you like to see your organization utilizing in order to really boost the the impact on the business to ensure the maximum impact in terms of customer retention, loyalty, upsell and the like? Automated insights came in number one, but also automated predictive analysis data preparation tools focused on automation. These are key priorities, so automation is very much a an enabler for business success.

13:02 – 13:45

James Kobielus

So when we look at the future of integration across both data ops and ML ops, EML becomes critical. It has become critical across the customer data analytics pipeline because, as I’ve indicated, it drives greater automation and enables greater closed loop human augmentation of various decisions and actions that need to be taken by your organization in order to make your customer happy. In terms of coordination between the frontend salespeople on the back end, people in terms of in the factory and logistics and so forth to to drive production and delivery and and remediation and repair and so forth.

13:45 – 14:08

James Kobielus

So in many ways, EML is the key to automation of this pipeline augmentation of human decision points that are critical for holding onto the customer, making them happy, but also in many ways for accelerating these analytics workloads. You need ML in line to everything in the pipeline across the entire customer relationship.

14:09 – 14:35

James Kobielus

So when we look now at what’s needed for for putting your customer data in shape for maximum impact, it’s quite clear. The most critical is quite clear that a modern data ops pipeline is very critical. That enables you to drive all analytics from a common pool of accurate plans, up to date current and relevant customer data. So that enables you to really.

14:35 – 15:06

James Kobielus

The benefit is to discover the best available customer data from diverse sources. EML is critical to reducing the cost of consolidating, transforming, cleansing and enriching all that data to automating every one of these processes in the distillation of the insights you need to serve the customer well. EML is clearly critical to augmenting the productivity of your data as professionals so they can keep up with the sheer scale of the amount of data, the amount of backend processes needed to put it in shape.

15:07 – 15:49

James Kobielus

EML is critical to contextualizing and annotating more of this data, so to enable greater curation of the data in terms of enabling you to identify what is more relevant to any particular decision that might be made curation and governance and really for accelerating every workflow in a world where terabytes are old hat, where we’re talking about petabytes beyond customer data are becoming part of the core wheelhouse of everyday data pro. That’s managing customer data in the business world. So those are the takeaways. I want to thank you and I’ll hand it back to Brendan and then we can hear from Tamr.

17:02 – 17:44

Louise Baldwin

Fantastic. Thanks very much, Brenda, and thank you, Jim, for setting the scene. There’s a lot of rich detail in what you just presented on why companies really have to get their customer data in shape. I’m very much looking forward to the conversation with with Mark and opening up some of his experience, you know, addressing some of some of the challenges, but also taking some of the opportunities that come from really leveraging customer data. So Mark, maybe starting off, you know, as someone who has spent a lot of their career, you know, leading data strategies at top companies, most recently at Thomson Reuters. I’m sure you’ve lived a lot of these themes that Jim has touched upon.

18:35 – 18:58

Louise Baldwin

For sure, for sure, and you know, within that, there’s the whole concept of how the data is being, you know, is it being seen as an asset? Is it being seen as a as a business asset? You know, there’s a lot of talk around the lifetime value of customers. Have you seen organizations make a shift in mentality to thinking more about customer data as a true business asset as a whole?

21:15 – 22:08

Louise Baldwin

It is really interesting to hear you describe it as an inflection point, because I guess so many companies are have gone through or continue to go through sort of the cloud shift. And I think with that they’re getting the infrastructure benefits of scale. But now starting to think to your point about the actual usability of the data and the quality of the underlying data in order to put it into action. And that is not really a lot of what we’re what we’re focused on at Tamr in terms of creating that mastered clean and accurate view. When when you think about, you know, companies moving to take action upon the data and maybe even sort of leverage a cloud transition, what do you think are the real challenges now? Like, are they still technical challenges or is it cultural challenges?? Or is it a combination of both in terms of actually going to use and leverage the data?

25:56 – 26:51

Louise Baldwin

Yeah, within, you know, unpacking a lot of kind of what you said within that, I love this idea you introduced of, you know, having nowhere to hide these days, really. You know, back in the days where data was completely siloed across systems and whatnot, just the data aggregation in the cloud really bringing a new level of transparency on the work that needs to be done really to make the data actually usable by having it, by having it centralized and also this, you know, theme that you bring up as well around the skills like within the tech org, how much can we expect in terms of the breadth of sort of the challenge of having to bridge both the business and the technical from a skill sets perspective, though, how do you think about that? You know, when you say we can’t expect that much from solely that it’s still it still leaves a lot to be done as a whole. How how do you think about that from a skills perspective of the data? Org.

31:45 – 32:40

Louise Baldwin

There’s a lot within the US going back to one of the themes that you mentioned in terms of getting input from those who know the data as soon as the subject matter experts. You know, a theme that is very core to team remark, as you know, is human guided machine learning. So to say, you know, machine learning is needed to benefit, you know, the scale advantage and everything that it brings with it. But at the same time, needing input from those who know the data best to guide the algorithms and to be able to have their input to make sure that the data is ultimately usable. What’s your take on machine learning approaches as a whole? And have you seen sort of a willingness to embrace machine learning because it sometimes can be considered a black box or, you know, just less transparent or scary to some organizations?

37:01 – 37:26

Louise Baldwin

Yeah, the theme that you’ve touched on of I’m going to go to summarize and probably not do it justice, but using what you got right, from what you describe in terms of the data of enriching what you have, leveraging what you have because often I think for a lot of companies, there is a lot of depth and richness to what they have, if it’s leveraged and used effectively and then using what you have in terms of your resources efficiently.

37:27 – 38:05

Louise Baldwin

So, you know, saving saving time, using their expertise or knowledge of the data in a constructive way when the technology is there to support them and hopefully do a lot of the heavy lifting, but still sort of benefiting from their their experience that goes with it, you know, thinking maybe about the the approach at the end, Jim very strongly suggests implementing a data ops pipeline and kind of leveraging the agility principle from DevOps and thinking through best of breed technologies. What have you seen of kind of the data? Approach the whole.

44:31 – 44:53

Louise Baldwin

Hopefully, it is a theme. That that continues to blow up and to your. Point on on being a pivotal moment Moore brought. Only for data management off the back. As a side, hopefully data ops is a thing the.

44:57 – 45:36

Louise Baldwin

Was with that. Yeah. You know, thinking, thinking through hopefully fruit. We will be next year. You know, Mark, you noted that we’ve talked a lot. But about customer data today, it is our central theme. But of course, companies have tons of data that could be cleaned up and, you know, at Tamr we think of at mass doing data as a whole. And for some very some of our customers, that means customer data. It could mean product data could mean reference data. How do you think about sort of the benefits of prioritizing customer data or at least as a starting point?

49:48 – 50:39

Louise Baldwin

It’s an important thing we’re raising. I do feel like very often it’s easier to point to the cost savings. You know, it’s the thing that people automatically go to when thinking through budgets. But your point customer data just unleashes this potential to think about the top line growth as a as a whole. It’s not easy. We try our best to partner with our customers to think through those implications and ripple effects of how customer data is used, because so often customer data is so critical to the organization as a whole. There are so many uses, you know, the sales team, the marketing teams to be the finance team’s operations, but really huge, huge ROI to be gained, a huge business value to be gains. If you can connect, connect those dots, mark within that know.

50:39 – 50:43

Louise Baldwin

Speaking of connecting the dots, where are you going to jump in, Mark?

50:47 – 51:06

Louise Baldwin

I was going to ask you about digital transformation with within that team because it is so often sort of from a business perspective, what what people points to. What’s your take on that sort of the direct connection between companies thinking about digital transformation and company thinking about clean, accurate data to back up decision making?

57:34 – 58:00

Louise Baldwin

We spoke a lot about the challenges, though it was great to hear that sort of you bring it back to the opportunity, right? And the strategic planning that companies really now almost have to be putting putting into their data. But the huge opportunities that releases from a business perspective. I know we’ve only a few minutes left and I know we’ve had some questions coming, so I’m going to hand it over to Brenda to give us some questions from the audience.

58:24 – 59:07

Louise Baldwin

Yes, it’s a great question, and it doesn’t in it, but it does greatly reduce the amount of effort that has to go in and typically with the manual effort be reduced by about 80 percent. So how are machine learning works or Tamr? Approach to it is a supervised approach. We call it human guided machine learning, where essentially we like to believe we’re putting sort of data curators at the place where they’re going to have the most impact. So giving feedback into our into our models, leveraging it sort of the transparency to to review how we clean and master the data, we’re doing it in an efficient way so that the bulk of the manual effort is is essentially reduced.

17:46 – 18:32

Mark Alvarez

Absolutely. I think we’ve been living through them for decades now. I think that Jim is quite spot on. Well, it’s the observation that I think is really important, and I’m glad James pulled it out so strongly. As you know, data has now emerged as an input to the business. Every function is becoming highly quantitative and statistical driven, and that’s placing a real premium on the ability to generate the data. You know, everybody’s got their own plans in place now to produce data content. So I think we’re at a tipping point across the economy where more and more firms are starting to face these issues and starting to make the associated investments. And that includes the investment and the value add with applications like Tamr.

19:01 – 19:42

Mark Alvarez

Well, I think it’s the data manager’s lament is that we always treat this as a technology problem, and the fact is it’s it’s really not a technology problem. It’s got its own, its own dimensions to it. I think we are seeing a shift. I think we’re starting to see it. I think we’re a long ways away. If you look across the Fortune 500, Fortune 1000. You know, I think we are still very much constrained by technology decisions that were made 20 years ago. Companies are, you know, faced with technologies that don’t necessarily scale or or can support some of the applications we’re talking about today. They’re very fragmented landscapes.

19:43 – 20:03

Mark Alvarez

You know, it’s not like anyone came into this with one single data model in mind and quite frankly, technology and capabilities across industry. And this knowledge in general and the arrival of cloud technologies suddenly is making available the ability to generate results which never existed before.

20:04 – 20:28

Mark Alvarez

So so I think we’re very much I view this and I write about this as well, but I view this very much as an inflection point starting to show itself in the economy. We’re starting to see firms undertake journeys that you know, are couched in programs like digital transformation that really place an emphasis on the ability to supply data, whether they they like it or not.

20:28 – 21:12

Mark Alvarez

And then to Jim’s point, or importantly, it’s not just a question of data supply, it’s a question of the quality of that supply. You know, with today’s capabilities and the sort of machine learning from, you know, that’s available at your fingertips from firms like Tamr and others, you know you’re able to get to a very robust and sophisticated statistical based understanding of things quite quickly nowadays. And I think we’re just beginning to scratch the surface. So I think we’ve seen the first wave of investment come. We’ve seen it, you know, augmented with AI and machine learning in particular. But I think, you know, the ship has a long, long ways to go before we fully understand and fully embrace the whole scope of it.

22:10 – 22:36

Mark Alvarez

Well, one, I think it’s a very complex combination of factors, and I think we probably collectively across the industry would have meant we haven’t fully embraced that complexity yet. I mean, look, the reality is as as we’ve all been doing cloud migrations for the past five 10 years, and we’ve really started to take the lid off of the incumbent technology stack that exists in any given firm.

22:36 – 23:21

Mark Alvarez

One of the lessons you learn about cloud technology is Boyle Boyd. You identify your bad data very quickly and really very quickly. Highlights to you what the usability and the reliability of what you’re producing in-house or what you’re buying and and then and then enriching and using in-house. So I think that’s one factor. As you migrate to cloud, the appetite for data in any given firm just goes up exponentially. And, you know, I think increasingly the the story is, you know, moving to cloud just isn’t in the lift and shift of infrastructures, even though it is that it does have benefits, but it opens up a far, far greater canvas of opportunities and demands on the organization.

23:22 – 23:46

Mark Alvarez

Look, I think the reality is, and certainly speaking from practical experience, the technologies to solve these problems are there and there’s no lack of technology capability out there and doesn’t seem to be a lack of capital to throw at this. So I really don’t think this is a technology problem, and I don’t and I don’t think viewing it as a technology issue is is really going to be very helpful.

23:46 – 24:19

Mark Alvarez

This is a domain that has its own set of knowledge base to it. It it places a premium on discipline project management. It places a premium on disciplined and and robust specification and definition. It places a premium on your ability to engage with your customers, as James and James is saying and ascertain where the future is going to be, where the opportunities are. Those are skills you would find in a technology organization, and I don’t think it’s fair to technologies organizations to to solve those problems.

24:20 – 24:51

Mark Alvarez

So I think, you know, you’ve got this whole combination of events that come around. And then to top it all off, you know, you need a high degree of subject matter expertise by definition. I mean, data is a record of of of of particular events within a given subject matter, whether it’s financial data or if it’s accounting data or whatever. And then the last part of the complexity is it none of it’s discrete. I mean, you need to use bits and pieces of all of it to come up with that 360 view that Jim was talking about.

24:52 – 25:24

Mark Alvarez

So that that most of that we’ve focused historically on the problems of distribution access permission, which our technology issues and technology is a big piece to play in all of this. But the reality is in today’s day and age, to get that value, to get that business result means a very, very different combination of skills is an hour before, and I think you can just take it as read with that, that the technologies are there. They’re they’re they’re mature. They were, I know from personal experience working with you, and Tamr. It’s there, it works.

25:25 – 25:54

Mark Alvarez

You know, I think the future is going to be quite bright. The question is, can you keep up with the pace of change? Can we keep up with the scale of the growth of data? You know, when I when I started in this business, gigabytes was the challenge of Tim’s talk of a petabytes. So these are all very, very dynamic and fluid factors, and they place a premium on. I’m taking a very strategic approach to it, not one that’s, you know, constrained to just the technology organization.

26:52 – 27:38

Mark Alvarez

I’m going back to your question. I’ve thought about it a little bit differently than most of my colleagues on the CTO circuit that I had. And you know, my view on this is this this? This shouldn’t be a challenge. What we should be doing is equipping our technology organization to be successful as possible, and we do that by getting away from asking the technology organization to do a bespoke integration of disparate sets of data which they may or may not be familiar with. And, you know, trying to come up with some sort of view that satisfies a point problem for the business. Instead, let’s shift to the model where you know and data vendors have been doing this for decades, but let’s consolidate the data for them and make it available as an actual service.

27:38 – 28:06

Mark Alvarez

When you start thinking of it in those terms, then some of the best practices that we’re we’re kind of painfully stumbling on as we move forward in this, in this environment start to make sense of things like centralizing under one command and control structure, centralizing on one data management technology stack, and there’s plenty to choose from. You know, it may. Maybe it’s cloud based. Maybe it’s not. I think increasingly it makes sense to be in the cloud and there’s lots of opportunities.

28:07 – 28:35

Mark Alvarez

So from the point of view of skills, I kind of think this is really about rebalancing the equation to where let’s use our technology people to deliver those solutions. And I think capital markets is a great place to look right now. But a lot of these technology solutions have a limited shelf shelf life. I mean, you know, if you if you create a better, a better mousetrap, your competitors are probably going to reverse engineer you sooner or later. And in case capital markets some sooner so.

28:36 – 29:09

Mark Alvarez

So I think you’ve got to kind of change the balance the way you’re doing things, you need to take a much more strategic, forward looking view. I think you need to be engaged with your customers at a level a data level now where you have the opportunity to really start to pick out the patterns, especially when using machine learning, you start to be able to pick out, you know, the bigger the bigger picture and you start to open new opportunities. And I think that that story applies equally within the firm, as it does to a firm, you know, trying to transact business with their customers.

29:09 – 30:03

Mark Alvarez

Here’s your opportunity to look at data as a service. Look at it, manage it as a service, get the economies of scale of publishing and persisting and quality assuring and and distributing this content, you know, on demand that’ll give you economies of scale that will position you for the future more important and let your technology people rely on the resource of data rather than having to do, you know, a bottom up, figure out what this data is and go and find a couple of data scientists to help them figure something out for, you know, a point solution. I think now we can start to look at this is much more of a holistic engineering approach to things. So I think that’s that bodes well for the key to the technology future. And I think we see this, we see this in the quality of our analytics center produced that we see this and you know, the robustness of those analytics, you know, but frankly, five years ago, we couldn’t even dreamed of producing.

30:04 – 30:43

Mark Alvarez

So, you know, I think it’s it’s all trending in the right direction, but for an organization, it means really thinking about what is the appropriate data management organization. I think Data’s I think the observation I’d make is data to any organization is is very, very organic. So you’re going to have to come up and think about this and put something together that works for you. I don’t think there’s going to be one playbook that solves everybody’s problems. And then I think you got to look for the right skills that fit your organization, whether those skills are in data science, because you’re doing some excessive normalization that nobody else can do to get a competitive advantage. Or maybe it’s an application development.

30:43 – 31:43

Mark Alvarez

Certainly some of the bigger pictures that are out there that I focus on, you know, I’m definitely looking for the ability to vision and articulate the ability to tell the company. What it’s doing at a very detailed level, the ability to architect quickly on solutions, you know, the ability to codify this into and deliver them through through rigorous project planning is probably the biggest, single biggest area of a win. Don’t just throw it over the fence to the technology department and expect them to give you a result. I think there’s a lot of work you can do to optimize for the technology department. So those are some of the skills that are that we’re seeing. I think you’re going to see a lot more skills around integrating multiple applications to make them work together as a single orchestra. That’s new ground. So that’s some of the areas where I’m thinking about and working with my clients on that. And I think we’re going to see a lot of that in the future.

32:42 – 33:32

Mark Alvarez

There is that, but I would also say that you don’t have a choice. It’s the new normal that everybody everybody’s expecting. That they’re getting as much value out of this resource that costs them a lot of money one way or another, whether it’s people or technology or or infrastructure that they’re buying. So, you know, I think it’s just become the new normal in past five, 10 years that you know, your customers, your users of data, whether they’re my users inside the firm or I’m selling a service on the internet, it doesn’t matter. I presume that we are applying knowledge base to make the content I’m looking at as relevant and consistent and reliable as possible. It’s just the new normal.

33:33 – 34:18

Mark Alvarez

My case, my most recent case working with Tamr was about enriching the data we had. And it wasn’t. It wasn’t adding more fields to the data, it was identifying the patterns and linkages within the data. That’s what really gave us a lot of value and I think is a value to everybody so that we know that our customer hierarchies are properly populated and properly maintained, or at least to a level that’s good enough to drive our business. And that’s that is can only be done with humans in the loop and not necessarily any humans. But these are humans who are comfortable and understand, you know, accounting and end client account management.

34:18 – 34:51

Mark Alvarez

So I think. Being being able to track that, being able to do it in a way that provides transparency for governance purposes is vital because nobody wants the liability of modifying their data haphazardly and then taking the consequences. That’s really just a liability business. You may realize that liability through a lack of a sale or a cancellation from a customer, and you may realize it through better relations and increased revenues.

34:52 – 35:31

Mark Alvarez

But I think, you know, I quite honestly, I think it’s misplaced to worry about these black boxes. I think the abilities to govern the whole exercise, you know, from start to finish exists. I think we’ve come a long ways when it comes to data governance. It’s having the will and the depth and the discipline to apply governance as part of your process, not as an afterthought, you know, and data governance is really taken off in response to the financial crisis of 2009, where we’ve really sought accountability. But the real value is if you can invest it in your operations and really drive the business.

35:32 – 36:19

Mark Alvarez

And I’ve seen this firsthand, you know where because we were able with teamwork helped by the way, to improve that customer data and because we were able to streamline its distribution through our CRM platform. As we standardize on a single global CRM platform, we were actually able to drive tangible, measurable results, like freeing up our new business salespeople. You know, something something in the order of five to six hours a week just through productivity enhancement of a tool they already used. So I think those are the types of things that can really move forward, and I think you only get there through having humans in the loop. And more importantly, the right humans operating in the right business process.

36:19 – 36:58

Mark Alvarez

So again, you’re back to the situation where this isn’t a simple problem. There are many different dimensions to it. And I think you have to go into it with a design mindset. You know, you have to understand and put boundaries on the problem and try stuff. I mean, I find I find using postseason and other other short term exercises very useful. And I think this is a fertile ground for that type of experimentation. And then you you need to operationalize and commercialize it as quickly as possible. So I think you’re going to see a lot more of that in the future. And quite honestly, the technologies today are very well placed to operate in that manner, I think.

38:08 – 39:01

Mark Alvarez

Very, very early days. Is. You know, I think. You know, I. Think there is a very big demand. And shown to making your data. Available as a service. That is very highly operating. Spiritual in nature and. And I think it’s been largely overlooked.

39:03 – 40:00

Mark Alvarez

But or the history of. Certainly my 35 plus. Tears in eyes in this industry. So I think it’s a very good. The time to start looking at those. And I think, you know. I think that there’s there’s nothing new here, you know? Standards exist in. In areas like project management and ISO nine. Thousand four, operations management of. Amongst other things. I think it’s time to dust a lot of those off and recast them for how to apply them in this data.

40:01 – 40:35

Mark Alvarez

The world the data vendor community has been doing this for decades. So I think they probably have. A pretty good handle on how. To produce and maintain. And more parental support content. And for use and. And far more importantly, for re-use across the organization. So I think you got to bring that in house now.

40:35 – 41:14

Mark Alvarez

Oh, and. And I think. The opportunity is theirs to look to your existing resources, to look at your existing methods of working and ask yourself, how can you leverage those? I think it’s going to be a lot easier to leverage. Resources you already have. Certainly with some of the projects I’m engaged in right now. Much easier to leverage the resources that are already. Just within the organization. For the purposes of getting.

41:16 – 41:39

Mark Alvarez

To a reliable. Supportable, scalable. Data as a service than it is. To try and stand it up from scratch, and I think I think some firms might be successful at doing. But not a lot of options.

41:40 – 42:33

Mark Alvarez

So when it comes to business process outsourcing, right? Now, which might build out. But I think, you know. Lot of that when we talk about data, especially we’re talking. But they did Jim’s talking about which. Is, you know, just. The customer. And your and your interaction. So with the customer, this is. Highly, highly organic and in. Internal stuff, that’s where the value is just, you know. And I think that’s the scene. You know where you want to mine, so I don’t.

42:36 – 43:00

Mark Alvarez

The outsourcing a lot of that. I mean. These are the books and records. And you’re now making those books and records of available through the organization. Asian through it, through a series of analytics and. Other services, so.

43:06 – 43:49

Mark Alvarez

  1. I think it’s going to happen. I. This thing, best practices will start the. Manifest themselves if they haven’t already. I think there are. Or significant on. Opportunities for improvement here. You know? Maybe next year, we’ll be talking about data. In the same way, we’re talking about machine learning. In today’s day and age.

43:56 – 44:20

Mark Alvarez

So I definitely. See, it is a trend that’s coming, and I also see it as. Essential, I think. You know, you have to get scale and the only way. Where you’re going to get scale is to formalize your. Data operations.

45:40 – 46:14

Mark Alvarez

Well, you know, I kind of I kind of think that that decision isn’t, you know, just isn’t an optional decision. I think every firm that I work with certainly has a sales organization, and I don’t think I’ve ever met one who would say they’re really happy with their data. So, you know, I think there are natural reasons that align with firms that are trying to grow. You know, for other businesses that, you know, biases that the whole data discussion towards the customer, that’s been an interesting finding.

46:15 – 47:10

Mark Alvarez

I also think though, if you have the discipline to view this as a business plan through view your data spend through the through the lens of a business plan and through the lens of how am I going to get it generate return on my investment here. I think it very quickly highlights the value of areas like customer like product because they point to and speak to very quickly to returns on investment that are composed of a combination of revenue growth and not just cost savings. I think if you’re entering the whole data management discussion and you’re only interested in saving costs, you’re probably going to fail. You’re going to you’re probably going to not realize the gains that you’re going to go for. You will probably because it’s a cost thing to constrain it. And this is a big reason why you really can’t put your leadership and management of this in technology.

47:12 – 47:45

Mark Alvarez

This is really about investment, and this is you should be looking for topline results, either either quantifiable or non quantifiable results. But whether the things like improving your net promoter score and the correlation between improvements in your net promoter score and your revenue growth numbers, those are the areas you’re going to look. And you know, I’ve done this analysis now several times, and the reality is investing in anything that drives your your efficiencies in the front of the house are most welcome and easy to recognize, easier to recognize.

47:46 – 48:44

Mark Alvarez

But there’s a very huge number of difficult dimensions to add to this discussion in that, you know, the maturity of the firm is really important. You know, how mature is the technology they’re using? Is it is it flexible enough to make these expansions and changes? What’s the firm’s ability to realize, actually realize again, you may spend all this money. And I mean, I’ve heard this lament many times. You know, you spend all this money. You don’t know where it’s going necessarily. It’s not accounted for. It gets budgeted once off, you know, in the in the technology budget and kind of goes into the black hole, you know, of budgeting at that point. So come into it really with the point of view of a business plan and come into it with leveraging your organization. Make sure you’ve got the accounting behind this and then it’ll point to you where you’re going to get your quick hits and more importantly, where you’re going to get your big hits.

48:45 – 49:10

Mark Alvarez

And so far, I have yet to see anything that shows the same combination of top line benefit and bottom line benefit that investing in customer data does. That may change? That may change my firm, but I think, you know, we’re going to see over time this type of pattern emerging and then that’s what will drive the best practices. It’s not easy is this is not easy.

49:10 – 49:45

Mark Alvarez

And again, I go back to your first question. This is not something you can ask the technology organization to do. They don’t know how to do the accounting. They don’t want to do the accounting. They want to get on and provide best in class technology and analytics. You know, we want to drive towards benefits that James is talking about. So I think, you know, you’re seeing this very, very big shift in the industry. And I think I think we’re going to see more. Like I said, I detect an approaching an inflection point to steal a phrase from Malcolm Gladwell.

50:43 – 50:44

Mark Alvarez

Well, that’s why I’m curious.

51:08 – 51:54

Mark Alvarez

Oh, well, yeah, I mean, I think you’ve put your finger on one of the most profound areas of investment in today’s economy. I think every firm survey and Fortune 500 Fortune 1000 is looking at how they can leverage digital technologies, modern state of the art technologies that we call digital to to drive their business. You know, I think a lot of the industries that I work in and work with, they’re hyper competitive. The options for growing your business are limited. They’re constrained, you know, everybody’s got access to the same technology, so you only can only ever expect to be as good as your next smartest competitor.

51:55 – 52:41

Mark Alvarez

And you know, I think this is a point where you can differentiate, where you can actually start to manage the data content to your advantage. And I think that points to, you know, certain key things you want to do in your organization, whether you’re doing a digital transformation program or not. But they’re eliminating friction in the use of the information your firm produces is, you know, I know that sounds like a statement of the bleeding obvious, but that seems to me, you know, an area that is ripe for innovation, ripe for capabilities to develop. I think that is inherent to a digital transformation program soon.

52:41 – 53:19

Mark Alvarez

As you turn loose today’s modern technologies and analytics on the data supply you have available, you know, you see pretty quickly what the problems are and you said and you certainly hear what the pain points are. So, you know, I don’t I don’t think there’s a choice. I think you, you you owe it to your, your investors, you owe it to your employees. You want your customers to do everything you can to eliminate that friction and to equip your firm to be as effective as possible. And I think James view on the 360 customer review backs that up. That’s that’s the modern way of working.

53:19 – 54:01

Mark Alvarez

Are we there yet? Absolutely not. I think we’re at the beginning of a very long journey here. So starting from that premise, you start to see that you can’t sit there and start writing. Business plans are under digital transformation programme without at the same time saying, by the way, we have to get our data service in order here. You know, we have the best digital program in the world. But if we’re populating it with bad data, it’s going to fail. And those are, you know, those are just basic axioms that I think I think anybody who’s worked in the technology space for the past 30 years would agree with. And I think you’re seeing this across the industry press now. We’re seeing this everywhere.

54:01 – 54:32

Mark Alvarez

And the other thing is, it’s a very it’s a very large scale problem bigger than you probably realize. There was a study done by MIT Sloan back about five or six years ago now with Experian and IBM, and they’re finding this survey of the Fortune 1000. And, you know, they came back with the astonishing number that, you know, for those firms between 15, 15 and 25 percent of revenues were being spent on data. So this is a huge latent problem that’s out there.

54:34 – 55:12

Mark Alvarez

You know, the the opportunities, the upsides, I think I mean, even, you know, I think the accountants are starting to see that there’s significant upside here. And it’s not just cost. I mean, I just think if you’re looking at it through as a cost saving initiative, you’re missing the boat. The opportunity here is to generate more value. It’s to broaden your business. That’s to accelerate your business. And, you know, I think it only works is if you get this handshake between the digital side of this, you know, the analytics and the service management and everything else and the data side, the content side, if that’s that’s where it’s going to work.

55:13 – 55:40

Mark Alvarez

I think every firm has a combination of a maturity of the ability to realize wins. We realize impact of the. Ability to influence their retention rates, the ability to influence their pricing tables, you know, through adopting some standardized pricing and global pricing practices, all of which manifest themselves as data change.

55:41 – 56:20

Mark Alvarez

So so I think you get pretty big bang for your buck in your digital if you invest in your data first. But don’t think that it’s it’s a one off investment if you just do it once. It’s probably going to peter out pretty quickly. You want it. You want to make sure it’s firmly entrenched in the program. You want to set goals, you want to set measurable goals for this stuff. And then I think you want to start to really understand, you know, what is it costing you to to generate the level of business that you have today? Versus what would it cost you to take it to the next level or at least understand what happens if you don’t make those investments?

56:20 – 57:04

Mark Alvarez

So the two, I think, are, you know, intricately coupled to pursue one without the other, I think, would be a mistake. I think you’ll only get limited upside if all you invested in was your data. But I think if you invest in both data and digital together and you optimize that mix, that’s right for your firm based on those factors and other factors. I think you’ll, you know, you’ll get to a point where it becomes much more like the annual sales planning exercise and much less like the current environment of Oh my god, we got a problem. It’s a big pain point. Let’s go fix it with money that we have to go fix it.

57:05 – 57:32

Mark Alvarez

So, you know, I think we’re moving away from these things that we used to be able to afford to starting to view this much more strategically. And I think that’s going to be good. I think that’s going to flow through to, you know, more efficient operations, better customer relationships. You know, I think this will shift to a digital economy offers significant upside across the board. If we get it right and we’re a long ways from that.

59:25 – 59:54

Mark Alvarez

Oh, great question. You know, full disclosure, my track record, I’ve been a buyer, you know, I don’t think it’s a one off purchase. I think it’s a combination of pieces that, you know, optimize how you produce and author and produce your content. It’s how you assure the quality of your content, how you enrich your content. Full disclosure on the team or customer use team, or very much the center of enrichment.

59:54 – 1:00:41

Mark Alvarez

So I think it’s a number of things. I think, you know, look, I think it’s. It’s, you know, up to each you organization to figure out the the right mix of resources here in today’s day and age, I don’t know of a single firm doesn’t consider themselves constrained on the technology front. It’s just that the skills the skills base is very sought after and it’s very expensive and getting more so. So I think that bias is my thinking anyways to to not just buy versus build anything. It’s too simple a way to look at it, but I think it’s buying configure and deploy and fine tune, you know, get the results versus doing it all.

1:00:41 – 1:01:15

Mark Alvarez

Bottom up my personal and my personal experience in the master data space. You know, most recently, it was a Fortune 500 multinational. Buying in a commercial platform was the right decision. It greatly accelerated our efforts. It also interestingly brought in a cultural dimension, which I hadn’t really thought of going into it. And that was that, you know, we actually bought in a platform deployed on cloud. You know, it pushed a lot of the right buttons at the firm said it wanted to do, but hand me a lot of progress on.

1:01:16 – 1:01:55

Mark Alvarez

And that’s created this sort of bandwagon effect where people started to embrace the fact that, you know what we could standardize, how we produce our data, we could standardize how we acquire the data into the platform. We could start to see the benefits, you know, in terms of reduced time to build out our databases. You know, we just just just comparing project plans was was was was was enough to tell us we were on the right path. So we started to get a, you know, started get a shift away from working with all the old creaky green screen stuff we’d been banging away on for the past 30 years and starting to use spreadsheet.