Data - Driven Service Development



Okay, great. Welcome, everyone, to today’s session on data driven service development presented by the AI for group build well labs, like to welcome everyone, no matter where you are in the world or whatever time zone you’re in. Welcome. Good afternoon, good evening. Today’s session, we are in today’s session, we will spend some time talking about data driven service development, and also what it means to develop data as a service infrastructure. We’ll be talking through a number of different issues in regards to getting platforms set up. What we won’t be discussing today are specific platforms. And so the material that we’ll be going through today, and some of the concepts and principles we’ll be covering today are meant to be a bit more overarching, and not really specific to a specific type of platform, there’s probably a whole session that can be just developed or that can be developed just to address what type of platform would be would be best for your particular need for your particular enterprise, for your particular company. So we won’t be going into details on platforms. But I think what we’re going to spend some time doing today is kind of going through some kind of overarching principles of data driven service development, and what it means to develop data as a service infrastructure. So let’s begin here. So a lot of what we’re going to be talking through. And again, it this will kind of hit people in different ways depending on kind of like your experience, what your company’s experience has been shifting from more traditional data services, to data as a service. And we’ll talk through what some of that shift looks like. But the shift to data as a service is a very sustainable one, it can provide a little bit more longevity to the way that your company uses data, accesses data, and makes data informed decisions. The basic idea between the shift is to kind of transfer data from what would traditionally be viewed as a cost center to a revenue generator. And we’ll talk about the different ways that that can be done. But one of the benefits of kind of making the shift is that you’d have flat, fast, reliable, secure data available both internally to the enterprise itself and externally to customers. Leaders really value data, but they want to see less cost and more ROI. And that’s what I think people have in mind when they’re thinking about making these types of shifts to data as a service models. Overall, it makes insights more accessible, company wide answer your customers. In a recent Gartner survey, 58% of chief information officers said that they were increasing their investment in business intelligence tools and data analytics tools in 2021. So this is definitely a shift that is prevalent across a diverse set of companies, a diverse set of sectors. This shift in mindset is something that is overarching across many different types of organizations. A data as a service approach to Analytics will also open up entirely new revenue streams, it helps to build bridges internally between tech and business, and increases values increases value to customers. So let’s talk about what this shift looks like. We talked about data being otherwise costly and expensive to maintain, which is absolutely true. The more the larger your data, the more costly it can be to maintain it. The more complex and sophisticated your data, the more costly can be to maintain it. A lot of that also involves retaining the appropriate talent within your organization to be able to maintain and draw intelligence from data. But what data as a service does that this model basically makes insights more accessible company wide answering customers. So this approach to analytics opens up entirely new revenue streams, it opens up a an entirely different way of viewing data and how you can use data to organize both internally and externally.



And it really helps to build a bridge between tech and business. So all the fancy sophisticated tech that you have kind of within your company. And you’re able to kind of manifest that out to customers in a way that increases value to the customers that you work with. So what exactly is a data as a service platform? And why should you invest in one? In the most general sense, a data as a service platform is an advanced analytics engine that sits between some type of data lake or data warehouse or some type of data management system that you have setup internally within your organization and some user, now that user could be an internal party could be a team member, it could be a member of like a different unit, or it could be external, meaning your customers. What is data platform, what does data service platform that allows you to do is allows you to analyze data, and then subsequently allow business users to access those same data sets in order to make day to day decisions. And so the idea is kind of, in a way, one, one way that you can think about it is this idea that you’re kind of democratizing the access of data, both internally and externally, internally, so that it’s shared and democratized within teams and externally, to where it’s shared and democratized along with a diverse set of customers that might have. One benefit of this approach is also that you would attain some level of consistency across teams and organizational units. In today’s world, CIOs and other executives really are of this mindset that Insight and Analytics should no longer solely reside within the purview of some data science team or some IT team. But for many companies, if someone in a business unit has a particular data need or data requests, the process can be slow and manual, the data is often outdated, by the time they receive some CSV or Excel file. And I think one of the things that a data as a service platform allows you to do is it kind of it kind of changes that paradigm a little bit equates a digestible, consistent and secure stream of data to your customers. It’s often it’s an offering that would benefit. Many companies that are kind of awash in data and really kind of overwhelmed by the data that they have, are not really quite sure how to make sense of their data. It’s also a departure from today’s kind of business intelligence tools, which are oftentimes unconstrained and very complex and difficult for non data experts to use. These kind of traditional BI tools allow users to manipulate data sets in different ways which may be made, which may not be so standardized, so that can cause for additional confusion, and breakdown in communication, different departments using the same data can reach very different conclusions, which is never a good thing. You would think that, you know, if two independent parties approached the same dataset, that they’d be able to reach a similar conclusion based on that data. So as a result of all this, of all these types of challenges that are associated with traditional BI tools, it makes it much more difficult to businesses to quickly and easily use insights to take



sticking into action. So why not do it yourself? There are a number of things to consider, before you decide that you want to build a data as a service



system or infrastructure internally, or whether you want to buy something kind of out of the box or in some way customized from some vendor. You know, there are a lot of reasons that you would do either one or the other. What I have here on the slide are kind of a series of questions that kind of get at, you know, what types of things you might want to think through as you kind of make that decision of whether to build the terminal you buy from some vendor. This kind of data as a service platform, whatever the platform is, you know, we talked a little bit about in the beginning that, you know, platforms differ in their scope, they differ their complexity, and so the platform that you ended up choosing to kind of build or go with, for your particular need, can be can be quite vary depending on the nature of your company, what you’re really looking to get out of your data and the complexity of the data that that you have, that you have to work with. So the first question is, you know, do you have, do you have the right talent? Do you have the right talent in house to build sub distributed analytic system that I think is probably the most important question that I think is going to be a huge driver as to whether you decide to go with the build or buy option. If we need to hire a New team is that where our resources are best spent. So it’s not necessarily true that just because you hire new team, but that’s where your resources are being allocated the most efficient. So that’s something else to consider. Do we actually have the time to build a new platform from scratch? Timing is everything. And in industries where the race to put out certain types of products or certain features within a product are very time sensitive, this is going to be an extremely important question that any enterprise would need to answer. Any organization will need to answer besides before deciding whether to build internally or buy some from, from some vendor. What is the revenue potential of adding a new platform? And does that figure into our time to market? So that, again, is another question that needs to be asked. And then the last one is also very important, you know, can I actually trust a partner to handle my customer data with sensitivity and security, that, you know, and if, if you can’t, or if you don’t feel comfortable doing that, and that’s a big reason as to why you might want to build a platform or data as a service platform internally. If you’re not able to build some level of trust with some vendor or some partner to handle your customer data with sensitivity and security, then you might want to look at options that are more in line with building something internally, so that some of those sensitivity and security values that your company that your company really holds dear to heart are kind of maintained. Alright, so data adds value for customers and drives revenue. So this is I think, kind of the basic kind of the main, the main benefit of developing a data as a service platform. Many companies are challenged to provide data to customers. It’s an arrangement characterized by bottlenecks that doesn’t necessarily benefit either party companies are strained by the number of requests they receive, or different types of data from customers. And customers, in turn, are unable to access timely data due to long wait times. So implementing a data as a service platform allows companies to really give customers easy access to the data that they want, while actually creating revenue, the process and the revenue model can can be very dynamic, it can be very customized, it can look like however you want to look at so that is a really nice feature of a data as a service platform. You can offer dynamic pricing. So a data as a service platform would give you the opportunity to kind of introduce tiered product pricing, which is always a nice way to kind of attract different types of customers. As an introduction, you may even want to give customers some free version with maybe some limited insights so that they can get a little bit more familiar with the platform. And then kind of, you know, beyond that trial period, that awkward tiered versions of some of some data product. Data Service platforms allow you to kind of build data literacy. So whenever you make data analytics, more broadly accessible to employees and customers are no longer kind of solely within the purview or small group, everyone becomes more data literate. And so this goes back to this idea of kind of democratizing data literacy across your customers or across your set of customers. The more data trust that exists within an org within your customers organization, the more value they will place in data as a service platform, which again, makes complete sense.



Prioritizing security is another nice kind of outcome of building or buying some data as a service platform. When making data available to your customers, it’s imperative to ensure data integrity by offering enhanced security and monitoring tools, particularly if you’re an organization that is kind of held by compliance regulations like HIPAA or GDPR. Keeping those things on top of mind is really important. And then finally, it allows you investing in some data as a service platform allows you to kind of stand out among competition. A platform like this really gives you the ability to help your customers in some meaningful way by providing data that can answer questions and help them make better decisions. And ultimately, that’s that’s the goal. By implementing some type of analytics solution, you’re able to kind of fill a void that your competitors hadn’t filled or haven’t had a chance to fill. And thus you can take on more of like leadership roles, industry or sector that you’re that you’re in as a whole. Data as a story Urbis empowers customers, and your team to scale. So the pandemic was sort of a wake up call, I think for organizations, enterprises at large, particularly organizations that really value data and the power of data and the impact that data can really have on your bottom line. I think the events of 2020 2021 were a reminder that enterprise organizations need to be prepared for the unexpected. And we use resources wisely and move quickly without sacrificing security. And I think if you layer on industry competition and the need to scale, it’s clear that many companies are kind of forced to lead with some type of innovation mindset, like focusing on investments that help businesses grow. And so for your customers, it means providing tools that will allow decision makers across the company to have more actionable insights available to them. And data as a service platforms really allow you to get to that. It’s a tool that again, can be used both internally by teams to help organize teams, again, democratize the availability of data within teams. It also allows you to provide data and analytics to customers to use. And we’ll talk about some of the benefits of doing that.



There are several benefits associated with data as a service when particularly applied to creating new products, innovative products, innovating features within our products. The first, the first kind of benefit is strategic deployment of resources. So once your team is up and running, with some type of data as a service platform, it’s easy to then just kind of plug and play, it’s easy to really kind of customize and tweak based on what you need. And that requires subsequently fewer resources to then maintain it, which saves time saves money, saves effort, and allows teams internally to be a little bit more innovative, because they’re not kind of bogged down with kind of the day to day maintenance of some type of, of some type of data or analytics system. Data as a service allows you to really prioritize the future. So with fewer resources dedicated to your revenue producing advanced analytics engine, your engineers can focus their attention on more core features and initiatives that might otherwise be put on the back burner. So this also allows for, like a data science team, for instance, within your organization to turn their attention to more meaningful projects, and longer term forecasting, instead of kind of one off customer insight requests that kind of is exist day to day. So much of the time of technical teams, particularly technical teams that are responsible for interacting and responding to customers, so much time is spent on kind of putting out these fires in this kind of one off way. And I think what a data service platform really allows you to do is build a more build out a more systemic way to kind of address customer insight requests. data duplication, infrastructure costs, these are other things to kind of consider when thinking about some of the benefits of data as a service. For product innovation, part of moving data from the expense to the revenue side is kind of reducing some of the investment costs associated with innovative projects. So you can really kind of mitigate some upfront investment associated with platform integration, to see some type of like real tangible, like return on return on investment. Right, we kind of talked about a few of these already. But just to kind of drive the point home a little bit more. There are several benefits for data as a service for customer growth. The first, and probably the most important is that there’s less time wasted. So the old kind of manual way of pulling insights often leads to inaccurate forecasting reports filled with errors of insights that aren’t very good insights that are very kind of inconsistent across an organization, which again, is not something that is preferable. You know, in addition, users also don’t get value from seeing data in isolation, they want it correlated with other datasets. And what a data as a service platform really does is it helps kind of migrates all the all of these or it helps kind of sort of mitigate all these issues, making insights and Linux more accessible, accurate, easily synthesized for anyone in the organization, it’s very easily it’s very easy to that kind of layer on additional complexity as you need it to some like bare platform, making those type making those more complex requests a little bit more manageable and easy to address. You definitely attain more autonomy. So a data as a service platform with a very simple interface and predetermined data assets can easily turn insights into some type of business strategy that anyone can tap into. So again, this goes back to the idea of kind of democratizing information democratizing data across a company’s. Many, you know, employees no matter what types of teams they’re on. And so one of the things that an organization might experiences that their customers no longer have to depend on analytics teams or customer service teams to build the data visualizations or extract insights. Because that data literacy was democratized internally. Now you have kind of like more hands available, more hands on deck to kind of address some of these common customer insight requests. Daily actionable insights is also another great feature of data as a service platforms. In order to scale any business decision makers need to need to have the ability to kind of move with speed and accuracy. And that is much easier with some type of data as a service platform.



governance across all data is the kind of final feature or the final benefit that I want to kind of discuss in regards to customer growth. And one of the things I think to keep in mind is that data as a service really does, does well in removing user error and inconsistent insights that commonly occur with other more traditional BI tools that are a little bit more, that are a little bit more specific and customize and not necessarily provided at the same level to all levels of an organization internally. And thus, you get this kind of like varying kind of data literacy, kind of within a company, the people that tend to be more data literate, obviously going to be people that work with tools day to day, whereas people that don’t work, the tools day to day are not going to get as literate just because they don’t have the they haven’t really put in the time, it’s not a part of their regular set of tasks, it’s you know, they haven’t really had the chance to kind of develop some of that data literacy or some of that muscle memory associated with, with working with some of these BI tools. And so when everyone works with the same assets, within some common data as a service platform, the insights themselves become much more powerful, because the insights themselves are then spread out across the organization. And they’re also then spread out across the customers users. And then customer users, in turn, have a little bit more competence to make decisions based on analysis that they were able to get from your data as a service platform. So I think one of the things that we’re really trying to kind of push through this webinar is to kind of get, get you all kind of reimagining or rethinking some of the ways that you will kind of use data internally. So we’ve kind of went over, you know, many different benefits of data as a service platform, we’ve talked about how data is valuable, absolutely valuable to the enterprise. For a number of different reasons. We’ve talked about different ways that data is absolutely valuable to the customer, and to many users of data within your customers organization. And so I think one of the things that we would really kind of prompt you to really think about is, you know, how do you succeed, and then Chris, in the increasingly sophisticated digital environment, where there is so much competition in terms of who can get to someplace quicker and faster and more efficiently and in a higher and higher quality manner. And I think one of the things that a data as a service platform really allows you to do is think about some of these things in a more organized and structured way. And it really allows you to, again, democratize kind of learnings and insights both within your organization and and to the organizations that you are all kinds of providing services to. And with that will conclude. Thank you all. Happy to field any questions you can get to us via Twitter, get to me via email, but always happy to talk through any issues in regards to building out data as a service for your particular enterprise or how data as a service might be of benefit to the customers that you are working with. Again, what we didn’t cover today. Were specific platforms. A lot of the principles and techniques that we’ve talked about all the benefits that we’ve talked about are very kind of agnostic of the specific platform and specific tool that you’d be using to develop a data as a service system or data as a service platform. There’s probably an entire webinar that can be devoted just to, you know, the many things that you’d have to think through in terms of how you actually pick out the ideal data as a service platform. But we hope that today’s webinar was able to at least kind of get get you all thinking about some of the things that you might want to consider, particularly in terms of the benefits of investing in and maintaining and developing a data service platform. Thank you