apidays LIVE Hong Kong 2021 - Composable data for the composable enterprise by Andrew Dent, Mulesoft
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apidays LIVE Hong Kong 2021 - API Ecosystem & Data Interchange August 25 & 26, 2021 Composable data for the composable enterprise - Liquefying data capital with APIs Andrew Dent, Director of Solution Engineering at Mulesoft
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apidays LIVE Hong Kong 2021 - Composable data for the composable enterprise by Andrew Dent, Mulesoft
- 1. All contents Ā© MuleSoft, LLC Composable Data for the Composable Enterprise Liquefying Data Capital through APIs Andrew Dent - CTO JAPAC andrew.dent@mulesoft.com How to value data How to unlock that value How APIs help
- 2. All contents Ā© MuleSoft, LLC Data is a valuable resource 2
- 3. All contents Ā© MuleSoft, LLC The Enterprise Data Reality Lack of supply Limited access Poor quality, inconsistency, fragmentation, saturation Limited access control
- 4. All contents Ā© MuleSoft, LLC We are only starting to understand the value of data 4 Cut across data sources Incorporate external data Real time More mobile Data is a key component for value creation
- 5. All contents Ā© MuleSoft, LLC 5 Data as part of our product. Data products that help us make decisions, differentiate, and satisfy customers.
- 6. All contents Ā© MuleSoft, LLC Rival vs. Non-rival Fungible vs. Non-fungible Experience Good vs. search good Non-Linear 1 + 1 = 3 Data is a non-rival, non-fungible experience good whose value increases in a non-linear way as it is collected and correlated.
- 7. All contents Ā© MuleSoft, LLC 7 Reusable Unique Correlation Insight Scalable, immediate, re-usable Data leakage Benefits Risks Unique data and insights are valuable Management overhead Insight requires analysis that can lead to more insight Looking for a needle in a haystack; false positives Correlating data drives up value Compromise privacy Data can be used reused with each piece being unique. We gain insight through analysis, and when correlated with other data it has the potential to increase in value exponentially.
- 8. All contents Ā© MuleSoft, LLC Maximizing value - Share it! Use it! Protect it! 8 Share it! Real-time data access inside the enterprise Protect it! Careful governance and access control Use it! Broad consumption in correlated contexts Reusable Unique Correlation Insights
- 9. All contents Ā© MuleSoft, LLC A data architecture - API-Led 9 Component Data (Crude) Composite Data (Refined) Contextualized Data (Consumable) Conciliation, curation Composition, correlation, climatization Collection Share it! Use it! Protect it!
- 10. All contents Ā© MuleSoft, LLC Contextualized data - liquidity Quality data from authoritative sources TARGETED Is correlatable and composable NOT CREEPY Proactively and predictively distributed to the anticipated point of use TIMELY Right data Right context Right time
- 11. All contents Ā© MuleSoft, LLC Example: COVID Data Platform 11 Secure and standardized model ingest & normalize MuleSoft Anypoint Platform deliver Tableau Prep Data Sources Data warehouse store Tableau MuleSoft Tableau Public AWS Data Exchange Data partners Salesforce Traction on Demand SI/ISV Partners MuleSoft Public COVID Data Tracking API Crisis Response Developer Portal curate Coming soon! Coming soon! Hyper API validated by industry experts consumers Component Composite Consumption
- 12. All contents Ā© MuleSoft, LLC Composable Data Summary 12 Treat data as a capital asset - a Product mindset Value data: use it, share it, protect it Data liquidity: the right data in the right context at the right time
- 13. All contents Ā© MuleSoft, LLC Questions or want to learn more? Head to the MuleSoft Virtual Booth located in the Partners Village
Editor's Notes
- My name is Andrew Dent - Mulesoftās CTO for the Asia Pacific Region. Hello to all of you and I hope you are faring well in these tough times... I have a very fancy title for my presentation, but really what I wanted to talk about today is very simple. <click> How to value data How we unlock that value, How APIs can help in this regard.
- I am a long-time subscriber to the Economist. One of the things I enjoy is their covers. I think this was one of the best they ever did - itās going back a few year now. The picture paints an analogy of data as the new oil as raw material that is extracted and exploited by the digital powerhouses of our age.
- We are all trying to get access to this new oil. Some companies, such as those discussed in this Economist article are pretty good with data. But for most us we have a little ways to go. Many of us work for companies, and on systems, that were invented long before tracking every bit of data was a thing. Our systems and processes are fundamentally transactional, many of them do not have instrumentation, and our reporting systems are basic. I would say in general data is still generally pretty poorly understood and managed. I often get asked to come and talk to Chief Data Officers to see how we might These conversations have traditionally not been that interesting as Mule is in the API Management business, and not the Data Management business. But over the last twelve months or so these conversations have changed, and we have A LOT more to talk Why? Well the driver in data has moved from a focus of business intelligence to one of data science and this is a key component of an organisations digital transformation initiatives. <click>
- To support DIGITAL TRANSFORMATION this we need cut across different data sources incorporate external data as well in real-time delivery. Data needs to be more timely, more mobile, and this has lead to the increased importance of APIs and integration. In the last 5 years or so I think we have come a long way to changing the way we think about data and how we value data. If we continue with the Economist oil analogy we are perhaps at the Kerosene phase: where folks figured oil was a good substitute for whale oil in lighting applications. We are still are far off from the sophisticated ecosystem that now surrounds the usage of oil in modern society. And part of the problem is we still view data as raw material, as opposed a more refined component of something larger.
- What needs to happen for us to industrialize the use of data? not a technology shift itās more of a MINDSHIFT We need to look at data through a product lens. Data needs to be considered as a capital input that we use to produce our product. Today Data is created in different parts of the organization to meet the needs of various departments, not for later use by others. Contrast that with a physical product, such as a car, where components such as the chassis and the starter are designed with the end product in mind. Additionally To date companies have concentrated on the technical capabilities of data management, which are controlled by the IT function focused more on infrastructure and much less on what is being produced We need to think about the output. <click> Data products that help us make decisions, differentiate, and satisfy customers. In economic terms Data collection often has a high up-front cost and low marginal cost. These up-front costs can prevent firms collecting data or make them look for a financial return. These costs can be a barrier to getting value from data. So we should always start with the end product in mind to drive back to the business case for data collection. Itās always a bit difficult for people, especially IT folks, to see data in this way. So how do start this mindshift. Well we need to talk about it that way, and to do that we need to think about data not in TECHNICAL terms, but in ECONOMIC/product terms.
- So what is the value of data in economics terms - well itās Say quote. Now if I bring this up at my next meeting with product management they are going to be slacking each other behind the scenes that I belong in the luni bin, and if you have no idea what I am talking about then I have effectively made my point. Here is the economics to english translations - Non-rival means re-usablity - that I can use it over and over again - unlike your cake you can only eat its once and its value is all gone. Non -Fungibility means replaceability - an airline ticket only for me where as a dollar bill one is as good as the other. Data is an experience good which means you need can realize value the experience after you acquire it. A restaurant was the typical example of an experience good, but now with the internet a restaurant reviews I can figure out what any restaurant is like before I walk in. Finally non-linear means that value can be more that the sum of the parts. So now we have translated let's look at the english definition of data <click>
- Read quote So now we understand the nature of data and itās value letās unpack this in more detail because their is also a dark side to data - the risks and complexities in its use... Reusable Unique EXAMPLE: Utility pole Insight EXAMPLE: Here is the data give me the insight. Correlation in the process of doing this though I might accidentally expose a whole bunch of private information - and this is the reason we have seen the FACEBOOK and emergence of such strict data laws led initially by the EU I think with all these risks putting a product lens over these cann help immensly Now I understand the value and risks in data how do I begin to mine this value whilst minimizing the risks?
- To support this we have put in place a very simple framework... Share it, Use it, and Protect it. Share it - make sure access is freely available in real-time within the enterprise. We need to share all of it because we are not sure which piece might yield the insight. Use it - Now that that data is flowing I need to mix it up to create value. This is where I might get those exponential returns. and finally Protect it - I cannot just share data willy-nilly outside my Org as this has privacy implications, and data leakage can create great problems. So now I have my data mantra, Share it, User it, and Protect it. How do I implent this?
- Here is an architecture which is modelled along the lines of API-Led connectivity In this model the Componets at the bottom are essentially our crude oil. These are the raw data elements that are refined into Component. These components use standardized schemas and taxonomies. There is also a security layer here with masks and restricts private informations to reduce data leakage to higher levels The next layers is the āCompositeā data. These are complex data components that are assembled by combination of the crude data assets. These are bespoke data products that are created to a specification as defined by product groups. Another attribute of composite data is that is reusable and can be incorporated into multiple finished goods, just like an alternator can be used in multiple cars. Think about an employee data - itās composed from data held in multiple systems - its requirements are driven from applications and it is needed in multiple end systems. Contextualised data is the point of consumption - lets look at that a little more detail
- Contextualized data is our finished goods. One final theoretical concept we should talk about is liquidity No value exists by simply having an asset; the value exists in the ease of deploying that asset. Cash is the ultimate liquid asset in finance. For data value to maximized data must be liquid. This means the right data, in the right context, at the right time. I think this is best illustrated with my insurance renewal If a competitive insurer is able to send me a targeted offer, offering a discount on my insurance at the point of time of my renewal with my existing provider that is a insurer that has liquid data! Letās now look at real world example that embodies many of the principles we have discussed. <click>
- When the COVID pandemic a team was formed between Salesforce, Tableau and MuleSoft to see how data could help people deal with and recover from the crisis. The team focused on what data was in demand case information (new infections, hospitalizations, fatalities) and policy information (national, regional, corporate). First off, the data the team was looking for was openly available Varied sources, varied formats The team normalized these formats for each to web APIs using MuleSoftās Anypoint platform. Having this API layer for crude data sources created a repeatable pattern to add new sources Using this the team built a data warehouse which was used for analytics, visualized through Tableau. But there to get the most value out of this data, they needed to integrate it with user-facing applications or PRODUCTs. To support this, they created a COMPOSITE APIs APIs as the basis to compose optimized, CONTEXTUALIZED APIs for each of the channels to serve the data: an public interface for Tableau, a policy-focused API for Salesforce Work.com application, and RESTful API for partner access. (un-editied talk track) When the COVID pandemic first hit in early 2020, a team was formed between Salesforce, Tableau and MuleSoft to see how data could help people deal with and recover from the crisis. In most cases, we recommend thinking through user problems and navigating to data and capabilities, but here was a case with incredible urgency where the data might need to come first. The team started by surveying the whole landscape of possible data, and eventually zeroed in on data that seemed to be in most demand: case information (new infections, hospitalizations, fatalities) and policy information (national, regional, corporate). Thereās a bigger story to share (link to podcast: https://blogs.mulesoft.com/web-series/apis-unplugged/covid-19-data-platform-2/), but for this talk I want to zero in on the approach to collecting, preparing, analyzing and distributing the data. First off, the data the team was looking for was openly available, but only through a wide range of sources who had pieces but not the whole picture. These sources were available through a variety of protocols: web APIs, rendered web pages, CSVās. The team normalized the protocols for each to web APIs using MuleSoftās Anypoint platform. Having this API layer for crude data sources made it much easier to add new sources, something the team did frequently. Using these pluggable sources, the team built a curated data warehouse which was used for analytics, visualized through Tableau. But there to get the most value out of this data, they needed to integrate it with user-facing applications. To support this, they created a layer of APIs to represent the core data entities and aggregates. They then used these core APIs as the basis to compose optimized, contextualized APIs for each of the channels to serve the data: an ultra-flexible interface for Tableau, a policy-foused API for Salesforceās Work.com application, and a structure RESTful API for partner and open access. These API layers decoupled the user-facing applications from the warehouse for change management purposes, while avoiding the need to create redundant data stores. This approach can be generalized for organizations who want to inject analytical data into their core applicationsā¦ (segue to next slide)
- In Summaryā¦ Treat data as a capital asset - a Product mindset Value data: use it, share it, protect it Data liquidity: the right data in the right context at the right time
- Talk track: IT needs one solution that solves all of your use cases and allows the whole organization to innovate together. Your teams need to go seamlessly from development to deployment, without wasting time to āintegrate the integration solutions.ā Most importantly, everything you build must fuel a flywheel of innovation, where everyone can discover and leverage whatās been done before to truly speed up innovation. Transition: Letās talk about how MuleSoft brings this vision to lifeā¦