Digital transformation is real, and it’s happening – our data lends more insight to the state of the transition. It is an inescapable truth that every business is becoming a digital business controlled by software, which is the manifestation of these digital transformations. As businesses continue to align around a digital culture, they need to invest in new approaches to remain relevant in the eyes of their customers. The overall – but seldom-voiced – goal is survival; just ask some of those in industries that have already seen their physical products turned into digital ones and not survived the transformation.
451 Research defines digital transformation as the result of IT innovation that is aligned with, and driven by, a well-planned business strategy with the goal of transforming how organizations: Serve customers, employees and partners Support continuous improvement in business operations Disrupt existing businesses and markets Invent new businesses and business models (Sudesh speaks about ow its not just about technology factors)
Sheryl (Steve to add on if needed)
Steve to present How valuable? Well at Cloudera we analyzed the S&P 500 and five of the eight most valuable companies on the planet over the course of the past decade. Those five companies are Amazon, Apple, Microsoft, Google, and Facebook.
The market capitalization growth of these companies has been extraordinary. And why is that? Well, it's simple, it's because these companies are data-driven. These companies make money by having more information about you, your buying habits, what you like to spend money on, what you don't like to spend money on, what music you listen to, and even who your friends are.
Steve – Importance of brining together “single view” of customer.
Why are these areas so important- because they can separate leaders from laggards- with a 24 point gap differential in leaders embracing AI, Machine learning and Intelligent business applications- It’s not about the individual AI technology but the embedded intelligence in the applications that drive business decision making on a daily basis
Other major differences include the ability for businesses to innovate, invest in intelligent personalization and prioritize shifting applications to the cloud.
Sheryl It's important that marketing understand the alphabet soup differentiation. MDM, DMP, CRM, CDP and CIPs all offer a variety of benefits, and businesses are still searching for the 'holy grail' solution. It's very difficult to build a CIP from scratch that does more than just house the data but also acts on that data in real time for multiple use cases. Businesses must shift away from 'he who holds the most data wins' attitudes. It's important to plan for all potential intelligent business application use cases of a customer 360 throughout the customer journey. Advanced machine learning that can take action on signals with real-time decision-making for 'in the moment' execution across both physical and digital experiences is essential. Additionally, ensuring that a company is compliant with the GDPR will mean combing all customer data to account for a variety of factors, including where and how data is stored, and ensuring that businesses always have the most current information. Since complying with the GDPR can be a massive cost undertaking, having a single, real-time customer view can motivate a business to turn it into a profit-making activity instead.
Steve to add on to Sheryl’s comments
Steve to comment (cost, scope, etc)
However, past approaches by companies that used combinations of CRM systems, master data management (MDM) and data lakes to create a single source of truth have all struggled to live up to the expectations of front-line business users in areas such as marketing, customer care and digital commerce. Looking ahead, however, the new requirement will be investment in customer intelligence platforms (CIPs) that do more than consolidate a single view of the customer: they add a layer of data governance, synthesis and identity, which powers a dynamic customer graph to fulfill the vision of contextual experiences.
The advancements in predictive ML intelligence build on a variety of algorithms to achieve real-time one-to-one capability (ideally in fewer than 20 milliseconds). Key advancements include data governance, synthesis and identity, which power a dynamic customer graph to fulfil the vision of contextual experiences. CIPs are not just about the data, but also the potential for delivery of dynamic rich media content, including images, videos and voice. A CIP must go a step further than a CDP by synthesizing data that dynamically links customer-customer and data-customers using an optimized mixture of matching techniques. It provides context from raw data for relationship discovery, with graphs, columnar data stores and in-memory high-performance indexes to drive multiple versions of the truth for different use cases. As it ingests and synthesizes more data into the customer 360, a CIP platform must also become more intelligent in identifying important trends and information for each customer, and better at summarizing the important intelligence for specific business users. Synthesis and reasoning must work in balance to ensure the CIP is usable; as more data is synthesized and the customer 360 becomes deeper and richer, the CIP must get better at summarizing the important intelligence for specific business users. Automated reasoning helps to make inferences and enrichments on each customer profile, and also helps line-of-business users predict the customer’s future actions such as churn, propensity to buy, proximity and location, etc. It provides a deeper understanding of individual customer journeys and unique interactions, combined with transactions, to accurately understand and improve customer experience.
When we talk about machine learning, we mean three buckets of things: pattern recognition, anomaly detection, and ultimately, prediction. On the other hand are what you do with analytics. This is about providing self-service intelligence, increasing productivity for all your knowledge workers, not just data scientists. And lastly, secure reporting. About 700 of our customers today – roughly 2/3 - are running SPARK in their Cloudera environments. Meanwhile, 750-plus are using Cloudera for analytic workloads leveraging Impala. So, we have a high percentage of our customers already using the latest and greatest technologies for both machine learning and analytics.
We deliver the modern platform for machine learning and analytics that's been optimized for delivery via the cloud. And you see the word that's highlighted here is "platform." That's the business that we're in. We don't make end solutions here at Cloudera, but we do build a platform for deriving value from your data. It's modern because it's based on the latest open source technologies. It's about machine learning because Cloudera has been doing machine learning for many, many years at a production level for hundreds of our customers. And it's about analytics because you can leverage what you're doing in SQL today but move beyond structured data and rigid monolithic database architectures. And last, but certainly not least, everything that we do with a name like Cloudera, you may guess, has been optimized for delivery via the cloud. Whatever we build has to be enterprise grade. It must be scalable. And last, it has to be available to run anywhere, whether that's on-premises, in the cloud, or in some combination thereof, in a hybrid or multi-cloud type of environment.
STEVE TO COVER PACKAGED VS. CUSTOM BUILT Here’s an expanded view of how we see the world. We like to refer to Cloudera Enterprise as the modern platform for ML and analytics optimized for the cloud
Modern is not just a current statement but a future statement as well. Want to continue evolving and innovating. Want to make sure our customers can continue to deploy new use cases as they need.
We’ve observed that the most interesting business applications today actually require 2 if not 3 or 4 of these different analytic capabilities in order to accomplish the end goal Example: Suppose a manufacturing company wants to analyze the continuous stream of data coming off the factory floor to improve their business. Well, First, the plant manager will probably want a real-time view of everything happening in the plant (requires Operational Database) Second, you’ll probably want a historical view as well for comparison purposes (requires Data Engineering) Third, you’ll want a predictive model to predict outages and downtimes (requires Data Science) Fourth, you’ll want to run a bunch of reports to enable the corporate team to analyze waste over last days and months, compare the plant vs. other plants, etc. (requires Data Warehousing capabilities) Finally, you’ll likely want to visualize the results of those reports (requires integration with third-party BI applications) And that is just one example from one industry – many more can be found in other industries as well For example, a retailer might…. Now, that’s a really hard application to build if you are trying to cobble together 4 different systems to do the work – even if they are from one vendor, but especially if from different vendors You’ll have to setup completely different pipelines to ingest, store, and secure data and you’ll have a heck of a time building a consistent catalog of schema and other metadata But with Cloudera, we’ve built all of this functionality into a single, unified platform such that each of our 4 core services share a common data storage, ingestion, security, and governance layer That makes it really easy to build multi-function applications like I’ve described Furthermore, that makes it really easy for different teams and departments within an enterprise to collaborate on all of these business’s data in an organized and scalable manner We call this unique capability SDX for Shared Data Experience
Steve Also mention there are Azure cloud credits available when proceeding with this path.
Steve We’ve covered a lot of information, but I wanted to share additional resources to help you learn more. Regardless of where you are in your big data or Customer 360 journey, these assets will help you position your organization for success. -Later today, we will post a Cloudera Vision Blog written by 451 Research’s Sheryl Kingstone that continues to dialogue from this discussion. -If you’re interested in learning more about the Customer 360 powered by Zero2Hero solution you can visit the Cloudera solutions gallery or Microsoft Azure Marketplace -On November 28th, we will have another webinar, this time focused on our SDX. During this webinar we will go into more detail around running Customer 360 workloads -On January 10th, we will host yet another webinar highlighting how Cloudera uses analytics and machine learning to inform marketing and sales strategy. This is a great webinar to attend if you want to hear a success story. -And of course, if you have any questions you can reach out to us at Customer360@cloudera.com -Let’s now open it up for questions.