WHY IT MATTERS: Digital Transformation

As the API or "application programming interface" becomes the primary interface for business (in much the same way physical storefronts gave way to applications and websites), we're seeing a new chapter in the story of software emerge. While it's playing out against the backdrop of other trends (microservices, software-defined networking, developers as buyers, and connectivity as a utility), the big idea here is that traditional, large programs are being broken down into and being offered as -- or with data exposed to -- services. But more importantly, these pieces and services are being recombined by other companies to create something new... it’s combinatorial innovation in action. In this talk (originally delivered at our most recent annual a16z Summit in November 2017), general partner Martin Casado describes the evolution of "the API economy" in the context of the history of computing; shares examples of companies whose entire business is based on or around APIs; considers implications for future infrastructure, IT business leaders, and others; and shares how APIs are already fueling the expansion of software eating the world.

Teaching page of Shervine Amidi, Graduate Student at Stanford University.

The Customer Data Platform Institute is a vendor-neutral organization dedicated to helping marketers manage customer data.

The vendor's focus on core R&D and channel-based selling proved a winning business strategy, but I think Sitecore has hit a ceiling in recent years.

Page weight is an important metric to track, but we should always consider using appropriate statistical methods when tracking it on the web as a whole. Certainly track it for your sites – because as you’ve seen here, the size of content does matter. It’s not the only thing that matters – but the correlation is strong enough that a spike in page weight should merit some investigation.

The analysis of Internet of Things (IoT) data is quickly becoming a mainstream activity. I’ve written about the Analytics of Things (AoT) before (some examples here, here, and here). For this blog, I’m going to focus on a few unique challenges that you’ll most likely encounter as you move to take IoT data into the AoT realm.

What is fog computing, and why is it necessary? This 2-minute video provides a quick overview and introduction.

Last month’s story about organizations exposing passwords and other sensitive data via collaborative online spaces at Trello.com only scratched the surface of the problem. A deeper dive suggests a large number of government agencies, marketing firms, healthcare organizations and IT support companies are publishing credentials via public Trello boards that quickly get indexed by the major search engines.

If only everything worked like this! Why are we still sending files around via email? Why aren’t there multiple branching versions of everything? Why do we pretend that there’s any canonical version of anything? (Because we have to make money.) Git acknowledges a long-held, shared, and hard-to-express truth, which is that the world is ever-shifting and nothing is ever finished.

Finally, though, the biggest surprise with this project was just how long it took - and related, the amount of compute resources it needed. The first surprise was in terms of calendar time. My original estimate was that as a side project it would take about 3 months. It actually took around 8 months. (And the original estimate was supposed to be pessimistic!) Some of that was down to underestimating how many hours each stage would take, but a big chunk of the underestimate was failing to anticipate other things coming up outside the project. It’s hard to say how well this generalises, but for side projects, taking your original (already pessimistic) time estimates and doubling them might not be a bad rule-of-thumb.

Some IT leaders are growing weary of the phrase “digital transformation.” Find here 10 articles on the subject to help grasp some wisdom:

1. How Landauer CIO built a revenue-generating mindset in IT

2. SunTrust CIO's formula for speed relies on cloud, DevOps

3. Increasing IT’s speed: 3 levers CIOs can use

4. Teaching IT to embrace change: 9 execs share advice

5. Matson CIO: The most painful, gut-wrenching part of leading transformation

6. How hybrid cloud aids digital transformation

7. Budgeting smarts for digital transformation: 4 tips

8. Digital transformation: 3 strategies to manage change

9. Digital transformation: 5 principles for the retail industry

10. What absolutely won’t work when it comes to digital transformation

Blockchain technology is hot right now! I've devised a 2 month study plan to help you learn how blockchains and cryptocurrency works. This is the kind of curriculum I'd create for myself to learn, but I'm open sourcing it for you guys since I love you. We'll start with cryptography, move on to Bitcoin, then Ethereum, other cryptos, and finally Decentralized Applications.

Curriculum for this video: https://github.com/llSourcell/Learn_Blockchain_in_2_months 

Apache PredictionIO is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task.

For most companies, agile is confined to software development—but it doesn’t need to be. Increasingly, forward-thinking companies are taking advantage of the same agile techniques that have transformed software development. Now they are successfully deploying these techniques in other core business units, from marketing to human resources to finance. When companies implement agile across their entire organizations, ways of working improve dramatically. Agile methods are more collaborative and creative and can be more efficient than other business models. But companies must first understand why their current business structures need to change.

Digital transformation - extending and exposing business processes outside the organisation - by implementing a digital strategy – a statement about the organisation’s digital positioning, operating model, competitors and customer and collaborator needs and behaviour through the delivery of digital solutions defined in a digital architecture – a future state application, data and technology view to achieve digital operating status - is potentially (very) complex. Digital architecture does not exist in isolation entirely separate from an organisation’s overall enterprise architecture. Digital architecture must exist within the within the wider enterprise architecture context. Enterprise architecture provides the tools and the approaches to manage the complexity of digital transformation. 

This guide is for anyone who is curious about machine learning but has no idea where to start. I imagine there are a lot of people who tried reading the wikipedia article, got frustrated and gave up wishing someone would just give them a high-level explanation. That’s what this is. The goal is be accessible to anyone — which means that there’s a lot of generalizations. But who cares? If this gets anyone more interested in ML, then mission accomplished.

This technology is called face recognition. Facebook’s algorithms are able to recognize your friends’ faces after they have been tagged only a few times. It’s pretty amazing technology — Facebook can recognize faces with 98% accuracy which is pretty much as good as humans can do!

Amazon has casually unveiled what could turn into a fundamentally different way to build software.

We want to create a better programming experience for learning and teaching programming. This paper describes Seymour, a live programming environment that features two visualizations of a program’s execution. One shows the execution’s details and another depicts it at a high level. These two visualizations come together to create a compelling user experience that we plan to test and improve with student feedback.

Google Data Center FAQ & Locations. Google data centers are the object of great fascination, and the intrigue about these facilities is only deepened by Google's secrecy about its operations. We've written a lot about Google's facilities, and thought it would be useful to summarize key information in a series of Frequently Asked Questions.

Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components—-a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. This becomes particularly challenging when data changes over time and fresh models need to be produced continuously. Unfortunately, such orchestration is often done ad hoc using glue code and custom scripts developed by individual teams for specific use cases, leading to duplicated effort and fragile systems with high technical debt. We present the anatomy of a general-purpose machine learning platform and one implementation of such a platform at Google. By integrating the aforementioned components into one platform, we were able to standardize the components, simplify the platform configuration, and reduce the time to production from the order of months to weeks, while providing platform stability that minimizes service disruptions. We present the case study of one deployment of the platform in the Google Play app store, where the machine learning models are refreshed continuously as new data arrive. Deploying the platform led to reduced custom code, faster experiment cycles, and a 2% increase in app installs resulting from improved data and model analysis.

The next step is to ensure everyone at BCG understands this new model, not just IT. "We have some smart people across the company who will ask someone they know in some startup to develop a new app for them," Kuppens says. "We tell them, 'Fine, but make sure to do the 3x3. Is it secure and serviceable? Will it break our architecture? What is the user experience? Can we run this on AWS or Azure? What is the expected uptime?'" If the new application or data business case passes the basic 3x3 demand-clearing assessment, the business can move forward with IT’s oversight.