Tag Archive for predictive analytics

Oracle: All the New Stuff Inside Everything

Oracle Open World 2017

As expected, Oracle OpenWorld 2017 (Oct. 1 – 4 2017) featured a large number of announcements. The debut of Oracle 18c, the latest version of Oracle’s eponymous database, grabbed the most attention. Given it’s billing as an autonomous database and Oracle’s flagship product, this is not suprising.. While the idea of a database infused with machine learning that automates all types of database management functions is intriguing, it overshadowed the real impact of Oracle releases. Oracle 18c was only one of several AI-infused “autonomous” products. Instead, the sum of Oracle’s presentations amounted to adding machine learning into all levels in the Big Red Cloud Stack. AI is now integrated into Oracle’s SaaS, PaaS, IaaS cloud products. Oracle didn’t stop with machine learning either. They have imbued their applications with analytics and blockchain technology too. Oracle have made this technology available from within Oracle Cloud Applications and Oracle+NetSuite, providing advanced technology to mid-market organizations through large enterprises.

In essence, Oracle has made sure that all the new technology that everyone has been hearing about for so long is everywhere in the Oracle ecosystem. That’s very exciting. Previously esoteric technology is now available to the corporate masses in a more cost-effective manner. This strategy mirrors Microsoft’s but with greater depth in large enterprise applications. Until recently, organizations that saw value in these new software technologies would have had to hire experts and maintain expensive systems themselves. By integrating them into enterprise applications in domain specific ways, organizations can reap the benefits of advanced software without the cost of building and maintaining it. This approach makes sense; Technology such as machine learning, analytics, or blockchain doesn’t need to be custom built for most organizations. Managing a supply chain using blockchain, for example, will be similar across organizations. The same is true for sales analytics and machine learning for recruiting.

If an enterprise does need to create specialized uses of these technologies, Oracle makes that easier by providing them as cloud infrastructure services. While data scientists and developers trained in blockchain are still needed, the cost and complexity of building, managing, and maintaining the infrastructure is borne by Oracle. Having these advanced technology stacks prepacked as cloud services also means a faster start. Developers can begin writing code immediately instead of having to waste time spinning up the infrastructure. Google, Microsoft, Amazon, and IBM all offer all or some of this technology via the cloud as well. For Oracle loyalists though, the decision to implement just became easier since they no longer have to introduce a new vendor to deploy these types of systems. The tie-in to enterprise cloud applications also simplifies adding customer capabilities to common enterprise applications.

By integrating these three new technologies into everyday enterprise and mid-market applications and providing them as a service, Oracle is making them more accessible to a greater number of organizations. Oracle customers can now gain the benefits of new technology with less of the work or distraction of building it all themselves.

Spotify Knows What I Like But Not What I Want


I’ve spent a fair amount of time looking at predicative analytics. Predicative analytics is made of software, data, and a model that should forecast, or predict, something. Consumer goods and retail companies use predicative analytics to try and determine what someone will want to buy. Media properties use predictive analytics to take a good guess at what you would like enough to consume. In other words, predictive analytics tries to determine what you want. This is tricky and sophisticated stuff. It requires lots of data about individuals and people like them, an understanding of the patterns that indicate taste, and software capable of processing all of that data incredibly fast. Which brings me to Spotify.

Full disclosure: I love Spotify. I love it enough that I’m willing to pay for it. On Spotify, I can find almost – almost – any kind of music I might want. And I like a lot of music. My taste is, to say the least, eclectic. Progressive rock from my early youth, punk and new wave from my teen and college years, electronic dance music (which everyone knows is the successor to 80’s techno), 90’s alternative, modern indie, blues, early music (as in 1500’s), reggae, rockabilly and so on. When it comes to music, I’m certain to give predictive analytics engines a fit. I recognize this upfront.

Spotify has been doing a lot with analytics of late. Recently they released something called Taste Rewind. Their software asks a user to pick a few bands from a group they display and then generates a playlist from each decade from the 1920’s onward. That’s a pretty straightforward analytics and big data task. That’s not to say it’s easy to do but at the heart of it, Taste Rewind is comparing the characteristics of the artists that you choose with other artists in their database for similar matches.

Last week they added something new to their service. Called Discover Weekly, Spotify puts together a weekly custom “mixtape of fresh music”. With Discover Weekly, Spotify is trying to predict music that I want to listen to. Based on my listening history, and comparing it to others with similar taste, they put together a playlist that should – and I mean should – give me music that I want and have not heard.

Alas, it’s close but not there yet. For the most part, the music is serves up is music I like a lot. That’s good. I don’t like all of their picks but I think that’s impossible. No forecast, whether human or software generated, is going to be 100% accurate. The bad is that little of it is particularly new to me. Of the 30 or so songs in a Discover Weekly playlist, on average I’ve heard probably 28. The worse is that, while it’s all good music, it’s not necessarily what I want to hear. Just because I like The Clash doesn’t mean I particularly want to hear them now. Instead of Discover Weekly, it should be called “Music that you probably like but might not be in the mood for right now” Weekly. That would be a terrible brand name though…

And this is the heart of the problem with predicative analytics. Humans are so complex that trying to determine what they want is too hard to do. It’s a gamble at best. Discover Weekly probably works for a great many people, especially those with limited taste. For listeners who have wide tastes and really get into their music it’s probably impossible to predict what they want to hear at any one moment. Unfortunately, those are likely to be Spotify’s best customers. They are the customers who really want to discover new and interesting music, and are willing to pay for it, whereas the teenage Beyoncé fans only want to know what’s popular.

Discover Weekly should get better over time as Spotify refines the data model and adds new sources of data. It will never be 100% accurate but hopefully will figure out how to server up really new tunes.

It will be at that moment when they kick Apple Music right to the curb.