Archive for 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.

Microsoft Infuses Products with Machine Learning and the Social Graph

Micsoroft plus LinkedIn Social Graph

This article was recently published on Amalgam Insights.

 

This past week (September 25 – 27, 2017) Microsoft held it’s Ignite and Envision Conferences. The co-conferences encompass both technology (Ignite) and the business of technology (Envision). Microsoft’s announcements reflected that duality with esoteric technology subjects such as mixed reality and quantum computing on equal footing with digital transformation, a mainstay of modern business transformation projects. There were two announcements that, in my opinion, will have the most impact in the short-term because they were more foundational.

The first announcement was that machine learning was being integrated into every Microsoft productivity and business product. Most large software companies are adding machine learning to their platforms but no company has Microsoft’s reach into modern businesses. Like IBM, SAP and Oracle, Microsoft can embed machine learning in business applications such as CRM. Microsoft can also integrate machine learning into productivity applications as can Google. IBM can do both but IBM’s office applications aren’t close to having the market penetration of Microsoft Office 365. Microsoft has the opportunity to embed machine learning everywhere in a business, a capability that none of their competitors have.

For the average knowledge or office worker, having machine learning embedded in the applications that they use every day means help doing their normal daily tasks. Machine learning in of itself is useful for analysis or automation. The real power of machine learning will be most evident when it is available to help with everyday tasks such as scheduling, analyzing data in spreadsheets, managing customers, developing financial projections, enhanced search capabilities, and creating impactful presentations. For corporate workers, this is the type of AI technology that helps them to do their jobs better, making them more valuable rather than obsolete.

The second was the integration of Microsoft 365, Microsoft’s social graph platform, with LinkedIn and Dynamics social graphs. Since Microsoft purchased LinkedIn, the big question has been “how will they leverage it?” The answer is finally here. By combining the Microsoft 365 (contacts internal to the company) and Dynamics (contacts external to the company) social graphs with the personal connections of employees, Microsoft products will help to surface and leverage useful relationships no matter what they are. There are some obvious advantages. First, it means being able to connect the right group of people together to accomplish something no matter how they are linked. By breaking down the walls between the personal, internal, and external relationships, Microsoft will allow knowledge workers to find and foster essential connections that are at the heart of business. The most obvious beneficiaries will be sales and marketing by surfacing paths to and intelligence about people that they need to do business with. Social graphs also represent rich data that can be mined for a variety of other purposes. Since the Microsoft 365 is extensible, other information about people, relationships, and location can be added to the extended social graph. This will create a rich pool of information that can be mined for a variety of purposes. Applications include finding and managing vendors and partners, recruiting new personnel from internal and external pools, identifying better ways to communicate, and seeking out M&A targets.

Microsoft’s announcements are, aside from the quantum computing announcement, more incremental but in a good way. They are taking highly hyped but useful technology and making it relevant to the masses. Both machine learning and social graphs will no longer be primarily the realm of specialized applications. Instead, through inclusion in Microsoft’s most ubiquitous apps especially Office 365, they have the potential to become part of the fabric of everyday work life.