4 Key Developer Responsibilities Where Machine Learning Can Help

Note: A version of this post was published to Tom’s Tech Take II

As the fall season of tech conferences starts to wind down, something is quite clear – machine learning (ML) is going to be everywhere. Box is putting ML in content management, Microsoft in office and CRM, and Oracle is infusing it into, well, everything. While not a great leap forward on the order of the Internet, smartphones, or PCs, the inclusion of ML technology into so many applications will make a lot of mundane tasks easier. This trend promises to be a boon for developers. The strength of machining learning is finding and exploiting patterns and anomalies. What could be more useful to developers?

Here are some examples:

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How does the 2017 version of HPE support enterprise IT departments?

Meg Whitman at HPE Discover 2017

Meg Whitman at HPE Discover 2017
Meg Whitman at HPE Discover 2017
Recently, Amalgam Insights attended HPE Discover, HPE’s semi-annual show devoted to its enterprise offerings. Our firm was especially interested in seeing how HPE would position itself after having divested much of its software portfolio to Micro Focus and then spin-merging its Enterprise Services division with CSC to form DXC Technology on April 1st of this year.

In HPE’s General Session and subsequent presentations, several key themes emerged in HPE’s positioning. The most obvious is that, in consolidating HPE’s offerings to servers, storage, and networking, the company is now focused on being the arms dealer for hybrid IT support. This is based both on the core HPE portfolio of technology and services as well as removing the business services and complementary technologies that were previously seen as competitive to potential HPE competitors. This fundamental change should serve HPE well.

How HPE found its focus

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Amalgam Insights’ primer on value-based pricing

money-graph
money-graph

Price is the ultimate test of value. Amalgam cannot emphasis this enough. No matter how valuable you think your product or service is, the ultimate business test of that value is whether someone is willing to buy it at the listed price.

One of my favorite topics in enterprise software is pricing. Despite the work done in value-based pricing over the past 50 years, the vast majority of pricing exercises still start with either a very basic cost-plus or percentage-based ROI model. This assumption has a key issue: it assumes that your product is a commodity. To explain why and to explain how to take a more value-based approach, consider what a price is.

There are many ways to break down price and many roles that price plays from a marketing and sales perspective. But as a starting point, the model AI uses to translate value into price comes from 3 basic components: Reference Price, Differentiated Value, and Price Positioning

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