They are going to be briefing majorly into key topics like data creation, management, and value creation lifecycle. They will also deliver unconventional intelligence and analysis of the key data issues challenging companies as 5G begins to roll-out and the Internet of Things continues to rise. It will bring together pioneers in all of these areas and will furnish best-in-class wisdom to those striving to understand the multiple legal and business issues that go into fabricating a world-class data management and exploitation strategy.
I am continuing my quest to define and visualize the API lifecycle across a diverse API toolbox. I am talking to anyone and everyone I possibly can when it comes getting their take on what the API lifecycle is, and what are the ways in which we can make more visible and tangible. I am meeting regularly with the Solace team to define the API lifecycle across a request and response, as well as an event-driven world. As part of our discussions Jonathan Schabowsky (@jschabowsky) shared his earlier vision of how he sees the event-driven API lifecycle which I though was worth documenting and including the visualization as part of my wider research. Jonathan breaks things down into four main areas or top level groups of the stops along his event-driven API lifecycle, but I think his outline provides a pretty interesting look at the API lifecycle from the view of an event-driven API service providers.
You have the toughest job in business--managing the increasingly complex and dizzying array of contracts across your company. At Exari, we understand the challenges you're up against and we're here to help. As a global leader in Contract Lifecycle Management (CLM) solutions, we give companies around the world the ability to automatically assemble contracts, track every contract in their organization, and analyze those contracts to ensure compliance and enhance revenues. Our powerful technology is the most flexible CLM system anywhere in the world.
In this initial phase, you'll develop clear goals and a plan of how to achieve those goals. You'll want to identify where your data is coming from, and what story you want your data to tell. If you plan on hypothesis testing your data, this is the stage where you'll develop a clear hypothesis and decide which hypothesis tests you'll use (for an overview, see: hypothesis tests in one picture). One way to think about this phase is that you're focusing on the business requirements, rather than the data itself. Data can be collected in this stage, but you won't be working with the data.
In our last article, Lifecycle mapping: uncovering rich, predictive data sources, we discussed the importance of mapping out your customer lifecycle to better understand where your most predictive customer data is hiding. Now, we'll pose some questions to help identify your predictive customer attributes and lifecycle events, pinpoint where that data is located, and recognize patterns to predict outcomes for future prospects, leads, and customers. Data discovery is the second stage in the customer lifecycle optimization (CLO) process. The primary task of this stage is to expand on your lifecycle map to identify authoritative data sources that establish progress. At each stage, prospects, leads, and customers will complete certain events that will individually or collectively trigger a transition in or out of that stage.