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Microsoft reveals Azure Stack’s pricing model and adds new features as the solution approaches general availability.
Azure Stack, Microsoft’s private cloud software built from the same bits used to create the public Azure cloud service, is now available in its third, and likely final, technical preview edition before being released to the public later this year.
TP3 has many new features, but it also clarifies Azure Stack’s pricing structure, and how Microsoft is addressing the specific problems customers are using Azure Stack for.
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When it comes to getting the most value out of data, successful companies take a practical approach, first defining their own data strategy and then determining the tools needed to get it done. A good example of this is Airbnb, which set their own data strategy and tools to help users more accurately price their home listings. Too often, however, companies fail to lay out a clear strategy, instead relying on the available tools to show them where they need to go. Unfortunately, these tools usually serve up packaged metrics with data that is too detailed and lacks cohesion.
The mobile marketing data landscape
In VentureBeat’s The State of Marketing Analytics: Insights in the age of the customer, author Jon Cifuentes writes:
“Enterprises are stuck between fragmented data silos…There’s customer data, inventory data, log data, search data, reporting, analytics, CRM, session data, et. al – with different vendors supporting each. While “real-time” customer data sounds nice in theory, the actual process of broadcasting this information through the organization is time-consuming, expensive, fragmented, and frustrating.”
These cobbled-together sources and tools provide directional insight but don’t align with initial expectations, particularly as companies start requiring custom insights and metrics. In fact, most companies quickly find themselves in exactly the situation they had hoped to avoid – working in increasingly complex systems with considerably higher non-value added workloads.
The challenge for companies is: how do you align your data vision with your unique acquisition, engagement and monetization strategies?
Purpose-built tools like app analytics, A/B testing, marketing automation, etc. have done a great job in recent years of allowing non-technical people to analyze data, run tests and engage users. However, since these tools were built for single-use cases and by separate companies with proprietary data stores, they have failed to address a core issue: the need to access the same user data in order to truly provide a personalized experience to each user.
Data-capture tools and user engagement tools also need to be integrated in order to provide a full picture of how changes impact the product downstream. For instance, teams need to be able to apply user actions from app analytics to run A/B tests, which will in turn impact the user experience.
The path forward
The solution exists at the platform level: unifying data sources before applications are built on top of them, with a flexible 2-way structure that enables real-time integration between event and user data, at all levels in the stack, and not just based on basic pre-determined rules with segmentations on top.
This type of structure makes it possible for events to be enriched by boundless user attributes (user state) and enables contextual analytics. This, in turn, produces a robust targeting framework, because now the user state can be updated in any manner, in real-time. For example, Glassdoor utilizes this methodology to deliver real-time dynamic notifications of job alerts to users based on their prior behavior when browsing the Glassdoor website.
While many marketing vendors are fighting to define themselves as integrated or unified marketing platforms, most still need to reach deeper down the stack and unify product and marketing tools with data tools at a platform level. Because they refer to the same data source, there will be no discrepancies between insights and actions. For example, segments defined for analytics will maintain the same properties in A/B testing or content delivery. Applications developed on top of unified data platforms will be inherently more flexible and manageable.
Omniata is coming out of beta on September 24th! You can reach us at [email protected] to learn more. Though just coming out of beta, we’re already tracking 300 million monthly active users, 2 billion events per day, and handling over 17,000 requests per second!
Alex Arias is the CEO and cofounder of Omniata, a unified data, analytics and user engagement platform. For more than 10 years, Alex has been an entrepreneur and driver of innovation in digital services, working previously at Digital Chocolate and EA. He’s been helping companies define their own Data Value Journey since cofounding Omniata two and a half years ago.
VMware has updated its stack of data center virtualization software, rounding out capabilities that allow an organization to run an entire data center operation and related cloud services as a single unified entity.
Among the new additions are components to the vCloud Air suite of software for running cloud services. The company has expanded its network virtualization software to provide more ways of moving a workload across a data center. And it has also released a new version of its OpenStack distribution for running cloud workloads.
VMware believes that the architecture best suited to deliver enterprise and cloud-native applications is one based on virtualizing organizations’ compute, network, and storage resources so they can be delivered on multiple platforms, said Mark Chuang, VMware senior director of product marketing and management.
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