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Understanding the concept of a hybrid cloud is easier if you first understand the key differences between public and private cloud services.

A public cloud is a virtual network of servers and services, exposed as compute resources, storage, and service APIs, which can be invoked by applications running within a publicly accessible virtual server environment. Azure and AWS are popular examples.  All the servers in the public cloud exist in the same network environment, managed by the cloud provider, and applications access services using access control systems and encrypted network protocols.  This simple and flexible model works  for many typical cloud applications scenarios.

But if an organization wants to take the step to add additional security and to manage your own cloud network, with control just like in the on-premises datacenter, you are looking at a private cloud.  A private cloud consists of a secured virtual server environment with fine levels of access control over the network architecture.   A private cloud could be as simple as a single server running a few virtual compute resources, or as a virtual private cloud deployed through a public cloud provider. Private clouds on premises must implement their own virtualization software, known as a hypervisor, like VMware, which manage the virtual compute resources.

A hybrid cloud, as you might be figuring out by now, is when a public cloud and private cloud is networked together into a single, virtual environment connected via WAN.  For many large, mature IT organizations, running a hybrid cloud architecture is necessary due to the sheer amount of existing infrastructure and investment already in place.  

Additionally, a public cloud-compatible management layer can allow administrators to manage compute and storage resources across both public and private clouds seamlessly, giving them greater flexibility to manage security and costs.  OpenStack is a popular example of this orchestration software layer.

It’s important to note that hybrid clouds can have a higher level of technical complexity than a public cloud, and often require significant expertise, particularly due to the complexity of implementing private clouds.  

Why Hybrid Cloud

There are number of reasons to move to a hybrid cloud.  As we’ve seen above, sometimes it’s often the only reasonable solution in the near term, as the cost, time and complexity of a full replatforming may be too much for many organizations.  But there are other, often strategic reasons to use a hybrid cloud infrastructure.

Hybrid clouds are often useful when creating a seamless extension of on-premises datacenter. This facilitates the migration of applications to the cloud over time, while retaining the mature security infrastructure and processes often developed over many years.  Organizations who take this approach often are selectively choosing whether to move applications to cloud or replace them with cloud-native solutions over a long period of time.

Another big reason reason is cost.  On-premises computing power is often significantly cheaper than cloud computing resources, particularly for intensive uses like graphics rendering, real-time transactions, and intensive data and analytics.  Expensive tasks can be handled in the datacenter with cheap, powerful, fast servers and commodity storage with high scale business applications can be handled in the cloud.  This is often attractive if an organization has already made an investment in high performance computing resources.

As cloud and SAAS-based products have become the norm, the tech industry has shifted from typical enterprise-style marketing to one that much more resembles consumer marketing. When the project is digital and the customer is using digital as the primary sales channel, acquiring new customers via digital is a logical and necessary strategy. Since many marketing programs in this sector drive customers to convert via a free trial or other digital experience, enabling not only a high-value conversion but the ability to collect rich in-app usage data from known customers. Compared to manufacturing or services, quantifying the return on a marketing investment in a SAAS app is often much simpler to produce, often in nearly real time.

For most B2B SAAS companies, customer aquisition via the internet is the primary focus, with brand becoming increasingly important as the space has grown crowded with so many similar products. Search and Social media marketing have emerged as the dominant marketing tactics in this space, fueling the massive and rapid growth of Google and Facebook who have captured xx% of the global share of marketing spends.

With most companies seeing 50%+ of their web traffic from Google organic search engine results, SEO has become a fixture in the toolkit for B2B SAAS marketers. Optimizing landing pages, developing product and feature content, blogging and social media presence all help to drive search traffic to leadgen forms and free trial signups.

Organic traffic is perceived as cost-effective, often even seen as free, although many marketers spend budget on specialists and agency to optimize organic search and produce high quality content on their behalf. This channel also has the advantage of mich higher [cite] conversion rates than ad channels.

The primary challenge of organic search marketing is the more limited ability the marketer has to affect the desired outcome. As search engines have matured and their technology has become more sophisticated, search results often favor well-known brands. Relying on search engine tricks that worked in the past, such as keyword stuffing or content duplication, now can result in penalties which could cause a major loss of traffic. Additionally, there is a latency to SEO results, often measured in months, as content is developed, published, shared and linked back to. With recent changes to Google’s reporting, marketers can no longer see keyword-level traffic to their site.

Typical organic search metrics include:
* keyword searches
* result impressions
* inbound sessions from search marketing

More and more B2B SAAS marketers are investing in Google’s AdWords and other pay-per-click search engine advertising methods. These products not only allow marketers to guarantee a top-of-page placement, they ensure that the company is capturing searches for their brand and key non-brand searches. These ads also have the added benefit of providing a better user experience for customers looking to narrow down a large set of results into just a few vendors. PPC ads allow much more control and flexibility to marketers. They offer the ability to do copy variations, can be mapped to A/B tests, and offer tremendous control for marketers willing to pay a premium for high quality traffic. While a typical B2B SAAS company may only receive 30% of traffic from PPC ads, this spend is often the largest component of an advertising budget and is often the biggest individual source of leads.

Typical paid search metrics include:
* keyword searchs
* result impressions
* average cost-per-click
* cost-per-conversion or cost-per-lead

Social advertising is also emerging as another popular tactic, in the B2B space most attention goes to LinkedIn, which allows for highly targeted advertising to specific roles, industries and titles . Native advertising, content boosting and pay-per-app-install models are all popular and have moved from experimental to core tactic for many B2B SAAS marketers. Particularly attractive to mobile SAAS apps are pay-per-app install ads, which allow an easy user experience directly from the ad, reducing the friction that many customers have when locating software in crowded app marketplaces. Even if a B2B SAAS businesses isn’t truly mobile-first, customers increasingly expect a seamless experience: they want to touch and feel the product right away, rather than engage in a complex enterprise sales process right off the bat.

Most B2B digital marketing programs include at least some display advertising, typically in the form of banner remarketing and display retargeting. These ads are displayed after a customer has visited the web site, and are cost-effective at both brand development and assisting conversions. Retargeting pools make excellent sources for marketing off of your web site. Compared to typical programmatic display or traditional ad banners, these are far more effective in keeping interested customers engaged, and are often a low cost way to remind your visitors to convert.

Measuring customer experience is another key element to ensuring your digital program is succeeding. A/B Testing is the most common and powerful way to capture customer engagement. Landing pages, tailored to ad or offer copy, is the typical way to drive ad impressions into prospects. Having a strong offer is important, and free trials, guided demos and webinars are a great way to encourage prospects to share their personal information

Mobile has also increased the depth and number of possible measures as well. Since most analytics tools allow for in-app data collection, it’s possible to get very rich customer experience analytics from a mobile application. Compared to a web app, mobile app events are much easier to define and track, and allow marketers to capture key user engagement metrics.

Finally, Email nurturing remains the cornerstone of any B2B marketing program. With sophisticated automation systems available from a wide variety of vendors, the ability to produce and send targeted emails has never been easier. Targeting the message based on user profiles, activity or behavior leads to higher conversion rates, and many B2B prospects are much more willing to engage via email in the intitial stages of their buying process than

Apache Cassandra is a distributed, highly scalable,  high performance NoSQL database.  It offers several advantages over traditional relational database management systems (RDBMS), particularly in write-intensive, globally distributed, high availability situations that span geographies and datacenters.

Apache Cassandra is Open Source and distributed under the Apache 2.0 license.  Originally developed by Facebook, Cassandra is used by many global enterprises including Apple, Cisco, and Netflix.

Unlike traditional RDBMS systems, Apache Cassandra is a NoSQL database.  Rather than relying on related tables to describe data, Cassandra uses a simplified data storage architecture known as ‘wide column’.  This allows for the simplicity of key value storage, but row data types can vary per row, allowing for the flexibility of tabular data storage.  Cassandra also has the concept of ‘Column Families’ which allow grouping of columns into tables, but rows do not all need to contain the same columns.  Key / Value storage is fundamental to NoSQL databases as they allow for fast indexing, writes, and retrieval.

NoSQL databases forego complex transactions and guaranteed consistency in favor of a highly scalable, strongly or eventually consistent model which is well-suited to internet-scale applications.

Apache Cassandra is masterless, meaning that all nodes in a Cassandra cluster are active and communicating with each other. Any node in the cluster can accept and serve requests, and in the event of a failure to a given node, traffic can be automatically redirected to another active node with no need for complex master – slave replication schemes.  Cassandra automatically distributes and maintains data across the cluster with no need for complex sharding and disk partitioning.

Additionally, Cassandra’s replication approach is much simpler than multi-master or master-slave architectures.  Once a replication schema is created, it is automatically managed across all nodes of the cluster without need for any additional administration.  

Cassandra also exposes an SQL-type query and management interface called Cassandra Query Language (CQL) which allows for developers and administrators to interface the system using familiar RDBMS queries.

Why use Apache Cassandra?

Due to it’s highly distributed and fault tolerant nature, Apache Cassandra is well-suited to globally distributed, write and read-intensive applications that require high availability and high scalability.   A few examples include Social Media data, IOT Sensor data, User Tracking and Messaging applications.

A common reason to use Apache Cassandra is to locate highly available database clusters close to end users in a globally available application.  Since Cassandra nodes can be replicated across any type of infrastructure, including private, public, and hybrid clouds, Cassandra is well suited to geographically distributed applications.   Reads and writes can be delivered with low latency, close to the end user, and replicated throughout the cluster from any node.  This is especially important in high throughput scenarios where locating data infrastructure close to the end user can result in a significant reduction in bandwidth costs.

Additionally, Apache Cassandra is well suited for applications that may require significant scaling up or down.  Adding and removing nodes in a Cassandra cluster is simple and requires no downtime.

Relational Database Systems (RDBMS) are the traditional software platform for storing, indexing, querying and analyzing data.  Relational Databases expose data as tables, where data can be grouped into rows, with columns defining the datatype to be stored, such as numeric, text, or binary data.

RDBMS can use relationships between tables to define a data schema which models the real-world business case. These relationships can define reference data. For example, it is common to store a list of countries, cities, and states in separate tables, and use references, known as keys, to link these lists together to create a final definition for a location. These references can be enforced via a function called ‘referential integrity’, which ensures that a new record cannot be created unless the references to other tables are also created at the same time.

These features are designed to accommodate a set of standard properties known as ACID, or Atomicity, Consistency, Isolation, Durability.  These features allow the database to process transactions which can be rolled back in the case of a system failure, ensuring a transaction can never be in an incomplete state.  Financial transactions are typically ACID-compliant.

Querying an RDBMS is accomplished via Structured Query Language (SQL), a simplified English-like programming language that allows retrieval, update, delete and manipulation of data.  SQL queries can also be used to manage the database system itself, including backups, recovery, physical storage and performance tuning.

A NoSQL database is similar to an RDBMS because it allows for data storage and indexing, however it eliminates or reduces many of the features in an RDBMS.   NoSQL databases typically use a simplified table design, often simply key-value pairs, does not enforce referential integrity, does not offer ACID-compliance or often any transaction processing and rollback capability.   And NoSQL databases typically do away with SQL in favor of a simple API, often exposed as a REST service.  NoSQL databases use a Key / Value pair as their primary storage mechanism, eliminating the need for multiple tables, joins, and keys, and simplifying the underlying data storage.

Why use a NoSQL Database?

Many applications do not require the overhead of an RDBMS and don’t take advantage of the features they offer.  These applications often are non-transactional, read-intensive, widely distributed, and deal with unstructured data.     As an example, social media posting is a good candidate for a NoSQL database, and indeed many Social Media properties rely on NoSQL databases as their primary approach for data storage.

In the common use cases of text storage, image storage and retrieval, a NoSQL database will often outperform an RDBMS at scale, due to the simplified nature of storage underlying a NoSQL database.   In applications where fast retrieval is important, this can be a critical feature.    In a clustered environment, NoSQL databases can often get significant performance improvement over an RDBMS by relying on ‘eventual consistency’, where data may be updated to child nodes over time, rather than needing to be updated immediately during a transaction.

NoSQL databases also makes it simple for developers to add persistent storage to their application.  Since data management operations are exposed via APIs, there is no need for a developer to embed or expose SQL into their code.  NoSQL databases also map much more closely to the object-oriented programming paradigm, which relies on simple, self-contained key value data schemas comprised of fields as opposed to table structures.

Any developer looking for a highly scalable, fast and simple database should take a good look at a NoSQL database.   If the application does not require the transaction processing features of an RDBMS, a NoSQL database may be a more simpler, more scalable approach.

Device manufacturers often struggle to grow awareness as they introduce new products to market.  Educating healthcare practitioners and patients can be a complex, time-consuming, and expensive process.  Despite rapid advances in sales and marketing tools, most device manufacturers stick to the tried-and-true tactics such as field sales, trade shows, and brochure-ware web presence.

Over the past few years, there have been significant changes in B2B sales approach, particularly the rise of account-based-marketing (ABM) strategies that use CRM, digital and third-party data to help focus on marketing into specific customer accounts, and often rely on tools that are able to identify engagement across a company and it’s multiple decision makers.  Artificial Intelligence (AI) and Machine Learning systems have proven the ability to automate away the complex process of  identifying and engaging with the target market.

I’ve identified some of the common strategic areas that can help you quickly and effectively determine the right tools, tactics and messaging for your medical device campaigns.

Measurement Matters
Understanding how to measure performance is a key first step to any successful digital program.  Determining the goals for the campaign and how it will be measured helps create alignment with sales, marketing and executive management, and ensures marketers don’t end up talking about ad impressions and brand reach while the CEO asks tough questions like “how much money did we make on this campaign?”
For most marketers in the medical device space, the focus is on conversions and the cost metrics around customer acquisition.  Determining return-on-investment (ROI) for marketing campaigns and channels is often complex and requires multiple data sources including CRM and ad-tech platforms, but will help deliver a clear picture of the value that a campaign brings to the business.   Most digital marketing platforms do a great job at measuring basic advertising metrics like impressions, engagement, and reach, but measuring ROI often requires marketers to go the extra mile and integrate their sales data to tell a real business story.

Build an Audience, then Activate it
One of the biggest shifts in marketing has been the declining effectiveness of many traditional advertising tactics and the shift to social media and content marketing. More than ever, consumers of all types, including corporate buyers, expect to learn about new products in high-trust environments, particularly those that contain some sort of peer validation, as opposed to advertisements.   Customers expect brands to deliver significant value in the marketing and sales process. Digital excels in this area, and there are a number of approaches that can help a brand build an audience of interested prospects and influencers without using often-wasteful mass advertising tactics.

Educational content marketing is particularly effective in new markets where demand may not yet be fully realized, particularly for emerging product categories or new technical break throughs. Device manufacturers can often own demand for information through some basic search-engine marketing techniques.   By owning the educational content for a category, the manufacturer can build and capture demand for the search terms around the category, and grow awareness as well as traffic back to the corporate site.   

The most common and successful approach for many brands is building an audience through value-add content marketing.  With this approach, the brand produces a high value content piece which requires prospective readers to give their email address to obtain a copy.  Once limited just to newsletters and email blasts, this category has become increasingly sophisticated to include vendor pricing and selection guides, salary surveys, how-to content, industry surveys, analysis and insights are all content items used by brands to help build their in-house lists.  Setting up customer journeys, drip campaigns and newsletters are all popular ways to keep prospective leads engaged and encourage them to convert.

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