DATA, ANALYTICS & AUTOMATION for better healthcare
Modern Data Management

Modern Data Management

Cloud Architecture

Your data is doubling in size every two years. Are you willing to spend $40m every two years to double your data center capacity too?

It used to be that building a new system required a capital investment in new technology infrastructure that left a trail of lingering hang-ups over speed, cost and shared responsibilities. Then came Cloud—and with it a drastic change in the economics of technology deployment and the elimination of past barriers to timely and deeply insightful analytics.

This explains why analytics solutions are the first thing that many healthcare companies have considered moving into the Cloud. It’s hard to overstate the appeal of being able to manage growth (and costs!) of new data warehouse implementations, big data capabilities or large business intelligence platforms incrementally and quickly respond to a high number of demands from a large variety of business groups. Leveraging Cloud to successfully deliver more value at lower cost for data management and analytics requires solutions that are:

  • Scalable as data volumes and needs grow
  • Deployed on demand
  • Applying commodity machine learning techniques

A Big Data Cluster for Every Job

Rapid deployment, scalability, and elasticity are three infrastructure characteristics that the cloud has made more widely available to organizations of every size.

Rapid Deployment -New environments for proofs of concept, developer teams, and testing scenarios can be made available to teams on-demand. This avoids the typical delays that used to slow down solution developers. With well thought out infrastructure templates, developers, testers and analysts can even deploy their own infrastructure without any IT involvement.

Scalability -Data volumes, application use and problem complexity are all growing simultaneously, putting complex capacity planning and growth demands on IT teams. The database needs more storage. The number of dashboard users has tripled in two months. The data science team had just deployed a new custom model for risk analysis and clustering.  Cloud infrastructure can grow at pace with these same needs.

Elasticity -More and more workloads are becoming real-time or transactional, but many legacy data feeds and large analytical jobs will always be periodic. IT teams usually have to make a compromise – how much hardware do I have to buy to keep that batch job from being intolerably slow. You don’t want to make a capital investment on hardware that will simply sit idle most of the month.

Beyond the foundational capabilities of cloud infrastructure as a service (IaaS), cloud vendors are releasing an ever-greater number of platforms as a service (PaaS) or cloud-native capabilities: Databases as a service. Hadoop as a service. Internet of Things (IoT) as a service.  Messaging as a service. Data warehouse as a service. All of these cloud-native capabilities being provided by vendors can scale to enormous capacity on demand to meet nearly any enterprise workload.

An AI in Every Report

Imagine having a workforce management report that is based on a continuously updated learning model, tied to predictions about the incoming patients, and optimized to create teams that work best together. The dream of embedding machine learning and even artificial intelligence into every analytics solution isn’t far from reality. With analytical capabilities as services from players like Amazon, Microsoft and Google already available, that concept is much more within reach.

With this ease of use, however, comes a greater risk of misuse. Leveraging appropriate advanced analytics techniques requires a firm understanding of how the techniques work and under what circumstances they make the most sense. Just because something is easy to do doesn’t mean it is the right thing to do.

Reliable Expertise in Cloud Analytics

It takes a thoughtful and well-planned cloud analytics strategy to see where the most impactful benefit with the least disruption can achieved. You’ll need experts who understand how traditional data warehouse environments work, where new cloud-based technologies can be leveraged to create value and what it takes to manage complicated migrations through to success. Amitech’s expertise can connect you with the vendors, tools and cloud architecture that will deliver the value you need and the results you want.