January 11, 2022
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
Under the Health Insurance Portability and Accountability Act (HIPAA), cloud service providers aren’t considered business associates, which are entities that use or disclose protected health information (PHI). Companies that perform services such as claims administration, quality assurance, benefits management, and billing qualifies as business associates. That said, Chung encouraged healthcare organizations to push their CSPs to sign a business associate agreement, or BAA, to ensure that the provider assumes responsibility for safeguarding the organization’s PHI. “If a CSP is not willing to sign a BAA, then you have to ask yourself, “Do they treasure your data as much as you do?” Chung said. “The BAA provides assurance to organizations that we protect their data, that we provide training to our employees, and that we store and process consumer data securely.” Healthcare’s traditional network perimeter no longer exists. Many physicians and nurses may work at multiple locations for the same institution, sometimes visiting several locations in one day, or clinical staff may conduct research at a nearby university.
In traditional environments, we have predefined builds that can be weekly, fortnightly or sometimes even monthly. One of the reasons is that these deployments take time. The problem with this approach is that we have to wait for the predefined dates to get the bugs fixed or to get the new features implemented, so there is a delay. The second reason was – by the time testers finish up with the testing and come up with bugs and defects, the programmers have moved on to different pieces of implementation and have less interest in resolving the bugs of the older application. This approach also delays the time for making the feature available in production. Building and deployments are the entities that are repetitive and sometimes boring.. ... Automating the testing behind the GUI is comparatively easier than automating the actual GUI. Another advantage is that irrespective of the UI changes, functionality remains intact. Even if some of the UI element is changed, the functionality of the feature does not change. This technique mainly focuses on the business logic and rules.
The major public clouds and several database vendors have implemented planet-scale distributed databases with underpinnings such as data fabrics, redundant interconnects, and distributed consensus algorithms that enable them to work efficiently and with up to five 9’s reliability (99.999% uptime). Cloud-specific examples include Google Cloud Spanner (relational), Azure Cosmos DB (multi-model), Amazon DynamoDB (key-value and document), and Amazon Aurora (relational). Vendor examples include CockroachDB (relational), PlanetScale (relational), Fauna (relational/serverless), Neo4j (graph), MongoDB Atlas (document), DataStax Astra (wide-column), and Couchbase Cloud (document). ... Companies with large investments in data centers often want to extend their existing applications and services into the cloud rather than replace them with cloud services. All the major cloud vendors now offer ways to accomplish that, both by using specific hybrid services (for example, databases that can span data centers and clouds) and on-premises servers and edge cloud resources that connect to the public cloud, often called hybrid clouds.
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Early success stories are encouraging, but they are the exception rather than the rule. More often, firms have started the transformation journey but have faltered along the way. Common reasons include a lack of ownership at senior levels and budgetary or strategic restraints that prevent project teams from executing effectively. The challenges of transforming service models are significant but not insurmountable. Indeed, as analytics use cases become more pervasive, implementation at scale becomes more achievable. In the following paragraphs, we present five ingredients of an analytics-based transformation (Exhibit 3). These can be supported by strong leadership, a rigorous focus on outcomes, and a willingness to embrace new ways of working. Indeed, managers who execute effectively will get ahead of the competition and be much more adept in meeting client needs. ... Analytics-driven transformations are often restricted to narrow silos occupied by a few committed experts. As a result, applications fail to pick up enough momentum to make a real difference to performance.
Firstly, data science has never been about re-inventing the wheel or building highly complex algorithms. The role of a data scientist is to add value to an organization with data. And in most companies, only a very small portion of this involves building ML algorithms. Secondly, there will always be problems that cannot be solved by automated tools. These tools have a fixed set of algorithms you can pick from, and if you do find a problem that requires a combination of approaches to solve, you will need to do it manually. And although this doesn’t happen often, it still does — and as an organization, you need to hire people skilled enough to do this. Also, tools like DataRobot can’t do data pre-processing or any of the heavy lifting that comes before model building. As someone who has created data-driven solutions for startups and large companies alike, the situation is very different from what it’s like dealing with Kaggle datasets. There is no fixed problem. Usually, you have a dataset, and you are given a business problem.?
More organizations are starting to fully adopt Infrastructure-as-Code (IaC) to create fully autonomous cloud-based environments. From a security perspective, ensuring that the supply chain from the code to production is protected and monitored is becoming an increasing concern for organizations. We are seeing tools in this space starting to mature, and new strategies are being implemented. For example, you can do things like pre-validation of configurations and architecture, ensuring your architecture and code are compliant and secured before it even moves to production. ... Multi-cloud strategies are here to stay – and many enterprises are picking technologies best suited for their platforms while also creating resilient architectures that utilize more than one cloud service provider. We will soon see this adoption model mature along with multi-cloud security practices and tools. Additionally, we see “multi-cloud” enveloping edge computing, which will continue to extend onto factory floors, as well as into branch offices and private data centers.?