What every business should know about Cloud Analytics

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The cloud is distinctively a well-accepted practice in today’s business world without being widely understood. It is a remote service for keeping and managing data – that much is clear. The how’s and why’s are best left to tech buffs in the company. But the most important matter, the impact and influence of the cloud on the future of the business, is often overlooked completely. Thus, the cloud is rarely at the center of the business strategy.

And yet, the most successful companies today have business models almost exclusively centred around the cloud. These are companies which have adopted the cloud-based Software-as-a-Service (SaaS) model to disrupt the general order of things, bringing a range of value-added services directly to the user’s device. Examples of SaaS include Netflix, HubSpot, Google, Uber, Amazon, AirBnB, Microsoft O365, Slack, and Shopify.

The idea is simple. Creating and hosting services on the cloud enables unrestricted access to a global market base, eliminates product delivery costs, and allows for updating the products from one central location, while also providing for customised instances. The cloud is also a great leveller, forcing the big guns to compete with start-ups for supremacy. It is a space where the interface, and not the product, is the creator of value. Which is why most SaaS companies – think Uber, Alibaba, or Amazon Prime Video – do not own the goods that they market. The cloud is a living demonstration of the world becoming increasingly virtualized.

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When you intersect the cloud with analytics, you enter the realm of Cloud Analytics. Cloud Analytics is a service that performs data analysis on the cloud and renders the results to you at a place and device of your choosing. It is your key to freedom and empowerment.

  1. How does cloud analytics benefit me?

As with the adoption of cloud model for any service, it is natural to wonder if a cloud analytics solution is indeed strategically beneficial over a non-cloud – or on-premise – analytics solution. In today’s digitally charged business environment, the better question to ask would be if an enterprise could survive without a cloud model. Consider these advantages:

Scalability: With data volumes exploding, storage and computing demands are met far more easily with your data analytics solution hosted on the flexible and scalable cloud model.

Availability: With a cloud-based solution, unplanned disruptions in the availability of data analytics service are passe, as the cloud analytics service provider guarantees near 100% uptime through high-availability and fault-tolerant systems.

Convenience: A cloud-based solution relieves your in-house teams from critical but vexing issues like maintaining an efficient data warehouse, reducing latency, constantly upgrading to the latest tools and technology, and handling surges and ebbs in real-time queries and requests.

Predictability: A cloud analytics solution takes the element of surprise out of your IT costs as cloud analytics services are rendered as fixed-price contracts with your service provider.

These are some of the obvious benefits of a cloud-based analytics model. At a subtler level, with a cloud analytics solution, business teams can view and share analytics results in a boundaryless way, thus increasing collaboration among diverse teams and simultaneously contributing to keeping the data accurate and reliable.

Arguably the biggest benefit of cloud-analytics is the empowerment of front-line business executives who may not be data analysts themselves but serve crucial customer-facing functions that require quick data-driven decisions. Think about it. If your competition is already profiting from it, can you afford to put it off by another day?

  1. What are the key business requirements that are served by the cloud analytics model?

The cloud analytics model scores high on versatility. Even as innovative applications and use-cases crop up with unfaltering regularity, there is an abundance of existing selections in the basket. The cloud analytics model can be used to meet a wide range of requirements that include data discovery, data warehousing, Business Intelligence and Analytics, Decision Intelligence, Dashboarding and reports, and KPI tracking – to name a few. The cloud model enables customized instances of all these anytime and anywhere, which makes it central to the enterprise’s business strategy and ability to compete. Once an enterprise has embraced the cloud analytics model, new requirements that can be met with it are a natural consequence. There are no boundaries.

  1. How do I move my data to the cloud?

Moving data to the cloud is not a fork-lift operation, but a carefully planned activity where the end-state may be quite different from the original state. It is a call to re-evaluate your data/application architectures and environment – which is probably overdue anyway but awaiting a “good time”. Having finalized the architecture, cloud strategy (single cloud or multiple clouds, public cloud or hybrid cloud, etc.), and requirements, the enterprise embarks upon the task of data migration to cloud.

As you migrate to the cloud, bear in mind that at least some applications may need to be modified to take advantage of cloud capabilities like auto-scaling and load balancing. As part of the process to migrate your data to a third-party provider of cloud services, you may need to define the service-levels on performance, user experience, and availability. It is important to carefully prepare and test the data migration and switchover plan. Depending on the size of your enterprise, you may either appoint a neutral cloud consultant to facilitate the migration, or build an internal team of a system architect, integration lead, DB administrators, and system administrators to work with the cloud service provider’s technical team as a project carefully steered by the company’s leadership.

The completion of the initial migration activity is only the beginning of a continuous process of collection, validation, and transformation of data on the cloud. This requires constant oversight and engagement between the enterprise’s own cloud team and the cloud-analytics service provider.

  1. How do I keep my cloud-based data secure?

Security of the company’s vital data on the cloud is probably the biggest inhibitor of the adoption of cloud data analytics, even after recognizing its many benefits. The truth is, you can never be over-cautious when it comes to the security of your most vital asset. But with due oversight, data on the cloud is probably a lot safer than on your hard disk. It is important to know that while placing your data in the hands of a third-party provider, you are still playing a vital part in ensuring its security and privacy. Here are some common but effective ways to ensure the sanctity of cloud data:

  • To keep your data safe from losses and corruption due to multiple users accessing data simultaneously, inadvertent deletion and corruption of data files, password sharing, and the like, use data loss prevention (DLP) tools that effectively disbar unauthorized users from gaining access to the data.
  • Advanced security measures like periodic Vulnerability Assessment and Penetration Testing (VAPT) of cloud service provider’s firewall to safeguard against intrusions and threats can help you prevent and overcome (potential) data breaches.
  • Investing in tools and services that provide real-time protection against advanced persistent threats like DDoS can be a big boost to ensuring uninterrupted services with cloud-based data.
  • Implementing SSL (Secure Socket Layer) encryption particularly when your data and apps on the cloud are in communication with other apps, devices, and networks is de rigueur.
  • Cloud data and services many also suffer unplanned outages, though more rarely. Business continuity planning and disaster recovery dedicated to cloud services are critical, as the impact of disruption could be much more catastrophic.
  • Most security threats, internal as well as external, must be detected before they have had a chance to cause damage. To enable this, it is a good practice to build a 24×7 security operations center to monitor traffic, detect patterns, correlate events, and send alerts as a preventive strategy.
  • An enlightened security behaviour by employees through ongoing security awareness programs is mandated even when you have migrated data to the cloud, over and above standard security protocols.
  1. What are the tools and technologies needed for cloud analytics?

The cloud is part of an ecosystem that includes several web-enabled technologies like big data, analytics, mobility, and applications. The real value of cloud analytics tools is derived only when the full digital ecosystem is firmly established.

Moving to a cloud-based analytics services model is typically not a major technological challenge for most enterprises, as it builds on the existing infrastructure that most companies have already built, except perhaps for adding the cloud-enabled analytics tools (or Cloud Analytics Tools) platform for visualizing enterprise analytics.

Cloud Analytics Tools are becoming an increasingly critical part of the enterprise application landscape as they enable a single window into a range of business indicators and metrics irrespective of where the user happens to be, resulting in greater workforce empowerment and more informed business decision-making. The dominant cloud infrastructure players, Microsoft Azure and Amazon AWS offer a versatile suite of cloud analytics tools for ETL, query, and advanced analytics like Azure data factory and Azure Synapse Analytics from Microsoft, and Redshift from Amazon. Each has unique features and capabilities which calls for a careful assessment of its suitability for your specific needs.

  1. What are the governance and quality requirements of cloud analytics?

As the enterprise eco-system expands to include several partners, service providers, contractors, employees, regulators, vendors, channels, and customers, there is an ever-increasing requirement to upscale the enterprise systems of governance, risk management, and control (GRC). With cloud-based systems managed by a third-party, this requirement is further intensified. Cloud softwares are typically offered as licensed-based subscriptions with strict compliance rules, the violations of which attract huge penalties. Considering the multiple users across locations, enterprises need to have strong oversight to prevent inadvertent violation or infringement of copyright rules. As GRC requirements increase in complexity, enterprises must adopt more structured and automated approaches towards managing the access, budget, and compliance across workloads on the cloud.

A GRC strategy for cloud-based analytics services is a multi-pronged approach. The client enterprise needs to run GRC within its own gamut with both software-enabled (automated) and person-based systems. These systems must include:

  • Cloud-data Confidentiality, Integrity, and Availability
  • Compliance to regulatory and third-party usage clauses
  • Policy framework
  • Auditing – budget, usage, compliance, access
  • Monitoring – slippages, fraud, patterns
  • Contracts review
  • Change Management process

The availability of automated cloud governance tools has made GRC quicker and more efficient, though it has not substituted the human perspicacity that many situations require. The tools provide rules-based alerts to track and report on unregulated changes, unauthorized access, non-compliances, and other unwarranted actions. Enterprises are increasingly leveraging cloud management tools from Amazon AWS, IBM, VMWare, NetApp, and others to ensure that operational bottlenecks, unmitigated risks, and budget over-runs do not become a millstone around their necks as they race for supremacy in the digital arena.

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Marketing KMS
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Marketing KMS
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