Cloud, the Next Data Analytics Frontier

Companies of all sizes are challenged to scale their business models while simultaneously keeping operations running responsively and efficiently. Organizations are struggling to keep pace with the explosion of data volumes and velocity and the resulting impact on insights.

Disparate analytics tools and siloed roles and processes prevent organizations from making faster, smarter decisions. Because of this, cloud-based analytics platforms are beginning to close the gap between the offerings of legacy systems and what modern enterprises need to compete and grow.

Cloud-based analytics help organizations become more competitive because they deliver data and analytic results directly to end-users, allowing them to make better business decisions. It has revolutionized big data and business intelligence. The profusion of data from a wide array of digital applications can be more easily collected and analyzed from a range of different sources and areas, from systems operations to employee performance.

However, as cloud-based analytics continues to become a popular transformative technology due to the increased cloud migration and adoption, what do companies need to consider, especially in data security, scalability and performance? How does the future look like for cloud analytics?

Security in the cloud

While cloud-based analytics provides access through mobile devices, the concern about data security grows. Organizations in more regulated industries such as finance, insurance, real estate, and government, where data privacy is a crucial concern, use private and hybrid clouds.

Yet, contrary to the concerns of many, cloud analytics provides more granular control of data access, increased auditing capabilities, and a single source of truth when it comes to an understanding of a company’s data. Cloud storage of analytics can also prove useful in safeguarding information during natural disasters and other emergencies.

In cases of cyberattacks, power outages, or equipment failure, traditional data recovery strategies are no longer suitable. The task of replicating a data center — with duplicate storage, servers, networking equipment, and other infrastructure — in preparation for a disaster is tedious, challenging, and expensive. Having the data stored in cloud infrastructure will allow your organization to recover from disasters faster, thus ensuring continued access to information and vital big data insights.

Latency in the cloud

Large volumes of both structured and unstructured data require increased processing power, storage, and more. The flipside of having easy connectivity to data in the cloud is that the data availability is highly reliant on the network connection. This dependence on the internet means that the system could be prone to service interruptions.

Indeed, the issue of latency in the cloud environment could well come into play given the volume of data that’s being transferred, analyzed, and processed at any given time. And more often, the problem is not necessarily latency but the unpredictability of it.

To mitigate the growing effects of latency, it is crucial to understand what causes it and how enterprises can reduce it. To overcome this unpredictability, organizations need to establish a baseline for performance and then keep as many cloud applications as possible performing to that level. 

Scalability in the cloud

The cloud provides not only readily-available infrastructure but also the ability to scale this infrastructure quickly so companies can manage large spikes in traffic or usage.

Analytics in the cloud allows companies to scale their analytics and avoid the expenses associated with on-premises data storage. With cloud computing, organizations can add data storage and data analysis capacity to reflect changes in the business.

An organization can quickly increase its cloud storage when the business grows or decreases if it slows down, which is a much simpler process than purchasing hardware. It allows the company to be responsive to new demands and quickly adjust its analytics capacity to meet consumer needs and seize opportunities as they come.

Where in-house, on-premises analytics solutions can become costly fast, cloud analytics does not require hardware, equipment, data centers, or continuous upgrades. It can result in significant savings and allow for a more flexible budget with simple subscription models.

Simplifying analytics experience

Empowering customers to connect, unify, and confidently predict business outcomes, solving the world’s most complex data-driven challenges, is the ethos of TIBCO. To support organizations that struggle to keep pace with the explosion of data volumes and velocity (resulting impact on insights), TIBCO offers innovative analytics tools and cloud solutions that help organizations make faster, smarter decisions.

With TIBCO Hyperconverged Analytics, the company offers a visionary approach for converged visual analytics, data science, and streaming across data and analytics roles. When coupled with TIBCO Cloud Data Streams, this takes users beyond traditional business intelligence with the ability to connect to real-time data streams for responsive business insights. Supporting next-generation business analysis needs, TIBCO Hyperconverged Analytics delivers superior visual analytics, data science, and streaming analytics capabilities through a single environment, Spotfire® 11.

Hyperconverged Analytics shortens the time to insights, while the new Spotfire 11 shortens the time to custom analytics creation. Together, they combine data management and data science with real-time data to drive business insights, decisions, and actions. TIBCO is bringing a “power to the people” analytics revolution with the introduction of TIBCO Spotfire® Mods. With this announcement, TIBCO further simplifies the complexity of immersive visual analytics and gives businesses extreme value from their data assets. The offering supports innovation for purpose-built analytic apps, fulfilling the promise of Hyperconverged Analytics.

TIBCO Cloud Data Streams is one of the first cloud-based, real-time streaming BI platforms, delivering click-simple access to real-time data for Spotfire visualizations. Now users can detect emerging trends and patterns as they occur, anticipate future conditions, and take action when it matters most. Extending ease-of-use to real-time analytics, TIBCO Cloud Data Streams allows customers to innovate beyond conventional approaches to BI and extends the democratization of high-value insights to streaming data.

The future: cloud and analytics working together

There’s a ton of rich functionality available in the cloud that organizations can spin up right now. Over the last few years, there’s been a real shift from heavyweight on-premises installations of data science and predictive analytics to the more lightweight approach that the cloud offers. 

The breadth of capabilities that the cloud providers have created combined with the ease of using a data science platform like TIBCO’s. Organizations can spin up environments very quickly without a great deal of IT overhead. This combination of scalability and flexibility is the central value of the cloud in doing analytics. In fact, with TIBCO® Data Science, organizations can create solutions across all of these various cloud environments without learning the nuances of each. 

Sounds easy? The proof is in TIBCO’s customers’ success stories that show how analytics and the cloud can work together in making impactful decisions.

Example one is Tipping Point Community which fights poverty with data. As a non-profit organization looking to understand the drivers behind poverty better, Tipping Point started a project to explore correlations between parking citations, late fees, and low-income individuals. Tipping Point found a disproportionate impact on low-income drivers using TIBCO Data Science’s collaborative interface for business users and deploying machine learning models in the cloud to discover insights. The data-driven recommendations that Tipping Point made to the San Francisco Office of Financial Justice led to a policy change to make the system fairer.

Leidos unlocks big data potential for healthcare analytics. Leidos partnered with TIBCO Data Science, an enterprise-class cloud platform that leverages Amazon Web Services (AWS) to allow users to create machine learning workflows. By using the cloud, Leidos opened up collaboration across teams and was able to perform quicker analyses. It analyzed healthcare data to determine the cause of disease outbreaks like HIV and Zika, consolidate data around emerging healthcare policies, and explore human factors affecting space exploration for NASA. 

Across these and many other examples, performing analytics in the cloud gives organizations the ability to uncover hidden patterns, anticipate outcomes, and react quickly to real-world events. Data science teams can spin up new systems in the cloud in a matter of hours, performing advanced analytics in a low-code, visual data science environment like TIBCO’s to find the answers to their most challenging problems fast.

Robert Merlicek, chief technology officer for Asia Pacific and Japan at TIBCO Software, wrote this article.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/metamorworks