Data analytics is not an entirely modern invention. The term “big data” was coined in the 1990s to describe massive data sets often used in the finance, science, and energy sectors.
Since then, both the amount of data produced and the computing power it requires have grown at an astonishing rate. The tools and techniques honed through various scientific disciplines provide a platform for businesses to accelerate growth and make the most of their place in the market.
As data analysis grew in ability and scale, it has increasingly moved into the cloud to leverage additional computing resources. In a short span of time, analytics on the cloud has improved the ease of use, accessibility, and capabilities of data analysis on exceptionally large data sets.
Cloud analytics have been yielding remarkable results in every industry area, including:
Identifying patterns and extracting data from audio files, images, and video
Testing genome data to identify markers of genetic disease and finding potential cures
Studying logistics capabilities to improve product availability and delivery
Analysis of infrastructure to optimize performance and costs
For marketplaces where data collection continues to grow at staggering rates, cloud analytics offers advantages that data scientists could hardly have imagined thirty years ago.
One of the most powerful features of analytics on the cloud is its agility. Cloud computing enables the scaling of storage and analytics as needed, enabling businesses to ask new questions about their data, on demand. These capabilities make it possible for organizations to respond in real time to changing conditions, traffic spikes, and major market events.
For supply chain managers, cloud analytics is essential to predict future demand and target greater efficiencies, thereby reducing the likelihood of supply chain issues emerging at a later stage. This data can then help them decide how to shift product lines when they become less profitable, or better target customer needs after the initial order.
Analytics on cloud solutions can also help to foster collaboration and information-sharing on an organizational scale. Using a central access point provides faster access to a shared point of reference, promoting better communications and shared insights.
Cloud analytics have benefited firms by providing:
Lower costs of ownership
Greater available resources to solve business challenges
Flexible platforms that scale to meet requirements
A single point of collection for diverse and distributed sets of data
A central point of access for clients worldwide
A powerful feature of the cloud is the access to data for any business department from a centralized location. This alone has solved many challenges associated with modern IT requirements.
A central cloud portal also enables the integrated of data from every part of the organization with ease. Leveraging additional data sets can deliver key insights that provide a competitive advantage or differentiate a product or brand. Breaking down data silos is one of the most productive ways of using analytics-on-cloud solutions to utilize existing business data.
Organizations are increasingly recognizing the advantages that analytics on the cloud can bring. Adoption of cloud analytics has more than doubled in recent years, providing thousands of businesses with a valuable boost to their revenue streams.
Analytics technology represents the starting point for business intelligence in most organizations. Data visualization services driven by cloud analytics are the most-used tools to deliver insights and improve decision-making.
As a leading voice in the field, the Trianz analytics cloud consulting team specializes in delivering solutions that put you in control of your business data streams. With decades of experience in the cloud, we find ourselves exceptionally well-positioned to deliver solutions that work across all types of organizations and data.
With Trianz’s approach to the analytics on cloud ecosystem — which expedites analytics implementation on cloud platforms — the focus is on providing the services and tools that deliver your teams detangled data for quick, actionable insights to power your business.
Components used to deliver our end-to-end analytics on cloud service:
With the world’s broadest selection of analytics services, Trianz experts are here to tailor your AWS ecosystem and data analytics needs to help you reinvent your business through data-driven insights.
Trianz has partnered with AWS to bring Athena customers new ways to connect to more data. We created a growing library of pre-built AFQ connectors in collaboration with the AWS Athena product team to service hybrid and multi-cloud sources.
Trianz accelerators were built to expedite the migration of your existing cloud and on-premises infrastructure. With Trianz EVOVE, we increase the velocity and consistency during complex migration tasks through automation and ETL management to minimize costs and risk while maximizing ROI during implementation.
With our industry-leading proprietary deployment frameworks, our clients benefit from custom in-house BI configurations and predictive/prescriptive analytics frameworks. This results in a distinct, data-driven competitive advantage for your enterprise.
Trianz experts use a data-driven approach to deliver tailored solutions rather than merely focusing on the technology itself. We place special emphasis on improving the digital customer experience (CX), driving customer loyalty and retention, and by association, long-term revenue growth.
Get in touch with our analytics experts today and take the first step towards fueling your business growth with cloud analytics.
What are the Differences? Though often used interchangeably, data pipelines and ETL are two different methodologies for managing and structuring data. ETL tools are used for data extraction, transformation, and loading. Whereas data pipelines encompass the entire set of processes applied to data as it moves from one system to another. Sometimes data pipelines involve transformation, and sometimes they do not.Explore
One Unified Dashboard In the past, most enterprises would have used a legacy business management system to track business needs and understand how IT resources can fulfill these needs. The problem with these legacy systems is the manual data collection process, which introduces the risk of human error and is much slower than newer automated solutions.Explore
Intelligent automation in the workplace is becoming more relevant in the modern market. As automation technology becomes more refined and smart business models allow business owners to optimize their workflow, more and more are turning to intelligent automation for their internal and client-facing processes alike.Explore
What is a Hybrid Data Center? A hybrid data center is a computing environment that combines on-premise and cloud-based infrastructure to enable the sharing of applications and data across physical data centers and multi-cloud environments. This allows organizations to balance the security provided by on-premise infrastructure and the agility found with a public cloud environment.Explore
Leverage Your Data to Discover Hidden Potential The amount of data in the insurance industry is exploding, and the number of opportunities to leverage this data to achieve large-scale business value has exploded along with it. Rapid integration of technology makes it possible to use advanced business analytics in insurance to discover potential markets, risks, customers, and competitors, as well as plan for natural disasters.Explore
Increased Use of Data Lakes As volumes of big data continue to explode, data lakes are becoming essential for companies to leverage their data for competitive advantage. Research by Aberdeen shows that organizations that have deployed and are using data lakes outperform similar companies by nine percent in organic revenue growth.Explore