If one thing is true about technology, it’s that it is constantly evolving into newer and better solutions.
This is particularly prevalent when it comes to the cloud—especially when it comes to maximizing the benefits. A recent Teradata report
from Vanson Bourne surveyed 700 global organizations with an average revenue of nearly $10B about their ambitions, fear, and investments in cloud analytics
. It found that more than ever before, cloud analytics is playing a major role in boardrooms the world over, with 92 percent feeling they are somewhat or very successful with their use of the public cloud.
In fact, survey results indicate that by the year 2023, most organizations want to run all their analytics in the cloud. But, an overwhelming 91 percent say that analytics should be moving to the public cloud at a faster rate. This was highlighted in a previous post, ‘The Surprising State of Analytics in the Cloud’.
With executives citing multiple reasons for reticence when it comes to embracing cloud analytics more quickly—from security (50 percent) and regulatory compliance (35 percent) to lack of trust (32 percent) and in-house skills—how can organizations provide a best-in-class, scalable, and effective environment for successful cloud implementations?
Analytics performance is a critical factor when selecting a cloud solution. The volume of data and sources involved is vast, as is the variety of people in the organization accessing and adding to it. Cloud-based platforms need to allow hundreds or thousands of users to query the system at the same time. This means speed and simplicity are key when it comes to moving information from a source system to the cloud-based analytics environment.
Slow or cumbersome data acquisition will impact how quickly decisions can be made within an organization. In turn, this can slow productivity and increase project costs. Size of storage is also important as it dictates the amount of history associated with business questions. The best analytic implementations allow companies to look back over a year or longer to identify trends and make better business comparisons.
From reporting and regulatory requirements for each department—which can often be quite regimented—to simultaneously being equipped to deal with ad-hoc requests that require links between disparate and non-optimized data sets, cloud-based solutions must be flexible. Being able to answer “what if” questions is also important, and this adds a layer of complexity where one question-and-answer combination leads to the next one.
All of these are common scenarios and supporting them is the hallmark of an agile and flexible platform capable of addressing both column and row-based storage strategies. Of course, the best solutions support a mixture to meet the range of demands for analytical workloads. It’s important, therefore, for the infrastructure to scale to meet these requirements — and to do so economically and with little system interruption. These flexibility issues can be as important as the technical specifications of a solution.
Successful analytics projects often evolve beyond their initial scope. After a time, most analytical environments will store more data than initially planned, experience an increase in user adoption of the platform, and grow requirements from those identified in the initial assessment. As such, it is critical to plan beyond the initial scope and push project requirements forward even though, initially, it may not seem necessary. As data-driven projects mature, they necessitate more advanced features and functions to handle additional demands. The ability to extend and integrate a cloud-based analytics environment with innovative functions creates an immediate need for a platform capable of executing this style of work. Ensuring organizations have the option to expand into new areas of execution can help a project stay on track, grow utility, and deliver unexpected value.
When it comes to cloud analytics, infrastructure is critical. It needs clear implementation and design to ensure information is appropriately categorized for the range of business users. It also requires advanced database management skills as well as expertise with data modeling, data integration, and security, just to name a few. Many companies have full-time employees who specialize in these tasks and can contribute to the process, but organizations should also be cognizant of the need for additional IT skills and securing the appropriate level of support for a new cloud-based project to match the required speed of implementation. Reliance on the cloud solution provider for professional services, training, and implementation assistance is paramount for success. Get it right and a cloud-based analytics platform can be a relatively fast implementation.
All analytics platforms need the ability to work within a wider ecosystem. Having the business questions and insights locked into a single platform limits the value of those insights—and coordination with downstream data consumers and administrative facilities such as archive, operational management, and monitoring make an analytics solution more effective. Leveraging the capabilities of advanced analytics or discovery platforms which span the larger ecosystem can enable sophisticated workloads and better business outcomes.
Planning for What’s Next
To prepare now for the needs of tomorrow, businesses should be looking for the best and most efficient ways to analyze anything, anywhere. Teradata is the leading cloud-based data and analytics company, recognized as the trusted gold standard for enterprise-class analytics. With modern, scalable analytics and consulting services built into our DNA, we harness data science and artificial intelligence to enable customers to perform predictive and prescriptive analytics to solve complex business problems and deliver high ROI. To find out more about how Teradata can help you analyze anything, anywhere and anytime, be sure to read about our Teradata Everywhere solution: https://www.teradata.com/Teradata-Everywhere