Applying cost-effective, off-premises cloud-based analytics
Cloud computing can serve as a catalyst that helps drive ease of consumption with operating expense (OpEx)–based pricing and low capitalization costs. These key factors allow for a consumption model that grants a real-time experience and also changes business behavior that enables leveraging technology to make strategic, data-driven decisions. Some specific scenarios illustrate this catalytic effect of cloud computing.
Minimizing processing time
In the first scenario, a large enterprise energy utility deployed off-premises, cloud-based financial analytics. Traditional compute for these analytics used to take a day, but cloud-based delivery trimmed the time period for these analytics to just a few minutes. As a result, the organization achieved significantly reduced total cost of ownership (TCO) compared to TCO from compute that required a full day.
The firm leverages a large, cloud-based provider to compare native reduced instruction set computing (RISC)–based processing of its enterprise financial analytics batch runs for planning, budgeting and financing (PBF). Its finance department runs these batch jobs to provide a near-real-time response. Instead of taking 24 hours for batch processes to be completed overnight, these batch runs now can require just a few minutes (see figure).
Because of the cloud-based delivery of analytics, the finance staff at this energy organization now views IT as a strategic partner and has brought IT professionals into its strategy discussions on future financial platforms. In addition, a peer-to-peer relationship was established, and a change in behavior brought about by the catalytic effect of commoditized cloud computing led to broad access of previously scarce computing resources.
Now, an expanded finance department has access to information once leveraged only by senior finance staff members. For this energy utility enterprise, consumption of financial reporting is now as easy as grabbing a cup of coffee compared to previous methods that required large capital investments and specialized, traditional IT services.
Making agile decisions in high-frequency trading
In another scenario, a trading organization that processes high-frequency transactions uses data analytics to discover key strategies from a handful of simple transaction situations and typical outcomes based on market data compared in tandem with trade execution. Previously, an analysis of this historical data had taken days to perform, but it now takes a few minutes through off-premises, cloud-based delivery of analytics. These analytics can empower trading staff to make changes in its trading behavior faster than ever when encountering market volatility (see figure). As a result, the staff can increase its speed in responding to unforeseen events.
The organization leverages a private on-premises cloud computing environment to perform historical analysis of trade execution data. That analysis can be used to gain insight into future trade executions. Attaining these results using a private cloud model allows for a highly agile, iterative approach to making trading strategy corrections that leverage near-real-time analyses.
In addition, the storage of data analytics has become commoditized, and that level of pricing allows for longer and deeper analytics storyboards than were previously employed. And they can be kept for multiple years. Quantitative staff can now play out scenario analyses perhaps within a few hours instead of taking weeks. This immediate access to information on the outcomes of trade execution scenarios enables an acutely surgical approach to high-frequency trading.
Consuming real-time data
A third scenario involves the idea that cloud-based technologies help drive analytics related to the consumption of beer to offer low risk and low TCO for beer distributors and bar establishment owners. Adoption is quick and simple, and line-of-business users can access cloud-based applications on standard web browsers anytime, anywhere (see figure).
A subscription-based, pay-as-you-go cloud services structure helps cut risk by requiring a low initial investment that typically is substantially reduced when compared to an on-premises infrastructure. In addition, cloud-based solutions provide the means for customers to easily opt out of the product, if they are dissatisfied with it. Therefore, the business risk is in the hands of the cloud-based vendor and not the company itself.
For example, a distributor or bar owner has the capability to capture analytics by installing flowmeters at the bar’s point of consumption (POC). In this way, each brand of beer poured can be measured in real time. Bar owners use the analytics to help increase revenue by monitoring and minimizing any beer waste at the tap and by invoking real-time pricing adjustments or discounts at specific times. This approach can facilitate applying discounts to specific brands of beer during a two-hour period to promote sales, such as between 4 and 6 p.m. It demonstrates a level of granularity that enables the distributor or bar owner to test products and promotions based on fine details.
Capitalizing on consumption models
Cloud computing enables new business capabilities that were previously not possible. It is providing organizations with the ability to rapidly provision new services on demand, while also achieving cost-effective enhanced performance and efficiency. As demonstrated here, several case studies across multiple industries—energy, food and beverage, and financial, in particular—illustrate how cloud computing can accelerate delivery of analytics capabilities.
For large enterprise utilities, enhanced performance of cloud-based analytics facilitates financial PBF processes and batch jobs can now be delivered cost-effectively in just a few minutes versus taking 24 hours to complete. Likewise, cloud-based deployment of analytics allows a high-frequency trading firm to gain innovative insights with trade execution. And using an off-premises cloud-based approach, the company is now able to quickly perform complex scenario analyses in a much shorter period of time than was previously possible. Cloud computing also continues to provide advanced capabilities that previously did not exist, such as real-time information regarding consumption for beer distributors and bar establishment owners. Real-time analytics can identify opportunities that help increase revenue and reduce costs.
While cloud computing offers numerous benefits, the ability to leverage the technology to make strategic, data-driven decisions with agility can be a real boon for many organizations.
Julie Roehm is the chief storyteller and senior vice president of marketing at SAP. Julie founded and ran a marketing consulting firm that provides expertise in all facets of business strategy and marketing execution. She also worked with start-ups and in the automotive, retail, financial services, telecommunications, private equity and venture capital, and media industries.
Ryan Somers is an ambitious, goal-oriented leader who offers expertise in driving revenue growth and profitability through strategic marketing and sales operations designed to maximize customer engagement and promote brand identity. Ryan also leads high-level strategic planning and execution to help ensure consistent alignment with vision and is a persuasive communicator who is able to influence executive-level decision making, build coalitions and foster a focus on achieving shared objectives.
Glenn Allison (@glennallison) is director of global networks at Kellogg Company. Glenn and his team are responsible for defining and delivering the network of the future developed by Kellogg that is expected to integrate global supply chain operations, interface with growing cloud services and prepare for the hyperconnected Internet of Everything. He has spoken at various technology events and contributed to several media channels covering technology.