By Jane Griffin
Cloud computing is not new. Remote access to applications, platforms and infrastructure has been a staple of corporate information technology operations for at least three decades. However, today, the difference is in the scale and speed with which cloud computing can be implemented.
Operating in the cloud gives companies the option to quickly deploy on-demand platforms and analytical applications, and to blend external and internal data. The cloud provides both cost and logistical advantages.
Leveraging data in the cloud is especially advantageous for three critical activities that many companies find elemental to their business: analytics on big data; uniting and delivering to a global mobile workforce and customer base; and leveraging social data to sense and respond to the workforce, shareholders and customer demand.
On-demand advanced analytics using big data is crucial to driving effective business performance and using that insight to enhance competitiveness. Leveraging analytics in the cloud takes advantage of its scalability, elasticity and dynamic provisioning to perform advanced analytics on large, diverse data sets.
Social data is virtually indispensable to many businesses as a predictor of customer sentiment. Cloud computing can enhance social customer interaction and reaction by helping companies span the enterprise ecosystem to embrace stakeholders, business functions and interactions to affect the end-to-end value chain—without being tethered by time and place.
Many mobile solutions have transcended utility to reinforce business innovation. The boldest plays in mobility combine cloud, geospatial and social computing technologies. They help companies—and their workforces—tap into diverse data sources, services and customer relationships based on physical location and desired action.
Analytics and the Cloud
Analytics applications are effective tools for turning data into actionable information that can help drive gains to the bottom line. And with data velocity, volume and variety growing ever larger, it is critical to exploit this data in order to drive revenue.
However, deploying analytics often take months to implement, and analytics applications are expensive to buy and maintain. In addition, many companies lack analytics talent.
To effectively interpret the insight that analytics applications deliver, it takes what are called “data scientists”: people with skill sets that span computer science, statistics and business analysis. They should also have good communication skills to interpret and present the results of analytics activities. For companies that don’t have the time or resources—or can’t afford to implement analytics—the cloud can be their best friend.
With cloud-based analytics, companies can move their analytics and hosting capabilities offsite and outsource the aggravation of maintaining the analytics applications to someone else: an analytics service provider. Outsourcing gives companies access to analytics capabilities that may be far beyond their ability—either financially or logistically—to implement on their own.
To be sure, this kind of outsourcing of data aggregation and analytics capabilities comes with its own set of costs. However, those costs are often more than offset by the benefits of sophisticated analytics delivered to decision-makers at the strategic or operational level.
The ways companies can use analytics in the cloud vary, but options include the following: the integration of internal data with subscriptions to big data as a service, packaged analytical applications, analytics development platforms, and insights and visualization as a service. Of course, selection of analytics capabilities should be balanced with budgets, desired internal core competencies and the desired time to deliver the capabilities to the user base.
The first method—outsourcing the platform only—owes its popularity to cloud computing. Outsourcing foundational IT infrastructure capabilities is not a new thing. Companies looking to save money have outsourced their IT functionality for years. However, what makes using the cloud to outsource analytics so different from the outsourcing of the past is the flexibility and potential analytics the cloud brings to the table.
Instead of maintaining complex, quickly evolving and expensive analytics infrastructure on premises, many companies can now turn to cloud vendors—via either public or private clouds—to store and process massive volumes of data for them. Thus, these companies’ resources can be more efficiently allocated, and they can focus their energies on building analytical teams to engage in sophisticated analysis and collaborative decision making. The costs can shift from technology to human capital, or from building applications to analysis of data.
The second way to utilize the analytics in the cloud is to completely outsource analytics activities to a cloud vendor via subscription analytics services. This option is appropriate for a range of companies—from small to midsize businesses that can’t afford analytics infrastructure or the analysts themselves, to larger companies that want to outsource a specific analytics capability that might be beyond their current level of expertise or the capacity of their resources to deliver quickly.
For example, a large company might want to build an analytics center of expertise, but it might not have the resources or organizational talent to make the option cost-effective. The company can turn to subscription analytics to solve the problem.
With an analytics collaborator, the company could gain access to complex analytical methodologies and results on an as-needed basis, without having to invest in the resources to gain the capability in-house. They would be able to have access to the information they need, when they needed it, with more efficient cost control built in from the start.
