CIOs are under tremendous pressure to quickly deliver big data platforms that can enable enterprises to unlock the potential of big data and better serve their customers, partners and internal stakeholders. Early adopter CIOs of big data report clear advantages of seriously considering and choosing the cloud for data analysis. These CIOs make a clear distinction between business critical and business enabling systems and processes. They understand the value that the cloud brings to data analysis and exploration and how it enables the business arm to innovate, react and grow the businesses.
Here are the 5 biggest reported advantages of choosing the cloud for data analysis
Speed – Faster Time to Market
Be it the speed of getting started with data analysis, the time it takes to have a software stack that can enable analysis or the time it takes to provision access to data, a cloud based system offers a faster boot time for the data initiative. This is music to the business ears as they are able to extract value from data sooner than later.
The cloud also offers faster exploration, experimentation, action and reaction based on data analysis. For example, a cloud-based system can be made to auto scale given the number of users querying the system, the number of concurrent ongoing analysis, the data that is entering the system and the data that is being stored or processed in the system. Without any long hardware procurement times, the cloud can often be the difference between critical data analysis that drives business growth and missed opportunities.
Another consideration mentioned by CIOs is the opportunity cost of building out full scale analytics systems. With limited budgets and time, focusing on generating core business value turns out to be more beneficial than spending those resources on reinventing a software stack that has already been built by a vendor.
Extensibility – Adjusting to Change
A very unique advantage of operating in the cloud is the ability to adjust to changes in business, the industry or competition. Dynamic enterprises introduce new products, kill underperforming products, invest in mergers and acquisitions. Each such activity creates new systems, processes and data sets. Having a cloud based stack that not just scales but offers a consistent interface reduces the problem of combining this data (and securing and maintaining) from a O(n!) problem to a O(n) problem making it a much cheaper proposition.
Cost – Lower, Cheaper
CIOs love the fact that cloud based data analysis stacks are cheaper to build and operate. Requiring no initial investment, CIOs get to pay for what they use and if the cloud auto scales, it makes for simpler capacity growth plans and easier to perform long term planning without the danger of over provisioning. Given the required data analysis capacity can often be spiky (varies sharply by time depending on planning and competitive activities), is impacted by how prevalent the data driven culture is in an enterprise (and how the culture changes over time) and the volume and variety of data sources (this can be change at the rate of how the enterprise grows and maneuvers), it is very hard for the CIO to predict required capacity. Imperfect estimates can lead to wasted resources or/and unused wasted capacity.
Risk Mitigation – Changing Technological Landscape
Data analysis technologies and options are in a flux. Especially in the area of big data, technologies are growing and maturing at different rates with new technologies being introduced regularly. In addition, it is very clear given the growth of these modern data processing and analysis tools and the recent activity of analytics and BI vendors, the current capabilities available to business are not addressing the pain points. There is a danger of moving in too early and adopting and depending on a certain stack might end up being the wrong decision or leave the CIO with a high cost to upgrade and maintain the stack at the rate it is changing. Investing in a cloud based data analysis system hedges this risk for the CIO. Among the options available for the CIO in the cloud are Infrastructure as a Service, Platform as a Service or Analytics as a Service and the CIO can choose the optimal solution for them depending on bigger tradeoffs and decisions beyond the data analysis use cases.
IT as the Enabler
Tasked with security and health of data and processes, CIOs see their role changing to an enabler role where they are able to ensure that the data and processes are protected while still maintaining control in the cloud. For example, identifying and tasking employees as the data stewards ensures that a single person or team understands the structure and relevancy of various data sets and can act as the guide and central point of authority to enable various employees to analyze and collaborate. The IT team’s role can now focus on acting as the Data Management team and ensure that feedback and business pain points are quickly addressed and the learnings are incorporated into the data analysis pipeline.
A cloud based data analysis system also offers the flexibility to let the analysis inform the business process and workflow design. A well designed cloud based data analysis solution and its insights should be pluggable into the enterprise’s business workflow through well defined clean interfaces such as an insight export API. This ensures that any lessons learnt by IT can be easily fed back as enhancements to the business.
Similarly, a cloud based data analysis solution is better designed for harmonization with external data sources, both public and premium. The effort required to integrate external data sources and build a refresh pipeline for these sources is sometimes not worth the initial cost given business needs to iterate with multiple such sources in their quest for critical insights. A cloud based analytics solution offers a central point for such external data to be collected. This frees up IT to focus on providing services to procure such external data sources and make them available for analysis as opposed to procurement and infrastructure services to provision the data sources.
A cloud based solution also enables IT to serve as deal maker of sorts by enabling data sharing through data evangelism. IT does not have to focus on many to many data sharing between multiple sub organizations and arms of the enterprise but serve as a data and insight publisher focusing on the proliferation of data set knowledge and insights across the enterprise and filling a critical gap in enterprises of missed data connections and insights that go uncovered.