Why Cloud Analytics is Better Analytics
Originally published at http://data-informed.com/why-cloud-analytics-is-better-analytics/
by Ajay Khanna
Information is a strategic asset. Companies acknowledge that value and are collecting huge volumes of data, from all possible sources. But very few companies can leverage that data to their competitive advantage. Challenges range from data accuracy and completeness to speed and complexity of implementing analytics.
An even bigger issue is that, once implemented, analytics remains so disconnected from operations that it is almost irrelevant. The insights revealed are generally at an aggregate level and provide information that is merely “good to know” and seldom actionable by operational teams.
Today, cloud and mobile technologies are providing enterprises of all sizes with opportunities to use big data and analytics to make better, data-driven decisions. New-generation platforms (cloud, big data, analytics) bring analytics and operational applications together to deliver demonstrable ROI.
Cloud computing allows organizations to consolidate data from all sources, across all communication channels, and do it at a big data scale. Without cloud, collecting data from all internal applications, social networks, devices, and data subscriptions would be cost prohibitive for most organizations. On-premises big data deployments could involve significant operational risks and expensive infrastructure. The ongoing maintenance of on-premises systems itself would be daunting enough to discourage many organizations.
Let’s consider some of the advantages that cloud offers over on-premises data analytics implementations.
Robust Data Foundation
Bringing together reliable data for analytics has always been a challenge. Analytics are not accurate if data is scattered, stale, and incomplete. Many of your applications and data sources, such as social and third-party data subscriptions, are in the cloud. In this environment, creating an on-premises data store is less than optimal. A cloud-based data management platform makes it easier for companies to blend data from all such sources and helps match, merge, and clean data. Real-time access to social and third-party data sources and real-time data stewardship enabled by cloud solutions keeps your data current, complete, and clean.
Once data is consolidated and cleansed, you can create a unified view of information that is readily available for your big data analytics. Now you can easily feed insights back into online data-driven applications. Because analytics and operations are running on top of the same data foundation, there is no mismatch, information gap, or time lag between the two.
Fast Time to Value
A modern data-management platform brings together master data management and big data analytics capabilities in the cloud so that business can create data-driven applications using the reliable data with relevant insights. The principal advantage of this unified cloud platform is faster time-to-value, keeping up with the pace of business. Whenever there is a need for a new, data-driven decision management application, you can create one in the cloud quickly. There is no need to setup infrastructure (hardware, operating systems, databases, application servers, analytics), create new integrations, or define data models or data uploads. In the cloud, everything is already set up and available. Use cases are limited only by your imagination. Sales operation teams can create better alignments and account planning applications, marketing teams can create segmentation for campaign planning, contact centers can uncover up-sell and cross-sell opportunities, and strategy groups can simulate pre and post-merger scenarios.
On-premises and disconnected systems make it tedious to develop analytical models collaboratively and to share the insights. Team members use emails and printouts to discuss ideas and consolidate feedback manually. Development takes time; many inputs are lost, and many members with valuable ideas are never included. The situation is even more complicated in globally distributed teams. Teams cannot learn from each other, and they spend expensive resources duplicating analytics already performed by others.
In cloud-based big data analytics, groups collaborate on data curation and analytics design and share insights across departments, across the globe. Insights are available in real time and, when included within operational applications, are actionable immediately. For example, marketing findings are not locked in marketing systems, but shared with all customer-facing teams. The information gathered by sales in the field is not left in spreadsheets, but is fed back to marketing, in a closed-loop, to improve the customer experience.
On-premises applications historically have seen slow adoption rates. Even after investment in training and skills development, utilization remains low and many applications are reduced to shelfware.
Built on the self-learning paradigm and user experience similar to consumer applications, cloud-based applications are easy to use and promote fast adoption. The cloud facilitates democratization of analytics across the organization, increasing the access and utilization. When insights from cloud-based analytics are presented within online operational applications, adoption improves even further. Users do not have to create one-off reports or log into separate systems to “run analytics.” It is just available within the current task. Data-driven applications in the cloud can be readily accessible to everyone from any place, any time, on any device.
Scalability and Elasticity
Another big benefit of analytics in the cloud is on-demand computational power. Whether it is a Fortune 500 company or small to medium business, they can access similar analytic resources. With on-premises installations, there is always a risk of over-spending or underestimating the computing needs. Adding servers is not easy, and reducing them is equally agonizing.
Elasticity in cloud computing has taken that uncertainty out of the equation. With cloud technologies, you can start small and expand as your business needs grow, and scale back if your strategy changes. You can access higher compute power on demand if you are running complex analysis, and scale back once you are back on a routine.
Lower Total Cost of Ownership
Companies are painfully aware of maintenance, upgrades, and migrations required by on-premises analytics platforms. Every 18 months or so, there is a massive effort to upgrade to a newer version. Not only is this costly, it affects business continuity. Not every new feature is backward compatible; businesses often end up struggling to redesign reports, redefine analysis, and redo integrations.
With cloud-based modern data management platforms with big data analytics, applications are always current. There are no upgrade issues, and enabling new capabilities requires minimal IT intervention. Companies can enjoy new features multiple times a year without big investments or downtime.
Reliable data is the foundation of analytics. If the data is not correct, complete, or current, you cannot expect much from the analytics. Cloud-based data management as a service helps organizations to blend master data and big data across all domains and formats, from all internal, third-party, and social media sources, to form a complete view of the business. This union of data, operations, and analytics, in a closed-loop, provides an unprecedented level of agility, collaboration, and responsiveness. All made possible by cloud technologies.
Ajay Khanna is the Vice President, Product Marketing at Reltio and an enterprise applications expert. Prior to joining Reltio, he held senior positions at Veeva Systems, Oracle, and other software companies including KANA, Progress, and Amdocs. Ajay holds an MBA in marketing and finance from Santa Clara University.