Adopt Agile Analytics To Become A Real-Time Enterprise

by Sohini Bagchi    Dec 16, 2016


Real-time analytics is rapidly becoming a key part of today’s data-driven economy, and smart businesses are cashing in on the opportunity to effectively serve their customers. In an exclusive interaction with CXOtoday, Subash Nambiar, Vice President, Oracle India explains some of the best business practices for CIOs who are looking to adopt agile analytics to enable a real-time enterprise.

Information management is no longer an option or premium capability for enterprises anymore. How has the data analytics landscape evolved over the past few years?

The data analytics landscape has evolved over the last few years. Cloud delivery, consumer applications, and ubiquitous mobility have fundamentally altered how users engage with their data, creating new expectations about ease-of-use and access.

Data generated and stored in the cloud has grown to the point where analyzing it in place, rather than shipping it to on-premises systems, has become a necessity. Cloud delivery also cuts the long deployment cycles typically associated with on-premises solutions. In both cases, the speed and efficacy with which data can be accessed and analyzed has grown, delivering greater access to a broader audience.

Mobility on the other hand allows in-the-moment data capture and response, opening a range of use cases – from retail assistants using CRM insights to better serve customers while they’re in the store, to factory managers adjusting production using real-time analysis. The range of mobile use cases is growing, opening new opportunities to make data-driven insight available to more decision-makers.

How important is it for a business to be a real-time enterprise?

While speeds and feeds do not usually justify a business case alone, in the case of a real-time time enterprise, speed is the business case. By capturing data faster, being able to move data faster, means analyzing it and acting on it faster.

Today more and more cases within IT are demanding the elimination of latency as part of solving a business problem. Data loses value at a faster rate and consequently the value one gets from your data diminishes when it is not tapped soon enough. Having a direct insight into data will help businesses build new services. For example, building a location based offer requires a collection of real-time information,geo-spatial technologies, as well as marketing data. Quick access to customer information, claims transactions, support information, social media metrics etc. can help businesses improve customer experience.

Faster data processing also helps improve efficiencies by helping IT managers offload hardware costs and improve asset utilization. By collecting events fast and eliminating latencies for reporting, companies can shorten their supply chain cycles while reducing gaps in key business processes.

How will data analytics in cloud enable businesses to eliminate much of the guesswork involved in trying to understand client needs?

Data analytics can help businesses eliminate guesswork involved in addressing client needs and to improve operational efficiencies.For example, in the financial services sector event correlation can inform trading behaviour, specifically algorithmic trading, by identifying opportunities or threats that indicate whether traders (or automatic trading systems) should buy or sell. Algorithmic trading is a growing trend in competitive financial markets.

In the airlines sector by applying a fast data, the operators can better ensure that the right bags get loaded correctly, ensure that flights leave the gate and take off on time, and ultimately manage airline operations in an intelligent, automated way. By correlating and filtering a vast number of events in real time, we can do intelligent traffic management, forensic analysis, identify traffic hotspots, assess crime density etc. By gaining insights as they occur, Telco’s can allocate network resources based on traffic and application requirements, network usage patterns etc.

 What are some of the best business practices for CIOs who are looking to adopt agile analytics to enable a real-time enterprise?

Some of the best practices include:- Manage and Govern Your Data - The first step in extending your analytics is to make the data accessible for your users.  CIOs should choose a flexible cloud platform that allows them to easily manage and view their data.

- Extend Your Insights- In addition to making data accessible, CIOs should choose a best-in-class platform that allows them to create advanced calculations and help them take advantage of the advanced analytic and predictive functions in their database environment.

- Simple to Manage - CIOs should opt for a cloud solution that is simple to manage and ensure that automatic mobile access shouldn’t take extra effort. Analytics should be immediately available on any device without special design, development, or administration on a CIOs part. Solutions such as Oracle Cloud ensures that infrastructure, maintenance, and upgrades are handled for customers so that they can focus on critical issues rather than having to manage their existing infrastructure.

- Platform should be simple and secure- Sharing information should also be simple and secure across all devices.  CIOs should be able to set up sharing based on roles, knowing that people will only see content appropriate to them, even if the original analysis includes data outside their scope.

How does Oracle’s agile analytics solution help in this regard?

With Oracle, BI Cloud Service, IT can offer users a best-in-class visual analytics platform that empowers them to do what they want, in a secure and governed environment. Users get the agility they want—with easier access to more data, quick ways to load and combine it, and the ability to analyze and share information right away—while IT gets a comprehensive, managed platform that is immediately available, 100% in the cloud, able to seamlessly integrate existing on-premises analytic models and applications, with no CAPEX costs.  The result is more value, more rapidly, for everyone. The services enable CIOs to analyze and understand their data like never before, offering new capabilities that automatically translate complicated analysis into intuitive visualizations -  all in the cloud, and all at a predictable, low cost. 

What are the technology trends that you foresee in 2017?

The technology trends that we foresee include-

Data Civilians Operate More and More Like Data Scientists. In the coming year, simpler big data discovery tools will let business analysts shop for data sets in enterprise Hadoop clusters, reshape them into new mashup combinations, and even analyze them with exploratory machine learning techniques. Extending this kind of exploration to a broader audience will both improve self-service access to big data and provide richer hypotheses and experiments that drive the next level of innovation.

Experimental Data Labs Take Off. With more hypotheses to investigate, professional data scientists will see increasing demand for their skills from established companies. For example, banks, insurers, and credit-rating firms will turn to algorithms to price risk and guard against fraud more effectively.

- DIY Gives Way to Solutions. Early big data adapters had no choice but to build their own big data clusters and environments. But building, managing and maintaining these unique systems built on Hadoop, Spark, and other emerging technologies is costly and time-consuming. In fact, average build time is six months. Over the next few months, we’ll see technologies mature and become more mainstream thanks to cloud services and appliances with pre-configured automation and standardization.

- Big data cloud services are the behind-the-scenes magic of the Internet of Things (IoT).  Expanding cloud services will not only catch sensor data but also feed it into big data analytics and algorithms to make use of it. Highly secure IoT cloud services will help manufacturers create new products that safely take action on the analyzed data without human intervention.

- New Security Classification Systems Balance Protection with Access. Increasing consumer awareness of the ways data can be collected, shared, stored—and stolen—will amplify calls for regulatory protections of personal information. Companies will increase use of classification systems that categorize documents and data into groups with pre-defined policies for access, redaction and masking. The continuous threat of ever more sophisticated hackers will prompt companies to both tighten security, as well as audit access and use of data.