Dr. Rao Mikkilineni is Chief Scientist and Co-Founder C3DNA Inc., a Silicon Valley start-up launched in 2013. He has also filled roles at Bell Systems and Hitachi. He will be teaching a class in Business Analytics Security in GGU’s Master in Business Analytics program this year.
What can you tell us about the data analytics career?
Data analytics requires data scientists who are trained in algorithms and tools that assist in extracting knowledge from raw data by correlating various items or using classification methods. The data scientists are in demand. For example, a search of LinkedIn shows that there are 1600+ jobs for data scientists at Microsoft right now (3/30/17).
Business Analytics is often written about in the context of consumer transactions. What is the connection to data security?
Information security is about protecting confidentiality, integrity, and availability of data, which is its enterprise asset. There are three states in which data is vulnerable to threats from outside: during execution where it resides in a process memory, in-flight (during its transmission from a source to a destination), and at rest (where it is stored).
As the data is collected from different sources in various forms, good analytics tools and technologies provide the agility required to react almost in real-time. The data have to be monitored, the collected data analyzed, any anomalies or suspicious behaviors identified and action has to be taken to prevent a security breach. If a security breach occurs, we must take various forensic actions involving data analysis from multiple sources.
A good business analytics master’s program provides a wide selection of business analytics classes and a disciplined process to using multiple subjects to develop enough mastery to start a career.
At my company, C3DNA, we use data analysis to auto-scale workloads in data center or cloud environments to meet large fluctuations in user demand — or changes in resources on which the applications are executed. We make any application run on any cloud without having to change the application, the operating system in which it executes, or the infrastructure (server, network, and storage) provisioning processes. This gives the enterprises a choice to use their data centers or any cloud from any provider on demand.
By making applications self-aware and self-managing (using a cognitive overlay just as biological systems do), we reduce the complexity of application management in a distributed network of clouds and save operational costs by an order of magnitude. The cognitive overlay allows us to provide highly available systems even on a not so reliable infrastructure.
Tell us about the Business Analytics Security course you teach at GGU?
The Business Analytics Security class is part of the Business Analytics master’s degree program and is designed to help both IT professionals and data analysts to understand how analytics assist in proactively manage information security. In a globally connected computing infrastructure, communication, collaboration, and commerce (at almost the speed of light) are demanding real-time management of information confidentiality, integrity, and availability.
If a security breach occurs, we must take various forensic actions involving data analysis from multiple sources.
Why is graduate-level work important for a business analytics career?
Data analyst or scientist careers demand expertise in multiple disciplines: probability and statistics, data exploration and visualization technologies; data ingestion, cleansing, and transformation technologies; introduction to machine learning and various tools and algorithms; and familiarity with tools such as R, Python, and Machine Learning A good master’s program provides a wide selection of business analytics classes and a disciplined process to using multiple subjects to develop enough mastery to start a career.
Stepping back the security context, why is Business Analytics important in general?
Data analytics tools, augmented with cognitive analytics, enhance the usefulness of data by orders of magnitude. Cognitive analytics refers to a field of analytics that tries to mimic the human brain by drawing inferences from existing data and patterns, draws conclusions based on existing knowledge bases, and inserts this back into the knowledge base for future inferences – a self-learning feedback loop.
An example is a bank using a cognitive filter to find and extract chunks of customer information and index them into detailed customer profiles—the building blocks of market segmentation. Analytics extend the reach of insights into the future and provide powerful tools for predicting user behavior, a shift in user interests, or any number of future events. In the customer service context, for example, predictive analytics enable powerful features and allow to businesses to: anticipate when a customer browsing the product website will need agent support; identify and reach valuable prospects before they decide to call the agent; or predict the number of customers calling based on behavior of your customers online.
With global connectivity of humans, machines, and devices, the exponential growth of data is putting new demands on technologies to harness information and gain insights into providing enterprises with the agility required to respond to their changing business requirements. IBM Chairman and CEO Ginni Rometty says 80% of data is not being used by business enterprises and with right analytics technologies this data becomes very useful to develop predictive strategic decisions.
Rao recommends these case studies for people who are considering a career in Data Analytics: Big Data Analytics Case-Studies (Teradata), Top Ten Case Studies: Big data analytics in healthcare (IBM), and Software Business Case Studies (IBM). We also invite you to contact us about the master’s degree in Business Analytics Program.