Blogs

3 key shifts to digital business using advanced analytics

Senior Managing Consultant, IBM

According to Gartner, digital business is the “creation of new business designs by blurring the digital and physical worlds." There are three key foundational shifts that are making the digital business phenomenon real. Interestingly, these tectonic shifts are also driving faster adoption of advanced analytics in order to capture new sources of revenue, increase operational cost efficiency and improve talent management for the enterprises.

Let’s define these shifts with some facts, commentaries and examples:

Shift one: Emergence of digital operation

DigitalBusiness_Blog.jpgDigital operation refers to the increasingly seamless and holistic integration of digital and physical interactions. A new set of transformation drivers, such as mobile revolution, social media explosion, hyper digitization (content creation and consumption) and power of analytics are impacting individuals (customers and workforce), organizations and industries.

For example, consider the recent merger of Staples and Office Depot, which is the beginning of a new digital era for this industry. Comparable store sales in North America haven’t grown at Staples or Office Depot since at least 2007, which highlights the state of off-line versus on-line commerce. Rajiv Lal, a retail professor at Harvard Business School and coauthor of “Retail Revolution: Will Your Brick & Mortar Store Survive?,” said that even after this merger, Staples will have to dramatically change the existing stores to operate a profitable brick-and-mortar business. PandoDaily’s Michael Carney argues the $1 billion savings in synergies should be directed toward “better competing in the online space and potentially building out an on-demand fulfillment infrastructure (possibly using local stores as pseudo-warehouses) to compete with Amazon.”

Shift two: Real-time data collection, storage and analytics are getting cheaper

We are generating 2.5 billion gigabytes (GBs) every day, of which 80 percent is unstructured (social media, video, audio, images, data from sensors). The cost of collecting, storing and analyzing real-time data is getting cheaper and will continue to do so. Some facts for consideration:

  • In 1980, the hard drive storage cost was $193,000/GB. In 2000, it came down to $14.30/GB. Today (2015), it has gone down to $0.03/GB.
     
  • Advancement of in-memory technologies is taking this to the next level by aggressively maintaining data in memory to reduce latency. Forbes' John Webster says, “Distributed cluster memory is utilized as primary storage for computation while disk becomes secondary storage for data protection and longer-term persistence.”
     
  • While increasing in sophistication, analytics software is greatly decreasing in price. “Five years ago, [what] might have cost...tens or hundreds of millions, now costs [only] a few hundred thousand,” paraphrasing Tamim Saleh, BCG’s lead for Big Data & Advanced Analytics.

Shift three: Pursuit of exponential growth with cognitive computing

COG · NI · TIVE / käg-nə-tiv (adjective): of, relating to or involving conscious mental activities (such as thinking, understanding, learning and remembering).

Cognitive-based systems extend the capabilities of humans. They achieve this by learning and building knowledge, understanding natural language and interacting more naturally with human beings than traditional programmable systems. Leading companies in all sectors are pursuing exponential growth by penetrating the complexity of big data, exploiting the power of natural language processing and machine learning. Here are some examples:

  • A Canadian public sector agency is implementing a learning management solution (LMS) for their talent management initiative. The machine learning algorithms of this software can be taught by the employees and HR for their own unique benefits. For employees, they get “personalized recommendations for courses, content and connections” to be more productive, while for HR, they get “recommendation of internal candidates for open positions, identify successors and even appropriate level of compensation for each employee.”
  • Professor Susan Athey of Stanford Graduate School of Business talks about the possibilities that can be unlocked through the secondary uses of data using machine learning. “You might gather data about taxi trips to monitor compliance with various regulations, but end up learning about commuting patterns, gaps in public transportation and even the propensity of different types of customers to tip.”
     
  • In a recent IBM Institute for Business Value study, “Your Cognitive Future,” the WellPoint case study is used as a reference. As one of the largest health benefits companies in the United States, they implemented a cognitive computing solution powered by IBM’s Watson that provides decision support for the pre-authorization process. The solution bases recommendations on its ability to interpret meaning and analyze queries in the context of complex medical data and human and natural language, including doctors’ notes, patient records, medical annotations and clinical feedback. As the solution learns, it becomes increasingly more accurate. Even if nurses have to do additional research on a request, Watson’s ability to aggregate the information and present it to them in a readable, structured format saves a lot of time.

While many challenges still lie ahead (economic head wind, the old guards and their way of doing commerce, data security and data value mapping, and quality control to name a few), the momentum is definitely behind the era of digital business. Leveraging advanced analytics to capture on that momentum is becoming the new norm.