March 20179 Digital native organizations such as Uber, Airbnb, Snapchat, and several others have created business models based on data and analytics. They are able to cause disruption by entering markets with surprising speed and creating a digital mesh of networks without any investment in physical assets. The majority of the organizations that are not digital natives are forced to quickly react to this new normal. They need to derive insights from massive amounts of data to prevent being disrupted and also to potentially create new revenue generating business models. Even though some have already recognized this and created market leading digital products, the sad reality is that most companies are capturing only a fraction of the potential value of data and analytics. Among the industries with the poorest value capture are healthcare and public sector which are capturing only 10-20 percent and manufacturing is capturing 10-30 percent. In contrast, retailers who leverage the full power of big data could increase their operating margins by as much as 60 percent.In order to drive this transformation in analytics, organizations need to recognize the disruptive models, define clear business use cases, redesign business processes to leverage data driven insights, empower end-user, and create frontline and managerial capability to proactively manage change. Massive data integration can help break organizational silos. It is estimated that in Retail Banking alone, $260B of benefits could be derived globally with massive data integration. There are several disruptive models in data analytics which industries can take advantage of. We will pick healthcare as an example and the disruptive models that it can take advantage of: · Orthogonal data, which is getting fresh types of data to augment domain data, such as combining travel and health data to assess medical risk before travelling· Radical personalization which could be incorporating the behavioral, genetic, and molecular data connected with many individual patients· Enhanced decision making by preventing medical errors by getting more data points from new sources to improve validation and adding automated algorithms. An example of this could be a doctor who could make better decisions by combining data sources from lab work, data from wearables and correlation with predictive analytic algorithms.Another technology trend that is a game changer for improving productivity and quality of life is deep learning (evolution of machine learning), using neural networks with many layers (and hence "deep"). It is estimated that more than 80 percent of the work activities associated with $14.5T of global wages could potentially be automated in the near future. This is compounded with breakthroughs in natural language processing.In order to keep up with the technology trends and increased complexity of data and analytics, several technology vendors are scrambling to combine analytical tools with business insights. While data generation, collection, and aggregation have become easier, the biggest opportunity is in analytics. Increasingly, complex data analytics will require sophisticated translation, and use cases will be very firm-specific. The need for right talent in this space has become more acute, not only for data scientists but for translators who combine data with business and functional expertise. The opportunity in this space is so immense that firms in other parts of the ecosystem are scrambling to stake out a niche in the analytics market. In addition to vendors who are providing analytics as a service by integrating clients' data, vendors are adding analytics to technology stacks. They are then leveraging the power of Cloud Computing and IBM Watson like services to help companies accelerate getting insights into their data.Never before in the history of BI has it been this exciting and transformational with the power to drive unforeseen innovation and market disruption. We are in exciting times where data is the new oil/currency and has an unprecedented potential to hyperscale the digital economy. Ramesh Munamarty
< Page 8 | Page 10 >