Big Data Business Value- Valutrics

The convergence across business domains has ushered in a new economic system that is redefining relationships among producers, distributors, and consumers or goods and services. In an increasingly complex world, business verticals are intertwined and what happens in one vertical has a direct impact on other verticals. Within an organization, this complexity makes it difficult for business leaders to rely solely on experience (or pure intuition) to make decisions. They need to rely on good data services for their decisions. By placing data at the heart of the business operations to provide access to new insights, organizations will then be able to compete more effectively.

Three things have come together to drive attention to Big Data:

1. The technologies to combine and interrogate Big Data have matured to a point where their  deployments are practical.
2. The underlying cost of the infrastructure to power the analysis has fallen dramatically,  making it economic to mine the information.
3. The competitive pressure on organizations has increased to the point where most traditional  strategies are offering only marginal benefits. Big Data has the potential to provide new  forms of competitive advantage for organizations.

 

For years, organizations have captured transactional structured data and used batch processes to place summaries of the data into traditional relational databases. The analysis of such data is retrospective and the investigations done on the datasets are on past patterns of business operations. In recent years, new technologies with lower costs have enabled improvements in data capture, data storage and data analysis. Organizations can now capture more data from many more sources and types (blogs, social media feeds, audio and video files). The options to optimally store and process the data have expanded dramatically and technologies such as MapReduce and in-memory computing (discussed in later sections) provide highly optimized capabilities for different business purposes. The analysis of data can be done in real time or close to real time, acting on full datasets rather than summarized elements. In addition, the number of options to interpret and analyze the data has also increased, with the use of various visualization technologies. All these developments represent the context within which “Big Data” is placed.

As the vendor ecosystem around Big Data matures and users begin exploring more strategic business use cases, the potential of Big Data’s impact on data management and business analytics initiatives will grow significantly.

A major reason for the growth of big data is financial. The idea that there’s gold in an enterprise’s data has spread through academic and analyst studies that suggest early adopters using big data to inform their decisions are more productive and win higher returns on equity than competitors that don’t. Where content is king, content is money.

Big Data not only helps organisations gain a multi-dimensional view of their ecosystem. but also generates powertul insights that can help them better execute their operations and take well-intormed decisions. Big Data is increasingly being leveraged through advanced data analytics tools and technidues to provide organisations with a better understanding of their customers. competitors. operations. suppliers and partners. High pertormance analytics. which previously took days or weeks to perform. can now be undertaken in seconds. minutes or hours through Big Data technologies. The public and private sectors are adopting Big Data analytics on a large scale to generate strategic insights and improve their product/service strategy. operational etticiency and gain a deeper understanding of their customers. competitors and suppliers. Big Data analytics is enabling them to predict the trends in near real-time. make more accurate forecasts and adjust their operations quickly to changing demand or new business opportunities.

 Public sector/Government: Big Data can be of immense use in the public/development sectors. It enables government departments and developmental organizations to analyze large amount of data across populations and to provide better governance and service. Big Data analytics can help them to improve transparency, enhance decision making, and adopt innovative practices in healthcare, public administration, defence, disaster management, transportation and energy. Revenue impacts of changes in tax policy and budget decisions can be understood quickly and with accuracy. The effect of tax simplification and code adjustments can be modeled efficiently. Identification of tax and  benefits fraud can be improved through analysis of structured and unstructured data.For example, Big Data has emerged as a new focal point for the US Government, which has announced a USD 200 million Big Data Research and Development Initiative in March 2012.

Financial services: Big Data analytics can enable financial institutions make better trading and risk decisions, protect themselves from frauds and security threats, and improve their products by better customer identification and marketing campaigns. Further, Big Data analytics is transitioning investment banks from relying on overnight batch data to make trading decisions. It has improved the risk decisions by leveraging real-time analysis of current data rather than the risk management models based on historical data. For Investment Banking – risk portfolios can be recalculated at very high speeds, accounting for movements in interest  rates, exchange rates and counter-party risks in real-time. Risk exposure, portfolio VAR and liquidity coverage can be  determined in minutes. For Retail Banking – customer relationship dynamic pricing allows assessment of bank products and services purchased with associated profitability. Banks can determine their level of credit exposure in their consumer-lending portfolio. For example, CITIC Bank Credit Card Center used Big Data technology to identify customers unlikely to activate their credit card services, and direct marketing incentives to those most likely to activate, thereby improving the effectiveness of the marketing campaign by 65 per cent, while Westpac New Zealand used Big Data technology to analyze social media data to gain real-time insights into the bank’s brand health and its product performance across different geographies by correlating specific branch performance to customer’s social data.

 Healthcare: The surge in volumes of clinical data on medication, allergies, and procedures owing to the implementation of electronic health records have led healthcare organizations to seek opportunities to predict and react more rapidly to critical clinical events, resulting in better care for patients and more effective cost management. For example, several of the United States’ largest integrated delivery networks such as Cleveland Clinic, MedStar, University Hospitals, St. Joseph Health System, Catholic Health Partners use the Big Data platform for real-time exploration, performance and predictive analytics of clinical data.

 Manufacturing: Organizations are increasingly leveraging Big Data and finding new opportunities to predict maintenance problems, enhance manufacturing quality and reduce costs using Big Data. For example, Volvo leverages Big Data to analyze information received from its vehicles, customer relationship management systems, product development and design systems, to identify, in advance, potential issues such as manufacturing and mechanical problems and proactively resolve the problems by adjusting its manufacturing process.

 Telecommunications: Organizations in the telecom industry are increasingly relying on real-time analysis of data generated by mobile devices including phone calls, text messages, applications, and web browsing for better customer service and to build on retention and loyalty. High-Performance Analytics allows real-time monitoring of network demand and forecasting of bandwidth in response to customer behavior.For instance, while Nokia collects a huge amount of unstructured data from phones in use, services, log files and other sources and uses it to gain insights and understand the collective behavior of consumers to improve the quality of its phones and their features, Cablecom deploys Big Data analytics to identify when a customer was most likely to make a decision to leave its network and offers special deals and incentives to retain the customer at the right time.

 Retail: With large amounts of data being generated from the point-of-sale at stores, online transactions, and social media posts, Big Data offers numerous opportunities to retailers to improve marketing, merchandising, operations, supply chain and develop new business models. Retailers are deploying Big Data analytics to improve the accuracy of forecasts, anticipate changes in demand and react accordingly. Alternate pricing scenarios can be run instantly, enabling inventories to be reduced and driving increases in profit margins.  Analysis of vast numbers of consumer segments can be used to decide what merchandise needs to be replenished.For example, the use of Big Data analytics led to significant growth in the number of active members of Sears’ loyalty program (membership crossed 80 million customers). 

 Other industries: Big Data can also be used in other industries. Data-intensive verticals such as utilities, oil & gas, and transportation, where data is generated through smart meters, GPS systems, and satellites are gradually using Big Data analytics to make real-time predictions of their operations.

As adoption of Big Data analytics by enterprises is gaining traction. players are also gearing up towards mainstream adoption. i.e.. BZC applications. Many Big Data players are solving difficult problems for consumers by providing Big Data applications on PCs. smartphones. tablets and other web-enabled devices. Consumers are using Big Data analytics for everyday chores such as locating vacant parking spaces more ettectively. and for real-time comparison of prices. With new applications coming into play everyday. the B2C market for Big Data is likely to replicate the success of current mobile applications in the coming years. While innovation is taking place in Big Data technologies. success would be determined by mass adoption and a large number of businesses getting valuable insights through the new and compelling end-user applications that allow regular business users or customers to quickly derive practical and actionable insights.

High-performance analytics performed upon big data can result in cost savings and revenue growth for firms, which in turn  boost profitability. A review of the academic and business literature suggests that there are six channels through which a  company can actualize these gains:
• An overall 3-6 per cent increase in output through instilling customer intelligence and predictive data analytics
• An overall 2-13 per cent decrease in input costs via supply chain analytics
• An overall 5-9 per cent decrease in input and labor costs by early-warning and quality analytics tools
• An overall 5-6 per cent increase in output through analytical risk management tools
• An overall 3-4 per cent increase in labor productivity through public performance management and performance
indicators
• An overall 4 per cent increase in detection rates through fraud detection tools.

 

In 2011, a  report from McKinsey predicted that demand for analytical talent in the U.S. would exceed supply by 50 to 60 percent by 2018. “There will be a shortage of talent necessary for organizations to take advantage of big data,” the report added. “The United States alone faces a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts to analyze big data and make decisions based on their findings.”

The McKinsey report also suggested the type of deep talent required among individual data analysts is difficult to produce, oftentimes requiring years of training. Fortunately, the managers overseeing the analysts can be retrained without “years of dedicated study.”

That leaves a short-term gap for analytical talent. Companies should be looking for data analysis and statistical gurus who possess a firm understanding of business processes. Those workers have likely developed and honed their skills over years of practical experience in the field—and chances are, if you identify a strong candidate and bring them onboard, they’ll probably have a network your organization can leverage to augment your B.I. team. Look to B.I. solutions providers, too, for guidance in identifying skilled people.