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Three Emerging Themes of Big Data Analytics


http://data-informed.com/three-emerging-themes-of-big-data-analytics/

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For years, analytics has been changing the face of business, but never to the degree we are witnessing now. Technologies that have shown promise for years are starting to deliver tangible results. New entrants are using analytics to disrupt established markets, and big data conversations have migrated from the IT department to the boardroom. The code has finally been cracked, and enterprises are taking notice.

With all the attention that big data receives, three emerging themes rise to surface in my daily interactions with senior executives: the realization of personalized marketing, the collapse of the middleman, and the recognition of data equity. Each of these themes demonstrates how analytics will affect the ways that companies operate and compete in the near term. I have witnessed each of these emerging trends develop first hand and expect market awareness to continue to expand this year and beyond.

 

Personalization: It’s a Matter of Time

After a decade of hearing about the promise of personalized one-to-one marketing, we are finally seeing successful execution on the front lines. And the timing couldn’t be better. Marketers are facing consumers with very high expectations. People expect relevancy, to be heard as individuals, and to have a relationship with the companies with which they do business. From a technology perspective, this requires marketers to gain an action-based understanding of individual customers and an ability to treat people based on what they do (behaviors), not what they are (demographics).

This year, marketers will respond to consumers’ high expectations by spending up to 20 percent of their budgets on supporting the customer experience. CMOs are also increasing their spend in channels that support deep personalization, such as email and mobile, while decreasing spend on impersonal communication channels such as print, radio, and direct mail. Considering that corporate marketing budgets can range from 5 percent to 10 percent of corporate revenues, it’s safe to assume that billions of dollars will be spent this year on personalizing the customer experience.

Time-series analytics identifies how people have responded to opportunities and offers over time, at an individual level. This is significantly more complex than marketing campaign strategies based on a snapshot in time, such as a recent trigger event. It’s this depth of historical understanding that delivers the intelligence needed to truly anticipate the near-term needs of individual consumers.Throughout the year, we will see significant advances in personalization based on two rapidly evolving technologies. First, we will continue to see advances in the sophistication of segmentation techniques. Nano-segments will be composed of individuals with commonality in their actions, not commonality in their demographic characteristics. Second, time-series analysis will play an increasingly important role in anticipating consumer behaviors.

Time-series analytics also will play a critical role in measuring the effectiveness of marketing spend. As the role of the CMO continues to evolve toward becoming the “chief customer advocate,” marketing leaders will look to big data analytics to measure individual customer satisfaction, behavior, and progress. Time-series analytics will highlight the effectiveness and ROI of various marketing strategies at an extremely granular level. These sophisticated measurement techniques will redefine the terminology of even the most data-driven marketers.

The Continued Collapse of the Middleman

By my estimates, approximately one quarter of the United States’ $17 trillion GDP is spent on some form of middleman, intermediary, or broker. These dollars are up for grabs each year, and  analytics is playing an increasing role in determining the winners. After all, the primary purpose of an intermediary is to provide an information-based advantage. For decades, proprietary information has allowed them to broker markets and deliver efficiencies to buyers and sellers. Big data is leveling the playing field.

Critical information no longer will be concentrated in the hands of a relative few, but will be available to buyers and sellers independently. The value-add of the intermediary will be diminished across all markets. From established industries to start-ups in the sharing economy, this migration of information will alter the competitive landscape.

The most advanced big data science and technology can consume data from any source and convert it into actionable intelligence on a continual basis. This allows the more innovative organizations to create new marketplaces and redefine complete value chains. For example, transportation companies can now merge historical purchase histories, online browsing patterns, customer service records, and flight delay details to determine whether an individual consumer is likely to accelerate or decelerate as a customer in the near future. This approach is significantly more effective than purchasing broad demographic profiles from third-party data providers.

The declining relevancy of the intermediary is only the beginning. In years to come, every market will be affected by these same factors. Only time will tell if middlemen will evolve their analytic sophistication faster than the buyers and sellers they hope to continue serving.

The Recognition of Data Equity

As big data applications become more prevalent across more industries, the use cases will demand the attention of value-focused investors. They’ll be searching for unique ways that companies are uncovering latent intelligence about their people, customers, operations, and markets. I refer to this phenomenon as data equity. It’s the missing link between an organization’s data assets and its market value.

Much like brand equity, data equity is considered an intangible asset. It’s an off–balance-sheet asset that may represent a significant portion of the overall corporate value. In essence, it’s the next frontier in financial valuation. It requires companies to demonstrate how well they distill intellectual capital from their data. Today, the data equity valuation practice is primarily qualitative in nature, but we are seeing increasing evidence that it is becoming a checklist item in the due diligence process.

Data equity is a natural byproduct of sound big data competency. Consider the everyday example of how a ridesharing company operates. Analytics are used to help drivers predict where the volume of needed rides will be greatest at any point in time. Pricing is dynamic based on real-time supply and demand, and customers are given accurate ETAs and travel times. Now consider the impact of applying that same level of analytical sophistication to a global company with perishable inventory (e.g., food, airline seats, cruise cabins, etc.) Sure, the cost savings and revenue growth derived from analytics eventually will show up in the financials, but by then it’s too late. The added value is already accounted for.

Data equity is a forward-looking indicator of value. The impact to the bottom line may lag, but it is inevitable. As I witness on a regular basis, corporate finance and IT departments are working feverishly to release the latent value hiding in their enterprise data systems. Eventually, more quantitative measures of data equity will dominate external market and analyst conversations and serve as the cornerstone of a company’s identity.

We are living in an exciting time indeed. We are on the cusp of inevitable breakthroughs in big data technology and disruptions in longstanding markets. With trillions of dollars up for grabs, analytics is increasingly being used to shuffle the deck and allow players to compete in a new dimension.

Arnab Gupta is founder and executive chairman of Opera Solutions, a global leader in machine learning and big data analytics. The company combines advanced science, technology, and domain expertise to find and transform signals — the valuable predictive and descriptive information in big data flows — into machine-generated best actions that drive frontline productivity and bottom-line growth. It serves premier companies in financial services, healthcare, government, supply chain, marketing, and other sectors through offices in North America, Europe, India, and China.

Prior to establishing Opera Solutions in 2004, Arnab founded and sold a number of other companies, including Mitchell Madison Group and Zeborg. He began his career at McKinsey & Co., where he rose to partner.

Since 2004, he has worked with the Bill & Melinda Gates Foundation on its India HIV-AIDS initiative. There, he has focused on stopping the spread of this disease by using innovative private-sector approaches.

Arnab earned an MBA from the Harvard Business School. 

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Personalization, Decentralization, Valuation. 

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