Though multiple streams and sources of product and customer information have been with us for some time, ‘Big Data’ gets a tremendous amount of attention. In part, this is because Big Data has become a catch phrase for the sheer volume of information in data sets now available for business intelligence and decision guidance. Over the past 20 to 30 years, business’ ability to generate transactional, interactional, and observational data has moved from basic structured (in SQL databases), to semi-structured, and now to unstructured data generation and mining. It is estimated that, each day, 2.5 quintillion bytes of data are created (so much that 90% of the information in the world today has been created in the last two years alone).
The essential concept of data (big, small, transactional, structured and unstructured, modeled and predictive, etc.) is neither new nor unique. However, the attention to, and focus on, challenges associated with ‘big data’ has grown to the point where key elements of its relevance – accuracy, quality, analytics, monetary value, etc. – are being called into question. Companies need to have the right, high-quality customer-related data, that is consolidated from multiple sources into a single view. Then, to build or sustain customer-centricity, those data need to be managed, analyzed, and reported to best, and most monetizing, effect.
Data are touching, and helping shape, our lives in many areas. For example:
– Hospitals – Pediatric units can monitor every baby heartbeat, combine with historical data to discover patterns and symptoms
– Fitness Bands – Data from exercise combined with health records
– Schools and Colleges – Instructors track student progress through data analytics, and can customize content
– Home Environment – Control all home devices vis iPhone app
– GPS – Location and speed combined to suggest optimal routes
– Loyalty Cards – Purchase history combined with social media data to offer coupons, discounts and personalized offers
– Personal Health – Sleep sensors (under mattresses) send heart rate data to smart phones
– Home Remodeling – Buildzoom has contractor and review info
– FBI – Combining data from social media, CCTV cameras, phones
– Elections – Microtargeting individual swing voters
– Commuter Travel Time – Google self-driving car soon a reality
Data can come from traditional sources, such as research and customer purchase records and databases, and new unstructured sources, such as emails, posts to social media sites (Twitter processes 400 million tweets every day), digital pictures and videos, cell phone GPS records, etc. Cisco has predicted that, in 2013, information flowing over the Internet would reach an annual volume of 667 exabytes, 2.6 million times the amount of information stored in the Library of Congress
Today, companies are challenged as never before to harness the volume, variety, velocity, and value, of Big Data as waves of information flow into the enterprise. The opportunity presented is to provide the business with greater marketing and brand-building decision-making flexibility and agility, and to address issues that were previously beyond the scope of most organizations. With the proliferation of customer data available to marketers, data quality has become an extremely important issue. Data must not only be complete and accurate, and consistent as to source, they must be relevant and easy to use and combine into data streams. The errors can range from duplicate names on files to poor merge-purge software. Many companies are utilizing frontline staff to conduct data quality checks; however, this is inefficient; and the responsibility can be handled more efficiently and less extensively through linkage software.
So, data represents opportunity – to improve processes and customer experiences, to develop products and services with higher value, and otherwise make monetizing changes that will resonate with customers. One application, for example, is reducing risk leading to potential defection, perhaps the most important stage in the customer life cycle. It’s critically important, however, that all customer-related data be a continuous flow, so that actionability is targeted and in as real-time a fashion as possible. For example, elimination of a loyalty program, and the customer data it produces, seriously jeopardizes a company’s ability to take value-driving action. It’s also very important that the generated data be of high quality, otherwise its utility is seriously diminished.
Finally, with respect to analysis and actionability, organizations should have a grasp of the most contemporary, and most useful analytical techniques. Otherwise, even the most complete reservoir of customer data can take an enterprise in the wrong direction. Going forward, actionability is just one of the challenges to be addressed within the world of Big Data. McKinsey has identified several others:
– Data policies – privacy, security,; intellectual property and liability (data as corporate asset), ownership and rights for use of data
– Technology and techniques – storage, computing, analytical software, dashboards and displays
– Organizational change and talent – war for talent, structure, workflows, incentives
– Access to data – integration from multiple sources, increased need for stakeholder sharing
– Industry structure – some sectors, like health care and government, have little transparency
Bottom line: ‘Big Data’ isn’t a flavor of the month, or even a flavor of the year. We’re really just entering a time when organizations are beginning to understand, access, and apply data from multiple sources for more efficient and effective decision-making. This will not only continue, it will grow, eventually reaching mid-size and smaller companies as well as the large companies where its use receives most attention and study.
More in-depth coverage of ‘Big Data’ issues, especially where customer-centricity is concerned, is provided in my new book, Customers Inside, Customers Outside: http://ebooks.businessexpertpress.com/Books/9781606498972