There has been a great deal of talk recently about “Big Data.” So much focus and effort is being channeled today by companies large and small in acquiring technology, products, and services by which to collect and store Big Data. Yet very little effort is being placed on the methodologies, techniques, and rigors that are required in deriving bigger and better insights from that data. Yes, Big Data requires new technologies to be powered, and it has to be stored properly. But Big Data must also result in Big Thinking, to ensure that it is becomes more valuable than traditional data in informing important business decisions.
With the concept of “Big Data” so new, I am hearing stories about how its wealth is often times underutilized. I recently attended a Big Data and Cloud Connect Conference in Chicago. During one of the sessions, it was mentioned that a large bank recently acquired social media data (among the many unstructured data that is available,) invested in updated infrastructure, and came to the obvious conclusion that its customers don’t like fees. Target Marketing Magazine recently published an article on why “Big Data is Baloney!” The article spoke about massive data, high powered systems, and expensive technologies, that, in the end, simply boiled down to engagement metrics.
With the largest variety of sources, fastest velocity of refreshing capability, and truly robust technology that powers it, Big Data means nothing without the business value that can ultimately be drawn from it. With the explosion of different channels by which consumers today, on a global basis, can communicate and consume (the Internet; social media networks; personal surveys; smart technologies on phones/parking meters/electric grids/thermostats; etc.), elevating the understanding of an agency’s clients’ consumers has become not only possible but essential. This data helps to provide offerings that are designed around the real time needs of these consumers.
An agency’s “Data Science Team” members should fully support the use of the robust technology, intelligent tools, and smart resources that can speed up the outcomes that make a difference for agency clients. The mission of a successful agency’s Data Science team should be to enhance the business value inherently found in traditional data, and embellish that with the variety, volume, and velocity of the Big Data that is also available today.
Some goals that a Direct Response Agency should seek to answer in regard to their clients’ business needs are (1) to identify and quantify the influence of Direct Response Television Advertising (DRTV) on the digital channel; (2) to measure the multi-channel impact of DRTV; (3) to uncover the moment by moment sentiments and behaviors of consumers that can drive action, and (4) to mine new targeting opportunities exhibiting similar consumption and behavior patterns as the base target.
Are there challenges involved with Big Data? Of course. I would say that Time and Automation are two critical success factors to consider in the effective use of Big Data. To build the infrastructure and process of collecting and storing meaningful Big Data takes time and investment.
Challenges with Big Data can be summed up into two themes: Technical and Analytical. Agencies have to deal with the typical technical issues that most organizations have had to deal with coming from legacy systems. Data silos exist. On top of that, the need to marry Big Data with traditional data makes integration, standardization, and data cleansing twice as challenging. Processing speed and storage capacity can also become issues as well, if not for the emerging tools and technology today that make it possible to scale very quickly. Championing these technical issues is just a part of overcoming the bigger challenge.
In my opinion, the biggest challenge today is in finding added value from these new data sets, in order for Big Data to have business significance and impact. Two things became crucial for an agency’s Data Science Team members to achieve: (1) they need to define new metrics, create new dimensions of attributes, and re-state business benchmarks, through the integrated lens of the old/traditional data, and the new, Big Data; and (2) the new data sets have to be converted in an analytical format that data scientists can explore, to uncover hidden patterns and find correlation to the current state of the business, while guided by the integrated lens of success measurements.
An advertising agency’s data development and implementation philosophy should be simple. An agency should move quickly to identify and leverage emerging data trends to better its clients’ ROMI without delay. This approach can have two phases: Phase 1 – acquire the data, prototype rapidly, analyze quickly and socialize the business value; and, Phase 2 – develop the automation, and test and implement horizontally, while planning for future vertical enhancements.
The results of data innovation and expertise in analytics are services that a modern agency must offer to its clients as part of its campaign planning and execution. Data Science Team members should be proud of the enhanced insights they are able to produce for clients. This forward movement will ultimately translate into organic growth and new opportunities for both existing and prospective new clients.
In conclusion, data today, Big or Small, robust technology, and analytics rigors have to intersect to create Big Business Impact. Only then can our industry truly say that “Big Data Equals Big Thinking!”
Tina Wisner is director of data science and analytics at Hawthorne Direct.
