To those who challenge the significance of big-data, I say, “Get real.”
Big-data absolutely matters for analytics and related disciplines such as market research and competitive intelligence. Why? Because it offers distinct benefits that can otherwise be hard, or even impossible, to come by.
I’m sure you’ve heard big-data described in terms of size, variety, and velocity, or what I call “real-timeliness.”
- Size pertains to data getting too big to be stored or analyzed by standard software.
- Variety refers to the many new types of data we have now readily available — social media data such as blogs, forum discussions, and postings on Twitter and Facebook; Google search data; clickstream data; retail scanner data; data from mobile devices. On top of all these, companies have their own data. This includes surveys, transactional data, financial data, customer complaint data, and so on.
- Real-timeliness refers to data being available much faster and sometimes in real-time. Standard market research easily takes more than a month before its results are fully available; internal data can be available in a week; clickstream data could probably be obtained an hour after it’s captured — provided the initial setup and coding has been done — and social media comments can be watched in real-time.
Let’s look at each characteristic from a benefits perspective.
Size: More and Better Analytics
More data often means we can do more with analytics, especially advanced analytics. Approaches such as time-series-based marketing mix modeling, for example, have taken off in a big way simply because the required data is now more easily available. Economists have used Google Trends to forecast car sales; researchers have used Twitter to predict the stock market; and in marketing I have found the impact of social media data to be a significant predictor of sales.
In a recent example, researchers grabbed online search data from 117 online sources, such as Edmunds.com, and used it to achieve a significant improvement in the quality of the analytical model that predicted demand for cars.
Variety: Easier and More Opportunities for Validation
Validation is a key success factor if a firm wants to benefit from analytical insights. The tools of choice are most often triangulation, replication, and in-market prediction. The variety of data available nowadays makes it easier to determine if certain insights are consistent with data from multiple sources (triangulation). Given the low cost of attaining and the size of available data, replication is now often easier, and anything online can be easily tested (validation, in-market prediction).
BauMax, an Austrian retailer, serves as one example. It quickly rolled out a pricing recommendation system in a few stores and could track whether the new pricing system actually resulted in higher profits. The company was fortunate to have in place a company-wide data warehouse in which it stored article-specific demand data, pricing data, location data, and more. BauMax reported that the recommendation system helped it achieve an 8 percent profit increase.
Real-timeliness: Faster Insights
In some cases they can inform decisions immediately. For example, global bank ING posts a question of the day, each day, on its Netherlands’s Website. It gets around 60,000 daily responses for use in its analytics.
In another example, Unilever launched Dove Pro-Age, a beauty skin product designed for mature women. The commercial for this product was considered too shocking, so the company aired a censored version of it. This got the media’s attention, and even led to Oprah’s coverage. Viewers visited the Pro-Age Website to watch the original commercial (see below) and posted thousands of comments. By scraping the text from the Website, Unilever was quickly able to gain an understanding of what thousands of women were thinking and whether it needed to take additional steps. The analytics showed it required no further intervention; as it turns out, women over the age of 50 championed Dove Pro-Age, as discussed in this case study.
Of course, benefits like these don’t come without the ability to handle potential challenges. If not handled well, big-data could mean big-disaster.