Big Data vs. Market Research: Which Can Increase Your Business Intelligence?
Market research has been around for nearly a century. The importance of market research has increased over time, and the techniques have changed dramatically.
In an increasingly global and competitive marketplace, no company can afford to ignore data about the people it wants to sell to. Big data is expanding on the concept of market research in ways that couldn’t be imagined a decade ago, and businesses ignore big data at their peril.
Analysis Options Make More Data More Useful to More Businesses
Use of data mining and predictive analytics is growing rapidly, with more businesses developing explicit data strategies that address business intelligence and marketing needs. Big data is becoming more readily available and affordable, and the processing power needed to derive insights from big data is easier to access, in large part due to an increase in cloud computing.
Big data is no longer solely the purview of the IT department, but is part of organizations’ overall business strategies. Getting the insights of big data into the hands of marketing professionals requires access to data, plus access to the processing that takes heterogeneous data and makes sense of it to produce actionable information. Used correctly, the insights gained from big data dwarf insights made through traditional market research.
What Advanced Analytics Can Do
New big data analytics tools have created what Nigel Wallis, research director with IDC Canada calls “the democratization of analytics.” It allows businesses to track much more than customer names and addresses. Now businesses can create detailed, specific customer profiles, and use data to offer products that real customers actually want rather than basing business decisions on limited data.
With big data analytics, companies are able to predict what customers will buy next based on detailed behavioral data, and they can market products to customers or potential customers with a level of precision that was unheard of with traditional market research practices.
Big Data vs. Marketing Research
Innovation and speed are keys to leveraging big data to create actionable, useful information from massive collections of data. While social media is one source of big data, other sources include logistics data, data from RFID tags, retail scanner data, and even data on things like weather and traffic patterns. The promise of big data is that it can integrate non-structured data collections from multiple sources, to combine analytics in new and innovative ways.
Big data concentrates on squeezing valid insight from massive, heterogeneous data collections.
Traditional market research, in contrast, focuses more on data collection. Before data was so abundant, market researchers had to concentrate on data collection, or they might not have enough information to lead to valuable insights. Big data takes the focus off collection and puts it on what is done with the data.
Representation, Relevance, and Detail
The market research of yesterday had trouble gathering data that was representative of markets. Focus groups and pen-and-paper surveys simply aren’t adequate to the marketing needs of today’s world of commerce, and they take too long to produce meaningful results that inspire confidence. Near real-time speed is necessary today, and old school market research methods simply aren’t relevant to many of today’s consumers.
Detail is another aspect of big data research that is advantageous. Searching for and identifying patterns in large, disparate data sets is what big data does, and it can do this even in the presence of data elements and trends that may appear to conflict. Researchers new to big data may be nonplussed by the idea of using data sets that appear to conflict, but it is the new normal that researchers must get used to, because contradictions are going to exist in data sets of the size that are analyzed today.
What Big Data Cannot Do
Big data can do a lot, but it can’t solve fundamental business problems, and it doesn’t do away with legacy systems altogether, because these systems often inform researchers on the best ways to dig into big data. Big data also won’t fit in with some of the ROI metrics you use now, like speed of transactions. Today, data utilization rates are the metrics you should pay more attention to. Finally, big data is not infallible, and you have to tolerate a certain level of inconclusiveness when you work with it.
It’s What You Do with Big Data that Makes the Difference
Big data analytics can close gaps between the data available to you and the insights you want to get from it. Syncsort offers organizations across the continuum of big data users better ways to collect and process data. By helping businesses and other organizations overcome the limits of data processing availability, Syncsort empowers its clients to use big data to drive better business insights quicker, with lower total cost of ownership.