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Big Data- Complex yet powerful

What do you think, the Big Data are complex powerful?

Big data is an evolving term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information. Such voluminous data can come from myriad different sources, such as business sales records, the collected results of scientific experiments or real-time sensors used in the internet of things.

Digital data vs Analogue data

Since the advent of digital storage devices and more precisely in 2002, the methods of storing data has changed completely. Now the data is available in digital form and is possible to analyze by algorithms and computers.

Digital data differs from analogue data:


Amount of data available in digital form has increased manifolds and we are soon reaching a point where the volume of digital data generated every minute is equal to the amount of analogue data generated of human history till 2002.


The time digital data takes to accumulate is minimal. Example- 4 hours of Youtube content is uploaded every minute.


The data can exist as sound, video or text. Further these data are available at varied platforms and different purposes.


Big data complements marketing

Big data is growing more and more valuable as the companies that make wise use of it are enhancing the experiences of their customers while simultaneously improving their business processes and optimizing their marketing efforts. However, while big data has the potential to provide big benefits, it does not give a fully comprehensive view of customers with insight on their motivations, future intentions, or competitive perceptions. To make big data truly actionable, companies must fill in these contextual gaps, making traditional market research its perfect complement.  Together, the pair of big data and market research can do the following:

  • Provide a 360-degree customer view: It is simply not enough to look only at your customers when analyzing interactions. How they interact with competitors can be equally valuable in understanding prospects and identifying the best approach to reach them.
  • Project future actions by understanding context for behavior: When analyzing behavior on a large scale, it is important to provide context as to why that behavior took place, such as the thoughts, perceptions, and motivations behind it. Customers’ purchase intentions cannot be revealed without connecting these dots. Understanding the context behind your data is crucial to projecting future actions.
  • Improve marketing campaigns with a refined targeting strategy: While big data can be used to successfully measure demographics, content preferences, past purchases and other campaign elements, it cannot always identify targeting characteristics directly related to product need or purchase intent. Strategically designed market research can shed light and provide more extensive analytics around customer behavior.
  • Test advertising pre-launch to minimize risk: A/B tests that are conducted live run the risk of exposing the audience to non-optimal messaging that could have a negative impact on your brand. Save time and money by integrating your in-market approach with pre-testing to optimize your communications and ensure a positive experience for your prospects.
  • Understand the purchase journey and your prospect’s experience: Data from your website can provide a vast amount of information on visitor behavior, but you still won’t know their motivations for visiting, how their experience has been, or where they are in the purchase journey. Market research, through a variety of surveys, can help obtain this information to tell the entire story.
  • Go from big data to bigger data: Though market research data tends to have a smaller sample size, it is full of information on motivations, perceptions, and context for behavior. Integrating it with your larger data sets can make your big data even bigger, and provide understanding of needs, opportunities, how to improve targeting, and where to innovate.
  • Humanize your data and make it actionable: Making sense of big data is essential to focus your organization and enable better decisions and action. Market research techniques such as customer quotes, polls, and video can really bring your data to life and make it relevant, interesting, and memorable.

Big data and market research offer different pieces of the puzzle. Combining them can give your organization a more comprehensive view of the customer and their behavior, attitude, and motivations. Integrating larger-scale big data with smaller-scale contextual market research data will yield greater insights for your brand and help to drive strategy in the right direction.

Challenges with big data?

Big data offers excellent opportunity to understand the customer needs, his behavior over a period of time and this in turn can help to increase the business or to create new products and services. However big data poses significant challenges too.


Having more data doesn’t necessarily lead to actionable insights. A key challenge for data science teams is to identify a clear business objective and the appropriate data sources to collect and analyze to meet that objective. The challenge doesn’t stop there, however. Once key patterns have been identified, businesses must be prepared to act and make necessary changes in order to derive business value from them.


Data quality is not a new concern, but the ability to store every piece of data a business produces in its original form compounds the problem.

Issues to consider are:

  • Fast-changing environment
  • Multiplicity and scaling
  • Spill-over effects
  • Knowledge of allocation and gift effects


Behavioral Big Data (BBD) focuses on human behavior over digital media. Collecting BBD through experiments or surveys typically requires researches to obtain approval by an ethics committee. Institutional Review Boards (IRB) provides approval to carry our study that involves human subjects. Guidelines focus on beneficence, justice, ad respect for persons. 


Keeping that vast lake of data secure is another big data challenge. Specific challenges include:

  1. User authentication for every team and team member accessing the data.
  2. Restricting access based on a user’s need.
  3. Recording data access histories and meeting other compliance regulations
  4. Proper use of encryption on data in-transit and at rest.


It’s difficult to project the cost of a big data project, and given how quickly they scale, can quickly eat up resources. The challenge lies in taking into account all costs of the project from acquiring new hardware, to paying a cloud provider, to hiring additional personnel. Businesses pursuing on-premises projects must remember the cost of training, maintenance and expansion.


  1. Galit Shmueli (2017), Analyzing Behavioral Big Data: Methodological, practical, ethical, and moral issues
  2. Agarwal, DK and B-C Chen (2016) Statistical Methods for Recommender Systems.
  3. Leeza Slessareva, (2016), Are market research and big data the perfect pair?
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