Big Data vs. Thick Data

Avasta on Big Data

Big data is the cumulation of many datasets that reveal patterns, trends, and relations around behavior to guide business strategy.  Used correctly, it can drive better assumptions, anticipate macro changes and give a better view of the market from a 30,000 ft level.However, its accuracy can only be verified retroactively.

Avasta on Thick Data

Thick data is qualitative, rich, and contextualized data that provides a deeper understanding of people’s intent, values, beliefs, motivations, and behaviors. It allows the data end-user to understand the underlying meanings and contexts behind people’s actions and decisions.

While big data can help identify patterns through large data sets, thick data helps understand the why behind the data and how variable it can be. Thick data uses smaller data sets, each of which has a more extensive breadth of data from which one can extract insights.

Furthermore, big data limits findings to what can be easily captured / data that's easily available so it may not present the full picture.

Because thick data is collected through more in-depth and qualitative methods, it can be more time-consuming and resource-intensive to collect and analyze. However, it can provide valuable insights that may not be captured by thin data alone. Big data is often referred to as thin data since it consists of a lot of data points with not as many valuable insights being drawn from it.


Big Data: Examples of big data would be general transaction records, internet clickstream logs, and information pulled from mobile apps and social media.

Thick Data: An example of thick data is what we use to help enable leaders to grow their business. It can involve in-depth research to determine the intent of purchase behavior and all the factors at play when a customer is considering buying in the category. For example, how many decision-makers are in the process of choosing to buy marketing software? Also, how long does the decision-making process take, what are their emotions towards your brand and competitor brands, the gaps from the customer’s point of view between your offerings and a competitor, and exactly how many businesses in the total available market are worth the sales investment.Companies can also use it to identify the minimum number of critical datapoints required to determine if your company is the right match for a prospective customer.


Conventionally, larger data sets have been more common than thick data in business. Since the 1990s, big data has been the backbone to guide business decisions and strategy by providing inferences about customers and the market. These inferences inform making bets on which direction the market will go and whether your business is the one to take advantage of those trends. You won’t know the extent of your success or failure until you’ve invested the time, money, and resources.

This is where thick data comes in. Thick data uses a concise number of data sets, each containing a vast amount of information. This thick data should be tailored to what’s specifically relevant to your decision-making, customers and objectives. It considers how long you should expect outcomes to be realized, enabling leadership teams to manage expectations of results instead of being misled by artificial targets.

Avasta Application

At Avasta, we gather thick data to identify the relevant decision makers we should talk to and the key areas and hypothesis to be explored. We then conduct quantitative research to either validate or invalidate the hypothesis. This arms us with the capability to capture the full picture of your business and market in a resourceful and efficient manner, creating actionable insights you can use with confidence to adapt your strategy.  

We then combine big data and thick data through common datapoints to identify how accurate the relationship is between the different datasources. Once Avasta has completed a full Spectra implementation, our clients have access to an accurate understanding of how their business does or can perform in the marketplace for the foreseeable future. They can then conduct scenarios to qualify potential strategies before deploying the time, money, and resources that may be better placed elsewhere to drive business performance.  


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