Big data challenges in market opportunity identification

Big data has become a buzzword in the product community.

Big data involves the speed at which data is generated, the amount of data that's generated, the types of questions that can be answered with it, and the number of sources it's coming from. In short, big data is about more than just size.

Big data describes the veritable explosion of data we're seeing from billions of people accessing the internet.

Product teams want to use big data to identify new market opportunities and new ways to target their customers. Yet, many companies are struggling with how to collect and analyze big data, particularly when the data is scattered across different sources.

Business executives are asking questions such as: How can we get useful information from all of this disparate data? How can we make better strategic decisions with our big data? How can we address the challenges of storing, organizing, and managing big data? And how can we increase the value of our big data?

Commonly, there are a few major challenges associated with managing big data for market opportunity identification:

• The first challenge is that it's difficult to find relevant predictive patterns in a sea of unrelated variables. For example, using traditional business intelligence (BI) techniques such as data munging and manual data mining, it might take weeks or even months to discover a predictive pattern in a large database of customer survey results. Imagine that your dataset includes demographics, firmographics, psychographics, purchase data, reviews, and more. That's a lot of information to look through, and a lot of variables that might not seem relevant at first glance.
• A second challenge is that traditional BI tools are not designed for efficient discovery of predictive patterns when analyzing big data, which is increasing in volume at a faster pace than traditional databases are being updated. Not only does this make it difficult to keep up with the latest insights, but it also becomes more costly and time-consuming to build insights into existing systems.
• Another major challenge with big data is simply getting it in the first place.

Large companies such as Google and Amazon have access to tremendous amounts of computing power and virtually unlimited storage thanks to their substantial investments in hardware and software services, but for smaller organizations – even those that are eager to harness the potential of big data – the story is different.

Data is coming in faster than ever before. The explosion of data being generated has outpaced the capabilities of traditional database systems to keep up with growth, but setting up and maintaining systems such as AWS or GCP requires dedicated engineers, given the requirements for technical know-how on scaling, security, data pipelines, and more.

Therefore, the problem is often that businesses lack the necessary resources that would allow them to collect, aggregate, analyze, and interpret such volumes of information without investing large sums of money in server clusters or other specialized infrastructure.

With AI, companies can solve these challenges more easily, and analyze big data to improve market opportunity identification.