Important signals about a brand are created every day and the consumer goods sector is unique in that many of these signals are created publicly. When a consumer reviews a product, that matters. When a brand posts on social media, that is an important indicator. When a company hires a new employee, that is a sign. Understanding these signals is vitally important for fully evaluating a brand. At the same time, the data equivalent of 250,000 Libraries of Congress is created every single day, so there is also a lot of noise produced alongside these signals that is less meaningful. Additionally, availability and relevance of data points can change significantly as time goes on. Do a quick Google search of your favorite brand today and you’ll see information on that brand’s products, distribution, team, consumer reviews, industry, competitive set, and social media presence. Do that same Google search again in a month, and the results are likely to look a lot different. Capturing these changes at scale is an incredibly difficult task, but it’s an important one. Finding the consequential information in this deluge of data can seem like finding the needle in a haystack, and in many ways, it is. That’s where Helio comes in.
Helio draws in billions of publicly available data points on a regular basis, collecting information on things like how customers are responding to content, how the company is describing itself, where the company’s products can be bought or where its stores are located, the work experience of people who are employed at the company, and much more.
As difficult as collecting massive amounts of information is, making sense of that information and storing it in a way that lends itself to analysis can be even more difficult. For example, if two different data sources refer to the same product in different ways, how do you systematically recognize that those two products are the same? This challenge, known as entity resolution, is one that many people who work with a diverse set of data sources struggle through and it is exacerbated as you work with more disparate data sources. There is also the problem of determining how to best transform and store the data in a way that is most useful to various algorithms. Normalizing data is a critical step in making it actionable. None of this is easy to do, and we’ve spent years getting Helio to the point where it can perform all these operations quickly and accurately.