The business value of data has exploded over the last decade. This is happening for two reasons: the internet enables people to continually create vast quantities of data, and computers have progressed to the point where they can store and analyze this data.
Modern organizations have two primary sources of data. These include internal data, such as from clients, customers, internal employees, partners, or website visitors, and external data, such as from influencer reviews, social media, and competitors. Historically, data was collected and analyzed by human hands, but drawing practical insights manually is a daunting, and in many cases impossible, task. Advanced analytics as a discipline is maturing but remains focused on structured sources, which are also mostly internal.
Enter advanced analytics, the automated examination of data or content using state of the art tools and techniques. Traditional business intelligence focuses on historical patterns, while advanced analytics uses collected data to uncover and predict future trends.
To help make sense of what advanced analytics is, let’s explore how it works in practice.
Advanced analytics is the central engine of a data fabric, that includes multiple elements, including data collection, data classification and data access. Advanced analytics follows a precise sequence:
Consider a relevant example from the coronavirus pandemic. Data scientists might look at, for example, the number of times people mention the immune system, then look at the number of products that talk about strengthening the immune system. If they discover that the immune system gets a lot of mentions, but there are very few products that claim this feature, that would indicate an unmet need in the market. There’s a lot of interest from the public, but very few companies talking about it.
This process is partially human, since the thought behind looking at the immune system is manual.
With advanced analytics, we go beyond this traditional business intelligence to uncover connections no one foresaw. Suppose, for example, we follow product sales, then we list the ingredients of those products. We may discover a lot of growth in products that contain Vitamin C. The system can surface something like that, and maybe connect it to trends like the coronavirus and interest in the immune system. Nobody has to think about that in advance. The computer is able to uncover this type of connection by breaking down sales data and connecting it to the ingredients of each product.
The real power comes from combining the human model and machine model. Data scientists can develop models for connecting things together and trying to understand what may impact what. They can use their industry knowledge to understand what attributes or characteristics, like ingredients or patents, are relevant. Analysts can then develop a model that breaks down incoming data to help uncover connections and connectivity they wouldn’t have discovered on their own.
This extra connectivity is a primary driver of business value in advanced analytics. The mix of human insights and powerful machine algorithms allows analysts to dig through mountains of data and find what’s relevant. They can then extend the impact of the data and analytics throughout the enterprise. This is one of the biggest challenges, because oftentimes the analytics is too generic to be applied to specific decisions. Moreover, when this is done in an automated manner, with real-time streaming from the constellation of external data sources, brands can uncover and respond to real-time shifts and trends.
Advanced analytics uses a variety of techniques to automate this six-step process.
These include: data/text mining, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, and neural networks.
Two of the most powerful techniques are natural language processing (NLP), machine learning, and image processing.
As a whole, these techniques apply the most cutting-edge mathematics, computer science and linguistics concepts. This empowers people across an organization to make evidence-based decisions rather than relying on conjecture.
Advanced analytics is applied across industries, but Signals Analytics focuses on three:
Advanced analytics platforms continually track consumer sentiments to uncover unaddressed needs, such as ingredient or category trends. They go beyond traditional market research methods to give evidence-based recommendations for marketing and messaging strategies. Product development gets strategic insights from tracking ingredient trends and predicting competitors’ future moves.
Consumer goods manufacturers leverage data created by customers, partners, and competitors to uncover trends and guide disruptive innovation. They uncover hidden needs, allowing brands to prioritize product development and identify fruitful areas of pursuit. Continual external data monitoring helps craft an evidence-based response to real-time disruptions in the marketplace. A deep understanding of consumer sentiments helps maximize marketing ROI.
Advanced analytics platforms tend to be very complex. Building and maintaining them requires advanced skill sets. Yet they also serve to democratize data, empowering decision-makers across your organization to utilize data insights for evidence-based decisions.
To ensure an advanced analytics platform is well-suited to deliver the most impactful insights possible, ask:
To learn more about what factors to consider as you compare platforms, check out our blog post on this topic.
About Signals Analytics: Signals Analytics is an advanced analytics platform built to empower business decision makers with real-time, actionable market intelligence. Our founders, two Israeli military intelligence officers, realized they could apply the same processes they used on the battlefield to make better decisions in the boardroom. We’re unique in our ability to connect more than 13,000 external data sources using proprietary NLP and machine learning techniques. Our clients include many of the world’s leading consumer brands.
To see how Signals Analytics works in practice, schedule a demo today.