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6 Factors to Consider When Choosing an Advanced Analytics Solution

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In its recent Market Guide for Augmented Analytics, Gartner projects that by next year, augmented analytics will be the primary driver for the purchase of business intelligence tools. It will be the future of how companies consume and manage data to drive strategic decisions because of its ability to present actionable trends and insights that can drive a decision or action. 

With so much data available for analysis, in the world of product decisions specifically, augmented analytics platforms can save organizations significant time and money, not only in conducting market research but in driving growth decisions that have long term impact on the bottom line.

Augmented analytics, however, is an emerging field, and many platforms are either too generic — meaning they will ingest the data and have nice visualization capabilities but still require significant data analyst resources to pull out actionable intelligence — or are limited to specific data types or data sources so the outcome is based on incomplete assumptions.


Here are some factors to consider when making a buying decision for an augmented analytics platform:

External vs. Internal Data 

Is the platform ingesting structured internal data, or can it support unstructured, external data feeds? More than 80 percent of all data generated today is considered unstructured, and this number will continue to rise with the amount of online activity. Finding the insight buried within unstructured data requires advanced analytics and a high level of domain expertise to be actionable and relevant. 

Data Sources and Data Types

The mean number of external data sources that companies ingest today is three; next-generation augmented analytics platforms can continuously tap into tens of thousands of data sources.

NLP and Machine Learning

A good question to ask is what Natural Language Processing (NLP) approaches are being deployed in the platform. Open source NLP technologies will not necessarily capture the ambiguities and nuances that are needed to extract relevant meaning across a wide range of data sources and data types. 

Focused techniques combined with business domain expertise that feeds into machine learning can generate much more accurate and scalable results. In the world of products, where the same product can have different names, or the same ingredients can make up different products, capturing the disparities is critical to the usability of the platform.


Black box solutions make it difficult for stakeholders to effectively understand what is going into the system and believe the results that the system delivers. Understanding the data sources and how the taxonomies are built can provide greater assurance that the platform can meet the needs of the organization from the get-go. 


A configurable platform is much more flexible than customized solutions, allowing different teams within the same organization to use it in the way that suits their needs. For example, an innovation team at a pharmaceutical company may be more focused on patent filings and clinical trials, whereas the strategy team may be interested in the market landscape and competitive developments.

Predictive Capabilities

Most predictive analytics platforms use historical models to predict the future, and most of the historical models are based on internal, structured data. The best information, however, exists outside a company. Being able to tap into it is the first step to improving predictive capabilities. Having access to multiple data types will also make the prediction more accurate by definition, assuming the ability to extract the proper context has been established.

For those embarking on a data-driven journey, partnering with the right type of augmented analytics platform is a very significant decision. In the world of product decisioning, the right augmented analytics platform will speed time to market, reduce post-launch modifications and costs, drive effective product positioning, unveil more targeted marketing campaigns, and improve brand affinity, which ultimately speaks to the top and bottom line. 

For more information on how Signals Analytics does this for pharmaceutical and consumer goods manufacturers, schedule a meeting with one of our solution consultants. 

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Chris Thatcher
5W Public Relations 646-430-5161
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