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6 Factors to Consider When Choosing an Advanced Analytics Solution

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This blog originally appeared in January 2020 and has been updated on April 30, 2020 to reflect the importance of advanced analytics in responding to the global health crisis.

As we continue to navigate through these unprecedented times, there is an ever-increasing need for data and analytics. In today’s uncertain business climate, organizational leaders are faced with both risks and opportunities emerging from the COVID-19 pandemic. Failing to listen to the data will result in significant competitive disadvantage. For sure, there will be winners and losers following the crisis.

With the rising importance of data and analytics, organizations should direct their finite resources and manpower towards extracting and putting these meaningful insights into motion. Using these precious resources to build a solution from the ground up is not only cost and labor-intensive, but also lacks the timeliness needed to respond to the global health crisis and plan for what lies ahead.

Configurable data platforms help organizations get up and running with advanced analytics quickly, without sucking resources. They provide immediate access to connected and continually updated data lakes, built-in analytic models and configurable taxonomies and off-the-shelf apps, built by domain experts in both cutting-edge analytics technology and specific vertical industries. The most advanced analytics platforms will provide full scalability and configurability to ingest new data sources, adapt taxonomies to address very specific business questions and be able to integrate into other business intelligence platforms for maximum usability and efficiency.

6 Factors to Consider When Evaluating Advanced Analytics Platforms

1. 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. 

2. 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. Simply collecting the multitude of data sets is a massive and cumbersome engineering feat in itself, even without the contextualization and enhancements needed to be meaningful to the business. This is especially a challenge with external and unstructured data, which is where most of the value lies.

3. 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.

4. Transparency

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. 

5. Configurability 

A configurable platform provides the tailored experience of customized solutions while also benefiting from the efficiencies of a turnkey, business-ready solution. Configuring the platform to your specific business needs with custom data sources, taxonomies, models, and outputs within the context of your ecosystem unifies the platform with your business strategy.

6. Predictive Analytics 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.

Harness the Power of Advanced Analytics

The uncertainty surrounding the pandemic creates new challenges, as well as new opportunities that only data-driven organizations can unlock. Partnering with the right type of advanced analytics platform is a very significant decision. In the world of product decisioning, the right advanced 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, food, beverage, 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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