What To Consider When Selecting An Advanced Analytics Solution

As businesses continue to face unique challenges and problems, there’s an increasing need for them to use advanced analytics. Company managers are currently facing opportunities and risks emerging from different factors, and failing to analyze data properly may significantly make them lose their competitive advantage. Therefore, organizations need to channel their resources and workforce towards analyzing and interpreting various data types to predict future trends correctly.

Fortunately, advanced analytics solutions can help you do that efficiently. However, you need to know what to consider when selecting an analytics solution.

  1. Internal Vs. External Data

Before selecting an advanced analytics solution to use, you need to understand whether you want to use it for internal or external data. According to research, more than 80% of generated data is unstructured, and this may continue to rise depending on the number of online activities.

To accurately analyze and interpret the data and use it to make valuable decisions for your business, you need to have advanced analytics software. That’s because most unstructured data has so many buried insights that cannot be viewed using other conventional analytic methods.

  1. Data Types And Sources

Companies ingest several external data sources that need advanced augmented analytics to interpret and to identify. Because of the sheer number of information that a company can access, collecting data sets may be cumbersome and tiring.

Besides that, traditional methods may give inaccurate results and take quite a long time, affecting the business’s ability to generate profits and satisfy consumer demands. Therefore, to properly implement advanced analytic solutions, you need to get data from the right sources and have an experienced team who can utilize machine learning tools and AI to analyze them.

  1. Machine Learning And NLP

When selecting an advanced analytic solution, it’s vital to ask the type of NLP (National Language Processing) approaches that the platform utilizes. That’s because open-source NLP technologies don’t capture the nuances and ambiguities required to extract relevant meaning across various data types and sources.

Instead, a good platform should use combined business domain expertise and techniques that use machine learning to generate more scalable and accurate results. In the business world of products where similar products may have different names, or similar ingredients can develop other products, capturing disparities is vital to usability.

  1. Transparency

Some analytic solutions make it difficult for companies’ stakeholders to know what the system ingests and believe the results it delivers. A good platform should understand data types and sources and how taxonomies are built to ensure that it meets the organizational needs from the onset.

  1. Configurability

An excellent advanced analytic solution platform should be configurable to give tailored experiences of customized solutions that benefit the business owners. Configuring a venue to suit particular business needs by using taxonomies, custom data sources, outputs, and models within your ecosystem’s context can help in unifying the platform with your business goals.


The uncertainties surrounding businesses in the current world make it necessary to use advanced analytics to find new opportunities and predict future outcomes. Knowing how to find the right advanced analytics solutions platform is significant if you want your business to speed market time, drive effective product positioning, minimize post-launch costs and modifications and identify more targeted marketing campaigns.

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