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intapiuser
Community Team Member
Community Team Member

Advanced Analytics

Advanced analytics is a part of data science that uses high-level methods and tools to focus on projecting future trends, events, and behaviors. This gives organizations the ability to perform ‘what-if’ calculations, as well as future-proof various aspects of their operations.
The term is an umbrella for several sub-fields of analytics that work together in their predictive capabilities. The major areas that make up advanced analytics are predictive data analytics, big data, and data mining. The process of advanced analytics includes all three areas at various times.
Data mining is a key aspect of advanced analytics, providing the raw data that will be used by both big data and predictive analytics. Big data analytics are useful in finding existing insights and creating connections between data points and sets, as well as cleaning data.
Finally, predictive analytics can use these clean sets and existing insights to extrapolate and make predictions and projections about future activity, trends, and consumer behaviors. Advanced analytics also include newer technologies such as machine learning and artificial intelligence, semantic analysis, visualizations, and even neural networks. Taken together, they help advanced data analytics software create an accurate enough canvas to make reliable predictions and generate actionable BI insights on a deeper level.

What Can I Use Advanced Analytics for in Sisense?

Because it involves so many disciplines and has such a broad applicability, there are several excellent uses for advanced analytics.
Making use of data analytics can be done in these steps:
  • Define the result you want, e.g. how to offer each customer additional products of interest.
  • Collect the data that will be needed (perhaps eCommerce site tracking data, CRM logs, etc.).
  • As necessary, prepare the data from each source, then combine the different data sets.
  • Make predictive analytics models, using statistical analysis to see which outcomes typically follow which events.
  • Apply your models to your business.
  • Review the models to ensure they are working properly.
User-friendly analytics software, such as Sisense, can make these steps accessible to business and non-technical users. You still need to decide which business benefit you want and identify the data required.

R Integration with Sisense

Using an endlessly growing library of R functions and advanced techniques for deeper statistical and predictive data analysis. Dramatically reduce the time needed to prepare data for analysis in R and ensure that R users are working with the same, single version of the truth as others.
Predictive analytics uses current and past data to let you make predictions about the future or other unknowns. You can see the likelihood of a coming event or a specific situation, given the data being analyzed. Predictive data analytics differs from general forecasting because it gives you insights into individual cases (individual customers, employees, and systems, in the examples above). Which makes it more actionable and it opens the door to immediate improvements and results by applying the insights from the analysis. Big data and predictive analytics often go together.
The richness of big data can be leveraged for the highly specific insights per visitor. An example of such big data is the individual clicks on different products and pages of each visitor on an eCommerce site. Analytics techniques must then be adapted to high volume, velocity, and variety of data. One technique is data mining to pick out patterns. Others are statistical algorithms to build models, and machine learning to update models as new data arrives.

Examples:

Here are some Advanced Analytics examples and knowledge community posts using Sisense
more to come...
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Last update:
‎03-02-2023 08:29 AM
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