Where the true results shine
Having all your data collected, transformed and stored is the foundation. Analytics is what you build on that foundation. Explore, analyze and visualize to reveal their truth.
From data to knowledge
Data analysis involves examining, cleaning, and interpreting data to extract meaningful insights. It helps understand historical trends, patterns, and relationships in data. Learn about customer behavior, preferences and market dynamics. Support decision-making by providing evidence-based recommendations for business strategies.
Showing what happened, by whom, where and why is what is takes to move from data to information, to knowledge and ultimately to wisdom.
Predictive & prescriptive analytics
Once what happened (descriptive) and why it happened (diagnostic) becomes apparent, predictive analytics is fueled to enable businesses towards stronger, more informed decisions about the future. Predictive analytics models evaluate past data, uncover patterns, analyze trends, and leverage that insight for forecasting future trends.
Combined with AI, those forecasts can even be turned into recommendations (prescriptive) so businesses don’t know just what will happen but also what to do to influence it positively.
Determining what predictive modeling techniques are best for your company is key to getting the most out of a predictive analytics solution.
Predictive Analytics Models
- Classification model: are best to answer yes or no questions, providing broad analysis that’s helpful for guiding decisive action
- Clustering Model: sorts data into separate, nested smart groups based on similar attributes
- Forecast model: deals in metric value prediction, estimating numeric value for new data based on learnings from historical data
- Outliers Model: is oriented around anomalous data entries within a dataset by identifying anomalous figures either by themselves or in conjunction with other numbers and categories
- Time Series Model: comprises a sequence of data points captured, using time as the input parameter
Why predictive & prescriptive analytics
• Risk management: Develop risk management strategies for potential risks, and even prioritize the risks that could be most detrimental.
• Customer insights: Better understand potential customers and what they need so that you can develop more specific marketing campaigns to reach them.
• Operational efficiency: By turning to historical data to understand resources and better manage them, predictive analytics can make companies operate more efficiently.
