Up to 80% of analytics is data cleaning and preperation — gain this time back through automation!
The problem for data-driven decision-makers
Businesses today rely more and more on data to detect threats and opportunities as early as possible. However, if those data are not accurate, then decision-makers will be busy acting on the wrong signals in the data, costing the company time, wasted resources, and missed revenues.
To this end, two factors drive the business impact of data-supported decision-making: Firstly, the speed of the decision and, secondly, the accuracy of the data supporting the decision. In a real-world scenario, attaining 100% accurate data is typically infeasible and not always necessary. However, rarely, does anyone define what is “good enough” for the problem at hand and, without such a definition, data cleaning efforts cannot be prioritised.
Inspirient's Automated Analytics Engine automates the data quality assessment and prioritizes actions to mitigate risk from low-quality data to enable decision-makers to estimate the effort and time to attain “good enough” data to make their decisions with confidence.
In detail, a comprehensive set of data quality controls are carried out for any given business dataset (see the full list of controls on the reference page). Issues arising from the assessment are assigned a quality score and mitigations are provided, based on data science best practices. If human assistance is not required for any proposed cleaning action, then it is automatically implemented — reducing the time to decision to the bare minimum.
Assessing the quality of any business dataset can be performed with unprecedented speed — automation to this degree enables decision-makers to make business decisions at the right time and with confidence!
The Inspirient Automated Analytics Engine automates the entire data analytics process end-to-end: From the assignment of input data, pattern and outlier detection, automated visualization of patterns, weak points and opportunities to automatic generation of textual explanations and recognition of the underlying relationships and rules. Most other analytics solutions rarely include these textual explanations and observations regarding the underlying data relations, which are both critical to provide a deeper level of analysis and more actionable conclusions.