Sisu Data is an analytics platform for structured data that uses machine learning and statistical analysis to automatically monitor changes in data sets and surface explanations. It can prioritize facts based on their impact and provide a detailed, interpretable context to refine and support conclusions. The product features fact boards, annotations and the ability to share facts and analysis across teams. Data teams and analysts start by creating common definitions of key performance indicators, which Sisu then utilizes to automatically test thousands of hypotheses to identify differences between groups.

Sisu introduced its new data exploration feature – Smart Waterfall Charts – earlier this year. This new feature takes advantage of Sisu’s decision engine to automatically identify key performance drivers, enabling analysts to find crucial factors more quickly. Sisu also redesigned its analytics platform, introducing a new, metrics-first interface that resembles an Excel workbook. Its tabs enable workers to examine metrics from multiple angles and quickly evaluate the data to speed up the delivery of insights.

Large volumes of data are streaming into organizations from all sides and with increasing frequency, forcing organizations to rethink how data is processed. And the changing nature of the frequency with which data informs decisions requires a corresponding change in analytic approaches. Our research shows that one-third of organizations (34%) need to process data every hour or in real time. To evaluate such large volumes of information in such a short time requires new types of analytical techniques based on artificial intelligence and ML algorithms. These algorithms can detect correlations in the data to highlight factors with the greatest impact on the metrics that matter most to the organization’s performance.

Sisu uses scalable ML to gain insights from large datasets. The platform automates data exploration so workers can find hidden facts in complex data more quickly. Sisu also introduced a new algorithm along with the Smart Waterfall feature that offers the ability to surface only the highest-impact, statistically valid drivers. This enables workers to quickly focus on only statistically relevant populations in the data.

Data is increasing at an exponential rate, adding complexity and challenges for data teams and analysts to identify which dimensions matter the most. Automating the exploration and evaluation of metrics enables organizations to make more informed decisions and implement more productive practices. I recommend that organizations looking to automate data exploration and speed up analytics processes evaluate Sisu’s capabilities when considering vendors.

Regards,

David Menninger