Align Your Data & Your Business Objectives
Let the Positronic Data Science team help you turn your Big Data into Smart Data. We'll help you examine your data, finding patterns and drawing conclusions by applying an algorithmic process to derive insights.
Big Data are data sets that are so voluminous (high volume) and complex (high variety) that traditional data-processing application software is now inadequate to the task of managing it.
Fast Data are data sets that stream in at a high velocity. Time-to-insight plays a large role in smart, informed decision making and companies gain an edge from having exclusive insight about the present or future.
Smart Data are data sets that have been cleaned (veracity), transformed, and combined to be fit for advanced analytics and machine learning to surface true value that can be applied to business processes.
The Data Science team at Positronic will help you build systems capable of gaining insights from your data.
The first step in the process is to collect the data. Depending on the characteristics of your data flow we'll select a data model. For example, if your data flow has high velocity, short lifetime, and can be wrangled cleanly into a defined schema, we'll help you build a Data Warehouse and the systems required to shape the incoming data. If instead you face a data challenge with low velocity and high complexity, a Data Lake may be more appropriate solution.
The second step is to clean & transform the data. Data cleansing is the process of detecting and correcting (or removing) corrupt or inaccurate records through batch processing and scripting. Data validation may be strict, such as rejecting any address that does not have a valid postal code, or fuzzy, such as correcting records that partially match existing, known records. In addition to cleaning the data at this step, the data will be transformed or augmented to be complete and fit for advanced analytics. For example, appending addresses with travel distance from a set location, harmonization of short codes (st, rd, etc.) to full words (street, road, etc.).
The third step is to identify patterns in the data. The Positronic team will apply techniques such as clustering, anomaly detection, and classification using state of the art machine learning algorithms. Items, events or observations which do not conform to an expected pattern or other items in a dataset will be isolated and analyzed. The result will be a thorough statistical analysis that fully identifies all of the patterns in the data.
Predictive Data Modeling
Lastly, the Positronic data science team will build machine learning models that can be operationalized to make predictions about new data. Driving insights into specific workflow steps of a business process is the ultimate value proposition of data science. Prioritize workload based on prediction of higher returns. For example, the California Franchise Tax Board increased revenue by more than $400M in the first two years of operation using a solution that prioritized workload according to how likely each case is to result in payment and how much that payment might be.