As organizations increase their reliance on analytics, the problems they face become harder, requiring more data of diverse data types for success. To solve these hard tasks, we must detect weak signals in the data by finding and extracting as much structure (or dependencies) as possible.

As our ability to collect and fuse data from different sources increases, advanced data types – with temporal, spatial, or link structure – are now moving into the analytic mainstream. At Elder Research our consultants are experienced with techniques for handling data from text, sequences, time-series, space, and graphs. Incorporating these techniques increases the power and accuracy of our analytics solutions.

Text Analytics

Much of the world’s information is locked away in unstructured textual data. Text analytics unifies techniques from both statistics and linguistics to unlock the information stored in unstructured text through the extraction of structured features. These new textual features can be combined with traditional numerical and categorical data and used to help prioritize workflows, identify sentiment, and/or detect emerging issues.

Text is stored in many formats including full text documents such as Microsoft Office, PDF, and HTML format, as well as semi-structured notes or comments fields in databases or spreadsheets. Elder Research can help convert that text into a unified format, apply statistical and linguistic feature extraction, and make the text available in a search engine, custom report, or data visualization to ensure that each piece of text is providing maximum value for your organization.

Learn more about text mining.

Graph Analytics

Graph analytics is designed for data describing interconnected entities and events. Understanding the relationships between entities is critical in many fields of predictive analytics including workflow prioritization, fraud detection, and rare event modeling. Due to the focus on relationships, graph analytics requires a different set of tools for efficiently storing, querying, and modeling data.

Elder Research consultants can help determine whether graph analytics is right for your problem, find the most appropriate graph analytics platform for your needs, help with the data conversion, and perform all levels of analytics. Making the switch to graph analytics provides a new dimension for discovering value in your data.

Streaming Analytics

While most analytics pertains to data at rest, streaming analytics focuses on analyzing data while it is in motion. Often seeing the entire data set is impossible or decisions need to happen in real time. In these cases streaming analytics can use rolling window statistics and other approximate computations to gather data and guide decisions.

From detecting anomalies to identifying complex events from multiple streams, Elder Research consulting engagements provide the statistical rigor needed to limit false positives and false negatives even when data is moving in real-time.

Predictive Analytics

Predictive analytic capabilities shed light on a large number of business problems, and can identify inefficiencies and opportunities for improvement in our client’s business processes. We help clients enhance efficiencies and performance by collecting and exploiting the data already available to them using innovative solutions.

Elder Research data scientists are experts in emerging open-source technologies such as R, Python and Spark, and available commercial analytics software packages. A key strength of the Elder Research team is our breadth of analysis techniques, including modern algorithms such as advanced ensemble models and deep learning techniques. We apply the right tools to optimize the value realized for each analytics consulting project.

Time Series

Rarely is any data static for long. Time series data tracks multiple variables over time when the patterns over time out weigh the level of interest of any single number. Elder Research data science consultants can help apply traditional time series techniques such as ARIMA, but when those techniques are not sufficient our decades of experience applying predictive modeling techniques to time series data can identify which techniques are best suited to a particular problem.

 

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