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AI & Analytics

Data Analytics

Leverage the power of Data Analytics to summarize data, identify trends, validate and quantify relationships, and detect outliers.

Most businesses rely on their data assets for decision-making, but many insights remain unachievable due to the sheer volume of data that is required to be processed to glean those insights. Data analysis reports can help provide a quick snapshot of your valuable data assets, including calculations and quantification of KPIs and metrics. These summaries can provide quick technical or visual summaries of key pieces of information allowing your data science and development teams to quickly capture useful information to accelerate their processes. Through detailed analysis, our data analysts can identify data associations and overlaps that can help optimize downstream analytical processes.

Clean, consolidate, and aggregate your data to feed large scale statistical assessment that will help your organization identify the most important factors for measuring and assessing the success of your processes. Determine the best ways to approach business problems, increase productivity, and decrease costs. Couple the identified variables and derived measures with dynamic visual reports that make it easier to react quickly to fluctuations and to assess the overall quality of your operational processes.

Sometimes data is stored in unstructured formats requiring alternative approaches to store and analyze this information. This includes graphical database systems used to analyze complex association between actors, events, processes, or procedural steps. Coupled with mathematical approaches, various types of metrics can be derived from a weighted directed graph that are useful for supporting business decisioning. In addition, our AI experts have extensive experience capturing information from unstructured text documents and audio & video signals.

Our team can help you with every aspect of Data Analytics including the defining, linking, and summarizing of your data assets, consolidating, and deriving features for downstream processing, and building quick insight reports for ease of business consumption.

Benefits

Comprehensive Insights
Analyze large amounts of data to discover previously invisible insights and trends. Consider patterns at scale, assessing and quantifying the various relationships between transactional activities and underlying descriptive information. Identify new opportunities for your business and stay ahead of your competition.

Trend Identification
Use statistical testing and analytical summaries to identify trends, patterns, and correlations that can be leveraged for data-driven decision making. Determine the influencing factors and the quantifiable impacts that adjustments to those factors are expected to have on net results.

Decision Support
Leverage statistical tests and insights to understand the key factors associated with a particular outcome. Understand and uncover the significant differences between groups of products, processes, or customers and effectively quantify the expected variations based on changes in inputs. Leverage these inputs, coupled with optimization techniques, to build an automated decision support system or for understanding and quantifying the impact of more powerful Machine Learning solutions.

Applications

Exploratory Discovery
Leverage powerful statistical tests, quick insights, and dynamic visualizations to enable your teams to perform rapid exploratory data analysis. Uncover and assess which variables and metrics are key for inclusion in your analytical processes when you require your models and insights to be parsimonious as well as to reduce the likelihood of confounding factors, thereby ensuring the validity of your outcomes and conclusions.

Summary Reporting
Analytical summary reports can help you interpret the current state of your business, what is driving trends and patterns, and uncover potentially problematic areas. By analyzing large volumes of historical data and converting them into easy-to-understand summaries, data-driven teams can obtain quick insights to facilitate their exploration and business executives can glean quick insights to support their decisioning process.

Weighted Graph Visualization and Analysis
Using parallel and distributed backends, significant volumes of information can be presented in dynamic and responsive frontends for the purposes of understanding associations between different actors, constituent parts, and processes. Through the use of weighted graphs and mathematical formulations, metrics that are relevant at a particular level within a hierarchical process can be propagated effectively to other steps within that process. Conventional operational data platforms make this kind of multi-step link analysis infeasible.

Use Cases

Data Consolidation and Reporting
Leverage an effective combination of business analysis, data analysis, data governance, and data science skillset to achieve a comprehensive understanding of your data assets. Understand the patterns and relationships between sources helping to streamline your reporting, analytics, and model development initiatives. Build comprehensive summary snapshots of data fields and sources to provide insightful information to those working day-to-day with the data sources. Through unsupervised customer segmentation, additional cluster-based information can be used to support informative insights.

Server Log Analysis
Server traffic analysis can help in determining the significance and timing of requests and required processing consumption. This analysis can be useful in predicting server stability and mitigating server failure through predictive maintenance. Newer use cases are emerging in the detection of anomalies and fraudulent accesses to identify and prevent potential cybersecurity threats.

Allocation Modelling
Graph-based representations can be used to visualize and quantify the relationships within knowledge graphs, supply chains, network flows, and production processes. Allocation modelling can be used to provision aggregate metrics and costs downstream to the individual product or actor level or consolidated and cross-referenced at the groupwise level. This can include production and operational costs in the manufacturing space, across various supply chains, or in support of risk quantifications within the banking or insurance space. Leveraging this information can be useful for supporting financial reporting.

Methodology

Our experts leverage their strong academic knowledge and development experience to provide end-to-end solutions for all your analytics needs. We will determine the best possible approach for your particular use case and implement generally touch on a combination of tasks from the 3 following phases depending on resource availability and the project scope:

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John Yawney

Chief Analytics Officer

John Yawney