Case Study
Data Analytics
We meticulously analyzed diverse datasets, including information on streams, water quality, emissions, spills, asbestos, mercury, lead, air quality, and utility maps. Utilizing GIS technology, we visualized spatial data and overlaid layers with contamination data to provide valuable insights into environmental risks and mitigation strategies. Our primary goal was to ensure that NIH/DEP remained compliant with EPA and Maryland environmental standards. Strategic Planning: Ark played a pivotal role in facilitating the development of mission statements, vision statements, objectives, and goals aligned with the agency’s mission. By defining key performance indicators (KPIs) and developing a detailed roadmap for enhancing data management practices, we enabled NIH/DEP to achieve their strategic objectives effectively.
Data Development Products
Data Visualization Tools:
Ark utilized Power BI, a robust data visualization tool, to create interactive dashboards and visualizations for NIH/DEP. These customized dashboards provided stakeholders with intuitive insights into environmental trends, pollution levels, habitat changes, and other critical factors. By leveraging Power BI’s capabilities, Ark empowered stakeholders to quickly grasp complex environmental data and make well- informed decisions.
Innovative Data Solutions: Ark delivers cutting-edge data solutions for organizations like NIH/DEP, enabling actionable insights from complex environmental
Data Curation and Cleaning Tools:
Ark implemented thorough data curation and cleaning processes to convert raw environmental data into a format suitable for analysis. This included employing tools like Alteryx or Python libraries like Pandas for systematic cleaning, normalization, and standardization of the data. Additionally, Ark utilized DAX queries within Power BI to furtherrefine and manipulate the data, ensuring that it was consistent and error-free. By combining these tools and techniques, Ark guaranteed the integrity and accuracy of the environmental data,facilitating precise and dependable analysis. The NIH Office of Management (OM) oversees critical administrative and operational functions, requiring efficient and modernized technology solutions. The Administrative Systems and Technology Officer (ASTO) within NIH/OM sought to advance the office’s administrative and technological capabilities by leveraging innovative tools and strategies. Ark, as a subcontractor to GDIT, contributed significantly to this effort by delivering a range of application development services.
Data Pipeline
At the NIH/DEP project, Ark implemented robust data pipelines to streamline the management of large volumes of environmental data sourced from diverse sources. Leveraging tools like Apache Kafka and Apache NiFi, we established efficient pipelines that automated the ingestion, processing, and transformation of environmental data. These pipelines facilitated seamless data movement, ensuring that information from various sources was aggregated and processed in a timely manner. The utilization of data pipelines was instrumental in enhancing the project’s efficiency and scalability. By automating data movement and processing tasks, Ark reduced manual intervention, minimized errors, and accelerated the overall data processing timeline. This not only improved the speed at which environmental data was made available for analysis but also optimized resource utilization, allowing team members to focus on higher-value tasks such as data analysis and interpretation.
The establishment of data pipelines contributed to the project’s reliability and consistency. By standardizing the data ingestion and processing workflows, Ark ensured that environmental data was handled consistently across the board, regardless of its source or format. This standardized approach enhanced data quality and integrity, providing stakeholders with confidence in the accuracy and reliability of the data being analyzed.
The implementation of data pipelines at the NIH/DEP project played a pivotal role in streamlining data management processes, improving efficiency, and ensuring data quality. By automating tedious and time-consuming tasks, Ark empowered the project team to focus on deriving insights from environmental data, ultimately driving informed decision-making and supporting the NIH/DEP’s mission of environmental protection and conservation.
Efficient Data Management: Ark optimizes data workflows, ensuring accuracy and reliability through robust pipelines and thorough curation processes.
Data Normalization for Environmental Data at NIH/DEP:
In the NIH/DEP project, Ark employed rigorous data normalization techniques to ensure that environmental data, including air quality, asbestos levels, energy usage, water quality, and storage tank information, was effectively organized, standardized, and optimized for analysis. Alongside leveraging DAX queries within Power BI, Ark utilized a range of complementary techniques to enhance the management and analysis of diverse environmental datasets:
Data Cleaning: Ark initiated the data normalization process by thoroughly cleaning the environmental datasets to remove errors, inconsistencies, and outliers. Techniques such as outlier detection, missing value imputation, and error correction were applied to enhance data quality and integrity
Standardization: Standardization techniques were utilized to scale numerical features to a common range or distribution. By standardizing variables, Ark ensured that data with different scales or units could be compared and analyzed effectively, contributing to more accurate insights.
Normalization: Ark applied normalization techniques to adjust the values of numeric features to a common scale, typically between 0 and 1 or -1 and 1. Methods such as min-max normalization and z-score normalization were employed to prevent features with larger magnitudes from dominating the analysis and to ensure equal contribution from each feature
Feature Engineering: Feature engineering played a crucial role in enhancing the analysis of environmental data. Ark utilized techniques such as log transformations, polynomial features, and binning to derive meaningful insights and improve the performance of analytical tasks.
Dimensionality Reduction: To simplify analysis and reduce computational complexity, Ark employed dimensionality reduction techniques. Methods like principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were utilized to reduce the number of features while preserving important information.
Data Integration: Ark integrated environmental data from various sources and formats into a unified dataset. By combining data through techniques such as data merging, fusion, and aggregation, Ark ensured that all relevant information was included in the analysis, eliminating redundancies.
Data Transformation: Data transformation techniques were applied to alter the structure or representation of environmental data, making it more suitable for analysis. This included log transformations, square root transformations, and Box-Cox transformations to stabilize variance, reduce skewness, and meet statistical assumptions
By implementing these data normalization techniques in conjunction with DAX queries within Power BI, Ark successfully prepared environmental data for comprehensive analysis, enabling stakeholders at NIH/DEP to derive accurate insights and make informed decisions in support of environmental management and conservation efforts.