Advanced Analytics Solutions
Hoonartek’s consulting division is focused on providing thought leadership and scalable enterprise-ready solutions to help organisations add value to their decision making process. Our consultants work with users and manage end-to-end deliverables across the entire life cycle of a project.
Our coverage includes planning and requirements, through to project execution and live deployment. Our expertise covers custom-built solutions such as scorecards and most leading COTS including SAS, Qlikview, Oracle BI & Analytics, Tableau, Microstrategy and Hadoop.
Data quality is a major challenge for organizations adopting advanced analytics and reporting capabilities. A successful data quality management strategy requires experienced data stewards and expertise to:
Identify, assess and monitor the impact of data quality issues on the business
Design structured processes for reporting and resolving data quality issues
Develop a technical solution that is scalable and flexible, with people, process and technology at its core
Enterprise analytics and reporting capabilities regularly suffer from data quality issues. These arise due to the lack of consistent terminology, the need for varying technology solutions and a lack of user education.
Businesses make critical decisions based on enterprise data. Poor data quality can lead to incorrect decisions, which can result in major setbacks and/or have significant financial implications. It follows that uncompromising data quality is a pre-requisite for any progressive business.
Hoonartek data quality services address the key ingredients of an effective data quality solution. These include:
- Enterprise data quality strategy roadmap
- Data quality tool selection and implementation
- Golden source-data identification
- Data certification in support of governance and regulatory requirements
BI Reporting and Data Visualization
Business Intelligence (BI) reports are used to track and monitor Key Performance Indicators (KPI’s) for any business function. The capability now includes data visualization technologies that provide intelligent visual insights into data.
Standardized KPI’s that can be monitored consistently by all relevant users in an organization, using the latest data visualization toolset
Serves as the single version of truth for KPI’s. Any discrepancy in numbers reported by multiple functions, within an organization, leads to flawed data
Should be set up as an enterprise capability, to ensure adherence to best practices
- Integrated data delivery strategy, encompassing analytics, BI reporting, data visualization and ad-hoc capability in desktop, mobile & cloud
- Assessment of enterprise reporting needs, including analytical to operational needs, alignment of reporting needs, selection of tools and implementation
- Reporting environment optimisation, audit of reporting and model accuracy, reconciliation to operations
Data Cleansing and Validation
The integration of data from multiple sources is a basic need for advanced analytics and reporting. Though imperative, these sources often fail the stringent quality standards of an organization. This makes data cleansing and validation essential to make it business-ready.
A robust data cleansing and validation mechanism will stand the test of time. It helps to avoid delays & data quality issues and prepares data for analytics and reporting.
At Hoonartek, our experience has taught us that data cleansing and validation processes typically matures in line with an organization’s adoption of advanced analytics and reporting capabilities. This helps to forge a strong partnership between the business and IT, which is critical for success.
- Business rules management framework, to ensure a single version of the truth exists across all business functions
- Starter library of business functions that is standardised and customisable for the enterprise
- Templates to define and capture business cleansing and validation rules
- Implementation of cleansing and validation, including fuzzy matches
Advanced analytics usually involves complex analytics models, such as predictive and regression, to generate important insights. Analysts spend a significant amount of time constructing, debugging, and optimizing algorithms for these analytical models. Their successful implementation requires a deep understanding of advanced mathematics and statistics, as well as computational methods and, most importantly, an extensive understanding of the business domain.
- Business analysis and an understanding of the enterprise’s needs for analytics
- Data preparation and creation of suitable analytical models (forecast, prediction etc.) using different techniques such as custom algorithms, decision tree etc.
- Implementation of various commercial tools for analytical modelling (SAS, R, Matlab)
- Model management and enhancement