Data Monetization Readiness & Roadmaps
We clearly define or validate business objectives to monetize your data with:
- Data Strategy and Prudent Roadmaps
- Data Management Readiness Assessments
- Analytics Readiness Assessments
Advanced Data Management Execution
Once critical pieces of the Foundational Data Management have been addressed, higher-valued data management areas can be leveraged to meet ever more valuation data monetization initiatives.
Business Intelligence. Evolve the organization to become data-driven and advance its analytical curiosity. Business Intelligence, supported by tools such as Qlik and Tableau to name a few, enable the organization to make timely decisions that improve value through exploration and investigation of data. This area helps answer critical questions through reporting and analysis to uncover insights and drive decisions.
Advanced Analytics & Predictive Models. Transform insights into actionable results using a repeatable and consistent method. For those evolving into a data-driven organization, models are critical to institutionalize corporate learnings. As an organization evolves further, they can embed advanced analytics and predictive models into their systems, greatly increasing the reaction time of the organization, while realizing significant value from their data.
Enterprise Data Integration. Merge data from disparate sources and deliver data sets consistently to and from systems, data repositories and analytical stores. This area is supported by tools such tools as Informatica, Talend, and SSIS.
Master Data Management (MDM). Create a single version of the truth from across the organization. Often it is difficult to create a single view of a customer or product. This area of expertise brings together information from disparate data sources and applies governed rules to determine which data should be used. This effort also eliminates duplicates and increases quality and consistency of delivered information. Many times, this area is also a source of unique monetization initiatives.
Foundational Data Management Execution
As most organizations grow, their systems typically become tactically glued together to meet business goals. As time progresses, this causes unforeseen challenges and limiters to future growth. The Foundational Data Management area of expertise is about pausing to look at the wider environment and how best to support future business needs.
Data Governance. Define or validate your processes for controlling data assets to ensure a common understanding, consistency, accuracy, and accessibility. This capability often includes Integress being the initial facilitator of a Data Governance Committee, helping create the charter, and refining the goals and desired outcomes. This effort often starts with governance around a specific challenge, such as common agreement and usage on KPIs or financial terms.
Data Architecture, Data Warehouses & other Data Repositories. Build or validate the readiness of your systems and processes. Implement improvements for enabling a foundational Modern Data Architecture to support Data Integration, Data Repository and Business Intelligence technologies. The Data Repository piece includes new technologies and best practices for storing, accessing, cleansing, and using data. This area includes such concepts as Data Warehouses, Operational Data Stores and Data Lakes and is supported by technologies such as AWS Redshift and Azure SQL Data Warehouse.
Data Quality. Improve the validity, accuracy, integrity and consistency of data throughout your enterprise. Data quality is one of the most important data management concepts. Without trusted data, the organization will be very hesitant to leverage data to make decisions and evolve the opportunities for data monetization. This area is supported by such tools as Talend and Informatica to name a few.
Data Operations. Improve capabilities to run, manage, monitor and support data assets across their entire life cycles. Without repeatable, consistent processes, organizational decision making and information flow is not possible. This area of expertise covers from current-state assessments to managing the data assets of a daily basis.
Data Risk. Define or validate the scope of risk analysis, monitoring and enforcement needed to mitigate loss exposures related to managing data assets. Data risk can come from both internal and external. On the internal side, risks such as data retention and backups to user access should be understood and mitigated. Beyond the assessments are implementing improvements as needed to minimize exposures.
Data Practice Evolution. The data management practices of an organization will not evolve over night. This area of expertise is laying out a controlled maturation of policies, processes and procedures to continuously improve and take advantage of an ever changing data management landscape.