Enterprise Data and Analytics

Data helps you understand and improve business processes so you can reduce wasted money and time. Every company feels the effects of waste. It depletes resources, squanders time, and ultimately impacts the bottom line.

DATA MANAGEMENT

Data Management is defined as the set of plans and strategies that define how data is managed across the enterprise level, within data governance, and with respect to data quality.

Additional items addressed include: communications; organizational alignment; business case development and program funding.

The data management current assessment identifies the management formalisms that define how planning, budgeting, staffing, communication, and execution are managed.

Review of management artifacts; plans for: data management, data governance, data quality
Availability of and review of operational models
Review and inventory data management policies
Review of work products that identify as they related to data management: subject areas; business areas; key data elements; key disciplines
Review of communication practices: how are policies, standards and processes promulgated; how stakeholders are kept informed
Assessment of organizational design.
Review Business Case & Funding practices. To what degree are data practices funded separately from projects and programs? Is there a clearly defined process to define, fund and resource data and analytical programs?
The data management assessment identifies the best practices for management formalisms that define how planning, budgeting, staffing, communication and execution should be managed. PlatinumDQ uses the CMMI / ISACA Data Management Maturity Framework. The key tasks are as follows:
1. Alignment of best practices with the business and strategy objectives.
2. Prioritization of gaps based on business objectives

Sales and marketing departments lose approximately 550 hours and as much as $32,000 per sales rep from using bad data

— (DiscoverOrg)

550 hours = 26% of annual working hours (2080)

DATA ANALYTICS

The W5 approach to analytics ties the insight generating activity into the operational workflow to ensure that insights are not only interesting but also actionable. The following are enterprise-level use cases:

Establishing Analytics as a Center of Excellence and or Business Intelligence Competency Center (BICC).

This business model will be evaluated to create a team of analytical or data science experts that can provide analytical services to the organization. Organizationally, this is often under the Chief Data Officer (CDO).

Establishing Analytics as a Service.

In this model, W5 will assess analytically structured data and tools available to operating units (lines of business) within the organization. The context of the W5 service offering is based on building out analytics and data governance at the enterprise level.

Understand and/or establish business case criterion that will focus future activity
Understand and document the current level of analytical maturity
Integrate the analytical governance process into the broader operational environment
Evaluate foundational data management capabilities and related governance that support data analysis
Schedule and conduct interviews/workshops
Analysis of current repositories and integration points
Collect and review in-flight & planned projects
Analytics and Reporting inventory collection
Compliance inventory
Review BI and Data documents
Define core processes, BI & Data project onboarding templates & agile methodology

ALIGNMENT OF PROCESS

The W5 team will emphasize on the alignment of process with function and their impact on key business success variables/metrics. This includes a comprehensive review of the current, future, and gap processes at the enterprise level, and within business and operational units.

Data Management Planning; business case; communication; Policy / standards management process
Data Governance: Governance management; Business Glossary stewardship; Dictionary management
Data Quality: Assessment process; profiling process; cleansing / remediation process
Data Operations: Requirements process; data onboarding; lifecycle management; data archiving / retention; ETL creation, scheduling, quality and error resolution processes. Risk Management; Configuration management
Data Architecture: Data acquisition process; vendor (Data provider) management; enterprise architecture integration process
Analytics Management: Analytics lifecycle management; analytics and data management lifecycle data process include:

Analytic governance process

Product validation processes with respect to analytic quality

Lineage / Provenance management

Data Product and Analytical Product packaging process – focus on: metadata management process; semantic / cross walk metadata management

Classification metadata management reference for data, vocabulary management taxonomy / ontology management

Operational Alignment. This activity will analyze the approach that the organization has taken to align the enterprise data management activities with those within the analytical function.