Business insights

Identify specific business decisions that require high quality insights and define data products that support these decisions. Integrate data and insights into specific business decisions to move toward data-driven decisions and away from heuristics.

Use data to understand and discover behaviors, relationships, correlations, and issues to resolve.

Drive from the top; engage senior stakeholders to advocate for using data and generating insights to make accurate informed decisions.

Use metrics and success stories to align data strategy to business strategy and to grow the data sponsorship within the organization.

Form cross functional teams focused on specific business use cases and building new data products to encourage operational innovation.

Support teams across business, analytics, and technology functions to collaborate and use data to measure metrics and improve business performance.

Use data to track metrics to measure business performance, identify operational inefficiencies, track product performance, and measure customer experience.

Encourage and elevate the data conversation in business teams.

Start to build data literacy and business taxonomy for known data.

Bring top priority data into a single, governed repository that supports consistent data quality and preparation for reporting and traceable improvement in quality in decisions.

Use data and insights to monitor and improve technical metrics, such as spotting siloed or poor-quality data.

Identify impactful business initiatives and operational functions that benefit from high quality insights, and define analytics requirements to deliver such insights and measure their impact.

Develop a portfolio of data initiatives, with some that address a known business challenge with established analytics methods (for example, reduce reporting time) and others that apply advanced methods to solve problems in a new way (such as forecasting inventory needs using ML).

Engage executive stakeholders to advocate that analytic insights are mandatory “value add” capability. Define roles required to develop new analytics and data products and assess technical and process readiness across multiple teams.

Expand data communities with active skill development focused on data visualization, new insights creation, and self-service access to curated data.

Encourage sharing of knowledge, hackathons, and innovation days to develop organizational excitement.

For select business goals and operational processes, embed data into day-to-day decision, enable self-serve insights, and automate tracking of KPIs measuring impact. Incorporate predictive insights to enhance traditional descriptive insights to improve planning and operational responsiveness (for example, assess customer sentiment and behaviors, predict equipment failures, improve supply chain forecasts).

Define and enforce common data standards and governance, architectures and development patterns to enable consistent and efficient insights creation.

Develop reusable, scalable architectures for the data value chain, including ingestion, transformations, cataloging, analytics, and consumption to support variety of data usage needs across the organization.

Enable automated insights alongside scheduled analytics insights for BAU processes, ad-hoc analysis, and innovative business requirements.

Include modern analytics technologies and expand technical expertise to run integrated analytics platforms in order to adapt to customer changes with agility.

Showcase the business value of a comprehensive data strategy and incorporate investments of data and analytics into strategic business planning.

Develop new markets, lines of business, and innovative products using analytic insights. Align data products teams responsible for analytics to the business and empower them to develop, test, and deploy insights using curated enterprise data and standardized DevOps methodologies.

Reward innovation and experimentation with data and develop clear and transparent methods to identify analytic insights with high potential for business impact and enterprise scalability.

Establish processes for aligning on business goals and opportunities, assessing their value, designing analytics solutions, developing, testing, and deploying faster than ever before.

Democratize data through thoughtful data governance so that data is treated as a business asset, there is a single source of truth for historical, real time data feeds, and information about the outcomes of decisions made in the past. This enables frictionless insights.

Develop standards, methods, and tools for data sharing across different business teams so that new data products can be developed with maximum independence while maintaining adherence to business policies and procedures.