Today, organizations compete in a dynamic market environment with ever-fluctuating opportunities and uncertainties. To better compete in the marketplace, organizations need data-driven business strategies and information products more than ever to make faster and smarter decisions. However, to maximize the potential of advanced analytics, companies need to design a responsive organizational structure that supports the business priorities with data and analytics.
Top-performing organizations in data and analytics are enabled by deep functional expertise, strategic partnerships, and a clear center of gravity for organizing analytics talent. These companies have developed an operating model and internal organization that is focused on ingraining data reliance and analytics into decision-making processes across the business units.
To better compete in the marketplace, organizations need data-driven business strategies and information products more than ever to make faster and smarter decisions.
Ensures alignment between business strategies, analytical initiatives, and technology infrastructure
Provides structure and processes to develop and promote best practices
Provide the governance structure for data and analytics and for sustaining programs, projects, practices, software, and information architecture
Provides the data and analytics strategic, programmatic, analytical, and technical skills required
Enables development of a coherent strategy aligned with the overall goals of the organization
Help build the capabilities for deployment of data and analytics toolsets for analytics initiatives
For a company aspiring to be a data-driven organization, these elements can be incorporated into any of several organizational models, each of which is effective as long as there is clear governance, and the company encourages an analytical culture across business units to learn and develop together. Answering a few key questions can help to identify the best model. One of the key questions that need to be addressed from an organization perspective is where analytics talent should reside in the organization. Should it be a stand-alone department within IT, embedded within the business, or a hybrid of both? In other words, the company should decide whether to create one centralized Analytics organization, in which Analytics stands alone in a center of excellence (COE) that supports the various business units or a decentralized organization, in which analytics is embedded in individual businesses; or a hybrid, which combines a centralized analytics unit with embedded analytics areas in some units.
The ideal organizational structure will depend on various factors including the current organizational culture and how advanced the company and it's business units are in their use of analytics.
Each of these models has strengths and weaknesses in particular settings, and it is important to note that the analytics organizations could evolve over time with increased organizational maturity with data and analytics. It is very common to see organizations start out decentralized and eventually move into a centralized function, while others that are centralized later move into a hybrid model of hubs and spokes. The critical thing is to prepare for these eventual changes and integrate flexibility and scalability into analytics programs with an eye toward the future.
Along with a robust organization, a collaborative analytics community is critical to harness data assets for effective adoption of analytics and wider organizational impact with data-driven insights. An effective analytics community is critical in fostering the fundamental cultural shift required to transition to a data-driven organization.
Any organization undertaking an analytics transformation typically has three types of obstacles to the adoption of analytics solutions – Cognitive, Process and Cultural obstacles. Unless these obstacles are resolved, analytics investments may not be able to attain their potential value and ROI from these investments will be limited. An effective analytics community can play a key role in identifying and overcoming obstacles to driving more value out of the Enterprise Analytics and BI Platforms. An effective analytics community plays a vital role in realizing the analytics vision - to leverage data and analytics for competitive advantage. It is a mandatory aspect of data and analytics, not just “nice to have.”
With a little planning and forethought, it is possible to design an analytics organization and community that is a key enabler for driving competitive advantage out of enterprises' data assets and reap the tremendous value and benefits of analytics.
Analytics Organization and Community Strategy and Design:
- Understanding of business priorities and organizational culture
- Assessment of various operating models based on data and analytics goals of the organization
- Design of roles, skills, and capabilities that would build analytics capabilities
- Identification of translators from the business who act as a bridge between the COE and business units
- Strategies to enable organizational capabilities to leverage data as a corporate asset
- Design of a robust and effective analytics community to drive analytics literacy and promote data-driven decision making
- Customized assessment and strategy workshops to help plan, deploy and optimize analytics investments
- Design and delivery of embedded insights for SAP business processes
S/4HANA Starter Pack
- Unified data and analytics solution in a multi-cloud SaaS environment
Data Warehouse Cloud
- Rapid deployment of focused analytics packaged solutions
- Visualization design, use case development and implementation
SAP Analytics Cloud
- Design of modern organization community and processes