Extracting insights from data may seem like old news, but do not get too comfortable. Machine learning and other advanced analytics technologies are set to bring an unprecedented impact on how real-time, contextual data is used business-wide.
Get ready for this new era with the data science framework from SAP.
A powerful combination of digital technology is sparking a revolution within business decision-making. Seemingly endless volumes of data are created every day. Cloud solutions are adding powerful processing capabilities. Now, the full potential of machine learning, artificial intelligence (AI), and predictive analytics is continuing to unfold.
Never before has the use of data progressed so quickly and with considerable relevance and importance. Gartner predicts that augmented analytics and continuous intelligence will be dominant drivers in improving decision-making with bias-free insight based on real-time, contextual data.
Awareness around the impact of such intelligent technologies is undoubtedly growing even though many executives and business leaders are not yet experts at applying these capabilities pragmatically. With a data science framework of the right methods and guidance, time-consuming, high-cost, complexity-inducing initiatives can become a vehicle for meaningful insights, experiences, outcomes, and competitive advantages.
The Path to Transformation is as Critical as the Technology
Based on experiences with customers across a broad spectrum of industries, the Business Transformation Services group of SAP Digital Business Services developed a methodical toolbox. Each item in this portfolio is designed to foster data-driven transformation with machine learning, AI, and advanced analytics.
The inspiration behind SAP’s framework is simple: to provide answers and solutions to fundamental questions and challenges that most companies face when adopting intelligent technology.
Critical topics covered include:
- Enabling capabilities for analytics, machine learning, data science, and Big Data
- Using data assets as a competitive differentiator
- Determining whether data is valuable and how to impact the different aspects of the business with it
- Knowing when an organization has the strategy and mindset that signal readiness for data-driven transformation
- Choosing the right use cases to deliver the most value
Our data science framework puts our customers on the right path by sharing data-driven approaches, considering analytics capabilities, and identifying and prioritizing appropriate business cases. It can be used in all lines of business across all industries, and ultimately delivers a road map to shape a holistic data strategy. More importantly, the framework offers clear, actionable explanations to keep customers competitive.
Foundational Data Framework
A data science framework from SAP consists of five components with each one serving as a building block for theoretical knowledge, analytical maturity, identification and prioritization of use cases, and validation of selected scenarios.
- Hands-on training: Acquire fundamental knowledge and gain a greater understanding of analytics, machine learning, and end-to-end machine learning workflow.
- Analytical maturity model: Gauge current analytical capabilities based on a self-service questionnaire. Customers can then develop a road map to enhance digital skills based on the assessment of seven possible action areas: data, enterprise, leadership, targets, analysts, technology, and analytics techniques.
- Use case identification: Explore common use cases along the entire value chain. This step enables customers to follow a step-by-step process for qualifying a business problem as a machine learning problem
- Use case prioritization: Rank potential business scenarios and use cases by evaluating use cases individually through a scorecard with appropriate key performance indicators.
- Proof of concept: After trying selected scenarios, customers can develop initial prototypes of their use cases by using our analytics engine free of licensing charges.
These data science framework components are supported by two pillars that provide the support, guidance, and expertise that SAP customers require.
- Analytics engine: Take advantage of various analytics and machine learning tools from SAP and our open source community. This engine serves as a quick and easy way to start innovating and developing individual analytics scenarios and accelerate the time to value of an initial proof of concept.
- Services and consulting: Experience a deeper engagement with selected services, when desired. Our consulting services team can guide tool evaluation, management consulting, and extended training and support showcases and provide references.
Value-Driven and Result-Oriented Engagement
A from SAP offers a unique perspective on the strengths of a business, as well as the critical success factors needed to capitalize them and address any gaps. Most importantly, it gives customers the freedom to set reasonable ambitions, enhance capabilities to meet current expectations, and prepare for the future.
Next week in the Enterprise Architecture and Landscape Strategy series, consider why a different approach to data management can further increase the value of business intelligence.
Ramin Norousi is a senior data scientist and business consultant for SAP Digital Business Services.
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