Key takeaways from Data Strategy program by UC Berkeley

The two months Data Strategy: Leveraging Data as a Competitive Advantage program from UC Berkeley Haas School of Business covers many important aspects related to formulating successful data strategy. This program complements my in-depth data and analytics experience. After completing this program, I would like to share my main takeaways from this program. These takeaways related to culture, data strategy type, integration, business value, data strategy vs data management strategy.

• Data-Driven Culture

The program highlights the importance of building data-driven culture instead of intuition and enthusiasm culture, where the decisions are based on figures and facts instead of intuition.

Also, the program highlights the importance of Chief data officer (CDO), Chief data and analytics officer (CDAO), or Chief digital officer (CDO) toward the success of the data strategy. According to Bloomberg survey: “among organizations that have created an office around the chief data officer, three primary business objectives stand out as driving forces. They are to: Improve efficiency (cited by 93 percent of CDOs surveyed by Gartner Group as a primary or secondary objective); increase competitive advantage (cited by 89 percent); and create greater customer intimacy (cited by 88 percent)”.

One of the hinders against the success of data initiatives and innovations is the organization bureaucracy. Thus, the creation of horizontal teams is essential toward achieving data initiatives and goals.

• Data Strategy Type

Many debates between data teams happens because data strategy type is not clearly defined. Debates around strong control or flexibility, single source of truth or multiple versions of truth. Choosing or balancing between defensive data strategy or offensive data strategy is important to settle such arguments.

According to Harvard business review article “What’s your data strategy? The key is to balance offensive and defensive”, defensive data strategy key objectives are: ensure data security, privacy, integrity, quality, regulatory compliance, and governance. The core activities: optimize data extraction, standardization, storage, and access. The data management orientation of the strategy is control. The enabling architecture is SSOT (Single source of truth).

On the other hand, the offensive data strategy key objective are: improve competitive position and profitability. Core activities: optimize data analytics, modeling, visualization, transformation, and enrichment. Data management orientation: flexibility. Enabling architecture: MOVTs (Multiple versions of the truth).

• Data strategy vs Data Management Strategy

As explained by Dr. Sunil Sharma , Data strategy focus on creating value out of the data by running several business cases linked to business strategy, while data management strategy focus on how to manage the data and meet compliance or protection requirements. Data management strategy complements the data strategy.

From my experience, many of the organizations have good data management strategy on how to manage the data following industry frameworks such as DAMA, however, this strategy usually handling by technical department without linking it with business strategy and goals. As a result, the business value is not quantified and not linked with business goals. That why it is important to come up with data strategy that ensures good data management practice is applied and achieve business goals and initiatives that has return on investments. Thus, streamlining the process of collecting the figures for performance measurement and business strategy is essential use case that support a successful data strategy, since it gives data team good understanding of the business strategy and performance measurement to link the rest of business cases.

• Importance of data integration and data management

From my experience I noticed many organizations invest significantly on the data generation and instrumentation, and data infrastructure, then the jump into trying to creating value from data using descriptive analytics or advance analytics such as machine learning and artificial intelligence. These organizations usually struggle on maximizing the value out data, since they pay less attention to data integration and management and treat it as an extension to applications support or infrastructure administration. This program explains the data process steps: Data generation and acquisition, Data integration and management, Data analysis, and Data operationalization and how their importance for the data strategy.

These are my key takeaways; the program covers future more valuable lessons and practical cases studies such as: Introduction to Data Strategy, Developing a Data Strategy, Data Management, Data Governance, Quality, and Security, Data Processes and Technology, Data Processes and Technology, Data Organization and Culture, and Data at the Leading Edge.

Finally, I would like to share my appreciation to the program professors for the great office hours and material: Sunil Sharma , Monique Barbanson , Richard Huntsinger , and Girish Venkatachaliah , and a special thanks to my section professor James Gray for great comments and advises on assignments and capstone project.