About the whitepaper
This whitepaper outlines the framework a technology decision-maker can use to select the right data processing approach for their organization. It discloses strategies for approaching data processing tasks within an organization based on its culture.
The digitization of business, society, and life at all is the main driver of data generation. The ecosystem is constantly evolving due to the complexity and speed of data arrival and aggregation. The most common business outcomes for enterprise is maximized ROI, reduced risk, and optimized operations.
However, the best business insights no longer come from traditional sources such as inventory, sales, and personnel data. The next generation of insights is often hidden across countless, often unstructured data points from myriad sources and systems. Extracting the insights requires specific blend of skills, tools, and strategy. Fortunately, there are plenty of modern data processing platforms to help you overcome this challenge.
The company culture is usually at the core of this strategic business decision. This article helps you succeed by navigating the options that enable a wide range of skill sets and personas to derive insights from data. Connecting your culture to the right tools is crucial when it comes to gaining competitive advantage by delivering better value from raw data.
Every organization is unique and requires a tailored approach to data processing. It would be irrelevant, for example, to compare an eCommerce store which built all of its processes in the big data era with a brick-and-mortar store which has been running legacy systems for many years. The KPIs and expected outcomes for the organizations would be different. Recognizing this, we will define the types of organizations and map out realistic expectations for each.
Table of contents
- The data value chain
- Ingestion: Setting the stage for data processing
- Data processing: A decision based on your organizational landscape
- Organizations driven by data analysts
- Organizations driven by data engineers
- Blended organizations and addressing data scientists
- Building your data-driven organization with Google Cloud
- Painting the picture: Data analyst organization
- Painting the picture: Data engineering/science organization
- Painting the picture: Blended organization
- Conclusion