In this post we explain our view on Data Strategy purpose and implementation. As the topic is quite broad, we break the narrative down to multiple sub-posts.
Proponents of Artificial Intelligence implementation solutions usually clash on 2 separate starting points:
a) Immediate roll-out of some highly touted package that has generated the latest buzz, and
b) Implementing a solid Data Program.
Without question, we are a staunch proponent of the latter option (b), as good data will always trump any latest magic algorithm. This brings to question, “what is good data, and how do you build it”?
Although good data has several easily recognizable properties, achieving good data requires a comprehensive and effective Data Strategy program to assess, augment, and monetize. In a series of posts, we will try to clarify how many inter-related issues come to play in producing good data from which Enterprises can then extract revenue and value. Below is a broadly described list of components to an effective Data Strategy. Follow the links on the list items for an more detailed explanation.
- Digital Transformation 1: Unifying / Streamlining IT Systems in use throughout the Organization.
- Digital Transformation 2: Data Ingestion to cloud Enterprise Data Warehouse from the IT systems in use.
- Implementing a Data Quality Program and a quality improvement action plan.
- Data Augmentation and Enrichment.
- Digital Transformation 3: Enterprise Cultural Transformation to become data-driven & data-literate.
- When the Organization grasps that Data is an Asset, it’s time to asses whether some Data could be a liability.
- Protecting Enterprise IT systems and the Data Assets derived from those systems.
Effective Data Strategy is not possible without past and continuous Digital Transformation. However, it is extremely crucial to correctly implement Digital Transformation to avoid future Data Strategy problems. Bad Digital Transformation implementation can result in massive costs, with yielding limited returns and monetization chances.