Data Strategy

Data Architecture describes how data is processed, stored, and utilized in a given system. Data architecture defines the types and sources of data needed to support the business, in a way that can be understood by stakeholders. One of the most important tasks that a Data Architect is often asked to help with is the creation of an Enterprise Data Strategy. The data strategy lays the foundation for the data and information architecture. 

Data and information is becoming more and more important as it will be an essential and integral part of future business models. Data and information from all over the enterprise, combined with data and information from external business partners and other sources needs to be managed. Big data initiatives will explore a huge amount of data to gain new insights with the objective to further optimize processes and decision making or create new services and business models.

The potentially disruptive shift driven by data and information centric initiatives opens up new markets and improvement potentials in many different domains including products, supply chain, manufacturing, production, sales and marketing, finance and accounting or research and development. Data and information and their “smart” management is one of the enablers for the digitization of organization.

“Top-performing organizations use data for competitive advantage and exploit more internal and external data. Technology doesn’t separate top performers from the rest of the pack; data governance and the alignment of data processes and business processes make the difference.” (Forrester Research, 2015)

The strategic recommendation is to have a joint approach of business and IT. The idea is to develop the data and information architecture with a professional data management and a flexible governance organization together with concrete use cases. This effort will generate sustainable business value and prepare the data and information landscape for future demands.

The Enterprise Data Strategy is:

·       Actionable

·       Relevant (e.g. contextual to the organization, not generic)

·       Evolutionary (e.g. it is expected to change on a regular basis)

·       Connected / Integrated – with everything that comes after it or from it

Reference

http://dataconomy.com/why-organizations-need-a-data-strategy/