Data is one of the most strategic assets for Business Entities and in the era of digital transformation, Data is the “The New Currency” or “The New Oil.” Organizations are looking forward to reusing their data for a myriad of applications like mining or analytics for more profound business insights. They need to ensure their Data Organization has the capability to support it and this is achieved by undergoing Data Disruption.
What is Data Disruption?
Data Disruption is a strategic and sharp transition from Current Data Organization to the Desired Organization, one which is more supportive of Mining and Analytics. Hence Data Disruption in the context of Data Management is centered on the acronym APE standing for Analyzing, Prioritizing, and Execution. In this article, along with Data Disruption APEs, we highlight DMM, Maturity Level, Road Map, and Change Management that allows organizations to become more data-driven.
Figure 1: How Analysis, Prioritizing and Execution (APE) should be combined to achieve successful Data Disruption.
At the very fundamental level, Data Disruption has to be backed by a business case which will clarify the need for Data Disruption and also will be instrumental in getting Budget Approvals and will continue to be the constitution until Data Disruption is achieved.
Beyond the business case, Data Disruption starts with two key steps. They are
Analyze the Current Data Organization
Analyze the Desired Data Organization.
The analytical output from above leads to the architectural component Data Maturity Model (DMM) which defines traits of the desired data organization in terms of:
Data Governance:
Defines key data organization roles such as Chief Data Officer, Data Stewards, Data Custodians, and Data Owners
Meta Data Management:
Defines “Data about Data” usually through standards such as Dublin Core Meta Data Properties.
Master and Reference Data Management:
Identifying Data Silos, Data Exchange Patterns, Lineage, and Dependencies.
Defining a unified data platform, tools, technologies, and high-level architecture.
Data Quality Man
Data Quality Management
Define Tools, Technologies, and Strategy to enforce rules regarding Data Quality.
Data Security and Compliance
Define Tools, Technologies, and Strategy to achieve Data Security and Compliance.
While defining Data Maturity Model, organizations also represent the Maturity Levels which determines the role of data in business activity. It differs from one organization to the other. Generally, there are five maturity levels an organization can use to grade itself and its business entities and lines of businesses as outlined in the table.
Figure 2: Data Maturity Levels with Maturity Level 1 being lowest and 5 being highest
Once the DMM and Maturity Levels are Analyzed, Road Maps can be Prioritized which will define the order and timeliness of changes that the organization needs to Execute. Change Management is a broad topic but will involve some principles and models such as the McKinsey 7-S model or Kotter’s Theory Method or any other that the organization is already using.
Engage with SAAL to understand how we can help with your data-disruption needs helping your business to transform from data-based to data-driven.