Large data governance: a maturity assessment framework

Goals: Target
Business Outcomes: Business Results
Enablers: Support elements
Organizational Structures & Awareness: Organizational Structure and understanding
Stewardship: Managers
Data riskmanagement: Risk Management
Policy: Strategy
Core disciplines: Key guidelines
Data Quality Management: Quality Management
Information Lifecycle Management: Information Lifecycle Management
Information and Privacy: Information Security and privacy
Supporting disciplines: Support guidelines
Data Architecture: Database architecture
Classification & Metadata: Classification and metadata
Audit Information Logging & Reporting: Audit information logging and reporting

Today's markets are awash with news, anecdotes and rumors about the ubiquity of big data. Marketers are struggling to turn the massive amounts of ZB data into revenue, while data scientists around the world are learning new technologies (such as streaming, Hadoop and other NoSQL storage), business software, and cloud computing to change the world Midnight Night.

Organizations see these technologies as a factor in changing the rules of the game, especially since some of these technologies support native-formatted data without having to convert or model the data. At this point in the large data lifecycle, organizations do not always understand which data sources are valuable and do not necessarily devote significant resources to gathering requirements and sponsoring formal information governance initiatives.

It is clear that the exploratory phase of the large data "special research and Development team" project has driven business value, resulting in formal planning, with the organization turning its attention to basic issues in the area of information management:

 Are we fully aware of the responsibilities associated with dealing with large data?
 How will large data change the traditional concept of information into an enterprise asset?
What are the new requirements for privacy-related ?
 How does all these big data technologies relate to our current IT infrastructure?

All this talk of big data has given CIOs more misgivings than they can prepare. According to our experience, many organizations lack adequate governance strategies, and they believe that big data is "different", which in some ways avoids the real problem. In short, large data technologies are increasingly integrated into operations (rather than exploration) and therefore require the use of governance guidelines similar to those of traditional data management methods.

One of the first steps in implementing an information governance plan is to assess the current maturity state and predict the desired future maturity status. "From a governance standpoint, large data has all the characteristics of ' small data '," said Banu Ekiz, vice president of Business Intelligence at Akbank Information Technologies, Turkey. The only difference is the complexity and diversity of the channels for large data sources. Although organizations need to devote more effort and resources to managing large data, the benefits of business value are also more substantial. If you can analyze large data from the WEB and take the necessary measures, the profits of the enterprise will be significantly affected. In this process, the large data governance maturity model is a critical first step. ”

We took advantage of the 11 classifications of the IBM Information Governance Committee maturity model, as shown in the figure. The following is a set of sample issues for evaluating large data governance maturity:

1. Operational results

 Have you identified key business stakeholders for a large data governance plan, such as:

O Marketing Department is responsible for social media governance
O Supply Chain department responsible for RFID governance
O Legal Department responsible for data retention policy
O HR Department is responsible for managing social media related to employees
O operation and Maintenance department is responsible for sensor data governance
o The Billing department in the telecommunications industry is responsible for call detail management
o Medical information and Claims management department in the medical insurance industry is responsible for claim records governance

 Do you quantify the financial benefits that large data governance can provide? For example:

• Reduced fines and legal action risks due to data violations
O reduces the likelihood of encountering default events
O Avoid negative publicity about improper use of data adversely affecting brands
o reduced likelihood of two payments to purchase the same data set (e.g. seismic data) due to inconsistent naming methods
o Increase cross-selling and up-selling opportunities through integration of social media with master data environments
o Predictable maintenance plans, sensor data, consistent and quality asset data combine to shorten equipment downtime.