The Chief Data & Analytics event in Melbourne is set to deliver its own set of insights from the gathering of data and analytics executives and practitioners. They will be keen to discuss, share and learn about the breadth of considerations one addresses while performing data management that benefits those enterprises investing in the practice.
CDO vs. CAO
The Chief Data Officer is accountable for the improvement of the data in the organisation. This is not a technical problem. When a CFO is appointed, their first job is not to replace the accounting system. However, for many CDOs, they are subjected to pressure to deliver technology projects. These will not improve data by themselves and often form a distraction.
The Chief Analytics Officer is accountable for delivering business understanding and insights through the management and analysis of data.
Although these positions are relatively new entries in the org chart, the roles are not new. For as long as information systems have been delivering data, someone has been tasked to ensure the data was understood, and was of the best quality, to produce meaningful and reliable reporting. It was left to someone in IT who took the time to understand the business, and work with subject matter representatives, to make decisions on how data would be collected, validated, integrated, and presented within software systems.
The Goals of Data Management
Data Management was once considered a technology function that included looking after the box of punch cards. Now, Data Management is defined as the planning, execution and oversight of policies, practices and projects that acquire, control, protect, deliver, and enhance the value of data. It is not about technology.
The goals of data management are to understand the information needs of the enterprise and all its stakeholders:
- To capture, store, protect and ensure the integrity of data assets,
- To continually improve the quality of data and information,
- To ensure privacy and confidentiality,
- To prevent unauthorised or inappropriate use of data and information,
- To maximise effective use and value of data and information assets.
Top Data-Centric Management Strategies
The Data Doctrine, www.thedatadoctrine.com, states that leaders can increase organisational effectiveness by focusing on data as a central, shared resource (or better still) as your sole, non- depletable, non-degrading, durable, strategic asset. To provide maximal organisational value, data- centric management strategies must adhere to the following tenets:
- Value Data Programmes Before Software Projects,
- Value Stable Data Structures Before Stable Code,
- Value Shared Data Before Completed Software,
- Value Reusable Data Before Reusable Code.
Today, the world of data availability grows at an exponential rate. Harnessing and utilising the vast quantities of data is the competitive advantage. Organisations that can engage both business and technology skills in the management of information will leverage data, creating a cost-efficient enterprise with an edge for getting value from data assets.
It is clear the Big Data V1.0 hype is over. The realisation that the means to quickly store vast quantities data does not give you the ability to understand, interrogate or exploit the data. We are seeing many organisations moving staff from analytics to data management. We are seeing emphasis on glossaries and catalogues and less on storing more and more.
The Chief Data Officer is focused upon:
- Leveraging the data asset,
- Ensuring the organisation is deriving value from the data,
- Using governance to assign stewardship, utilise standards, determine vocabularies and control data quality
The Chief Data Officer works in partnership with the Chief Information Officer to capture, protect, consolidate, integrate, and deliver the data. The Chief Data Officer directs the business and data architectures, whilst the Chief Information Officer directs the application and technology architectures.
The Chief Analytics Officer relies on this partnership, allowing the analytics team to focus on analysis and not data sourcing or cleansing.