If You Can’t Measure It, You Can’t Improve It. This quota from Peter Drucker is true more than ever before in the age of digital marketing. Perhaps more important than measuring it though, it understanding what you should be measuring. With so many metrics from impressions, to click through rates, post engagement to say nothing of the plethora of demographic terms thrown around, it can be easy to get lost in the maze of advertising metrics and data. This is not helped by the sheer number of platforms out there and the fact that many of them don’t export their data in a standardised manner making cross-platform comparisons difficult. The most import thing to remember when trying to measure any advertising campaign is the define the campaign goals and how those will be measured before the campaign is designed and executed. This way you can standardise the Key Performance Indicators (KPI’s) and understand how they interact with one another. Its also important that these KPI’...
Data Governance is a set of practices and processes that help ensure the formal management of data assets within an organisation.
Data Governance VS Data Management
Data Governance is often thought to be the same as data management, however, data management is the actions that an organisation takes into account in order to execute a business’ data management framework. To add to that, data governance acts on the decision-making functions that hold authority over data management. In practical use, a company using a CRM system that holds information about their employees is a form of data management, whereas data governance put in to practice means that the team overseeing this department has outlined how this information in the CRM will be used by employees.
Examples of Data Governance
Creating a data governance framework:
By creating a data governance framework, businesses will be able to determine how they will secure, manage and distribute their data. The framework outlines and informs employees where data is stored, who is managing it and who can distribute it.
1. Create a goal.
- Define the purpose of why the organisation needs a data governance framework and what it aims to achieve. This includes establishing short-term goals, long-term goals, and KPIs that set out to analyse whether their framework is effective.
2. Establish a data governance team
- An organisation must appoint people that will oversee that the practices and processes for storing and distributing data are incorrect and they will also be in charge of making decisions in relation to managing data governance.
3. Decide on a data model
- Establish a hierarchy on who will be storing and distributing data. The model should fit the goals set out for the framework therefore, rules and regulations on how data will be shared to employees and what data will require authorisation prior to being released. (ie. top-down, bottom-up, collaborative model)
4. Agree on a distribution model.
- Some data will be more confidential than some, therefore privacy is essential. Decide who will have access to the data, how data will be distributed, and in which channels it will be distributed to.
Sources: DBS E-learning Material on Data Governance, (Talend, n.d.)
Author: Jerli Padios
keywords: data governance, data management, organisation, authorisation, data
Comments
AIB have stated in an interview there company is coping with remote working by comunicating through Zoom, Microsoft teams and email “People are used to working collaboratively and have nurtured relationships over time. Those relationships have generally been developed through physical interaction and communication. Moving that 100 per cent into a virtual world has been the biggest challenge.”
I believe CRM's will be the strongest tool to communicate with teams and create innovation within company's staff. When sharing Data one of the main concerns is security threats and Data breaches. Which CRM's offer the strongest protection to threats online?