Starting With a Data Assessment
Analytics, machine learning, and AI are all topics that are in the crosshairs at large organizations everywhere. Being able to properly predict and react to market changes and real-time news updates gives businesses a competitive advantage. The tools that supply decision-makers with those insights are built on top of a data foundation – which enables organizations to understand what is happening in their business, learn from their mistakes, and ultimately anticipate future opportunities for their business.
Therefore, it’s important to start with a data assessment. Conducting a systematic data assessment gives you insight into where your data stands in managing its information effectively. It is the first step in creating a roadmap to consolidate data from various systems, apply consistent metadata, and maximize workflow, security, and your return on investment. The data assessment should explore the following questions:
What is your data?
- What information is being captured in your data?
- What format is your data? Is it accessible? What about all the good old data in the archive?
Where is your data?
- Where is your data stored, both physically and digitally?
- Which teams are responsible for data storage? Who is managing this?
How will you access your data?
- How will you distribute your data?
What are you trying to do with it?
- What is the end goal for your data endeavor? Search? Analytics? Consumer experience? AI?
How are you able to use your data?
- What state is your data in? Is it authoritative? Is it accurate? Is it being managed?
- What type of data cleansing needs to take place? Think of using standardized terms or a controlled vocabulary (more on this in later chapters).
- What can you do, and more importantly not do with your data? Think of licensing and rights management, and compliance issues.
This assessment gives you the opportunity to map out where your data foundation is strong, and where it needs additional support. It gives you a roadmap for action, allowing you to be proactive in how you think about your DAM data.
As you build out your data foundation, it’s important to remember it must account for both structured, quantitative data, and unstructured, qualitative data. It needs to be accessible to the multiple teams in your organization so they can apply it in their own applications, whether that’s within the DAM or an integrated system.
Your data foundation should be three things:
- Do not create a data foundation in a bubble
- Look to a few key stakeholders outside of the power users
- Think of indirect technologies your implementation will affect
- Collaborate on how to name things to ensure adoption
- Everyone needs to be speaking the same language for findability
- If two teams call the same asset by different names, confusion will prevail
- If you set a data foundation but don’t enforce it, then it is all for nought
- People will go back to calling data as their first reaction, not as the organization agrees to classify it
- Set a standard of procedure, set up governance teams, and hold people accountable for their data
Building a data foundation that is cross-functional, consistent and enforced will help you optimize the content lifecycle, and this data assessment will start you on your journey.