Once you have clear ownership within your core team, you need to get a global view of how data is used at your company. Once you’ve accounted for all the different moving pieces, it can be easier to:

  • Communicate clearly to the right people
  • Represent your team’s priorities to the rest of the org
  • Involve the right people in relevant decision-making processes
  • Have the right scope when planning new projects
  • Get more use out of your data by increasing its audience
  • Ensure that org-wide critical tasks and relationships have clear ownership within your Core Team
  • Keep leadership informed about the value your data provides, and the level of effort it takes to maintain it
  • Enlist resources to fill in gaps in your organization

To do this, I recommend mapping out the rest of your ecosystem. This will help break down those silos and give the individuals at your company who use the data the direction and support they need to get value out of the data.

Map out your ecosystem

This task can be surprisingly revealing, and may require some creative thinking. First, map out the obvious ones: marketers, analysts, developers and consultants. Don’t forget personalization, optimization, web development, privacy, project managers, data scientists, product owners and so on. Make sure to include executive sponsors and leadership.

List your company’s data tools

Next, list out the tools your company uses that touches data: your digital analytics tool of choice (Adobe or Google Analytics for instance), Optimization (eg Adobe Target or Optimizely), Content Management (eg Demandware), Customer Relationship Management (eg Saleforce), marketing (eg Kochava, Floodlight, Adwords), User Experience (eg Clicktale), Voice of Customer (eg ForeSee, Opinionlab)… feeling overwhelmed yet? Don’t worry, you can use this as a sort of head start:

Define responsibilities for each point of contact

For each component, figure out a point of contact- for instance, for your CRM, who will your Core team be working with? Reach out to the appropriate parties in your org. At bare minimum, send them an email, highlighting how they fit into the “Big Picture” for data at your company. If you are just now establishing a governance model, it may be worthwhile to even schedule a quick touch-base with each key person/team in your ecosystem to:

  • Make sure they know how they fit into the bigger Data-driven scheme and seek out feedback for what they’d love to get out of analytics
  • Establish who on your team is their main point of contact. Encourage them to keep you in the loop for any changes they are aware of that might impact (or benefit from) analytics
  • Ensure they have access to tools and resources (like variable maps or documentation on processes) in some centrally-located repository (like Sharepoint, Confluence, or Google Drive if need be)
  • Establish reasonable expectations and scope on new initiatives. If they understand that you have a queue for analytics initiatives, and a process to follow that may take __ weeks/months to change the solution or kick off something new.
  • Give them visibility into the type of work you currently have on your roadmap and how that fits into company priorities.
  • Ask if there are any areas in the company not currently using analytics that might get value out of being included in these conversations.

You may or may not want a regular meeting with them, but it’s important to make sure the relationship always exists, that they see the active role Analytics has in your org, they feel involved, and have a clear line of communication with you.

“Mapping takes too much of our time!”

I understand that this may require an investment of resources to get up and running, and that it may exceed the current scope of analytics at many companies. But, similar to establishing a strong data core team, this upfront investment of time and resources will, at bare minimum, help a company get more value out of its data, and may actually reduce the amount of resources needed in the long run. Establishing communication and relationships will give focus to analytics initiatives, reduce rework, and include analytics in conversations sooner (getting rid of the pre-release scramble to get analytics added and validated).

About the author

Jennifer Kunz

I'm a principal Analytics Engineer with 10 years of experience implementing digital analytics. I work with Matt Alexander and our team of Analytics Engineers to ensure our clients have quality, actionable data by helping with everything from JavaScript and app development to governance and processes. You can find me on twitter at @jenn_kunz.

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