ServiceNow Strategy

From Data-Driven to “Data-Informed”: How Acorio Overhauled Our Data Model

It’s no secret that data and a strategic data model are vital to a services company like Acorio. Just like most companies founded in the past decade (or really, past two decades), Acorio has had data since our inception, but it was originally used simply as a tool to get to the information we were seeking, to help organize our business. We used to store all of our data and collateral in an online depository with no real structure in place, but just enough structure to organize the content by subject and and enforce access controls. A search for a document was similar to searching in Google; you would find a whole lot of content, but there was no guarantee it would be the correct version.

In the past few years, Acorio’s need for data has changed. We have grown 100% year over year, increasing our requirements for staffing projects and managing users in all of our tools. Without a Change Management process or CAB in place, we did what everyone requested and gave them all of the data they had access to, to fit their needs. It worked; for a little bit. Employees were happy with the data they could access, but as does everything, our data increased over time. With so much information, data began to interlace itself within other parts of the organization that we originally had not intended and had not even envisioned it to.

How could we transition away from our small company tools to an enterprise system of data management?

It’s a lot to wrap your head around. Let’s try thinking of data as puzzles. As any puzzle addict knows, these games are very complex and can become all-consuming if you don’t go in with a strategy.

Hitting Local Maximum

Hitting our “Local Maximum,” Acorio knew we had to move away from a “data-driven” way of thinking to an approach that would enable the team to be “data-informed.” Without drastically overhauling our environment, our consumption of data, and a solid understanding of all our use cases for that data, we would have ended up with 42,000 more pieces of data added to the puzzle, furthering the divergence between need and access.

To prevent this, we decided to take a look beyond what data we were bringing over to our new processes, and also start looking for the why.

As a services consultancy, we handled the issue just like we would for any client and created a project for this effort. The questions that we would ask our clients, we started asking internally too. From there, a small team of our employees developed an “Acorio Data Plan” to collect all of our critical tools’ needs and document the requirements.

Transitioning to “Data-Informed”

To overhaul our methods, we looked at our existing data and data access from three perspectives.

First, we looked at the who, what, where, when, why, and how. Who is requesting the data (Department, Practice, Service, Leadership)? What is this data used for? Where do we get this information from? When do the triggers update this information? Why are we pulling it in? How is the data interacting with the system as a whole?

Second, the audience. Who will see this data? Is it external and public facing or limited based on the person accessing the information? Is the data internal and available to all employees or do we need to expand the ACLs already defined to create either a curtain or a wall? Or could it be HR specialized data where specific privileges are required?

Third, we looked at KPIs. What was our data in the past? What does the present data look like? What future state metrics will we need to know?

After documenting requirements for all of our systems, stakeholders, and data, we compared each based on the demand.

Next came the question on whether to prioritize by speed or criticality. One might think the answer is to address the most critical requirements first, but more often than not, your most critical data points are the most challenging to set up. However, if you can find a critical and easy win, take it. Automated user imports are always the first win that most companies try to solve, freeing you up to focus on larger efforts.

Next, we looked at important pieces of information needed to run a services company. Tying in our Sales History and Pipeline will give those access to what is coming and allow key stakeholders a view into planning days, weeks, and months ahead. Along with Sales Data, we have a product list hierarchy that allows us to group our products into a functional product tree for when we bring in Projects. Projects have product data and knowing what projects we have worked on with the people who were resourced on them, allows us to better track skills and experience for staffing future projects. Below are the data elements we thought were important to share.

Highlighted Data Elements

HR Employees:

  • Uni-directional
  • Daily integration: Active Directory or HR Source Feed
  • Low Risk/High Impact

Sales Data:

  • Uni-directional
  • Via hourly Integration or Webhook Push
  • Medium Risk/High Impact
  • Insert/Update


  • Realignment of Product offerings & Sales Data
  • Product Hierarchy
  • Medium Risk/High Impact

Operations Project Data:

  • Uni-directional
  • Via daily integration
  • Medium Risk/Medium Impact
  • Insert/Update

User API Provider (External Tools/Instances):

  • Collaboration/Productivity/LMS
  • Custom Scripted API; Control Endpoints and Data
  • Users – Switches from Consumer to Provider
  • Push/Daily Import
  • Low Risk/Medium Impact
  • Insert/Update

Reshaping Business

The above changes are helping us reshape our business. While we had access to a lot of this data, it was scattered throughout the system in an inefficient and, ultimately, ineffective way.

Over the past two years, our efforts to revamp Acorio’s data have changed the way we look at our Production ServiceNow instance and how we use our data to help further the understanding of our business. Since we began the critical process, we have:

  • Implemented a Change Management process with a weekly CAB
  • Gathered requirements for many tools in our repertoire
  • Separated out previous functionality, to prepare for growth
  • Restructured integral data components that drive the core of our business
  • Automated large but tedious employee tasks
  • Imported relational data to help trigger dot-walked lookups in ServiceNow.
  • Prepared APIs to be made available for use internally and externally.

Looking to review your data or process as a part of an Organizational Change? Let one of our ServiceNow experts review your instance as an integration health check.

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