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Data Rebirth

Data takes many shapes and sizes and often occurs in discreet places in a manager’s daily world.  Sales figures, revenue forecasts, advertising spend, online transactions, site visits, store traffic, supply chain, CRM, retention data, PR/marketing executions – all of these related facts, figures and KPIs live in so many different places, it’s enough to make your head spin.

The evolution of data visualization and consolidation tools is in full stride and the results are apparent among those who have fully realized the power of consolidating their disparate datasets.  Becoming savvy and receptive to figuring out how your data and reporting platforms can become a single ecosystem of information is a critical competitive edge in the near term and will be the standard over time.

Brands with truly integrated data visualization platforms can see their full business data mosaic.  Without the need to stitch together reports based on multiple data streams, teams can spend their valuable time finding insights in data that has already been ingested, curated, integrated and visualized via an automated visualization tool. Additionally, in the right platform, you can build “joined” datasets for advanced analytics and (potentially) programmatic artificial intelligence.

As more and more businesses become privy to the advantages of building a single-view of multiple data sources, the number of tools on the market also grows. Domo, Google Data Studio, Einstein Analytics (Salesforce) and a host of others all seek to help businesses achieve a more efficient way to analyze data to inform their decisions – but the proper implementation of these tools is a critical part of reaching this end goal.

Proper implementation and use of these tools can save significant time, manpower, and ultimately overhead -- increasing the quality of information you have to make educated decisions and decreasing headcount needed for time-intensive data assembly.  However, improper implementation can often create inefficiencies, missed opportunities or a narrow view of the many interconnected parts of your business.

To begin the journey of finding the right platform, and ensuring proper implementation, some key questions to ask yourself, your stakeholders, and your implementation team are:

What data do you want to use?

It’s important to start off the project with a digestible set of data sets to integrate and build from there. While there can be a good amount of datasets that you want to be integrated, creating too expansive a list to start can interfere with the initial build goals. Identify your key metrics (see question #4) and build out your datasets from there.

Do your vendors use APIs?

If you’re like most businesses, your data comes from different vendors and programs. Check to see if these vendors have APIs that will connect with a data visualization tool. If not, is there an automated file generator that can programmatically send the file to a certain location? Making sure all the data you want to use has an automated export out of its platform is critical.  If they don’t have an API or are unable to deliver an automated file export and don’t mix well with data consolidation platforms, then that should be considered when understanding a platform’s true capabilities. Consider negotiating some kind of FTP export program as an add-on to your license renewal if an API is not available.

Who are the audiences?

Most good data visualization tools have provisioning so that the C-Level can see one set of reports, Sales Team another, Marketing another, Customer service another, and so on. Each group has its own goals and KPIs and generating the right data ecosystem for each makes happy users. In terms of implementation, make sure the main stakeholders are involved and they are able to express what data views they need. Additionally, make sure you have a good feel for the capabilities of the BI/Data Visualization tool in provisioning views by user log-in.

What are the key metrics?

A data visualization tool or section of a tool can become unwieldy if there are too many KPIs and dimensions to look at the KPIs against. Try to prioritize and boil down into key sections of your platform and grow from there. Defining key metrics for each audience will help determine what information will be provided for different audiences, and how these metrics ladder up to the broader objectives of the company as a whole.


Benchmarks are always helpful visually and analytically. Know what to expect to see for specific measures that you are tracking or building norms in the platform are helpful for focusing on outcomes and as a result, can help simplify the early stages of your build. You may not have benchmarks to start, but using the platform to help build these benchmarks and then integrating them into the platform gives you extremely helpful context.

What type of tool should I use?

Scope and complexity are critical when you are assessing this. If your data is more marketing driven and limited to a specific set of data feeds related to marketing and media, then perhaps a less evolved tool can fulfill your needs.

If you are looking for a truly “enterprise platform” in which you are also ingesting sales, revenue, CRM, supply chain, retention tracking, accounting, etc., other more evolved tools are a better fit.

Be careful though, many of the more evolved data visualization tools cannot process disparate data. They will require you to curate and onboard the data into your own database first and will still require a data platform to help ingest all of the different datasets before being able to visualize and transform. At the same time, other tools have built-in “connectors” and more flexible ETL (Extract, Transfer and Load) systems that allow you to bypass data-basing platforms and onboard APIs and feed directly into that tool – saving potentially hundreds of thousands of dollars.

If you are interested in pursuing data visualization in a way that will work for your business, making sure you have a team of both knowledgeable analytics and technology experts is important.  If they are not involved, you may be asking the data visualization/consolidation vendor the wrong questions and you can end up signing up for a tool that you cannot use, or does not deliver on your needs.

If you don’t have the in-house expertise to find and implement a data consolidation and visualization tool yourself, an agency or analytics shop can be a viable option. Having an objective outside source to dig into your data, learn how various teams are using this data, and finding connections between them, can prove a more efficient way to determine data sets to use, provisional views to plan for and more.

Whichever path you choose, it’s clear that organizations often struggle to sift through raw data in a way that is quickly actionable. Data consolidation and visualization tools, and the proper implementation of them, can help everyone from sales teams to customer service to the c-suite see and interpret data, understand trends, and act upon them faster than ever.

“It’s really easy for us to take for granted how much data we can use now in our business, but it wasn’t even 2 years ago that we were all squinting in the fog and guessing a lot.”-

HMR Client Marketing Lead

For more information about Primacy's Data Analytics practice, or to get started on a Data Visualization project at your organization, contact us here