How to Escape the Data Bottleneck : Part 1 of 2


We are collecting and analyzing more data than ever. Companies count on it to derive real insight and create strategic plans for their business. There are dozens of tools and platforms available that will meet any company’s needs, however, many will struggle with the process of rationalizing and operationalizing their data environment.

  • “How is it that we collected all this data, invested in a Business Intelligence (BI) tool, and yet I do not feel I truly know how the sales team is performing?”
  • “I am spending so much time scanning through data before I can get anything valuable out of it. Then, I am constantly using VLOOKUP’s to combine other datasets. This takes up way too much of my day.”
  • “I need to have the TPS report on my desk by this afternoon, but the report request has been taking forever! So, if you could find solution… that would be great.”

Do you feel the same way, or ask some of the same questions yourself?

This is the result of the dreaded…

Data Bottleneck

What do we mean by the Data Bottleneck? Well, it’s a twofold problem:

  • Timeliness – Not having access to data fast enough – the data request process is slow and drawn out
  • Completeness – Not having enough of the data you need – or, incomplete datasets

Not every scenario or environment will be the same, but what I hope to provide in this 3-part series are some guiding principles that any company can adapt, apply and accelerate their time-to-value.

So, let’s talk about how to escape the Data Bottleneck, which starts with focusing on three key areas.

Clear channel of communication and collaboration

Often the root of the data bottleneck issue starts with poor internal communication between departments. If the foundation hasn’t been established, and all stakeholders brought into the conversation, it usually results in:

  • A reactive manual data analysis environment
  • The data request process being drawn out
  • Datasets that don’t reflect the company as a whole or don’t cover all subject areas
  • IT, Finance and the Business not having a mutual understanding of the data

This can be avoided by simply having better communication and more collaboration from the start.

Involving the necessary business units and stakeholders early on in the data foundation process, you will have a better idea of the core data needs throughout the company. All parties will be on the same page and can collectively contribute to the data environment, making the whole process more efficient and future proof.

Set the foundation from which to build on – Take the time to do it right

Let’s face it, all of us are busy. Rarely do we have the luxury of a clean slate from which to build the foundation of data analysis on. Still, you need to find a way.

This means investing time to:

  • Define the data needs of the business at a high level, as well as those of specific subject areas
  • Gather all relevant data sources
  • Create in-depth documentation and continue the business discovery process
  • Anticipate future requirements
  • Cultivate an environment that values data and strives to maintain this

It will be worth it in the long run when the data flow and analytics process is streamlined. Through this approach you are being proactive instead of reactive, and access to data will be available when new requirements demand it.

Understanding the business context of your data will help establish a taxonomy that is relevant throughout your company and data elements that are relatable to each other.

Strategic data modeling – Based on overall company objectives

When I use the word strategic, I don’t mean to, “boil the ocean.” In other words…….gathering data for data’s sake and pulling in too much, as this will only add to the bottleneck. Strategic data modeling is being thoughtful about which data to include and categorizing it to be useful to the business which increases efficiency.

Does this mean gathering only data that is immediately relevant? Well, no…….because that will slow down the process once a new business question inevitably arises.

There needs to be a balance in the data model between relevant/specific data, and that which is more open to meet future needs. As long as it is structured and standardized within the model, the data will be usable when it is needed, and…….most importantly: relatable to other data points.

Whenever possible, identify relationships in your data to avoid silos and allow for a more complete picture.



Let’s take a look at how clear communication, foundation building, and strategic data modeling helps you address the challenges of Timeliness and Completeness.


  • Clear communication and collaboration builds a collective understanding of your business and the context of your data which increases process efficiency
  • Building a foundation for data analysis from the start will create an environment that is proactive instead of reactive
  • Strategic modeling will increase efficiency since it is categorized in such a way to be useful to the business, and easy to analyze


  • Clear communication early in the process helps ensure your datasets meet current and future requirements
  • Defining and understanding the business context of your data helps create a relevant and relatable data structure
  • Building a comprehensive data model will provide access to data that can be combined and cross referenced giving the ability to view a more complete picture



In Part 2 we will continue exploring how to escape the Data Bottleneck. Specifically, how to start building out reporting and analysis solutions – by creating a hierarchy of detail.