Using Data Synergy to Combat Information Silos

As an organization grows and roles become more specialized, it becomes increasingly difficult to grasp how other parts of the business work together. This leads to a decrease in cross-collaboration and an increase in isolation amongst different job functions.  Teams working in isolation strive to best optimize their own workflow and KPIs. Without the context of the other business functions, this results in different departments inadvertently working against each other. Eventually, if left unchecked, it will become a major problem for the organization. So then, how can we remedy this phenomenon? 

At their root, information silos form due to a fundamental lack of understanding between teams.  While it might be difficult or impractical to impose cross-functionality to teams, that shouldn’t be the case from a reporting/business insights point of view.  In large organizations, teams and their respective skill sets inevitably, out of efficiency and necessity, will become specialized. Enterprise level reporting however, should not suffer from silo-ing.    This might seem intuitive but frequently reporting and analytical tools are generated with only a single facet of the business in mind. As a result it becomes difficult to combine existing reporting if the reporting is even compatible at all. Ultimately this limits the type of insights that could be generated and misses an opportunity for different parts of the business to understand each other. 

The Challenge

Let’s explore the following situation and its potential outcomes. The company is running an ad campaign promoting a new product.  The customer decides that in conjunction with this promotion they would like to place an order of said product, however the shelf space designated for this item is atypical and requires the product to be repacked into different piece counts.  There is also an upcoming holiday that this product ties in too and the customer would like to have the packaging to be updated to include some of the coloring typically associated with the holiday. This is typical and happens frequently throughout the year so as a result there is a dedicated team that handles these requests with a well-established process/infrastructure. Based on the complexity of the changes and considering the established operating procedures the entire project should take 5 weeks to produce and deliver.  The customer wanting to be better prepared for the upcoming holiday wants the product to be delivered in 3 weeks. This sale accounts for a significant portion of the monthly sales target and would be a huge win for the new product. Not surprisingly the logistics team responsible for the delivery of the product is concerned and initially pushes back, citing lead time issues. Consider the possible outcomes detailed below:

Outcome 1: The logistics team’s concerns are over-ruled, the sale is committed to, and delivery is made on time. To meet on-time delivery production schedules are changed, overtime is utilized, and expedites are used, eroding the profitability of the sale and causing downstream issues with other projects. 

Outcome 2: Logistics team’s concerns overrule the sales team and no sale is made.  This results in the sales team having to scramble to make up the sales gap and potentially miss their target. The new project also loses early visibility 

Outcome 3: After a lot of back and forth between the customer, sales team, and logistics everyone agrees to a 4-week delivery date but by then 2/3rds of a week has already gone by.  Logistics is able to accommodate with less disruption but there is some additional cost for expedites and overtime.

The Hurdles

There are quite a few things to unpack when considering the challenges, solutions and optimal solution for this situation from both a technological and process standpoint.  The interdependency of which will be made clear as we further delve into the details. To start off, being that this is already an established process the sales team knew that the speed in which the customer wanted the project completed and delivered wasn’t possible under normal circumstances.  Viewing this from the Sales perspective, with this project making up a significant part of their monthly target, it’s an easy decision to make the delivery the number one priority. However, from a logistics point of view the project skirts the line of what is physically possible. The overtime, expedites and production rescheduling required to deliver on time not only would be very expensive but could have other downstream effects potentially putting other projects at risk.  As far as the logistics team is concerned it doesn’t make sense to make a sale and execute a project that will cause that business to lose money. To summarize this sale is great for the Sales team and their KPIs but awful for the Logistics team and their KPIs. This is a situation that frequently arises in a business where two teams with opposing targets are in conflict. The correct choice is these cases is always what’s best for the business overall, but the question is how something like that is determined. 

To make an informed decision its clear that some data needs to be gathered.  Lets break down the information we would need from the sales perspective. The goal is to know what the real benefit of the delivery of this project would be to the business top and bottom line.  Since this is a project involves a new item and is launching in conjunction with a marketing campaign, to fully capture the benefit of this project the value generated from a marketing standpoint needs to be considered as well. Here in lies our central challenge, that will become a theme throughout the course of developing an optimal solution to this problem.  To understand the impact of the successful delivery of this project we need to combine historic sales information along with historic marketing information. The issue is the information needed is scattered amongst different systems. In this state the only way to find the real impact of this sale data needs to be downloaded and combined manually and an ad hoc report needs to be generated.  A classic example is combining the data into a spreadsheet and further relating the data using vlookups and other functions of that nature, then creating a pivot table/graphs. This method has many downsides. Its time consuming, requires significant manual intervention and are highly error prone. The report is also likely designed for a specific use case so the time invested doesn’t provide any payoff elsewhere in the business.  In the case that a report of this nature does become a tool that is regularly used they rely heavily on the person who built them. A reporting environment built like this becomes reliant on a few key people, making a key function vulnerable to turnover. This results in a phenomena where as time passes an organization actually lose insights into their own business rather than growing them. There is no bigger waste of resources then solving a problem that was already once solved. 

The situations is similar with the logistics portion of the analysis.  The actual movement of goods and cost associated with that is stored in one system while the cost of production and overtime is housed in another one.  Actual lead times (from historic information), expedite cost, and production costs are all needed to complete the analysis and will need to be combined with the sales and marketing information to decide on how the business should proceed with the project. 

The End Goal

If we again take a look at the above three outcomes its clear that outcome 3 is the most ideal conclusion but is there a better option? Ultimately everyone has their own targets that they are measured against and as a result different teams sometimes might have act in a way that benefits their targets while hurting someone elses.  The more isolated a team is the worse this line of thinking can become until eventually choices are being made that are bad for business overall.  Once a silo mentality takes hold it also becomes apparent in the way information and reporting systems develop.  Teams start becoming more guarded and secretive of their reporting or developing tools and reports that are incompatible with other existing reporting, making it impossible to develop cross functional reporting.  The business loses out because they were unable to take advantage of potential business insights that could have come from cross functional views. In this situation the sales team is incentivized to sell as much as possible with little insight to the downstream 

Short term goals:

Sales team has automated timeline projections that takes advantage of historical information broken down not only under standard operating procedures but also including the cost impact of different timeline lengths.  The end operating state allows the sales team to immediately see ideal timeline for a project as well as how different timeline length effect things like the margin of a project.

Logistics using sales information will be able to have production priority and scheduling generated allowing logistics to quickly change production schedules to accommodate.

Long term goals:

Data science and machine learning to give more granularity to project types and to better dial in project lead times.  Predictive tools could be further extended to recommending sales strategies and calling out risk to existing jobs. The longer data is collected properly the bigger the potential for insights to be determined. 


Using historical data you can predict that it is in the organization’s best interests to come up with a solution that will benefit both the logistics team and field sales team. The optimal solution comes as result of a cohesive data aggregation strategy. Not only does this do the job of combatting silos, it eases the communication gap between teams that are involved in completely different parts of the business. Its orders of magnitudes easier to for different parts of the business to agree to something if the information is clearly presented to everyone.  So much time is wasted while different business functions send endless emails to each other or meetings come to a halt because not everyone is on the same page. Despite the advantages of cross functional reporting this facet is often over-looked when an enterprise revisits/expands their digitization efforts.