How to Destroy Data Silos and Boost your Bottom Line

Today, businesses are drowning in data but starving for actionable insights.  The good news is that most departments are already familiar with some sort of Business Intelligence (BI) or Data Analytics solution with the power to make sense of massive amounts of data.  According to our recent survey with D&D Daily, 55% of respondents reported that their Loss Prevention department utilizes a BI or analytics solution and 48% reported that Operations uses this type of solution.  Other responses include Finance (35%), Marketing (29%), and HR (10%).  The bad news is that simply using a solution within a department isn’t enough to deliver enterprise-wide insights. 

One of the major issues standing in the way of transforming the deluge of big data into decision-making fuel are data silos.  When every department has their own data repository isolated from the rest of the organization, the result is many conflicting versions of the truth.  Silos have made it difficult to manage, analyze, and activate data, which hampers the identification of valuable opportunities for organization-wide improvement. 

What are Data Silos?

A data silo is a repository of fixed data belonging to one department or business function that is isolated from the rest of the organization, much like grain in a farm silo.  Silos tend to arise naturally in organizations over time because each department or business function has different goals, priorities, responsibilities, processes, and (most importantly) systems. Often, especially in legacy solutions, these systems don’t prioritize sharing data to other departments while maintaining data integrity. 

4 Signs your Data is Trapped in Silos and How it's Hurting your Business

1. Uncoordinated or Ineffective Strategies

Any plan created in a siloed environment is conceived in a vacuum with an incomplete picture. Instead of a holistic and accurate view, companies are making decisions based on information that’s contradictory, misleading, or altogether wrong. In some cases, information is so untrustworthy that key business decisions are made without utilizing data as a guiding factor. The most intelligent business decisions are made when experience is combined with solid, reliable data analytics.

2. Poor Data Integrity and Duplicate Information

Data silos also negatively impact data integrity.  For example, when two or more silos contain the same data, their contents are likely to differ.  This creates confusion about which repository represents the most up-to-date or accurate data and causes inaccuracies in reporting.  Without the benefit of organization-wide standards and goals, data valuable to one department may not be recognized by another. As a result, the information and data output of these systems may render rich details but cannot be used accurately.  Communications between departments is key to knowing what data is available and how systems can be utilized by each department to positively impact the business.

3. Lost Opportunities for Improvement

Data silos bury data that could otherwise reveal opportunities for improvement or growth.  For example, sharing sales and marketing data can reveal promotional performance, unearth patterns in user behavior or reveal market trends that provide a competitive advantage.  POS history combined with video surveillance, and inventory data can reveal a plethora of theft and fraud cases that could have otherwise slipped through the cracks.  Losses do not just come from theft; Broken processes impact the bottom line of a business much greater than any thief could.  Fixing a broken process could return hundreds of thousands to millions of dollars back to the bottom line.

4. Broken Customer Experiences

One of the most common symptoms of siloed data is a disjointed customer experience.  The omnichannel customer experience is quickly becoming the gold standard in any industry.    But when your customer data is stuck in channel silos, your customer experience will never be as seamless, consistent, or optimized as modern consumers require.

Create a Single Version of Truth

The end goal of any advanced data analytics strategy is to make a company data-driven – that is, to consistently benefit from reliable data in an organization-wide manner.  Of course, this is easier said than done and often requires a cultural change throughout the company.  Below, I’ll outline three steps to begin the process by breaking down existing data silos.

How to Breakdown Data Silos

1. Appoint a Data Champion

Most organizations require executive-level support to prioritize and execute a data strategy.  It’s beneficial to have a single data champion that is responsible for tying the disparate departments together.  Ultimately, there should be someone who interacts with each of the leaders within each department to distill the information and prioritize data integrity while transforming the data into valuable insights.  Depending on the size of the organization, this responsibility may fall to the Chief Data Officer, Data Scientist/Analyst, or a Head of Insights.  This person should be responsible for helping drive the decisions governing steps 2 & 3.