At Net at Work we engage in discussion everyday with our customers and prospects looking for technology to help them solve problems.  Every conversation is unique, everyone has a specific set of challenges, but one trend that we see over and over again is they are looking to transform a manual or disconnected set of processes into an automated more streamlined process that is driven by data.

Sometimes this process is referred to as a Digital Transformation, but at the core is all aspect of managing data:

1. How we Capture Data
2. Strategy to Store and Centralize Data
3. Track and Process Data
4. Access and Consume Data
5. Protect Data
6. Interact and use Data to do our Jobs
7. Present Data to our Customers
8. Present Data to our Shareholders

Because every business is unique they will inherently create data silos that need to be manually reconnected creating data inefficiency in the process.  For example, a business may have created a data silo by excepting credit cards outside of their accounting solution.  Yes, they have accomplished the goal of capturing payments, but have created a set of downstream issues with the next 7 items on the list above.  Digital Transformation is not just about making transitions digital, but rather allowing a natural progression of the digital transaction to the next downhill data process.

We see a lot of data silos that need to re-connected or reengineered for businesses to unleash their potential through a true Digital Transformation.  Here are a few examples:

• Credit Card Processing – Implementing nonintegrated solutions that require additional effort to capture data within their ERP, reconcile the data, all at a greater cost to the business.
• Disconnected Systems and Infrastructure- This is a simple question of where data is stored and how are we going to consolidate, centralize and access the data. This is the foundation of any system; more and more companies are looking to take away this complexity and put it all in a cloud environment.  Is that the right solution for you?
• Manufacturing and Inventory Control – All to often are isolated from your PO and AP processes not to mention updating costs to ensure COGS data is accurate.  These disconnected processes may result in inaccurate reporting to your shareholders.
• Document Management – Starts with replacing paper with a digital image, so in many cases the data silo is the actual filling cabinet that is the data for any audit trail. Turning that paper to a searchable digital image that can be routed with rules and workflow is a game changer.
• Business Intelligence and Reporting – Consuming data is typically difficult if you don’t get the first three steps right, without centralizing, and updating data in real time it’s hard to provide accurate KPI’s (Key Performance Indicators) to help you accurately run your business looking forward.
• eCommerce – eCommerce done right is a cumulation of several data elements coming together to create a fantastic online customer experience.  Done wrong can be a disaster for both the business and the customer.
• Sales Tax Compliance – Tracking tax compliance in one state let alone across multiple states creates a data matrix that is constantly changing and difficult to ensure compliance. The data used to capture sales tax resides in your ERP data but must be interpreted in real-time to remain in compliance.  From State to State, transaction to transaction data needs to be accumulated to identify if you have established Tax Nexus for any state, city, county that you do business in.  Data silos are often created by entering sales data in multiple system, further compounding your risk.

The list of data silos is long and as I said somewhat unique for every business.  One of the tools that we use to help identify Data Silos at Net at Work is our Business Health Assessment.  If you can see that your organization has data silos, request a Business Health Assessment from your Account Manager to help you re-establish your technology roadmap to ensure that your digital transformation considers not only the immediate problem you are trying to solve but also any downstream problems that the resulting data may present.