How to Overcome Data Silos for Improved Insights

Data silos aren’t just a tech problem - they’re an operational bottleneck that slows decision - making, erodes trust, and wastes millions in duplicated efforts. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North break free by shifting how they approach ownership, governance, and discovery. Here’s the 6-part framework that consistently works: 1️⃣ Empower domains with a Data Center of Excellence. Teams take ownership of their data, while a central group ensures governance and shared tooling. 2️⃣ Establish a clear governance structure. Data isn’t just dumped into a warehouse—it’s owned, documented, and accessible with clear accountability. 3️⃣ Build trust through standards. Consistent naming, documentation, and validation ensure teams don’t waste time second-guessing their reports. 4️⃣ Create a unified discovery layer. A single “Google for your data” makes it easy for teams to find, understand, and use the right datasets instantly. 5️⃣ Implement automated governance. Policies aren’t just slides in a deck—they’re enforced through automation, scaling governance without manual overhead. 6️⃣ Connect tools and processes. When governance, discovery, and workflows are seamlessly integrated, data flows instead of getting stuck in silos. We’ve seen this transform data cultures - reducing wasted effort, increasing trust, and unlocking real business value. So if your team is still struggling to find and trust data, what’s stopping you from fixing it?

Are you struggling to provide accurate analytics because of multiple disconnected ERP systems? Do you think the best way to solve the problem is to physically consolidate these systems? If yes, you should reconsider. Large-scale, multi-year ERP consolidations are a consultants dream come true, but in my many years of experience, I've yet to see one complete on time, or on budget. Many only partially complete, and for those that do, the victory is short-lived because the acquisition of new companies doesn't stop. While a Gartner analyst, I spoke often with CDOs or CIOs who had inherited an ERP consolidation project that had gone sideways, where months (or even years) had passed with companies spending fantastic sums of money on consultants, but with very little to show for their efforts. Others had been convinced by vendors that moving all analytics to the cloud would solve this problem, but it doesn't. That's because having all of your data in a single place won't solve a problem caused by disparate business processes and governance policies. So what's a better solution? Master Data Management (MDM) platforms are purpose-built to break silos and enable widespread sharing of data that matters the most to your company - like your customers, vendors, materials, and employees. MDM allows for the configuration and management of a set of shared business rules that allows you to virtually consolidate disparate, siloed systems instead of physically consolidating them. Other advantages: ✔ MDM's can support both analytical and operational use cases. Not only will you have more accurate consolidated reports, but you can also use an MDM hub as a source of truth for widely shared data in downstream operational systems (like CRMs, HRMs, etc.). ✔ MDMs can be delivering significant value supporting analytical use cases in weeks - not months or years. ✔ Using MDM's to break silos, like multiple ERP systems, is FAR less disruptive and less risky than a physical consolidation of systems. ✔ Should a physical consolidation of systems be required, by acting as a 'bridge' between the current and future states, MDM's are an extremely useful tool to support ERP consolidations - but with far more flexibility than building some DIY data staging area between legacy systems. ✔ MDM's allow organizations to benefit from the centralization of some shared data (and supporting business processes), while still allowing individual business units autonomy over their 'local' data. ✔ For companies struggling with governance, MDM programs are a great way to operationalize governance efforts around a limited set of high priority data to deliver highly tangible benefits to an organization. If you have multiple ERPs - or any other challenge related to data silos - MDM programs, supported by robust MDM technology, should be at the top of your list of possible solutions. #cio #erp #cdo #erpmodernization

Moving towards a staff-level data practitioner? The key skill that will make you successful won’t be any code you deploy or an architecture diagram you design—instead, it’s influence. More specifically, your ability to influence beyond your team is the key to showing how you can be a 10x multiplier within the organization. Here is an example from my career where I had to influence a non-technical team to improve data quality significantly. We had multiple data silos: product analytics, ARR from Salesforce, and time-tracking data. By combining this data, operations could determine which accounts had high support costs but low ARR, helping us pinpoint problem customers and identify those best aligned for expansion. Everything seemed to be working well until we noticed a major issue: when we broke down the data month by month, it stopped making sense. The culprit? Customer Success (CS) was responsible for time tracking, which was supposed to be filled out weekly. However, the data revealed that it was being inputted and backfilled only during the last week of each quarter. This inconsistency was skewing our metrics and undermining the accuracy of our insights. To solve this, I needed to get the CS team to care about entering their data weekly. The first step was getting the CS leader on board. I used the new ARR metrics to secure an introduction and explained how accurate time-tracking data could help their team better balance staffing across accounts. During our conversation, I quickly realized that the CS team was feeling the impact of unbalanced workloads—some team members were working excessive hours on certain accounts, while others didn’t have enough work and were worried about showing value. With this pain point in mind, I proposed a solution. I identified a couple of CS team members who were feeling the strain and enlisted them to help develop a proposal. I shared one of my proposal templates, and together, we filled it out with their domain knowledge and my data insights. I highlighted key statistics and created visualizations to support our case. Once we had a solid proposal, I had the CS leader review it, and then the CS team members presented it at a broader CS team meeting. They explained why it was in their best interest to start inputting time-tracking data weekly, how it would solve the issue of unbalanced working hours, and how it would showcase the success of the CS team to the wider business. The proposal received strong buy-in, and shortly thereafter, the time-tracking data improved dramatically. Not a single line of code was deployed, yet we achieved a significant improvement in data quality for a key business metric that directly impacted revenue. This experience reinforced a critical lesson: as technical professionals, we often overlook the power of people and processes as levers for change. Influence is a key skill as you grow into a technical leadership role, and it’s often what drives the most impactful outcomes.

"You stay on your side of the fence, and I’ll stay on mine!” Ok sure that works for feuding neighbors but NOT when it’s departments in the same company! Data silos are when teams hoard their own systems, creating isolated data pools that nobody else can touch. This just screws up your ability to make smart decisions. So, how do you fix this mess? First, get a solid data governance framework going—set policies for quality and access. Then, roll out data integration tools to bring all that info together. Think data warehouses or good old ETL. But it’s not just about the tech. You gotta foster a culture that values data sharing. Get those cross-functional teams working together! And for crying out loud, train your people! They need to understand why sharing data matters and how to use the tools. So stop playing fence wars and start breaking down those data silos!