Having good data is no guarantee that a business will make good decisions. But being saddled with low-quality data at the most critical points of strategic decision-making is a recipe for failure — and a lot of lost money.
How much? Industry experts at Gartner estimate that poor data quality costs businesses about $13 million a year. And here’s the hard truth: Even businesses with high-quality data can still lose ground if that critical information is stuck in silos.
Luckily, things are changing fast. Today’s business leaders are increasingly turning to platforms that use timely intelligence signals and scalable, automated workflows to free data from its silos.
To get the full picture, you’ll need to understand:
- How data exists today in the majority of businesses
- Why the data management status quo is almost certainly harming your business
- How to break the restraints holding you back from making the best data-driven decisions
What Are Data Silos?
A data silo is a specific collection of data owned, managed, and accessed by one group of users within a business.
Control and governance of business data, especially when there are regulatory and compliance standards at hand, are mission-critical tasks for any enterprise company. But that control and ownership can quickly spiral into disconnection and disorganization.
Data silos make it difficult to share information across an entire company. Only one department — and sometimes only one individual — can access and make use of the information in a data silo. And despite their obvious downsides, data silos are startlingly common.
In a recent survey for Airtable, Forrester Consulting found that large companies use more than 360 software tools across their teams on average — each using, producing, and storing its own set of data. Forrester also reported that 80% of decision-makers said cutting through the data silos that result from this tech sprawl is a top priority for their businesses.
Data silos can develop slowly and nearly imperceptibly as both volume and diversity of data grows. Company culture and old-school organizational structure from a time before cloud storage can also contribute to siloed data.
By the time teams can see that they’ve got troubling data silos, it’s probably been negatively impacting communication, collaboration, and their ability to make well-informed decisions for a while.
Reasons to Break Down Data Silos
While data silos may be an inevitable part of modern business, it doesn’t mean your go-to-market (GTM) team has to live with them. Here are four key roadblocks you can solve by breaking data out of its internal silos:
Make better business decisions
Data done right can lead to powerful business insights, illuminate new revenue opportunities, and uncover ways to make existing revenue operations more efficient.
But data silos make it impossible to build a true 360-degree view of customers and prospects required to unlock a company’s growth potential. That leads to poor business intelligence — and that means poor business predictions and decisions.
Siloed data is also often out of date and incorrect because it can lack connections to automated, cross-functional enrichment and orchestration systems that would otherwise keep data fresh.
Gain alignment on business goals
What’s your North Star Metric — the measure that every department in your business focuses on in their own way to impact overall growth?
Imagine how difficult it is for leaders in each department to align their actions to this goal when they’re blocked from seeing what other departments are doing, how the business is performing, and how they can apply those insights and impacts to their own strategies.
Then, consider the added difficulties that come when leadership teams can’t see how each division is moving the needle, aside from manual, lagging monthly reports. Removing data silos helps ensure that teams can access the metrics that matter for them, and leaders can see how everyone’s efforts line up with broader initiatives.
Spend less time on rework
Data silos mean different teams are working with different versions of truth, and not seeing the complete picture.
For example, when marketing and sales teams are working on the same lead, but from different platforms, they’re likely to spend much more time than necessary gathering information for the same record — and then trying to reconcile everything to work together across the customer journey.
Not only is all that rework time-consuming, it also introduces the opportunity for errors to creep in.
Improve customer experience
Today’s B2B customer journey touches nearly every department: marketing, sales, support, and billing. And business buyers are increasingly prioritizing a strong customer experience just as much as the products and services they’re evaluating.
If each team involved in this journey is unable to access accurate and up-to-the-minute customer data, it’s all too easy to lose the thread and ruin the experience.
How to Break Down Data Silos in 5 Steps
At any large enterprise, leaders are bound to encounter data silos. Preventing silos from forming is clearly a priority — but perhaps more important is how leaders go about unwinding legacy practices, integrating new acquisitions, and untangling poor practices that will inevitably crop up.
1. Win stakeholder buy-in
Getting support for business-wide changes — especially ones that come with costs, new tools that will need IT clearance, and possibly even some organizational restructuring — requires a clear path to value.
Here are some tips to help you start building a business case for data quality improvement:
- Identify the most critical business priorities of stakeholders and tie your data quality improvement project to positive outcomes in those areas.
- Outline metrics that show how improved data quality impacts the success of those business priorities. Focus on financial or operational performance metrics in particular.
- Benchmark current data quality and define the goals for each measurement. It’s important to emphasize the continuing nature of the project. Goals like yearly milestones may be one way to help get that message across.
- Focus on financial upsides. What financial benefits can stakeholders anticipate from data improvement? Calculate this to help win them over and dive into the research to come up with tangible project costs after you get initial approval.
2. Outline your goals and timelines
Goals that will connect data quality and data management with your overarching business objectives are key for a comprehensive data integration project. Goals should be informed by the same critical business priorities, and attached success metrics, that you presented in your initial business case.
It may be helpful to spread these goals along a timeline to give stakeholders, your team, and even yourself a realistic understanding of the scale of the project.
At this time, you may also choose to work with department leaders to break out their specific data goals, associated timelines, and any expected contributions from their teams.
3. Identify your data silos
One problem with solving data silos is that, by nature, they are not transparent. There are a few tactics for getting around this foundational problem:
- Talk to people who have been vocal about poor or inconsistent data
- Check in with leaders who work together closely (such as within the GTM team) to see who struggles to share data
- Ask IT for a list of data platforms to see if duplicate or similar tools exist in your ecosystem
- Ask IT where they get the most complaints about data sourcing or accuracy
4. Invest in data orchestration
It’s time to get tactical — how will you unify existing data and ensure the new data you collect will remain actionable for revenue teams?
With data orchestration.
Data orchestration captures data from various sources and cleans, matches, enriches, and makes it available to your various tech platforms. This prevents siloing and provides a unified system of record that revenue professionals and business decision-makers can actually use.
ZoomInfo’s DaaS solution, OperationsOS, provides robust end-to-end data orchestration to keep information from every corner of your company aligned, standardized, and protected against future quality degradation, all with the help of automation.
There are three components of making data orchestration work for you:
- Data Sourcing: First, you want to make sure you’re getting all the GTM data you need on companies and contacts, and centralizing it for your revenue team. ZoomInfo can funnel this information right into the systems where you already work.
- Data Governance: Data governance is pivotal to keeping data organized and high-quality.
- Data Integration: Integration is the key to making sure new data silos don’t crop back up. There are several ways to move data from disparate sources to one source, like a data lake or data warehouse, that connects with your revenue workflow:
- Custom scripts created by IT
- On-premises or cloud-based ETL (extract, transform, load) tools
- An orchestration app like OperationsOS that integrates data into the systems you use like HubSpot, Salesforce, and beyond
5. Build a culture of data governance
Data governance involves establishing and maintaining standards around effective data management.
A data governance strategy should encompass the processes, roles, tools, and metrics that keep data secure, accurate, available, and usable.
Here are the core elements of an effective data governance strategy:
- Policies that clearly define how data is to be used and managed, and who will be in charge of it
- Standardized metrics to define data quality
- A plan for handling data security and integration
- A reporting system to show how data and governance positively impact team performance and business goals
- Training to help employees across the company understand how to work with and appreciate data
- Reviews to ensure the data standards, workflows, metrics, and other systems are performing as expected
Data governance is often overlooked, especially in businesses that are still crawling out from under data silos. That’s why leaders need to build a culture that encourages data governance and create systems that ensure data is used and respected in day-to-day activities.
Optimize Your Business Data With ZoomInfo
Eliminating data silos to improve data quality and make better data-backed decisions is difficult without the right technology.
A successful data orchestration strategy includes processes to automate deduplication, normalization, enriching, segmenting, and scoring — critical steps to making data usable across a company.
With ZoomInfo OperationsOS, your team gets the best B2B commercial data delivered into any tool — accessible, flexible, and primed to accelerate your business.
See how easy it can be to source, maintain, and orchestrate the high-quality data your business needs to thrive. Request a demo of ZoomInfo OperationsOS.