Despite huge investments in data, you might be surprised to learn that most executives don't trust their company's data. It might be that they're skeptical of the data they're using, or simply don't know how to interpret the information at hand.
In fact, Havard Business Review reported that 90% of business leaders believe data literacy is crucial for company success. However, only 25% of employees feel confident when working with their organization's data.
The stats shout loud and clear that if you struggle to trust your business data, you're absolutely not alone. However, this doesn't make it any less important to fix it.
Untrustworthy data has repercussions across your entire organization. You run the risk of:
- Pivoting strategies based on incorrect assumptions
- Lacking a clear picture of business performance and ROI
- Delivering poor customer experiences
- Reducing job satisfaction for your team because of manual tasks and frustrations
- Being hesitant to share important insights across the team
Instead, every business's goal should be data integrity. Data integrity refers to the quality and reliability of your business data, including how precise, consistent, timely, and well-preserved that data is.
With high data integrity, your business can also benefit from the surge in opportunities that big data brings.
Here's our guide to what to do when you can't trust your reporting data. Learn how to turn things around long-term, so your data spend isn't spoilt by leaky processes and frameworks.
How to Make Your Data More Trustworthy
It might sound obvious, but if your business has been wrangling with unreliable data for some time, to create a different outcome you need to do things differently.
Fixing untrustworthy data requires changes to your organization's:
- Processes
- Mindset
- Skillset
Let's explore the best ways to make your data more trustworthy so you can benefit from accurate and timely analytics that pave the way for informed decisions.
1. Go back to the basics.
To make your data more trustworthy, let's go back to the very beginning. Imagine you're starting your database entirely from scratch with a clean slate. Now answer these questions:
- What data do you need to collect?
- What format do you need to collect it in?
- What data don't you need?
- What's the clutter or noise you would like to avoid?
- How do you need to integrate your apps?
You can use these valuable insights to inform:
- New processes for data collection, management, and integration
- What to clean up and prune from your database
- How to educate your team and increase data literacy in your organization
Once you're clear on what needs to happen, start creating an action plan to put it into place and make your data more trustworthy.
2. Follow the data trail back to the source.
Whenever you're faced with unreliable data, follow the trail back to the source. Where did the inaccurate data originate?
This includes looking at form fields and checking for consistent and standardized data collection. It also means making sure that Google Analytics tags are set up correctly, or that your SQL scripts for your business intelligence platform are flawless.
If this stretches your tech knowledge, perhaps because the person who implemented your systems has left the company, consider bringing in a data specialist to help you out. You could also get their help simplifying your data processes so it's more manageable in-house going forward.
3. Tick the boxes for data best practice
No matter the industry or company size, there are some best practices that every company should follow for trustworthy data. These include:
- Consistency – Maintain the same format across systems by using consistent and standardized fields and collection processes. When you integrate your apps, use customizable field mapping to ensure that the right data is synced to the right places.
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Completeness – For each piece of data, you need to know the full picture. A few examples are the source of your marketing leads, sales history for your customers, or conversion path for new deals. Is your data complete?
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Centralized and enriched data – Rather than having fragmented and incomplete data spread across several systems, maintain one centralized database with the most up-to-date and trustworthy information. This can be your CRM for your customer data, and a system like Chartio or Supermetrics for your company performance data. Create two-way integrations between your centralized database and connected apps to enrich your data everywhere.
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Access control – Set permissions and policies that ensure only the right people see certain data. This is about balancing accessibility and transparency with security.
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Validation – 28% of customer and prospect data is suspected to be inaccurate in some way, according to Experian. For accurate data, you need a method for checking and validating it. This can include automated processes for checking for anomalies and missing fields, backed up by some manual checks.
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Real-time updates – For the best results from your data, it needs to be up-to-date. Look for real-time updates when choosing a business intelligence system and a data integration solution.
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Quality sources – Make sure you know where all of your data is coming from and that you can guarantee its integrity. Maintaining a neat and tidy database that you know you can trust beats having highly advanced data sets that you struggle to make sense of or control.
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Cleanliness – Considering B2B data decays at a rate of 2% per year, your database needs frequent clean-ups. It's important to freshen up your data by removing duplicates, inaccuracies, and other data that's turned from value into clutter.
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Security and protection – Maintaining high security is crucial for data protection regulations such as GDPR in Europe, but it's also just a basic principle for being a trustworthy brand. It's also absolutely crucial if you want valuable data at your fingertips (and only yours).
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Integrations – Over 80% of business operations leaders say data integrations are important for day-to-day operations at their organization. Data integrations reduce data silos and make data more accessible to everyone at your company, so employees don't have to track down other coworkers to find specific information stored in their department's database.
4. Document processes.
One common trap that organizations fall into is relying on one person to set up and manage their data processes. When that person leaves the organization, chaos is often unleashed.
You can avoid this by creating clearly documented processes that are stored in your company wiki, Google Drive, or a tool like Notion. And remember: overly complicated processes might end up doing you more harm than good. The simpler your processes, the better.
5. Simplify everything.
Complexity is often the root of bad data you can't trust. For complex data analytics to work successfully, you need the time, resources, and knowledge to back it up.
For most organizations, it's more effective to keep your data and reporting as simple as possible instead.
Simplifying your data means:
- Only collecting the data you need
- Organizing data consistently and in standardized formats
- Avoiding complicated workflows and systems
- Reducing your reporting dashboards
- Avoiding multiple systems for the same job
- Creating documentation that's clear and easy to understand
- Amending processes so anyone can quickly understand them
To make your data the most trustworthy, ask yourself: where can you simplify your data collection, management, and integration processes?
6. Keep the sunk cost fallacy in mind.
You've invested a lot of money, you have complex systems in place... and you don't want to throw that away. So instead of starting afresh, you build on top of what you have – and hope it will cover up what's underneath.
Investopedia describes the sunk cost fallacy, or the sunk cost trap, as "a tendency for people to irrationally follow through on an activity that is not meeting their expectations. This is because of the time and/or money they have already invested."
This is all too common when it comes to business data and analytics.
If you keep building on unsound foundations, it will come back to bite you. Begin by understanding exactly what you're dealing with and the problems at hand. Bring in a second opinion here if you need it. Then, make as unbiased a decision as possible about what you need to do to increase data integrity.
Over the long term, it might be easiest to go back to the drawing board, create a much more straightforward and accurate strategy, and trash what you had in place before.
7. Communicate with stakeholders.
While concerns over untrustworthy data are often valid, sometimes you or your organization's stakeholders still don't trust your data when everything is sound.
If this is the case, clear communication is your way forward. Explain why your business analytics data is trustworthy and how it's set up to ensure reliability. Answer questions to help stakeholders understand how data is collected, managed, and integrated between your apps. Also, encourage concerns to be voiced so that you can explore their validity or irrelevance together.
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