Despite huge investments in data, you may be surprised to learn that most executives do not trust their company data. They may be skeptical of the data they use or simply do not know how to interpret the information available.
In fact, Havard Business Review reported that 90% of business leaders believe that data reading is critical to a company’s success. However, only 25% of employees feel safe when working with their organization’s data.
The statistics shout out loud and clear that if you are struggling to trust your business data, you are definitely not alone. However, this does not make it any less important to rectify it.
Uncertain data has consequences throughout your organization. You risk:
- Rotating strategies based on wrong assumptions
- Lacks a clear picture of company performance and ROI
- Delivers bad customer experiences
- Reducing job satisfaction for your team due to manual tasks and frustrations
- Hesitant to share important insights across the team
Instead, the goal of any business should be data integrity. Data integrity refers to the quality and reliability of your business data, including how accurate, consistent, timely and well-preserved this data is.
With high data integrity, your business can also benefit from the increase in opportunities that big data brings.
Here’s our guide to what to do when you cannot trust your reporting data. Learn how to turn things around long-term so that your data expenses are not ruined by leaky processes and frameworks.
How to make your data more credible
It may sound obvious, but if your business has long struggled with unreliable data, you need to do things differently to create a different result.
Correcting unreliable data requires changes to your organization:
Let us explore the best ways to make your data more reliable so you can benefit from accurate and timely analysis that paves the way for informed decisions.
1. Go back to the basics.
To make your data more reliable, let’s go back to the beginning. Imagine starting your database from scratch with a clean slate. Now answer these questions:
- What data do you need to collect?
- What format do you need to assemble it in?
- What data do you not need?
- What is the mess or noise you want to avoid?
- How 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 trim from your database
- How to train your team and increase data readability in your organization
Once you are aware of what is going to happen, you need to start creating an action plan to get them in place and make your data more credible.
2. Follow the data track back to the source.
When faced with unreliable data, follow the trail back to the source. Where does the inaccurate data come from?
This includes looking at form fields and checking for consistent and standardized data collection. It also means ensuring that Google Analytics tags are configured correctly or that your SQL scripts for your business intelligence platform are error-free.
If this stretches your technical knowledge, perhaps because the person who implemented your systems has left the company, you may want to consider hiring a data specialist to help you. You can also get their help to simplify your data processes so that it is more manageable internally in the future.
3. Select the Best Practice Data checkboxes
Regardless of the industry or company size, there are some best practices that any business should follow for reliable data. These include:
- Consistency – Maintain the same format across systems using consistent and standardized fields and collection processes. When integrating your apps, use custom field mapping to ensure the right data is synced to the right places.
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 to new offers. Is your data complete?
Centralized and enriched data -Instead of having fragmented and incomplete data spread across multiple systems, you need to maintain a centralized database with the most up-to-date and reliable information. This can be your CRM for your customer data and a system like Chartio or Supermetrics for your company’s performance data. Create two-way integrations between your centralized database and connected apps to enrich your data anywhere.
Access control – Set permissions and policies that ensure that only the right people see certain data. This is about balancing accessibility and transparency with security.
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 to verify and validate them. This may include automated processes for checking for irregularities and missing fields, backed up by some manual checks.
Real-time updates -To get the best results from your data, they need to be updated. Look for real-time updates when choosing a business intelligence system and a data integration solution.
Sources of quality – Make sure you know where all your data comes from and that you can guarantee its integrity. Maintaining a neat and tidy database that you know you can rely on beats that have very advanced datasets that you struggle to make sense of or control.
Purity -Considering B2B data decaying at a rate of 2% per year, your database needs frequent cleanups. It is important to update your data by removing duplicates, inaccuracies and other data that has been converted from value to clutter.
Security and protection – Maintaining high security is crucial for data protection rules such as GDPR in Europe, but it is also just a basic principle of being a credible brand. It is also absolutely crucial if you want valuable data at your fingertips (and only yours).
Integrations -Over 80% of business leaders say that data integrations are important for the day-to-day operations of their organization. Data integrations reduce data silos and make data more accessible to everyone in your company, so employees do not have to track other colleagues to find specific information stored in the department’s database.
4. Document processes.
A common trap that organizations go into is relying on someone to set up and manage their data processes. When this person leaves the organization, chaos is often released.
You can avoid this by creating clearly documented processes stored in your company wiki, Google Drive or a tool like Notion. And remember: overly complicated processes can end up doing you more harm than good. The simpler your processes are, the better.
5. Simplify everything.
Complexity is often the root of bad data you can not trust. For complex data analytics to work properly, you need time, resources and knowledge to back it up.
For most organizations, it is more efficient to keep your data and reporting as simple as possible instead.
Simplifying your data means:
- Collects only the data you need
- Organizing data consistently and in standardized formats
- Avoid complicated workflows and systems
- Reducing your reporting dashboards
- Avoid multiple systems for the same job
- Creating documentation that is clear and easy to understand
- Changing processes so that everyone can quickly understand them
To make your data the most reliable, ask yourself: where can you simplify your data collection, management and integration processes?
6. Remember the sunken cost error in mind.
You’ve invested a lot of money, you have complex systems in place … and you do not want to throw it away. So instead of starting over, you build on top of what you have – and hope it covers what’s below.
Investopedia describes the reduced cost error or the lowered cost trap as “a tendency for people to irrationally follow up on an activity that does not live up to 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 unhealthy foundations, it will come back to bite you. Start by understanding exactly what you are dealing with and the issues. Come up with another opinion here if you need it. Then make as impartial a decision as possible about what you need to do to increase data integrity.
In the long run, it may be easiest to go back to the drawing board, create a much more straightforward and precise strategy, and throw away what you had in place before.
7. Communicate with stakeholders.
Although concerns about unreliable data are often valid, it is sometimes you or your organization’s stakeholders still Do not rely on your data when everything is sound.
If so, clear communication is your way forward. Explain why your business analytics data is reliable and how it is configured to ensure reliability. Answer questions to help stakeholders understand how data is collected, managed, and integrated between your apps. Also encourage worries about being expressed so that together you can examine their validity or irrelevance.