As you have probably experienced already, data is an important asset for your business and organisation. There is no doubt about it. However, managing data can easily become a full-time task, just like managing other business assets such as customers, cash flow, or branding.
There is no single or perfect way to manage data in the age of AI. Every organisation is different. That said, these five principles can guide you on where to start and which areas you should be tracking or keeping an eye on.
1. One Source of Truth
This can sound like a dream for many organisations, especially when vendors promote UDL solutions (Unified Data Layers). Still, working with this mindset from the moment we join forces with an organisation is critical for long-term success.
Data silos create many problems. Each team manages its own data, profit means something different for each department, and KPIs are interpreted in different ways depending on who is looking at them.
One source of truth means:
- ONE place where the customer list lives
- ONE place where all invoices are stored
- ONE place where product names are managed
This approach sets the organisation up for success as data maturity grows. It is not easy, but it should always be the north star.
2. Data Process Audits
This principle may feel a bit too advanced for where your organisation is today. After all, the word “audit” implies tracking, reviewing, and measuring how data is processed.
But let me ask you this: do you know what records were imported into your system in the last 10 days from one of your data pipelines?
This may sound irrelevant because the automation has been running fine for years. However, identifying anomalies is critical if you want reliable data. Just because something is automated does not mean it should not be monitored.
3. Automation Over Manual Tasks
Yes, it is exactly what it sounds like. AI promises to revolutionise organisations, but behind the scenes, automation is what really keeps things running smoothly.
Saying “I will update this spreadsheet” might not seem like a big deal. But once you are not there, it can quickly become a nightmare for the person backing you up. Every time a well-designed process can be automated, do it. It is worth the time and effort.
Tasks that seem like they take five minutes often take much longer. We have seen processes that were described as five-minute tasks actually taking one hour every day. If the same task has been repeated five times in the last ten days, it probably needs automation.
4. Focus on One Insight or KPI at a Time
Creating KPIs is exciting when organisations start working with data. However, creating more KPIs does not automatically make a business better.
In most cases, teams cannot realistically manage more than three to five KPIs at a time, unless responsibility is clearly spread across the organisation.
Just because something can be measured does not mean it should become a KPI. Often, a KPI is an aggregation of multiple data points. For example, customer churn is influenced by the number of purchases, purchase value, seasonality, and other factors. Each of these does not need to be its own KPI.
Focus on one key insight at a time.
5. Data Simplicity
Simplicity requires effort. Data clarity comes from building databases, data catalogues, and data dictionaries that anyone in the organisation can understand.
We have seen systems with unclear column names, missing descriptions, confusing flags, and aggregated fields that are impossible to interpret. This creates frustration and slows teams down.
Data simplicity is what enables confidence, trust, and adoption. Over time, it becomes a key driver for the organisation.
To finalise…
Let me ask you:
What stage is your organisation at?
Do you feel something is missing?
If you have questions, bring them to one of our free data webinars or masterclasses. We are here to help. Sign up and find the session that best suits your agenda.
