Messy data causes a headache in companies of every size. Many businesses we speak to think that they need to tidy up their data before they can embark on a data warehousing and analytics project.
What is messy data?
To understand the cure for messy data, first you must understand what messy data is. From a business user perspective, Execs often feel that they need to “do something with data”, but don’t know where to start. This can be a result of:
- A lack of data input
- Lack of access to data
- Data in multiple places
- Duplicates of data
This situation leads businesses to believe that their data is in a mess, with no clear idea how to remedy the situation. In turn, it can breed a sense of inertia which can harm growth.
Sources of messy data
On a technical level, messy data is a term to describe data from which it is impossible to extract clearly interpretable information. This is a result of data gathered without a process and without consideration for its analytical value.
Data sets can require cleansing for a multitude of reasons. For example, data that doesn’t adhere to a set standard.
Standardisation of data, such as rectifying fields for phone numbers which include dashes when they should only be numerical is part of the cleaning process. However, there are many other issues that can cause messy data, such as:
- Missing data – as a result of a manual process that’s dependent on team/individual
- Unstructured data
- Multiple variables in one column
- Switched columns and rows
- Extra spaces
- Misuse of free text fields in third party sources (CRM, HR systems etc.)
Messy data is often the result of poor process in human interaction with systems. For example, using free text custom fields in your CRM can result in data that has little value analytically and cannot be categorised.
Tidying up the mess
Data needs to be focussed with a consistent structure in order to be tidy. To achieve this, firstly a process for capturing and inputting data must be established.
Do this with an analytical mindset and you’ll find your data far more valuable. Not only will this positively impact your BI output – reports and dashboards – it will also improve your operational efficiency. Categorising your data and enforcing a structure in the tools that you use improves the operational functionality of your business.
The kleene solution
Messy data requires technical expertise to rectify. kleene can clean up the mess, building your single source of truth.
Your data doesn’t need to be perfect before building a data analytics infrastructure. Through kleene’s Build Your Warehouse service, our analysts can clean up the mess. The process can expose technical debt, which your analyst is on hand to explore solutions to rectify.
Throughout the Build Your Warehouse phase, our analysts will clean and organise your data, ensuring that all data in the warehouse has analytical value.
Want to find out more? Get in touch to see how kleene can help.