By Kim Koster, Director of Product Marketing at Unanet
Have you really thought about how much money poor quality data is costing your company? IBM estimates that the yearly cost of poor quality data, in the US alone, in 2016 was $3.1 trillion. I was floored when I saw that statistic. I then had to think through why this number would be so high and it quickly became apparent. Below are just a few reasons that come to mind:
- Many manual processes and off book calculations
- Reconciling and re-reconciling incorrect data
- Processes not being documented or followed
- Correcting errors and begging forgiveness
- Lots and lots of email, Excel spreadsheets, and PowerPoint
- Disparate accounting, time collection, resource mgmt., and reporting tools
- Making poor decisions based on old or incorrect data
I bet at least 4 of these reasons pertain to most companies. If this is true for your company, these extra costs are degrading your profitability.
I have visited many companies over the years that have “Data Churn,” and it is just accepted as part of the fabric of their organization. Just a quick example: Jim on Project XYZ has a spreadsheet that his project manager loves. Jim hand-jams the information into the spreadsheet from the time collection system, the ERP, and an EAC tool. Sue on Project 123 also has a spreadsheet and it is being used for resource management and she is calculating project specific metrics. Project XYZ has a different set of metrics than Project 123. All the disparate spreadsheets must then be consolidated by accounting or finance. What a nightmare! No one wants to let go of their custom spreadsheets! How many times have you heard, “My project is so unique that I can’t use a standard set of processes and tools.” Every time someone says that you should be hearing a LOUD cha-ching in the back of your mind. That’s the sound of unnecessary expenses adding up.
As a project manager or finance professional the consequences of poor quality data are a lack of confidence in your project status, long hours making up reports and charts, a constant nagging feeling that the information you just gave the boss is wrong, and embarrassment both internally and externally.
For the organization as a whole, the consequences are loss of revenue, decreased profit, reputation, that the KPIs being used for decision making are wrong, and putting future wins at risk.
Now that we have established why bad data is prevalent and how bad the consequences can be for your company, what should you do about it?
- Admit that there is a problem with data quality
- Make the decision that you want to fix the problem and put a plan in place
- Involve the entire organization, and commit the discipline needed to make the changes needed
- Involve executive management upfront and keep them involved to get the best results
- Look at your current policies and procedures:
- Interview owners of different types of projects to understand their needs
- Review the existing policies and procedures and ask for input
- Update as required
- Make sure your policies and procedures are tailorable to different types of projects eliminating perceived uniqueness
- Make sure the you have a self-audit plan in place to assure adherence to the policies and procedures
- Schedule informal internal audits to help project team readiness and compliance
- As a part of the self-audit perform data quality checks
- Make sure you have tools that will enable the teams to be successful. Basic characteristics of a GREAT ERP system:
- Cloud based
- Fully integrated suite supporting people, projects, and financials
- Easy to use for all stakeholders
- Real-time reporting and dashboards
- Robust resource management
- Budgeting and Planning
- Time Collection
- Assess your current people, making sure you have the talent needed to be successful
- Look to hire strategically in gap areas
- Perform role-based training on policies and procedures
- Perform tools training for all stakeholders
- Establish standard KPIs across all projects and make sure they tie to corporate goals
- Adjust as bottlenecks are found, and continually improve
The hidden nature of these costs and inefficiencies make them difficult to identify and hard to fix. So many times, I hear, “Let’s not fix it if it’s not broken, Sally is doing fine with her Excel spreadsheets.” The reality is that poor data quality is costing companies a fortune and slowing growth. Companies that recognize this will enjoy more success, both project execution wise and financially.
Now is the time to assess the cost of poor quality data and start fixing it today!