Data & Innovation: How Poor Data Quality Hurts Innovation

Poor Data effects Innovation

Dan Beck | Director | PT 2.0

As we come into a new financial year, one of the biggest chores a lot of firms go through is cleaning up their client database.  Nobody likes it, it takes too long and it’s inconsistent – but what if it didn’t have to be?

We live in a data-fuelled world and work in a data-driven industry, and there is no surprise that a lot of the innovations that have arisen in this space are designed to reduce time and deliver value to clients. However, all of these systems rely on one crucial (and sometimes difficult) component: that the data is clean!  The worrying thing is that unless you can embrace some of these innovations to improve efficiency and save time, you may not be leveraging the same benefits that your competitors are.

Recently we talked to FeeSynergy firms in Brisbane and Sydney about ways to help keep data clean.

Here are the key takeaways from various studies:

  • Up to 32% of your data is missing, incorrect, incomplete or inconsistent;
  • Ovum research claims companies lose about 30% of revenues due to poor data quality;
  • 77% of businesses believe their bottom line is affected by bad data;
  • Most firms cannot complete a simple mail merge across their entire active client base due to missing or incorrect client records;
  • The accounting firms we have worked with have, on average, between 60,000 to 80,000 easily identifiable data issues.

Here are some key tips on how to start getting your data quality under control:

  • If you are on APS or MYOB AE, we have developed a tool to help identify data issues in your practice management systems, which provides the fastest way to identify missing client and matters\assignment data;
  • Identify which of your data is crucial (must have for compliance), useful (good for marketing and analysis) and wasteful (collected, but never used, nor likely to be);
  • Start small – target only one or two missing attributes (start with crucial, then useful, get rid of wasteful), start with those that are missing data, then the inconsistent ones;
  • Segment your database into useful categories, don’t try and clean it all in one go;
  • Put a partner\director in charge of data cleanliness – data governance is becoming more important for your entire business;
  • Stop manually replicating data between systems, there are better ways to automate this process;
  • Create a data culture in your organisation and lead from the top.

Doing this opens up a world of possibilities for your firm such as improved targeting and marketing, better business management by having better decision-making data, automating the right things, and being able to analyse your whole business (for more details, see our recent BLOG:

PT 2.0 Data Quality

If you would like any more information on how you can cut down data cleaning times, analysing your practice performance, benchmarking, automating processes or delivering data advisory services to your clients, please contact Dan and his team at, or their website

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