It’s well recognised in many corporate environments that data quality is a large and expensive issue.
For banks & insurance companies with risk data, retailers with stock, marketers with poor lists, and countless others who rely on accurate financial info – poor data quality is an issue as it breeds inefficiency, increases expense and robs capital. For the big boys, the cost of this can generally be absorbed as they have the scale to cover it. It’s the smaller players in the SME market who I find get knocked around the most by poor data quality.
Don’t think that data just means financial numbers – all sorts of data have errors.
What is an issue for many businesses, is that they have no awareness around the quality of their data……that is of course, until something goes wrong and they lose money.
So to see how your business stands in this area, check out the 4 most common data quality issues that we see – identifying why these are a problem and what you can do to address them.
Any business which involves quoting or estimating for a job runs the risk of financial loss if the quoting is flawed. Typically this is off the back of a poor understanding of the brief, lack of information collection or just a poor process.
- To minimise errors in this area, it is important to ensure you have clear detail on the project or job. For some businesses this will mean getting accurate measurements, drawings or specs, whereas for others, it will be clarity around the severity of an issue or the hours to be applied. Regardless of what elements you need, gaining accurate measurement is a critical first step, followed closely by an accurate formula, method or process to do the quote calculation. Naturally this varies significantly depending on the business and industry, but if you have no rigour around this area – then you will pay the price for poor quoting.
Staff activity and their productivity represent an important success factor for most businesses, as wages represent such a large expense cost. Naturally this factor increases in importance in line with the number of staff you have. It is continually surprising how few businesses understand what the productivity of their staff is like.
- To understand staff productivity, you need to have effective monitoring and measuring systems in place. Some businesses use software, others direct supervision or production output, but regardless of the type, it needs to be consistent and objective. The output data from these should then be reviewed on an ongoing basis to allow corrective action.
Incorrect client data or its management, brings with it several ways to lose money. To start, if you have poor capture systems/processes, then you may lose clients, lose prospects, lose stock or damage reputation. Add to this, if you are not handing private client data correctly, you could quickly become liable. To finish, the poor management of prospect data is a great way of ensuring that the “prospect” stays exactly that.
- To maintain quality client information, the best solutions incorporate the integration of quality processes, good staff training, intuitive software systems and a continuous review/monitoring program. Really focus on “how” your data is captured and handled, as it is far cheaper to collect and handle the data correctly up front, then to go back and cleanse it down the track.
Poor quality accounting or financial numbers mean that the business is not being managed with the benefit of accurate information. This is often made worse by the fact that the reader – the business owner, may not be skilled in reading, interpreting and understanding this type of information. Even small errors in the capture or classification of financial data can result in misleading data.
- To combat this, I find that experience counts. Using an experienced and professional bookkeeper to manage the financial data will significantly aid accuracy. Regular and consistent review of the data – perhaps with some interpretative analysis by someone capable, will enable the recognition of errors whilst enabling better quality business decisions to be made.
So with these common data quality issues in mind, the most important aspect for any business is ……to take action. The longer data quality issues are tolerated, the greater the cost.
Once data quality issues have been discovered;
- Fully identify the data quality issues you have,
- Cleanse the data to correct the errors,
- Address the deficiency in your process or system which allowed the data error to occur in the first place (typically includes a large element of staff training) and then,
- Establish appropriate monitoring programs to keep the data clean.
Follow these simple steps and you can effectively arrest the data quality issues in you business. Be warned however, the clean up of data generally takes more effort than expected, so it is so much better to start with a good data process and avoid the issue altogether.
Quality data, which is secure and well maintained is a business asset and can be extremely valuable.