DATA QUALITY – YOU CAN’T PUT DIESEL IN A PETROL ENGINE

YOU CAN’T PUT DIESEL IN A PETROL ENGINE

Data Quality

So how good is your data and how are you using it?  Like any machine it is only as good as the fuel (data) you put into it.  You won’t get very far putting diesel into a petrol engine.  The same can be said of your database(s).  Your data return will only be useful if you have good input.

Quality information is defined as “information that is suitable for all of an organisation’s purposes, not just my purposes.”

What are the steps to securing good data?

  1. One of the key factors of good information is its relationship with the business in question. Making sure your data links up and answers the questions you want answered in order to provide the service required or the relevant information to your audience.
  2. Being aware of the data you are collecting is an essential part of good data. Having process in place where data can be checked, cleansed and edited will affect your data quality. Allowing editing or checking without parameters in place will place your data in danger, this can lead to loss of good data on one persons judgement.
  3. Discarding documentation and design standards is a large problem. Over time data quality guidelines are discarded through employee turnover and data familiarity. The data can become “When do we ever use this”. If point number one is adhered to, it is useful information for some part of the business. To prevent this frequent training and updating of data guidelines is necessary.
  4. The main link of all the points above is communication. Without good channels of communication your data quality will suffer. This has to start from the outset involving all who will be using the data and supplying the data. This can seem like the most logic step but it can be the biggest hurdle to getting good information.

When dealing with poor data quality, Marsh (2005) summarises the findings from various industry research as follows:

“88 per cent of all data integration projects either fail completely or significantly over-run their budgets”

• “75 per cent of organisations have identified costs stemming from dirty data”

• “33 per cent of organisations have delayed or cancelled new IT systems because of poor data”

• “$611bn per year is lost in the US in poorly targeted mailings and staff overheads alone”

• “According to Gartner, bad data is the number one cause of CRM system failure”

• “Less than 50 per cent of companies claim to be very confident in the quality of their data”

• “Business intelligence (BI) projects often fail due to dirty data, so it is imperative that BI-based business decisions are based on clean data”

• “Only 15 per cent of companies are very confident in the quality of external data supplied to them

• “Customer data typically degenerates at 2 per cent per month or 25 per cent annually”

• “Organisations typically overestimate the quality of their data and underestimate the cost of errors”

• “Business processes, customer expectations, source systems and compliance rules are constantly changing. Data quality management systems must reflect this”

• “Vast amounts of time and money are spent on custom coding and traditional methods – usually fire-fighting to dampen an immediate crisis rather than dealing with the long-term problem”

Data Quality 2

 

What Can Quality Data Do?
Good data is all about providing the tools to get information that will assist in making good decisions. With good quality data you can

  1. Gain information
  2. Gain knowledge
  3. Make decisions
  4. Get results

With the world of data changing rapidly the level of data being collected has increased immensely.  As this happens the levels of data become more difficult to manage and this can allow the quality levels to drop.  It has been proven through various research that good data quality can improve customer satisfaction, decrease running costs, assist in more efficient decision making and increase employee performance and job satisfaction (Kahn et al., 2003; Leo et al., 2002; Redman, 1998).

Data Quality 3

 

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