A survey by Hackett Group some time back showed that over 85% of the respondents, representing various industries, cited POOR DATA QUALITY as the greatest barrier to effective planning and analysis. Even today, these challenges continue to hinder a procurement function’s efficiency and effectiveness.
While half of the respondent companies agreed that procurement analytics improves the quality of business decision-making and helps create significant value, over 85% also said that they have an inefficient analytical process.
Dirty Data certainly creates the biggest hurdle in procurement value delivery.
A recent IBM survey revealed that, in the USA alone, such bad data costs the economy over $3.1 trillion every year.
How can dirty data inflict so much damage?
First, the decision makers and managers increasingly rely on the data in their daily work. Second, the data has plenty of errors and in the face of a critical deadline, the tendency is to make quick corrections instead of eliminating the errors at the source (Master Data).
For a long time that we have been conducting spend analytics for variety of organizations – large and small, a consistent theme that emerged was – Poor Data Quality.
Our observations are absolutely in line with that of the Hackett and IBM survey results. For one, in all the cases, it was extremely difficult and a prolonged process to get hold of the right and relevant data. Further, once somehow you finally get it, the quality of the data renders itself useless without further cleansing efforts. It is astonishing to see this happening in an era of big-data analytics and information technology explosion.
Today, technological advances should ideally support the data being available on the fingertips to help make instant decisions. However, it is a pity that most organizations still use the data and analytics primarily for reporting updates rather than for business insights and real-time decision-making.
Here is an interesting observation that should push us all to shift priorities on how we manage the data. While companies make huge capital outlay to implement best of the business management technologies, they consistently lack in implementing governance mechanism to maintain and sustain high data quality. Typically, master data management (MDM) in the organization is left to some lower-level team or a non-relevant function.
In many companies, we found that the master data management is typically delegated to an IT function. While maintenance of data systems and reporting may be a relevant responsibility, will IT department have necessary expertise and understanding of the business/procurement data, or can they be the effective custodian of such data?
Companies must address such issues that are core to maintaining high data quality. No doubt then, the data quality is so poor that real-time Procurement Analytics is still a dream. Any meaningful analysis typically takes days and weeks if not months.
A recent conversation with the CFO of a mid-sized company revealed that it was taking weeks for the finance team to present the working capital analysis and inventory recommendations. Large part of the effort was going into data cleansing for reliability. We all know how dynamic the inventory needs to be managed. If you do not have real-time insights, how do we expect a company to make quick decisions to stay competitive.
