Importance of data quality has been emphasized a lot by several dignitaries across data and analytics domain, and everything that they said stands true. However; apart from the obvious costs of poor quality of data, there are other hidden costs as well, which somehow it seems have been missed out to be mentioned.
Managing data from sources
The hidden costs, as mentioned above, include the need of manually configuring downloads of data from source systems for every software project, forever. Lack of complete and trustworthy enterprise model with detailed business metadata is the reason for it. The additional work of redefining the data forms a vital process of every project; and hence is required to be done over and over again.
Sale that did not materialize
Another hidden cost, which is less obvious or not paid heed to, comes from sales that never materialize. Inventory displayed to a customer on a webpage of an eCommerce site is not equipped with backend data to display all potential products up for sale – items not reflecting there would never be sold. This data inefficiency in its simplest form could be the gap on an order page. Or, it could be amongst those irritating instances of customer web order that informs the buyer only at the time of checkout that the product is no longer available in stock. In a reverse scenario, the product or the item was actually available, but the inventory system did not update the web pages and here as well, the sale of that product did not materialize.
Customer journey ends abruptly
This is not it. In this case, it is not merely that the sale did not happen. The customer lost trust on the webpage that supplied products. They would start buying or making purchases from more trustworthy sources elsewhere. The stinker here is that you or your business will not get a notification, when these customers start doing it. The only notification that you get is that your business continually will have diminishing number of repeat customers, and by the time you realize – it’s too late.
One of the key problems with subtle loss of such customers is, they never tell you that now they make purchases from your competition. They just walk away. You cannot capture their feelings from surveys, as lost customers are usually not in the mood to complete surveys for companies, they no longer are associated with. The saddest part is that they do not even remember the reason for the tiff – why they left, and what is the reason for distrust. But one thing is for sure – the sales from them vanished due to poor data quality.
Managers, who don’t find their reports trustworthy, bring in decision latency. A report suggesting particular sales figures is when countered with different numbers by another group; managers choose to wait and watch, to see a series of other reports to ensure that each supposed market trend is real. All this leads to wasted opportunity cost; as these managers reach out late to every new marketplace, trend and change. In other words, you can say they are providing undue advantage to agile competitors. These competitors are agile as they trust their reports. They trust their data. Trustworthy integrated data is a competitive advantage.
Keeping or maintaining customer databases separate for every department also brings in subtle business losses. Managing the data quality of such databases, never is at par and hence a challenge. Customer addresses change all the time, people get married, they have kids, they change their office addresses and a lot more is what the company is required to manage in their databases – and that too separate for each department. It proves to be a herculean task. And this fact remains same for both, corporate and individual retail customers.
Companies can assess whether they have been incurring this subtle cost due to bad data or not, by investigating the data of mailings and shipment returns. They then can tabulate the employee time invested in rectifying poor addresses and the cost incurred for re-shipping the products. So if a company has 100 employees and more than 45 of them are doing “scrap and rework”, company is merrily wasting money and resources. This goes unaccounted for, as finance department usually never counts employee time as an extra cost.
Increased shipping costs, wasted employee time and interest in work, and late customer delivery; are the elements to measure the cost of poor data quality. Companies are incurring losses big time by spending more money to degrade their customer’s experience. Never forget, data quality is directly attached to the company’s bottom line as well as the customer’s heart.