Is Bad Data More Expensive than No Data?

Several advances are happening in mobile, internet and storage technology. As a result, increasing amount of data is being generated by individuals and businesses. The generation, storage, analysis of data (termed as Big Data) and its use in decision making presents itself as a tremendous opportunity for organizations like governments and enterprises. By the estimates of research firm IDC the market for Big Data is about US$ 136 Billion.

There is immense potential for building businesses around the processing of Big Data. Enterprises often times confront the fact that a large amount of data in Big Data is incorrect and bad data. An estimate by IBM mentions that the cost of Bad Data in US is about US$ 3.1 Trillion! It is but natural to ask if it is better to collect less but correct data rather than more data that is suspect and subject to later corrections.

What are the consequences of Bad Data for decision making?

Here are some consequences of basing decisions on bad data:

1. Faulty Assumptions: The assumptions made using bad data often times are faulty. For example, assume a sales team approaches a client based on faulty assumptions. Sooner or later they would discover that the premise on which they began a process itself was incorrect.
2. Incorrect Conclusions: This could be a consequence of the Faulty Assumptions made at the beginning. For example, Faulty Assumptions derived from bad data can mislead the sales team to make Incorrect Conclusions.
3. Loss of Time & Money: It derives from Faulty Assumptions and Incorrect Conclusions. A sales team not only loses time in its pursuit of a customer deal it is also in danger of losing the deal itself. This will prevent it from achieving its sales targets.
4. Inefficient Processes: An Enterprise may design inefficient processes on the premise of Bad data. This in turn will affect the ability of the Enterprise to service its customers as well as its employees.

An example where some or all the above is visible  is in after sales service. Most Enterprises provide this to a customer who has bought their product. Assume the salesperson has not captured the correct product and customer details. The service department and the customer will experience considerable strain before getting the correct information in place. To overcome this Enterprises must focus on  defining data correction as part of their customer services process. This is a wasteful and redundant process. Collection of correct data at source can obviate this.

One of the solutions to overcome this situation is, to provide the field sales force with custom mobile apps that  can capture customer data at the source, easily and accurately. This will

• make the data collection process efficient,
• ensure that the Enterprise has the most accurate information available across all customer touchpoints at all times.