Analyzing raw information to try and find useful patterns and trends in it is not easy to accomplish. As new tools and technologies emerge, data mining is becoming an expensive process. There are several costs involved in the process. The data has to be cleaned and integrated. Next, it has to undergo additional processing to convert into a data set which would allow efficient mining. In fact, according to some estimates, such preprocessing tasks take up as much as 50-80% of the time and effort that goes into the mining process. The time and money spent on these efforts comprises much of the cost involved in data mining. The paradox is that companies that are reluctant to invest in mining relevant data cannot compete effectively in their niche.
Businesses usually mine data to understand their customers, predict their behavior, and plan for the future. ION Geophysical Corporation (ION), a leading technology-focused seismic solutions company, is widening its data mining applications. For the last 20 years, the company had been mining data to solve customer problems. Recently, it started using these tools in its business to shape its budgeting and capital-spending plans, which would definitely increase its operational costs.
Costs involved in Data Mining
Costs of Hardware: Significant storage and computational resources are required for data mining. Setting up the necessary infrastructure for data mining and its maintenance, is costly.
Costs for licensing or purchasing data mining software: Cost associated with software may vary according to the features they provide. Even though, some of them are freely accessible, others have licensing fees.
Costs for Data collection: Collecting data from multiple sources can make the data mining process more efficient. For instance, ION collects data from customer-relationship-management system, earnings-call transcripts and analyst reports. Sometimes, due to unavailability of data, it may have to be purchased from some source. Expensive data collection strategies may have to be implemented to collect data.
Costs for Data Preparation: To make the data ready, data cleansing has be performed.
Cost of Staffing: Data mining does not work merely by loading raw data and software on to the computer. To handle data mining efficiently, the service of a trained data mining analyst is indispensable. Higher-level platforms are better for data mining than outdated ones. Training employees on the new software can be expensive and time consuming. It is difficult for employees accustomed with an older system to engage with the new.
The following measures can help cut the costs of mining data:
- Redesigning processes helps to eliminate the duplication of time and effort
- Eliminating inefficiency by converting paper-based systems into digital format
- Make more use of technology and automation
According to Henry Ristuccia, Deloitte’s global leader of governance, culling data to extract intelligent information (that steers investments) is the scariest part for most companies. If companies don’t know what to do with the data, then all their efforts will be in vain. The process may take much of the company’s time and resources. A viable solution to this is to associate with a professional BPO company that offers data mining services at affordable rates.