Specific big data can provide operational agility leading to the reformation of business processes and ultimately better performance.
System of record
The other element of big data that doesn't get talked about as much as breakthrough information producing competitive advantage, is the operational agility that effective big data analytics can produce. It is the structured, system of record, data that contributes the most to this agility - and its contribution is vested in the reformation of business processes that can be tuned for better performance.
Here are some current business process headaches that poor system of record data in companies creates:
- Part descriptions initially entered by the company's engineering department do not reflect the nomenclature that is used in the field, so someone has to manually go in and change them. Meanwhile, service reps have a hard time determining the correct parts to use in their daily work.
- Part and assembly revision levels are difficult to synchronize for an aerospace company that must maintain three different sets of part and assembly levels - one that is internal, one that reflects the original part numbers from OEMs, and one that reflects part and assembly numbers that military customers want assigned.
- Corporate charts of accounts become so complicated that it's difficult to meet month-end close dates.
These situations have been "accepted" by companies. For years, they have put their shoulders to the wheel - correcting data "on the go" and as needed in the course of a business day. But now that the rise of big data has suddenly made dealing with data fashionable, they are beginning to look at the cost of poor and replicated data that manifests itself in inefficient business processes that take too long and limit corporate agility.
Relevance from data
"Companies want to see business relevance from their data," said Rex Ahlstrom, Chief Strategy Officer at BackOffice Associates, which focuses on the quality of ERP (enterprise resource planning) data and has "Expect More from Your Data" as its company motto." In the past, data quality has been a focus of IT, but now more companies want to see an alignment of the business with IT so they can improve the quality of the data that support their business processes."
Ahlstrom explained how automated tools could analyze data from systems of record, determining which data really were necessarily to support a business process, and which were extraneous.
"What we are getting at here is data that is not only good from a quality standpoint, but data that is business-relevant," said Ahlstrom. "In one case, a mining and exploration company had over 2,500 chart of account entries, but when they performed an analysis of their data, they found that they only needed 250."
Ahlstrom explained how the automated process worked.
"The tool takes a look at all of the data elements and then performs an analysis that determines which of these data elements are actively used by a business process. We then use the content to build a series of reports that give individuals in the organization the opportunity to look at the data that really supports the business process."
The technique enables companies to fine-tune their business processes - and also the amount of data being pulled into them. As extraneous data is pared from the business process (as in the chart of accounts example), the business process becomes more agile and quicker and easier to perform.
"We're seeing a change in mindset now," said Ahlstrom, "From a time when companies performed more passive data governance to a new time when users begin to understand the impact of data across different business processes."
How far companies decide to take business process-oriented big data initiatives remains to be seen. But if a retailer can improve the visibility and the processes along its supply chain or an oil and gas company can reduce time to market for a new product because of business process simplification brought on by data reforms - the economics can be compelling.