Companies deciding to launch into business analytics will likely wonder what technological and organizational competencies can ensure their success. Business intelligence projects are certainly a sound idea in the current data-saturated age. They are not, however, automatic successes and require finesse and forethought. TechTarget recently published industry expert Wayne Eckerson's advice on the subject, wherein he prescribed several must-have components for business intelligence, including data quality
.Building a foundation
There are several pillars that companies must develop before they can launch a business intelligence process with confidence. After all, the function of such programs is to give users insights they can trust when they make decisions. If the underlying facts are not correct, the results will be similarly tainted. Eckerson noted several different data quality metrics companies will need to monitor to make sure their processes are solid. They have to ensure their information is not missing and that it reflects the current state of events. Consistency throughout archives is also vital.
Eckerson explained that trust is what is really at issue in data quality challenges. He noted that users may stop believing in the value of their information if it shows persistent problems. Even if things improve or are good on the whole, a few bad experiences with data can sour a company on the entire concept.
Making sure users actually take advantage of analytics software is critical. Strong modern systems are only an improvement on past practices and a real competitive advantage if they see use.
There are many different considerations surrounding enterprise data usage in an analytics context. According to Eckerson, the enthusiasm for such projects should extend to the very top of the corporate structure. He noted that the "culture" of analytics at a firm is based on the policies embraced by executives, whether these are sketched out and implied or enacted directly and codified.Advanced dangers
As companies add more data to their offerings and become faster, the threats of data problems remains. According to Search Engine Journal contributor Riza Berkan, the debut of big data systems could be tainted by a lack of cleansing. Companies building complicated frameworks to turn contextual data into monetary returns could be in for an unfortunate shock if they never subject the information to any kind of consistency check. Even the latest technology can fall short of the mark if the underlying content is weak.