Original big data solutions vital, says Teradata
Business should consider creating their own big data strategies, rather than going for what others are doing, according to Teradata.
The company says that while most organisations are likely to benefit from big data, the technology needed to underpin a data analytic strategy often looks different for various organisations.
“Rather than simply going for what others are using, organisations need to identify the right mix of technology and skills to meet their business needs,” says Ross Farrelly, chief data scientist, Teradata ANZ.
Farrelly provides three tips to help companies create original big data projects that will fit their business needs:
Don’t simply copy technology decisions made by others
Technology enables an end goal. It shouldn’t be an end in itself. As long as the technology can meet business requirements, it doesn’t really matter what that technology looks like.
Organisations should define the business goals of a big data strategy before choosing the technology to drive it. This will result in a better fit to meet long-term business goals.
Don’t restrict capabilities by hiring the wrong people
Many of the job descriptions for big data roles look similar, with specific technology expertise and industry experience cropping up again and again. While some of the attributes of potential data scientist candidates will naturally be similar, it is important for organisations to look at the bigger picture.
“If companies only look for one standard set of qualifications, this can restrict the broader skillset available to them,” Farrelly says.
“Companies need people with different backgrounds and expertise, and good data scientists will pick up new technology and systems easily.”
Don’t ask the same questions over and over again
It’s not uncommon for companies to have similar end goals from successive big data projects. Higher revenue, deeper insight, and better customer engagement are recurring themes, for example.
As such, companies might have a tendency to ask their data scientists to replicate what has been done previously to achieve these goals.