The second one is, you cannot leverage new-age technology if your business processes are still old fashioned. You'll require new positions and new skills—especially in mathematical and statistically oriented knowledge—to fully utilise those tools. If not, project take-off will be much slower than it should be.
What new skills must be acquired to fully take advantage of analytics? Where should organisations be looking for such skills or could they be acquired through in-house training or external skills upgrading?
New technology and new skills will certainly be required, in addition to understanding how structured and unstructured data can be used to bring new business insights. One role is obviously that of a data scientist who understands data, quantum arithmetic, and who has the knack for recognising or spotting data patterns so that he or she need to work on say only 5 percent of the data that is really useful instead of the entire data set.
An organisation may soon require a chief data officer who can lead its big data endeavours. Already, there are many learning institutions offering training courses on data science. We see that internally, the organisation might place a pure scientist with statistical and mathematical training to work together with other employees with domain expertise, as one approach towards fulfilling that role. For now, there will be a struggle to find such a person.
According to The Data Directive research sponsored by Wipro, those companies that claim to be best at extracting insights from data are not necessarily looking to their CIOs to lead on this initiative. The CIO may be the default leader for many of them, but increasingly, there's preference towards the CEO and other C-suite leaders.
This group is also nearly eight times as likely to have a data management strategy; and four times as likely to have changed the way they make strategic decisions. As with the high-growth firms sub-set, they are typically far more likely to see scope for radical transformation of the business through better use of data.
What's involved in real-time analysis of data? Can organisations take advantage of what they already have, or must there be new platforms and apps to be deployed first?
First is the need for compute power, but this requires a fairly different approach to how it is used in the past. Take for example of an online purchase recommendation engine to online customers. To come up with the right suggestions, the engine barely has a few seconds to generate some accurate results (based on mouse clicks, screen navigation, product selection history, etc.) for the customer to stick around. The ability to handle all this structured and unstructured data in real-time then becomes very important.
Sign up for Computerworld eNewsletters.