It’s no secret that the past decade has seen a significant rise of knowledge work; this trend will only continue to increase. However, what is fundamentally different about today’s knowledge workers is that effective and influential employees aren’t those who have all of the information (or those who hoard it), but those who know how to look for it and find it by asking the right questions. In a way, they are defined by the questions they ask and the answers they can find, not by the ones they already have.
Empirical studies of knowledge workers (Reinhardt et al., 2011, p. 160) highlight that the (expected) typical knowledge actions almost always includes analyzing information. TIBCO CEO Vivek Ranadivé outlined in 2011 how “math trumps science” and that “our job is to find the patterns.” In essence, we don’t necessarily need to understand why something happens, but what has happened, and therefore what will happen, by leveraging available data.
A Shift in the Way We Think
To thrive, let alone survive, in today’s economy, leading organizations place emphasis on asking the right questions, and pushing past reservations to challenge the status quo. Those who are stuck in their ways won’t last. In fact, new entrants to today’s job market—the “Generation whY” type, accustomed to asking questions and having been nurtured to do so—and newcomers to an organization readily find their place as they ask questions and come with a fresh perspective.
Supporting this transformational shift requires most than just a new mindset: It requires new paradigms and solutions, supported by technology, such as pattern recognition, data quality and governance, and analytics. Used wisely, these solutions redefine an organization’s relationship with its customers, partners, suppliers, and employees. Together, they are the knowledge worker’s jet propulsion system.
Organize Data to Ask Tough Questions
With Big Data upon us, it becomes all the more important to “make sense of it all.” Although hoards of data are being collected and archived, it is only useful if leveraged to its full potential. It’s naïve, at best, to think that all of this data can be properly managed and stored in a massive data warehouse or operational data store, especially since big chunks of data are time-sensitive. For example, if a customer clicks “Like” on Facebook or pins an article on Pinterest, the value of this information is only good for a relatively short period of time. The same holds true for clicks on a website. Therefore, the ability to act on this information in real time is key. In support of this, pattern matching and machine learning engines help find similarities in mountains of data with record speed and accuracy. This nifty online demo shows what’s possible.
Standards work by ensuring normalization in language and dialogue, which allows us to communicate effectively. Can the same be said for your data? If your sales and marketing data can’t be combined to form an adequate representation of your customer, it’s not possible to understand how effective promotions and campaigns are in driving sales. In manufacturing, if supplier data isn’t effectively integrated to manage your supply chain, it becomes very difficult to ask the tough questions that help improve process. Both examples show why it’s important to have adequate normalization of data, or data quality, and ongoing governance to ensure that everyone speaks the same language. (An IDC Case Study is available here.) Only then can the questions we ask be understood by those involved.
It all comes together when real-time (short-lived) data and long-term data from data warehouses are combined to ask “what-if” type questions. The combination of the two enables knowledge workers to drill down to specific items and look for the patterns and trends, so as to understand how they will affect the organization down the road.
In essence, a knowledge worker’s value in today’s workplace is about the questions he asks and what the organization does with them. In support of this digital transformation are a series of solutions that TIBCO customers have come to rely upon to further their business. Now, what will you do? Will you ask the right questions and prove your value to the organization?
Ensure your company is equipped to ask tough questions by reading Four Clues Your Organization Suffers from Inefficient Integration, ERP Integration Part 1.