In a relatively short amount of time, we’ve gone from talking about “big data” to “analytics.” We’ve gone from churning out reports explaining the data to using analytics as a predictive tool, to forecast how customers might respond to certain events whether they had to do with politics, news or marketing.
Today, some organizations are going even further, developing approaches that create “prescriptive analytics,” where models test scenarios and recommend courses of action to achieve desired business goals.
Prescriptive analytics are a part of what the International Institute for Analytics, an independent research firm, calls “Analytics 3.0.” It defines that as a “stage of maturity where leading organizations realize measurable business impact from the combination of traditional analytics and big data.”
By embedding analytics directly into business processes and adopting new technologies, Analytics 3.0 companies “generate insights in the millions per second” rather than content themselves with sorting for useful information at a more leisurely pace.
The IIA’s ebook on the subject is worth checking out. You can find it here.
In this new world, analytics aren’t simply part of the business – they are the business.
In this brave new world, analytics aren’t simply a part of the business – they are the business. Think of Amazon, which uses analytics to present personalized product recommendations; or LinkedIn, which offers lists of people its users may know.
Both companies are pioneers of Analytics 3.0. But what strikes me as more interesting is the IIA’s vision of such capabilities spreading to organizations that operate outside of the technology world. For example, GE now places sensors in jet engines, turbines, locomotives and the like so that it can develop new services around the resulting data and analysis.
The integration of analytics into the company’s core business is sure to put new demands on your team.
Not surprisingly, integrating analytics into a company’s core business is sure to put new demands on your team. Today’s data scientists need curiosity, focus, skepticism and attention to detail. The team of the future – your Analytics 3.0 team – will have to be proficient in building relationships, as well.
- Collaboration with colleagues across multiple functional areas, especially in companies whose core business lies beyond the realms of online services or big data itself, will become increasingly necessary. As analytics becomes a core part of your organization’s culture, your team will work more closely with everyone from business-unit managers and marketing team leaders to factory foremen and transportation coordinators.
- Industry expertise. It’s always important to understand the context in which analytics are being applied. As your team members collaborate more, they’ll have to speak the language of other departments and understand how analytics decisions may play out in specialized fields.
- Closer teamwork. The organization’s focus on data will push the analytics team itself to operate as a more cohesive unit. For example, the IIA points out, the “data hackers” who extract and structure data will be required to work even more closely with the quantitative analysts who model it.
- Understanding IT. Nearly everyone involved in analytics will work closely with IT, whose professionals bear responsibility for implementing the systems used to explore, analyze and exploit data that is delivered at an increasingly rapid rate. That doesn’t mean your team needs to become technical experts themselves, but they’ll have to understand the methodologies IT follows and the often-conflicting challenges it faces.
All of this will happen under the direction of the chief analytics officer, a senior executive charged with ensuring the necessary capabilities are developed, supported and made available to stakeholders throughout the business. Already, IIA reports, companies like AIG, Bank of America and Teradata have created such C-level roles.
It shouldn’t come as a surprise that as the role of analytics expands, the skills required of data scientists and other professionals will have to evolve. When planning for your team’s future needs, be sure to consider the role of senior leadership and the demands of more versatile, collaborative professionals.