Now that companies have replaced rigid hierarchies with flatter, more fluid structures to promote agile ways of working, they have also made it harder for employees to chart a path for growth and advancement. This challenge is also a concern for employers, who must — for the sake of engagement and retention — show high performers how they can progress within the organization. Analytics can help highlight opportunities for getting ahead.
The disadvantages of asking people to rate themselves are obvious. For instance: You could fake your way to a higher score, or you might lack self-awareness. But self-report surveys have advantages, too. They make data collection efficient, and nobody but you has 24-7 access to your thoughts, feelings, and behavior. And here’s another benefit many people don’t consider: The act of answering the questions can promote greater self-awareness, which opens the door to self-development.
- Research Feature
- Read Time: 20 min
Volatility in an industry should concern not only the companies within it but also the people who work for them. To stay ahead of developments that may disrupt your professional life, you must make two evidence-based diagnoses: How volatile is your industry? And what explains the volatility? The answers will equip you to disrupt your own career preemptively.
- Read Time: 8 min
B2B Marketing departments never have enough time, manpower, or money. AI solutions such as automated emails and predictive analytics can help companies push back against these constraints. AI marketing products can act as a force multiplier. Imagine filling your headquarters with thousands of brilliant marketers, expertly analyzing data and providing actionable insights that increase the productivity of your existing marketing team.
A good psychometric test can easily outperform a résumé scan and interview at predicting job performance and retention. Yet personality testing and other ways of analyzing potential present some significant challenges: For instance, not all assessments pass the sniff test, and people’s personalities vary from moment to moment, often depending on the challenge at hand. We need a finer-grained understanding of human potential.
Baseball teams routinely use analytics to shift fielders’ positions so they can be placed where a hitter is most likely to hit the ball. This works well for preventing the opposing team from hitting and scoring — but it’s not so great for the game, which relies on base hits and scored runs to keep fans excited and engaged. Should “shifting” be banned for the sake of the fans?
Findings don’t have to be earth-shattering to be useful. In fact, obvious insights can help you overcome three barriers to change in your organization: resistance to new data (“But that’s not what my experience has shown”), resistance to change itself (“But that’s the way we’ve always done it”), and organizational uniqueness bias (“That will never work here”). You can also gain trust by confirming what people already believe.
MIT SMR Connections | Content Commissioned by SAS
Success in analytics requires having data you can trust. On Feb. 14, MIT SMR Connections and SAS will hold a Twitter chat on the best practices of building a foundation of trust toward data.
- Read Time: 6 min
The benefits of smart devices are wide-ranging: the IoT-powered communication and analytics have the potential to do more than just add convenience to our daily lives, but to actually radically improve the way we work and live. But without taking the right cybersecurity measures, products can fail before they have a chance to see wide adoption.
- Read Time: 3 min
According to a 2019 NewVantage Partners survey, fear of being disrupted is a leading factor for executives making heavy investments in AI and big data. While many are already seeing measurable results, companies that invest in their people and processes in tandem with technology may see the highest adoption.
Football players who seem mediocre in college suddenly flourish as top pro performers, while hot prospects flounder when they reach the NFL. Can teams’ recruiters and coaches accurately identify the key players that will help their team win games based on the players’ past performance? In this episode of Counterpoints, Wharton professor Cade Massey, host of “Wharton Moneyball,” argues that they can’t.
| Runtime: 0:59:36
The workforce is changing, with more and more skilled workers electing to work for themselves or become entrepreneurs. As the competition for talent heightens, intuition is no longer adequate to identify and attract — not to mention keep — the best potential employees. In this webinar, ManpowerGroup’s chief talent scientist Tomas Chamorro-Premuzic discusses the current workplace dynamic and the innovative methods to solve the talent problem, including digital tools for talent assessment.
This episode of Counterpoints examines the strategic value of data analytics — and more to the point, whether the data scientists creating the analysis are being rewarded appropriately for their contribution to strategy.
MIT SMR Connections | Content Commissioned by SAS
New research by MIT SMR Connections and SAS shows that organizations with advanced use of analytics and AI are intentionally building a foundation of trust across three critical dimensions to gain value from these technologies. Those applying analytics that incorporate AI-based technologies are fostering trust in data quality, safeguarding data assets and customer privacy, and developing organizational cultures that trust data-driven decisions.
While many businesses have embraced the idea that analytics can help improve performance, there are plenty of skeptics. Can analytics really show business leaders something old-fashioned intuition can’t? In this podcast episode, analytics expert Ben Alamar seeks proof that analytics really do lead to improved results.
- Read Time: 4 min
The value of big data is being captured by large companies, but many small businesses are being left behind. One reason: Investors get more data from larger companies, so that’s where they place their bets. Startup and small business owners must think about their data as a new class of economic asset and understand their data helps investors assess them—which affects their ability to raise capital.
- Read Time: 6 min
Companies are racing to apply machine learning to important business decisions, only to realize that the data they need doesn’t even exist yet. In essence, the fancy new AI systems are being asked to apply new techniques to the same old material. The result is a visible arms race as companies bring on machine learning coders and kick off AI initiatives alongside a behind-the-scenes, panicked race for new and different data.
In this episode of the sports analytics podcast, Counterpoints looks at the unusual case of Larry Murphy, a right-handed hockey defenseman whose support for Hall of Fame lefthanders helped two teams win the Stanley Cup in the 1990s. Was this outcome due to a unique quality Murphy brought to the game, or does a more general strategy of finding complementary talents improve team performance?
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