How New Managers Can Bring Teams Together Effectively

[Source image: Eugene Mymrin/Getty Images]

For centuries, managers have been befuddled by team dynamics, particularly why some team members hit it off in a heartbeat while others simply don’t. Moving from anecdotes to data-based best practices took humankind all the way until 2012, when Google embarked on a two-year journey to find an answer to what drives effective teaming once and for all.

What they found laid the foundations for modern corporate team building as we know it today.  The results of Project Aristotle, as Google called the study, are simple: After hundreds of interviews and two years of data-crunching, the study found that the best performing teams share five particular features in common, namely psychological safety, dependability, structure and clarity, meaning, and impact.

All of this sounds self-evident today, which is partially testament to how far and wide the findings have trickled down through MBA courses and leadership self-help books over the past decade. Some of Google’s findings were markedly more counterintuitive, including why consensus-driven decision making, co-location, individual performance, and extroversion seem to have little to no impact on their effectiveness.

Even though Project Aristotle wrapped up long before anyone had heard of the metaverse or quiet quitting, the study is well worth any manager’s time today.  First, given everything we know of millennials and Gen Z, it’s clear that many of the five tenets that are driving performance have become only more important over time, particularly psychological safety, meaning, and impact.

It is equally clear that our workplaces have adopted practices that are failing employees miserably by over-managing for structure and clarity to the detriment of all other drivers of teaming success.Not knowing where employees are and what they are doing presents real discomfort. In response, many managers have doubled down on clarifying roles and processes in a way that would make Frederic Taylor look like a slouch.

Daily check-ins and check-outs, software assisted monitoring, and hourly production breakdowns have become increasingly prevalent ways for managers to regain a resemblance of control over their minions who might or might not be logging in from Aruba at any given moment.

At the same time, managers across disciplines have adopted Agile ways of working in hopes of replicating the successes the process has brought to the field of IT development in particular. There is much good to be said about Agile for good reason, but when applied with the wrong intentions, it can quickly turn into micromanaging on steroids.

The first casualty of over-compartmentalization and overmanagement of work is the ability of employees to self-actualize and find meaning and impact in their work.  Omnipresent monitoring and hourly timesheets also wreak havoc on dependability, making it a matter of rote compliance instead of a carefully cultivated sense of trust and accountability.

Now to the good news: Hybrid, and full-remote workforces are absolutely compatible with effective teaming. Here are two ways to help you push the pendulum back toward a more balanced approach across the five drivers in case you find yourself having gone too deep on structure and clarity.

Trust

The first step is bringing trust back to the equation.The world didn’t collapse even though everyone worked from home, and your team will be just fine even if you don’t know exactly how many minutes your staff worked on any given task or how many keystrokes they produce per minute.

In fact, they’ll be far better for it. Leaning in on the flexibility and adaptability of modern ways of working will foster psychological safety and promote engagement and ownership in ways that will have far greater dividends than monitoring and oversight ever could. People are fundamentally reciprocal, and the best way of building trust is to exhibit trust.

One particularly effective method is for managers to focus on setting goals and milestones and giving the team the autonomy to seek out the best way to accomplish them. Tolerating failures and the occasional missteps is critical for making the autonomy feel real, and it goes a long way in making employees feel safe to explore their roles.

Another equally powerful method is to transform stands-ups and check-ins from reporting to supporting events. Recasting regularly scheduled meetings as opportunities for helping team members overcome blockers instead of simply checking on progress made is by far one of the most powerful moves a manager can do to breed trust and self-actualization. Co-location is another practice that is ripe for transformation.

Biting the bullet and going fully flexible might feel risky, but there is no reason to believe that any arbitrary pre-established mix of in-office and at-home days works better than allowing teams to find the norms that work best for them. Trusting your teams to work effectively from wherever they are is the right choice in particular because of how it immediately casts dependability in terms availability and outputs instead of physical presence.

Instead of the default option, the office should be recast as an enticing alternative to which teams naturally gravitate to when they need to share physical spaces for collaboration and team building. Brown bag seminars, in-office baristas, and a few Pelotons can go much further than you think, and putting the emphasis on tangible moments of collaboration is key.

Self-actualization

Second, you should ensure that the roles and processes set in place leave space for self-actualization and personalized ownership of one’s work. If you’re using Agile, be cognizant that a 100-meter dash doesn’t come anywhere close to the sense of meaning and impact that running a marathon brings.

Not everyone’s work will be foundational, but everyone’s contributions should feel necessary and acknowledged as contributing to goal deeper than wrapping up a two-week work package.The trick is to make sure that even when working on their own units each employee can visualize a clear pathway from their work to the grand “why” of it all.

This is where the importance of setting goals mentioned earlier becomes particularly clear. The best thing a manager can do when running multiple sprints is to maintain a cohesive narrative of how the work contributes to the larger organizational goal. Often this means distilling vision statements, contractual milestones and even change requests into meaningful arguments about why the work we do today matters.

This may not always be simple to pull off, but who said being a good manager was supposed to be easy? Ultimately, what particular steps you end up taking matter far less than the sincerity of your commitment to making effective teaming a priority for your organization. As for now, it’s more than likely to be pushing back on the overemphasis on structure and clarity.

By Alexander Puutio

Alexander Puutio is an adjunct professor at NYU Stern where he explores the interplay between business leadership and our society at large.

Source: How to manage teams effectively

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How The Power Of Predictive Analytics Can Transform Business

Tableau analytics visual

With the acceleration of digital transformation in business, most CTOs, CIOs, and even middle management or analysts are now asking, “What’s next with data?” and what ongoing role will technology play in both digital and data transformations. Other questions that keep these individuals up at night include:

  • How can people throughout all organizational levels be more empowered to use data and help others make better decisions?
  • What prevents people from more deeply exploring and using data?
  • In what ways can analytics tools and methods help more people use data in the daily routine of business—asking questions, exploring hypotheses, and testing ideas?

With this in mind, plus observations and discussions with many Tableau customers and partners, it seems that today’s circumstances, behaviors, and needs make it the right time for predictive data analytics to help businesses and their people solve problems effectively.

Current realities and barriers to scale smarter decision-making with AI 

With growing, diverse data sets being collected, the analytics use cases to transform data into valuable insights are growing just as fast. Today, a wide range of tools and focused teams specialize in uncovering data insights to inform decision-making, but where organizations struggle is striking the right balance between activating highly technical data experts and business teams with deep domain experience.

Until now, using artificial intelligence (AI), machine learning (ML), and other statistical methods to solve business problems was mostly the domain of data scientists. Many organizations have small data science teams focused on specific, mission-critical, and highly scalable problems, but those teams usually have a long project list to handle.

At the same time though, there are a large number of business decisions that rely on experience, knowledge, and data—and that would greatly benefit from applying more advanced analysis techniques. People with domain knowledge and proximity to the business data could benefit greatly, if they had access to these techniques.

Instead, there’s currently a back-and-forth process of relying on data scientists and ML practitioners to build and deploy custom models—a cycle that lacks agility and the ability to iterate quickly. By the end, the data that the model was trained on could be stale and the process starts again. But organizations depend on business users to make key decisions daily that don’t rise to the priority level of their central data science team.

The opportunity to solve data science challenges

This is where there’s an opportunity to democratize data science capabilities, minimizing the trade-offs between extreme precision and control versus the time to insight—and the ability to take action on these insights. If we can give people tools or enhanced features to better apply predictive analytics techniques to business problems, data scientists can gain time back to focus on more complex problems. With this approach, business leaders can enable more teams to make data-driven decisions while continuing to keep up with the pace of business. Additional benefits gained from democratizing data science in this way include:

  • Reducing data exploration and prep work
  • Empowering analyst experts to deliver data science outputs at lower costs
  • Increasing the likelihood of producing successful models with more exploration of use cases by domain experts
  • Extending, automating, and accelerating analysis for business groups and domain experts
  • Reducing time and costs spent on deploying and integrating models
  • Promoting responsible use of data and AI with improved transparency and receiving guidance on how to minimize or address bias

Business scenarios that benefit from predictive analytics 

There are several business scenarios where predictive capabilities can be immensely useful.

Sales and marketing departments can apply it to lead scoring, opportunity scoring, predicting time to close, and many other CRM-related cases. Manufacturers and retailers can use it to help with supply chain distribution and optimization, forecasting consumer demand, and exploring adding new products to their mix. Human resources can use it to assess the likelihood of candidates accepting an offer, and how they can adjust salary and benefits to meet a candidate’s values. And companies can use it to explore office space options and costs. These are just a few of the potential scenarios.

A solution to consider: Tableau Business Science

We are only at the beginning of exploring what predictive capabilities in the hands of people closely aligned with the business will unlock. AI and ML will continue to advance. More organizations, in a similar focus as Tableau, will also keep looking for techniques that can help people closest to the business see, understand, and use data in new ways to ask and answer questions, uncover insights, solve problems, and take action.

This spring Tableau introduced a new class of AI-powered analytics that gives predictive capabilities to people who are close to the business. In this next stage of expanded data exploration and use, we hope business leaders embrace data to help others make better decisions, and to provide transparent insight into the factors influencing those decisions.

When people can think with their data—when analysis is more about asking and answering questions than learning complex software or skills—that’s when human potential will be unleashed, leading to amazing outcomes. Learn more about Tableau Business Science, what this technology gives business teams, and the value it delivers to existing workflows.

Olivia Nix is a Senior Manager of Product Marketing at Tableau. She leads a team focused on the use of AI and ML in analytics and engagement, including how to use technology to enable more people in organizations to make data-driven decisions. Olivia has been at Tableau for four years where she has worked closely with development teams on new product launches. Prior to Tableau, Olivia worked as an analyst at the Pew Center on Global Climate Change (now C2ES) and Johnson Controls. She has her MBA from the UCLA Anderson School of Management.

Source: How The Power Of Predictive Analytics Can Transform Business

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Critics:

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.

The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.

Predictive analytics is used in actuarial science,marketing,business management, sports/fantasy sports, insurance,policing, telecommunications,retail, travel, mobility, healthcare, child protection, pharmaceuticals,capacity planning, social networking and other fields.

One of the best-known applications is credit scoring,[1] which is used throughout business management. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.

Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining.Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.

For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.

Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, “Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.”In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization.

See also

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