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Nod your head if you have ever heard, seen or (heaven forbid), quoted this statistic: “70% of transformational change efforts fail.”

You nodded, right? Let’s face it; the 70% failure statistic is dramatic. It builds the case for hiring experienced transformational change practitioners. It cautions implementers to learn about change management practices and integrate them into their tactical tasks.

Unfortunately, it’s a made-up number. Back in the 90s, Michael Hammer speculated about the success rate of re-engineering projects and since then, authors and speakers have cited 70% as the failure rate for all types of change programs. Several transformational change practitioners have dug into the change archives and vigorously refuted it. (See here and here) Yet, it persists.

Even if no one had refuted the number, I stopped believing it years ago. As a measurement practitioner, I have found that:

• Few organizations are disciplined or adept at identifying measures of success at the outset of their projects;
• The data to measure success is often difficult to collect;
• The evidence of success can rarely be attributed solely to the change effort;
• Leaders move the finish line or unexpected circumstances cause it to move;
• The initial sponsor leaves and her replacement does not revisit the measures.

Given all this evidence against it, how can anyone state with such certainty that 70% of change projects fail?

Make Measurement Simple, But Not Simplistic

Here is a suggestion: instead of quoting a fabricated and meaningless statistic, why not apply some discipline to your change projects and evaluate your own success rate? Conceptually, it’s simple. You identify what you expect to happen, you measure what happened, and you determine why you achieved or did not achieve your goals.

“Ah,” you say, “the world is complicated! Organizations, leaders, and goals change. The competitive landscape shifts. The economy doesn’t behave as predicted. And the larger the project, the greater the risk and uncertainty we face, which makes measurement that much more challenging.”

It’s true that measuring large, complex, multi-year projects can be daunting. But what are your options? Throw up your arms and avoid measurement altogether? Set goals but never evaluate your success? Or, adopt a simple but flexible framework that helps you learn as you go, adjust your direction, and achieve your goals? I suggest the last option.

Your measurement process doesn’t need to be complicated. First, break your project into phases such as:

1. Diagnosis and solution design
2. Planning and resourcing
3. Execution
4. Realizing the future state

At each stage, ask a series of questions to assess your readiness to move to the next stage. The questions are basic and essential for any major project, transformational or not. You can and should customize these questions for your organization based on what you expect to achieve. Regardless of the questions chosen, the primary aim at this point is to identify and agree on what questions to answer (see the graphic below).

After you identify the questions and get agreement from the program sponsors and key stakeholders, identify the measurement indicators. This step should be easy if you have identified the right questions. Below are possible indicators for each of the four stages and questions outlined above.

How Does This Work in the Wild?

You may be thinking: “This sounds like a lot of work.” Yes, measuring the success of transformational change takes time, but it also helps the organization stay focused on the goal. Without clear goals and a measurement process, how will you know if the investment in time, money, and resources has produced value for clients, shareholders, and employees?

Let me share a recent client experience with a Silicon Valley technology firm to illustrate how this process works. For the past five years, the organization had been quite successful. However, one segment of the business faced major competitive threats but could not effectively compete due to an aging services portfolio. They were often late to market on new services and had lost touch with shifting customer expectations. They were ill-equipped to address these gaps due to inefficient work processes, an outdated organizational structure, and technology that did not support continuous innovation.

Their opportunity was to transform the organization by rebuilding its services portfolio and go-to-market processes. The sponsors set an 18-month time frame from project launch to full implementation. Throughout the project, the sponsors reiterated the famous Darwin quote: “It is not the strongest of the species that survive, nor the most intelligent, but the ones most responsive to change.”

The project was complex and required involvement from three different teams reporting to three different senior leaders. Project discipline was key to success. In addition, the program sponsors insisted on measuring success not only as the project moved from stage to stage but most critically, for the outcome of the entire effort. They defined outcome indicators at four levels: financial, customer/partners, operational, and learning /culture and constructed a measurement model aligned to the project roadmap (see below).

As the team drilled into each measure, they discovered that much of the data was not available or too difficult to gather. The sponsors agreed to simplify their approach. They replaced some of the hard indicators with ‘soft’ indicators from surveys and used proxy measures where data was readily available. They also recognized that evidence of the outcomes could take several years. To ensure the transformation was moving in the right direction, the team added progress measures to the mix. Everyone agreed that imperfect data was better than no data at all. Their final measurement framework is shown below.

When the project completed the “Make it Work” stage, the senior leaders concluded they should move the project into operations where segment leaders would adopt the measures as part of their goals. (Find more information on this case study here.)

Twelve months later, I followed up with several members of the project team. They felt the project had accomplished many, but not all the objectives. They acknowledged they still had work to do, including process improvements and reduced cycle time for new services. The segment leaders acknowledged the need for continued focus on the future state and ongoing communication about the vision and its importance to the business.

Was the project successful? In the minds of the sponsor and the team, yes. Did it achieve 100% of its goals? No. Was the investment worth it? The organization believes it was.

Final Thoughts

Transformational projects often take years to complete. Often, what senior leaders envision for the future state shifts as the effort progresses. Some portions of the project work, some don’t and ideally, the team learns from both.

Is it realistic to evaluate a program based on a single overall metric of success or failure? I don’t think so.  Rather, identify a suite of balanced measures and evaluate your project against all of them. At that point, you don’t need to worry about the 70% number.

The post Stop Guessing: How to Measure the Impact of Transformational Change appeared first on gothamCulture.

Think about the Internet. Most of us weren’t even aware of it until the mid 90’s, but where would we be today without it? Although I type here from the comfort of my office chair, my office is at home and I rarely need to venture into NYC thanks to technology. My office material comes from Amazon.com and my calls are handled over a VOIP platform. All driven by the web.

Knowledge management is a similar area you’ve probably never paid attention to. Maybe you haven’t heard about it yet, but knowledge management is already affecting how you live and work.

At home, no one owns an Encyclopedia Britannica anymore. We find that information elsewhere, like Wikipedia. When we’re on the go, as long as we have our smartphones, apps like Yelp, Waze, Groupon, Instagram, and YouTube are allowing us to contribute to and draw from knowledge management.

Can you see the commonality?

• Providing information to a group expressing interest in the topic
• Harnessing feedback from that group to further refine the content
• Further educating the group, turning them into subject matter experts
• Creating a “center of gravity” for experts and information alike

What is Knowledge Management?

The definition of knowledge management is how an organization creates, shares, uses, and manages information. It refers to improving the organization organically by making the best use of the knowledge at its disposal.

Imagine being able to extract the lessons learned from every piece of work in the company portfolio. Or finding what you’re looking for without rifling through folders or needing to ask dozens of coworkers. Wouldn’t that make your daily work life easier?

In the simplest variant, a Knowledge Management system consists of the following:

• A simple internal process to capture company knowledge
• A Community of Practice (internal experts) to review information that comes in
• Technology to support this process and the Community of Practice

The good news is this: Your company is probably already halfway there and you didn’t even know it! At work, are you using a Microsoft SharePoint server, cloud storage, or a shared drive for people to store documents instead of on individual desktops? Maybe you use Adobe EchoSign to quickly sign documents electronically? And it’s not just technology and software. Simple changes to staffing and process workflow can have equally large impacts (and should be considered prior to software purchases anyway).

The Benefits of Knowledge Management Systems

Now for the bad news. Knowledge management may provide large benefits, but you’re not going to get there overnight. You’re going to have to approach this step-by-step. To implement knowledge management the very first time, adhere to the following process:

1. Develop your problem statement / define the problem you’re trying to solve
2. Isolate a portion of your organization that you think is either interested in or is competent to brainstorm solutions to this problem. This becomes your initial network
3. Create a call to action and brand your campaign / get your entire organization on board and get them interested!
4. Develop a simple process to capture ideas (suggestion box/feedback card/website)
5. Solicit ideas/review ideas with your initial network
6. Implement quick wins & offer praise to the problem contributors

Organizational learning and development becomes more difficult to manage as your company grows. Without managing critical information, team members may take important pieces of tribal knowledge with them when they leave, new employees are forced to learn their roles without any guidance, and a tremendous amount of time is wasted learning and relearning the same processes in inconsistent ways.

But as an organization matures, it forms stronger linkages between leadership, culture, and strategy to allow for longer-term operations planning and enhanced daily operational performance. So why not start considering knowledge management to help support this?

Knowledge management helps gather the power from your entire organization and use it to incrementally improve your daily operations. It enables organizations to learn more intuitively, allowing companies to innovate better through knowledge-sharing organizational structures, processes, and tools. By making their jobs easier and providing a platform to learn new skills, you can engage your workforce by making their work more interesting and relevant. That effort ultimately leads to a series of advantages which, day by day, helps your company stack up better than your competition.

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“But I’m not a trained researcher, or a data scientist, or a….” I hear you begin to clamber.

Luckily, you don’t need letters after your name to be a smart consumer of data and findings. You only need patience and confidence as you thoughtfully consider the information in front of you. Remember that while you may not be a statistical wizard, you do bring your own flavor of insight and expertise to the table.

Here are a few suggestions to help you become a better consumer of the data and other information you have available.

Focus on knowing enough not to be dangerous

While many people may tell you that you need to know enough to be dangerous, I argue the opposite is true. Knowing enough to know what you don’t know is so powerful, especially when dealing with large amounts of information. Being honest with yourself about your skill and knowledge levels will help you understand who you may need work with to better understand the data and its implications.

No one expects you to be an expert at everything, and you shouldn’t expect that either. Take this as an opportunity to learn and collaborate, and you will not only better understand what you’re looking at, you might also walk away with additional knowledge to help you the next time around.

Ask questions

You may not be a researcher or the data analyst, so ask the team in charge of the data how it was collected and what they see as the most interesting findings. Partnering with the experts in your organization will allow you to get the answers you need, even if you can’t find them yourself. It’s your responsibility, though, to not assume that they’ll simply hand you everything you need. You need to ask insightful follow-up questions and dig deeper into the data to uncover any assumptions or contextual factors that might have influenced the reported findings.

Focus on context

Data and findings do not exist in a vacuum; they have meaning once you begin to interpret them and put what you learn into the context of the bigger picture. At your organization, you know more about the context and environment than an outside consultant or data specialist ever will. You know your organization’s stories, unspoken rules, and knowledge that get exchanged at the water cooler. Your organizational knowledge will help you look at the data from the right angle and ask the right questions to better understand the findings.

Look for trends, not snapshots

Pulse data can tell you a lot about your organization and the people in it, but it can also leave you blind to bigger trends that might trump what you learn from a single data point. When looking at employee engagement data from a single year, for example, you may find numbers to be lower than you might like. However, they may be much higher (or lower) than they were the year before, which would tell you about how your organization is trending.

This trend data gives you more robust and contextual insight into the state of your organization. Were changes implemented following last year’s low engagement numbers? Does this year’s increase suggest that these changes impacted engagement? Looking at data across time can help you more accurately tell your organization’s story.

Data analysis and interpretation may never be your day job. But you have more tools and experiences at your fingertips than you may think. You can use what you know and who you know to make sense of the data at hand. With a little confidence—and perhaps a little practice—you’ll lend unique insights to the meaning and implications of the information.

Don’t let your inexperience or discomfort around data deter you from digging in and understanding how this information can improve your work, and your organization as a whole, through informed decision-making.

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But the real challenge goes well beyond merely accessing more data. The key is accessing data in the right way, at the right time, and in the right format to generate beneficial insights.

This process is no small feat. It requires both technology and human analysis in order to identify these critical insights for business leaders. It has historically meant a team of highly specialized data analysts spending hours upon hours sifting through terabytes (or more!) of information to make it digestible and useful.

But that’s all about to change in a big way.

Meet Watson, IBM’s groundbreaking effort to change the future as we know it using Artificial Intelligence (AI).

 “Five years ago, IBM built this system made up of 90 servers and 15 terabytes of memory—enough capacity to process all the books in the American Library of Congress,” states Charlie Rose in a recent 60 Minutes feature. “That was necessary because Watson is an avid reader—able to consume the equivalent of a million books per second. Today, Watson’s hardware is much smaller, but it is just as smart.”

IBM has invested billions of dollars and gathered some of the most brilliant scientific minds available to move AI past its current application in smart phones and home appliances.

Back in 2011, Watson landed a spot on the game show “Jeopardy!” to compete against two of the show’s champions. IBM’s computer system won that night, but IBM has its sights set on much bigger and more meaningful goals.

How Watson is Helping Cancer Patients

Oncology teams currently review cancer patient cases and studies themselves, working diligently to develop treatment strategies. But consider this: each day over 8,000 new clinical trials and studies are published on the topic. The sheer amount of data is simply impossible to keep up with. Meaning, these review teams must design treatment strategies based on incomplete or outdated information.

Regardless of how hard these teams work, they can’t possibly be expected to keep up with the most current information available; information that could possibly inform the development of less destructive, more effective cancer treatment strategies.

Watson is currently being tutored by twenty top cancer institutes. It can read the equivalent of one million books per second, and it never forgets. As Watson is given data, it continues to learn, something that its computer forefathers were incapable of doing.

Using machine learning algorithms, Watson is able to draw its own conclusions based off of the massive amounts of information it consumes. After reading twenty-five million medical studies and trials (its first week on the job, mind you) and scanning the internet for additional trial information, Watson was put to the test, analyzing one thousand actual oncology cases alongside oncology review teams.

The result? In 99 percent of cases, Watson drew the same conclusions as its human counterparts. But that’s not the really exciting part. In 30 percent of those cases, Watson found something new.

By accessing the latest, up-to-the-minute research, Watson was able to identify new strategies that the review team would have otherwise missed.

And now, IBM is making this technology available to employees. As of January 1^st, 2017, qualifying IBMers and their covered family members and partners will have access to Watson’s oncology services. This announcement opens new horizons for their employees, as well as another opportunity to develop Watson through actual case studies.

What’s Next for Watson?

For those of you imagining the day Terminators take over, rest easy that IBM’s vision is for AI to be used for very specific tasks that augment human decision-making, keeping machines dependent on man.

Take, for instance, how Watson was used as a teaching assistant in an AI class at the Georgia Institute of Technology. “By utilizing AI to handle routine questions and tasks, we are able to free up people’s time so they can focus on adding value in other ways,” says Bob Schultz, General Manager of Smarter Workforce, IBM. “The system can be utilized to drive key insights from massive amounts of information, and it can learn how to present it in a useful way as it learns.”

Imagine the possibilities if your business had this capability to manage data. From recruiting to production, from strategy to employee engagement, work life as we know it would be disrupted in ways that we can’t yet imagine.

While some well-known folks fall on both sides of the AI debate, one thing is undeniable: we’re only just beginning to uncover the potential of this new and exciting technology, and I’ll be continuing to explore this topic further as we learn more.

This article originally appeared on Forbes.

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There are several barriers that can keep an organization from adopting Lean. This article will cover 2 of them: Lean history and corporate biases. I will also show how to make an office environment Lean.

Lean History

Your ability to optimize something depends largely on what your history has been.

For example, the United States has been awash with cheap gasoline for most of the past century. Europe, however, has always had higher taxes on fuel since WW2 to fund war reparations and major infrastructure repairs. Fuel prices in Europe today range from 2-5x the price for US petrol. With that in mind, how do the US and Europe generally monitor vehicle fuel consumption?

In the US, we track miles per gallon. At one point, I’m sure 10 mpg might have been a good measure. Today, extremely efficient vehicles are 42 mpg or more. But I ask you, what is the ideal? The mpg metric is not transparent and therefore, not in line with Lean thinking. In Europe, by contrast, mpg is not used at all. They track “liters consumed per 100 kilometers”. 10 years ago, vehicles requiring 15 Liters per 100 km were commonplace. Today, very efficient vehicles consume 5 Liters per 100 kilometers, showing that fuel efficiency has tripled within 1 decade! This L/100km equation shows optimization potential in a way that mpg cannot.

It all comes down to perspective and transparency to see where waste exists.

Corporate Biases

Another big reason a lean initiative won’t get off the ground is because of a general corporate bias against Lean thinking.

Let’s face it—Lean was developed in production environments and nowadays fewer of us work in factories than ever before. “We’re different here!” some would say. So if it wasn’t developed or piloted in your own area, it’s easy to write it off as part of the ‘Not Invented Here’ syndrome.

Corporations and cultures can become like animals, taking on a life of their own with groupthink prevailing. Again, history plays a role here. For example, if a manager has progressed through his or her career by working a series of difficult jobs in order to be promoted, they feel the next generation must be willing to sacrifice the same or more to get ahead.

With most organizations being hierarchical, having pyramid shaped corporate ladders, it’s easy for workers to see the “up or out” principle is alive and well. Therefore, organizations can become ingrained in their thinking, waiting for leadership to address problems even though the workers who face the problems daily may be best equipped to mitigate them.

But, rocking the boat and empowering people to do their jobs better isn’t always a senior manager’s top priority. This is not to downplay the typical manager out there who certainly has to work hard to stay relevant within the organization; it’s just easier to dictate what needs to be done than listen to suggestions from the shop floor because workers lack the top-level view.

In the end, corporate biases like these can create a lot of wasted resources. There’s a difference between being BUSY and being PRODUCTIVE. It’s about time companies start to appreciate that.

Lean Office: The Final Frontier!

Office spaces are the final frontier of lean. Lean within factories has started to mature and the concepts are relatively easy to grasp. If you can make an office think Lean, then you can replicate Lean anywhere.

With this in mind, the necessary steps to create a Lean office are the same to create a Lean factory, although the wastes you find look very different:

• Specify Value

Know who your customer is and what they consider value-adding.

• Map the Value Stream

Start with the first touch point, and work all the way to the final customer. How long does it take to add value? Identify everything else as waste.

• Establish Flow

Remove wastes and harmonize the time to produce through workload leveling and standard work. Eliminate bottlenecks as they appear and shift.

• Implement Pull

Once a flow has been created, start to produce only what is needed to the customer’s tact. Do not do extra. Build quality in and eliminate rework.

• Work to Perfection.

A lean journey is never complete. Reexamine, refine, re-implement.

It’s important that companies today seize the initiative to make office environments lean, as the transformation to more remote work environments will prove to be disastrous otherwise. Offices are flooded in waste, even more so than factories! The heart of Lean methodology is to decrease waste, so we must have the proper metrics in place to measure the effectiveness.

Take stock of the wasted resources in your office. How do you specify the value of an email exchange? Or on a company meeting? If your company sets low expense approval levels for managers (say $500), for example, yet you regularly host corporate meetings with 20 people for 1 hour, is this the most efficient use of resources? Instead of chasing costs, I propose that Return on Invested Capital is the single best top-level metric available today.

If the goal is a relentless pursuit of perfection, then a lean journey is never truly complete. My advice is to stop looking for silver bullets! Look for what is proven and works—discipline. Start measuring what is valued by your customers if you’re not already doing it. Remember Deming’s words, “If it can be measured, it can be improved.”

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But, where to start? If you are a business leader who has taken on the task of trying to identify the right tools for your organization, you no doubt came to the realization that there are an endless number of app companies that want to pitch you.

The hurdle for many business leaders in trying to solve these problems is determining which platform, or platforms, are best suited for their organization. And the total proliferation of apps claiming to solve all of your business pains only adds to the confusion.

I recently read a nice overview of the HR app market from The Starr Conspiracy Intelligence Unit. They did a fantastic job of providing a detailed review of the major players, and bucketing them into three categories: Recognition, wellness, or measurement. An overview like this is a great start, but let’s dig deeper into some key considerations to look for in a people-related tech solution for your team or organization.

What You Should Know Before Investing in an App

There are a lot of fantastic options out there to meet a wide variety of needs, and it can be quite overwhelming to figure out the most appropriate solution, or suite of solutions, for your organization. Here are a few things to consider:

1. Understand what you’re really trying to solve before searching for solutions. Doing the upfront evaluation work to determine if the app you are considering—or any app or tool available—can meet your organization’s needs may save you thousands of dollars and hours of headache.
2. There is no silver bullet. Taking time to really understand the full functionality and what solutions each app provides is critical. No app does everything and deploying one won’t miraculously make everything in your organization better.
3. Expect the vendor landscape to evolve. As new players enter the market and different tech solutions are validated by early adopters, you can expect a bit of volatility as those that successfully seat themselves as market leaders acquire smaller competitors and consolidate the space. This will mean that customers may have to live through the market shakeup that occurs as things mature.
4. There may be deals to be found if you’re willing to be an early adopter. As new tech startups enter the market, those on a budget may be able to negotiate good terms in exchange for helping the vendor refine their products. But buyer beware, in these situations, you can likely expect the road to be bumpy as these vendors continue to add new functionality. This is likely more suited for smaller, startup situations than for established companies who are looking for a validated enterprise solution.
5. Have a plan for what you’re going to do with the information. Collecting data, regardless of the method, can do more harm than good if you don’t plan on doing anything with it. Take time to understand how data is presented back to you via the application and be intentional about what actions you and your team will take with the data you collect.
6. Be sure about alignment to your approach and organizational language. Implementing a new software application into an enterprise can be a monumental effort. There are many reasons for this, but for the purposes of these types of applications, it is imperative that the methodology behind the software is both valid and that it aligns with your approach to work.
7. Be clear about the level of support the vendor is willing to provide during implementation and beyond. Taylor Wallace, co-founder of WeVue, a culture management app, adds, “We hear a lot of feedback from people who have tried to implement everything from enterprise social networks to online survey tools. There can often be quite a bit of confusion about roles and responsibilities (who owns what) and the extent to which the app vendor will support the company with actual implementation and sustainability.” Your vendor should have plenty to best practice data available illustrating how other organizations have successfully rolled out the solution and what potential challenges you should be aware of in the process.
8. Make sure you’re willing to go the distance before you sound the starting pistol. “Getting feedback without acting on it is a terrible thing,” says Matt Hulett, chief product officer at TINYpulse, a Seattle-based employee engagement, and peer recognition platform. He strongly suggests that business leaders ask themselves if they’re honestly committed to shaping a culture of performance. Leaders should be prepared to change their communication style and their way of working based on the feedback that these tools provide.

Advances in technology in the last decade have afforded the opportunity to fundamentally change the way companies interact with their stakeholders. While time will tell how this new market evolves, and which companies stick around, we can’t deny that HR technology is forcing businesses to rethink how they engage with their employees and other stakeholders.

Will an app solve all of your company’s problems? No. But when these tech solutions are selected and deployed in alignment with your organizational culture and strategy, they can be powerful tools to rally your team, drive engagement, and collect the data necessary to drive meaningful change.

This article originally appeared on Forbes.

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Luckily, in addition to the recent influx of interest, there are many established experts in this space who are helping to guide the conversation about how people data should, and should not be used, both from an ethical and practical standpoint.

As an experienced researcher focused on understanding how people – employees, customers, and users – perceive and experience the environments and systems they interact with, I am a data junkie at my core. The insane amount of data available to us in the current era is both amazing and staggering, and there is a reason that the outcome of some organizational and product research is analysis paralysis rather than informed decision making.

I love rooting around in a huge data set as much as the next person who shed blood, sweat, and tears to collect and organize it, but this type of data exploration with no basis in theory often leads us to find spurious relationships and draw erroneous conclusions.

Providing Context to Data Collection

In this era of seemingly limitless data, we need to take a step back to look at how we are deciding what questions to ask and what data to collect. Without this planning, without this basis in sound methods, we find ourselves forging exciting new paths, chock full of information, but often with little to no basis in practical application or significance. And without the proper scientific or subject matter expertise, we cannot determine if what we found is, in fact, accurate, useful, or even plausible.

Thus, when faced with copious amounts of data, we need to get back to (research) basics to make sure we stay focused on how data can help inform our understanding of organizations, systems, and their members/users, rather than get caught up in the latest and greatest analysis technique. Here are a few big questions to consider.

What is the basis of our research?

Clarity and truth should be sought out from the beginning, and this starts with defining goals and purpose. We have greatest ability to modify our plans and our questions at this stage of the process based on what we learn as we consult stakeholders and those who know the situation best.

As researchers, we have our own set of expertise, but we cannot afford to overlook the valuable insight provided by those closest to the subject matter we are trying to understand. This helps define our research question, data collection plan, and analytics strategy—all crucial to finding meaningful insights.

What data do we have or can we gather? How do we handle the data once we have it?

Once we’ve defined what we want to know, figure out how to get there. Determining how to get the data needed can have important implications for what questions to answer and what insights we may find. If collecting new data, we have control over how, when, and from whom data is collected, but we must also spend the time, energy, and budget to do it. These resources can be tight, and we are not always able to ask employees or users more questions.

We should leverage stakeholders and those closest to the subject matter to help us understand what existing data helps us in our research, as well as explain the context around how it was collected, from whom, when, etc. I am all about leveraging what we already have, as long as we acknowledge the need to clearly understand the limitations and caveats of the data.

We must also be sensitive to the fact that we are using people data, which requires varying levels of increased sensitivity when we handle and analyze it. We oftentimes promise our participants anonymity or confidentiality, and it is our prerogative to respect those promises, especially since ignoring them hurts the trust we have established. Being careful to match our approach to the context—not just the content—of our data will ensure we draw appropriate conclusions in a way that respects the individuals we’re hoping to better understand in the first place.

So what, and what next?

Having collected and analyzed our data, we find ourselves ready to answer the questions we set out to investigate in the first place. Sometimes, we find great insights and are equipped to inform decisions or ask more complex questions. Other times, we find ourselves without answers, but with a solid basis for going back to our stakeholders (and the drawing board) and trying again. In other cases, we find unexpected answers but these can still be used to inform the path forward.

No matter what we find, we need to ask ourselves why it matters. What is the value? Can we take action based on what’s been learned? Are the differences significant in a practical sense? Is there anything that could be done differently based on what we know? Though it’s easy to be seduced by pretty graphs and low p-values, these do not tell us anything that makes life better for our stakeholders.

When it comes to data, I may be trained as a researcher and statistician, but I practice as a pragmatist and skeptic in my daily work. In a world where we have so much at our fingertips, we must rely on our ability to focus on what efforts will lead to the most useful insights so we can make the most positive change for our employees and users.

The post People Analytics: Why Methodology Matters More than Data appeared first on gothamCulture.

As organizations evolve their ways of working to drive innovation and speed to market, a reliance on technology to automate the more transactional aspects of work makes sense. But organizations don’t embrace new technologies because they exist. New technologies are created because organizations have a real need for them.

So, what are the benefits and risks associated with this rapid adoption and reliance (some may say overreliance) on digital communication tools in the workplace? I spoke with Brigette McInnis-Day, EVP of HR at SAP, and Taylor Wallace, co-founder of WeVue, to learn more.

The Benefits of Digital Communication

“SAP, as a global company, has embraced the use of technology to get work done for years. It’s an integral part of the way we operate across time zones and geographies and it helps us run at a much faster clip than we would be able to do otherwise,” says McInnis-Day.

Brigette explains that within SAP, the use of internally developed technology platforms such as SuccessFactors in conjunction with providing hardware options for employees allows the team to customize their technology resources in ways that support their individual productivity.

Outside the walls of the physical office, digital communication has been the linchpin for remote or dispersed teams. McInnis-Day sees one of the key advantages of digital communication being the ability to create relationships and connections across the globe and have some fun doing it. “Digital allows us the ability to speed communications while also being precise and having some fun with it. Things as simple as emoji’s, for example, are a fun way to be expressive while also being clear.”

Taylor adds that “digital communication in our personal lives has far outpaced the way we communicate in the workplace – so workers are now demanding better communication tools. To expect people to work any other way is a massively missed opportunity.”

“Digital communication allows organizations to have access to a global talent pool that can connect across borders, languages, time zones, and generations,” says Wallace. “Teams can connect authentically, as they would in person, using the latest in enterprise photo and video sharing.”

The Risks of Digital Communication

The reliance on digital communication tools in the workplace is certainly not without its challenges, however. First and foremost, all of these tools and platforms keep people tethered to work all the time. We all need to periodically unplug, and without setting clear boundaries this can be quite difficult to achieve. We might not always have 100% trust in who is on the other end of the phone as they can give incorrect information. However, there is a tool which can illuminate this. By using reverse phone lookup it can give you all the information you may need when you need it.

Choosing the right tools for your organization and its workflow can be a frustrating process. Considering the sheer number of apps coming to market every week, it can be difficult to determine which one is right for your team. And when you find “the one”, it may not work as expected as time goes on.

Wallace adds, “a common complaint with Slack, for example, is that organizations look at the usage data and realize that some employees are spending ALL of their time communicating and hardly any time actually working. Now, with instant communication so accessible through these tools, it can be easy to be less intentional about when and what we ask of our colleagues.”

Adoption of these tools is also a challenge. Today’s multigenerational workforce has vastly different rituals, routines, and expectations with regard to communication, and it can create situations where people feel alienated, disrespected or just plain left out of the loop as organizations adopt new communication processes. This is why as a business it might be good to check out something like this communication skills training, to make sure that no one is left out.

How Can Leaders Prepare Their Organization?

What skills must leaders master in order to effectively lead in today’s digital, tech-centric business environment? McInnis-Day suggests that leaders must focus on and develop a critical set of skills in order to lead effectively in this digital environment.

1. Develop a local and a global mindset. Digital tools have allowed organizations to work more seamlessly across the globe. “Digital communication channels open up a global talent pool that can connect across borders, languages, time zones, and generations,” adds Wallace.
2. Learn to exhibit empathy for others. Communicating digitally is a different experience than conversations in-person or over the phone. It’s easy to misconstrue the meaning behind text-based messages. Helping your team practice empathy can help eliminate some of the friction that may occur as a result of these communication tools.
3. Gain experience on global projects with culturally diverse teams. People across the globe work differently based on their cultural upbringing. When you work on a remote or global team, take some time to learn more about peoples’ cultures and business practices through collaboration. Not only will it help you develop new skills, you can also apply your learnings to your larger business strategy as your organization continues to expand and grow.
4. Learn to be articulate and precise in your communications. Along with developing empathy for others in your communications, making an effort to be clear in your communication can help eliminate any misunderstanding. Phone calls are different than email. And email is different than Slack or IM. Adapt your communication style to the medium you’re using.
5. Learn how to adapt your communication style to your audience so that you are reaching them effectively. This may be particularly true when working with a multigenerational or global team. People understand things in different ways. Language barriers, slang, and cultural values all play a part. Adapting your communication style to both the platform and the people can help bridge any gaps in understanding. This, of course, takes empathy on your part, as a leader of a culturally diverse team.

“Many organizations are sold the belief that digital communication tools are a cure-all for their organizational challenges,” says Wallace. “Likewise, many leaders believe that by simply implementing some new tool, teams will embrace that channel and things will just ‘happen’. Leaders need to engage with the tools they want their teams to use, carefully craft implementation plans, and assume that no technology is a cure-all.”

It’s no secret that the way we communicate in our daily lives—both personally and professionally—has changed, and it has forever changed the way work gets done. Digital communication in the workplace is no longer an option. How you as a leader choose, adopt, and implement the tools in your organization will determine whether they’re successful.

This article originally appeared on Forbes.

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