Friday, February 2, 2018
Thursday, February 1, 2018
Change Management Is Becoming Increasingly Data-Driven. Companies Aren’t Ready
Source: https://goo.gl/4UsCCV
Data science is becoming a reality for change management, and although it may not have arrived yet, it is time for organizations to get ready. The companies best positioned to change in the next decade will be the ones that set themselves up well now, by collecting the right kind of data and investing in their analytics capacity.
The key to building predictive models is knowing what you want to predict and collecting large and diverse data sets that may enable you to do so. Although predictive models for change management are still a ways off, organizations can get themselves on the right path by adopting the right tools and capturing the right data. We see five no-regrets steps that organizations can take:
Start Using Digital Engagement Tools
There is a new generation of real-time employee opinion tools that are starting to replace old-fashioned employee opinion surveys — tools that tell you far more than just what employees think every year. These tools have obvious relevance to change management and can help answer questions like: Is a change being equally well received across locations? Are some managers better than others at delivering messages to employees?
We are working with a large travel and tourism firm to introduce a system for real-time employee feedback. This is giving us the opportunity to experiment with different change strategies within chosen populations in the company. The real-time feedback means we will learn very rapidly how communications or engagement tactics have been received, thus optimizing our actions in days rather than weeks, as might be the case with traditional approaches. This data can then feed into a predictive model, helping us know with precision the actions that are going to accelerate adoption of a new practice, process, or behavior by a given employee group. Commercially available tools, such as culture IQ polls, sample groups of employees through a smartphone app on a daily or weekly basis to generate real-time insights. Waggl.com goes further, creating an ongoing conversation with employees about a change effort, allowing change managers to tie this dialogue to the progress of initiatives they are undertaking. These tools can already have a big impact on change programs, but the data stream they create could be even more important as we learn to build predictive models of change. Deploying them now is critical to ensure success with data-driven change initiatives in the future.
Apply Social Media Analytics to Identify Stakeholder Sentiment
Change managers can also look beyond the confines of the enterprise for insight about the impact of change programs. Customers, channel partners, suppliers, and investors, to name just a few, are all key stakeholders for change programs. They are also more likely than employees to comment on social media about changes a company is making, thus giving potentially vital insight into how they are responding. At EY, where some of us work, we have developed a tool for social media analytics called SMAART that is able to understand sentiment within consumer and influencer groups. In a project for a pharmaceutical company, we were able to isolate the specific information sources that drove positive and negative sentiment toward the client’s brand. We are now starting to apply these techniques to understand the external impact of change efforts, and it’s a simple leap to extend these techniques within the enterprise. Advances in linguistic analysis of text mean we can now capture clues about behavior from people’s word choice; even the use of articles and pronouns can help reveal how someone feels. Applying these tools to anonymized company email or the dialogue on tools like waggl.com will give fresh insight into change readiness and the reactions of employees to different initiatives. And the insights from analyzing internal communication will be stronger when combined with external social media data.
Capture Reference Data About Current Change Projects
Organizations often seem obsessed by measuring fractional shifts in operational performance, capturing data on sales, inventory turns, and manufacturing efficiency. However, when it comes to change, few track performance from project to project beyond knowing which ones met their goals. Although projects have unique features, there are many similarities between process improvement, system change, M&A, and reorganization projects. There are opportunities to capture information about the team involved, the population engaged in the change, how long it took to implement, what tactics were used, and so on. Building a reference data set like this may not yield immediate benefit, but as the overall data set grows, it will make it easier to build accurate predictive models of organizational change.
Use Data to Select People for Change Roles
For decades companies have been using data-driven methods to select candidates for senior roles. And today some businesses such as retailers are using predictive analytics for hiring frontline staff. Applying these tools when building a team could both improve project performance and help to build another new data set. If every change leader and team member underwent psychometric testing and evaluation before the project, this data would become variables to include as you search for a causal model on what leads to successful change projects. This can even be extended to more informal roles like “change agents,” allowing organizations to optimize selection based on what they know about successful personalities for these roles. Along these lines, California startup LEDR Technologies is pioneering techniques to predict team performance. It integrates data sources and uses them to help teams anticipate the challenges they may have with team dynamics, so they can head them off before they occur.
Build a Dashboard
We imagine each organization having a bespoke dashboard, developed in partnership with the firm’s leadership team, reflecting their priorities, competitive position, and future plans. In this way, dashboards can bring insight to specific transformation investments the organization is making. Much of the data that makes up these indicators is already available today but is not being collected. One client of Change Logic’s has built a dashboard for identifying recruitment and attrition in must-win talent populations. It’s not as sophisticated as some of the models we expect to see, but nevertheless it is teaching the executive team to use data to inform people-related decisions.
It will take time to build these sorts of tools. We believe organizations should start building dashboards now and, where possible, automate them. Today, change dashboards are vulnerable to version control issues, human error, and internal politics. Automating the dashboard can make it more transparent and objective.
As organizations collect more data and build more-accurate models, change managers will be able to confidently use them to prescribe strategies to enable organizations to meet their goals. Which stakeholders are involved? What approaches work with groups that share these characteristics? What risks are associated with programs that share these features? What are the techniques that accelerate delivery of business benefit, and what are their relative costs? What is the cause and effect of specific types of investment — for example, leadership development, large group events, and communications cascades? These are all questions that will be answered with data and that will inform customized transformation plans.
Developing these sorts of metrics will not be quick or easy. These are not one-and-done installations, but rather multiyear commitments to capture data, build models, and refine dashboards. Establishing reliable data sets with which to work takes time. Data quality is an issue everywhere; so too is the need for a common data language that allows organizations to know they are measuring what they intend to measure. This has been a problem for data analytics in other fields; there is no reason why change management will be any different.
Although it will take time, we will finally be able to close the causal loop and make reliable predictions for how an action or initiative in a change program will shift a given metric. This will move investment in change from an act of faith to one of data-informed judgment. Change management will move from a project-based discipline struggling to justify adequate investment to one that is advising on business outcomes and how to deliver them. This should lead, at last, to a decline in the one metric about change programs that we all know — failure rate. And along the way we may finally solve the great puzzle of why so many transformation efforts fail.
9 skills CFOs will need to be successful in the future
Written by: James Kosur
Source: https://goo.gl/xrWEpc
The role of the CFO is rapidly changing. A chief financial officer can no longer simply rely on crunching numbers while focusing solely on a company's balance sheet.
To succeed from here on, CFOs will need to shift their focus towards analytics, people management, disruptive technology, and several other crucial and newly forming variables.
Business Insider sat down with Ash Noah, vice president of CGMA External Relations at the American Institute of CPAs (AICPA). Noah is the former CFO of the international unit of TNT Express, a global transport and logistics provider. In his role, he led finance teams in 45 countries through a significant transformation. Today his team travels all over the world, talking to CFOs and other financial experts about company needs, transformative changes in their industry, and much more.
Noah painted a picture of what the CFO of the future will look like, and it's a stark contrast from today's financial leaders.
1. CFOs must become analytics wizards.
Credit: Thomas Lohnes / Getty Images
The current role of the CFO is very transactional. However, that is starting to change. Noah says CFOs are quickly becoming more analytical at their core.
"We should only be spending about 10% of our efforts and time on transactional and 90% of our time on analytics," he says. "Only about 3% of organizations are at the desired state. Most organizations are in the 50/50 range, and most companies are trying to move up that chain. The more time you spend on analytics the more you help the business and enable effective business partnering. The CFO of the future will be a true business partner who will provide these insights."
Noah says there is already a "pull from businesses" as they seek out more analytics data and that CFOs are cognizant of that pull.
2. CFOs must manage an increasing amount of risk.
Credit: Sean Gallup / Getty Images
The level of risk and the rate at which risk is increasing is unprecedented. This calls for highly skilled risk management processes, says Noah.
The "clear and present danger" is that a business model is going to be "disrupted by technology," he says. CFOs are now constantly asking, "Who is going to put me out of business tomorrow?"
The CFO of the future needs to be beside the CEO, helping to navigate these risks. "Making your business resilient and strengthening your business model is what the CFO of the future will have to do," Noah says.
To underscore his message, he points to the S&P 500. In the 1950s, most companies featured on the list would remain there for 60 years. Clayton Christensen of Harvard University says today's average S&P 500 company will drop off the list in 18 years. Based on those numbers, 75% of the current S&P 500 will not be featured in 2027.
3. CFOs must adapt to new technology.
Credit: Carl Court / Getty Images
Noah says you must look at your business model and figure out how it can constantly be adapted and how you can finance that evolution. "How you use new metrics to enable innovation is an absolutely essential question for a CFO."
CFOs can no longer rely on old tools and the traditional return on capital, return on investment, payback period, and all the measures that CFOs are so familiar with.
"The old way of thinking stifles innovation and ruins new business creation," Noah says. "The CFO of the future must enable innovation and manage risk better."
4. CFOs must become better at managing people.
Noah says you have to use your technical skills in the context of the business to influence people and to lead change.
"Leadership, people, and business skills are absolutely crucial to the future of the CFO function," Noah explains. "The more you move up, the more you want to make sure you have the right people and leadership skills. Typically finance functions have not paid attention to that in the past."
5. The CFO of the future must guide decisions in a politically charged atmosphere.
Credit: Spencer Platt / Getty Images
The role of a CFO is to represent the organization and fight for the business models of the new world. Noah cites the example of Uber, which has been attacked by government agencies over its contractor-versus-employee structure and its general services.
"A CFO of the future needs to be at the forefront of providing the business with financial and business implications," he says. "I would stop short of saying a CFO should lobby, but they need to be able to partner with their business in areas such as union regulations and minimum wages laws."
He adds that CFOs need to be able to "measure political impact and show the effects it has in terms of dollars and risk. They also need to enable their partners to lobby and send the right type of messages."
6. CFOs must manage big data as a large part of business operations.
Credit: Nigel Treblin / Getty Images
Data and analytics are becoming increasingly crucial to businesses, not just in terms of finding new markets and new segments in order to achieve revenue generation, but also for understanding, variabilizing, and controlling costs.
According to Noah, "We did a report on big data a few years ago, and 93% of people surveyed said the way decisions are made in the future will change because of data. We are not expecting CFOs to be data scientists — they don't have to understand the algorithms — but the CFO of the future will need to be the connector between the business and data scientists."
Noah also points out that CFOs in the future will need to be better adept at finding a business' needs and solving problems as they arise. "They are the ones that will translate that to the data scientists," he says. "They are the ones that will help them build the right queries and get the answer the business needs and then be able to take those insights back to the business in order to make them actionable."
7. CFOs will make effective decisions with analytics from outside of the enterprise.
Credit: Spencer Platt / Getty Images
"CFOs need to develop the right question when creating datasets," Noah says.
He notes that a lot of companies are using big data to drive decisions outside of the enterprise: "Fundamentally it is unstructured; it is outside the boundaries of the enterprise. Right now a lot of the non-financial data is not trusted by owners of that data. However, 74% of companies are saying their gathered information is accurate."
Non-financial data may include machine-versus-human labor and productivity, and how many products were consumed or sold in the marketplace. "To really get insights into a business you have to look at the underlying non-financial drivers to the financial information," he says.
8. CFOs need to understand business drivers and the underlying non-financial information that drives the financials of their company.
Credit: Handout / Getty Images
While analytics outside of the enterprises will become increasingly important, CFOs must also be able to look at the value their company is creating based on variables such as intellectual property and intangible assets. Those non-financial business drivers are becoming increasingly important.
Noah explains that "when we look at trends, the valuation of a company was traditionally based on 80% of the value created in the balance sheet by tangible items and about 20% of the value was intangible. There was a gap between the market cap of the company and the balance sheet valuation." A study in 2010 found that if you look at S&P 500 valuations today, the gap between the balance sheet and the market valuation has become 80%. That means 80% of the valuation is no longer in the balance sheet.
"If the CFO is focused on the balance sheet and is focused on compliance, focused on processes and procedures and conformance, he's not engaging in value creation," Noah says. "The more significant value is now created from your intangible assets. The CFO of the future really needs to understand and become more of a P&L CFO to engage in value creation."
He adds, "Value creation today is a knowledge-based economy. Companies are now more likely to create value using intangible assets and intellectual property. "
9. Hiring decisions will become a major part of the job for future CFOs.
Credit: Joe Raedle / Getty Images
CFOs will need to drive talent acquisition and retention.
"A CFO will need to have a structured approach to create the competencies a company needs, and they will need to fill in the gaps," Noah says. "A CFO must adopt a structured-competencies framework and recruit and train new workers based on that framework."
He also believes the CFO will need to find creative ways to retain workers based on the company's desired competencies.
Tuesday, January 30, 2018
Big Data Combined With Machine Learning Helps Businesses Make Much Smarter Decisions
Machine learning unveils the hidden potentials of big data to solve complex business problems
By: Deji Atoyebi • Guest Writer
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Source: https://goo.gl/gno8Ur
Today, the importance of machine learning and big data to businesses cannot be overemphasized; both are revolutionizing business operations and consistently providing lots of new opportunities.
Although machine learning dates back to the 1950's, it's presently more subject to practical, large-scale applications than it has ever been. Big data, on the other hand, became a thing in 2013, after it was discovered that 90 percent of the world's data was produced in the previous two years.
The spate of data generation, therefore, became a challenge as well as an opportunity. As an opportunity, big data enables businesses to not grope in the dark but make wise real-time decisions by providing them with insights into various market situations and ensuring a better understanding of consumers' behaviors and preferences.
It's however noteworthy that big data by itself is of little value. To be useful, it has to be operated on by various analytical methods, many of which don't go beyond providing mere statistical insights.
Machine learning comes in handy as it goes further to unveil the hidden potentials of big data by producing and implementing solutions to complex business problems.
Here are some four ways by which combining big data with machine learning has helped improve business intelligence, and some takeaways for business owners.
1. Facilitating Customer Segmentation
It is not uncommon to find distinct groups -- each comprising individuals who share a wide range of similarities - within a business's customer base. In fact, discovering such groups is a crucial step every business should take.
Fortunately, machine learning clustering algorithms are perfect for achieving this kind of a segmentation. Many such algorithms are unsupervised in that they don't require special human direction to operate. Rather, an unsupervised clustering algorithm requires only data for exploration, so as to discover similarities and differences (where they exist), and come up with distinct clusters based on a number of features.
In 2009, Orbitz created a machine learning team to facilitate segmentation, among other reasons. Three years later, it discovered a pattern from the data at its disposal: Mac users were willing to spend as much as 30 percent more per night for hotel rooms, when compared to Windows users. This discovery made it (Orbitz) swing into action in a way I'll touch on, shortly, as it obviously helped to lay the grounds for segmenting the business's customer base based on the relative propensity to pay for varying hotel types.
Your business can also harness the power of machine learning and big data to achieve segmentation. But, first, you need to discover whether segmentation holds any potential benefit for your organization. If you believe it does, then it will become necessary to invest heavily in data analytics, make your business machine learning ready, and then employ a machine learning team. As you'll soon see, machine learning will not only help to accurately and efficiently make sense of the data at your disposal, but also help to implement core business strategies.
2. Making Targeting Feasible and Effective:
Merely knowing that your customer base is composed of different groups doesn't cut it -– you have to devise means to cater to divergent needs.
Orbitz responded to the earlier stated discovery by targeting customers differently: costlier hotels were displayed to Apple users. It's quite reasonable to suggest that this move was a wise one, for such a strategic targeting must have been highly profitable.
On the other hand, it's sometimes necessary to view one's customer base as comprising different individuals with various preferences rather than a conglomeration of different groups. This perspective will make it more pragmatic to tailor products to each individual based on his or her specific behavior and perceived preferences. Again, machine learning, under the aegis of big data, facilitates this.
Google, for example, uses big data to better understand your preferences and combines it with complex (machine learning) algorithms to provide supposedly relevant results for every query you make. This is why your past choices (for example, the sites you've visited) end up impacting on some of the results you're shown.
Machine learning and big data are also breaking grounds in targeted advertising. Pixar, for example, targets its audience with different movie advertisements which are based on learned preferences. Netflix also estimates that its algorithms produce $1 billion a year in value from customer retention, thanks to the "Netflix addiction" which is mostly spurred by accurate recommendations fostered by both user and item-based collaborative filtering.
In other words, business owners need to understand that targeting consumers differently makes a lot of sense, and that machine learning makes personalization, which is key to providing a better user experience, possible. Say you run an ecommerce business, machine learning can help you personalize your ads so that people see only products that are most likely suited to their needs. This will definitely add an unobstrusive touch to your platform and may improve your bottom line by increasing sales and engendering customer retention. Again, the "Netflix addiction" speaks volumes about the potentials of machine learning-induced targeting.
3. Fostering Predictive Analysis:
After gaining insight into consumer behavior from big data, you'll want to use machine learning to develop generalizations and thus make predictions regarding various business issues.
In other words, machine learning models can learn behavior patterns from data and determine how likely it is for a person or a set of people to take certain actions, such as subscribing for a service. This makes it possible to anticipate events and make futuristic decisions.
The American Express Company used big data to analyze and predict consumer behavior by learning from historical transactions. Through this, it was able to predict 24 percent of accounts in its Australian market that were about to close within four months. T-mobile also uses big data to predict consumer fluctuations.
To make these kind of predictions, you must employ machine learning expertise to help grapple with your business's data. Classification algorithms are usually used as the foundation for such predictions.
Related: Artificial Intelligence Is Likely to Make a Career in Finance, Medicine or Law a Lot Less Lucrative
4. Providing Foundations for Risk Analysis and Regulation:
Big data enables machine learning models to extensively analyze and regulate risks.
For fraud detection, American Express applies machine learning to analyze large historical datasets. In fact, the machine learning system is considered to differ from the previously existent fraud detection systems which included only manually created rules, and is better off because it's likely to improve with more data inputs. It also saves the company millions of dollars, said Bernard Marr.
Your business can also make use of machine learning to decrease financial irregularities. Many organizations are, in fact, developing systems to make the process easier. IBM, for example, provides financial institutions with a machine learning system on IBM z/OS in order to aid financial risk management. This system pays particular attention to credit scoring and is targeted at deducing credit worthiness which it uses to gauge risks.
Employing machine learning models can go a long way in ensuring anti-money laundering compliance, detecting rouge trading and other trade anomalies, so it's best to not starve your business of these elements of sanity.
Machine learning and big data are presently gaining the attention they deserve, and there's no doubting that both depend on each other's strength. More importantly, both have consistently made major impacts on how we undertake business operations. This article revealed some four ways by which a combination of machine learning and big data, if applied, can be a fillip to business intelligence. It's therefore left to you to up your game as an entrepreneur.
Friday, January 19, 2018
Wednesday, January 17, 2018
5 Rare But Essential Productivity Practices
Productivity is defined as “the quality, state, or fact of being able to generate, create, enhance, or bring forth goods and services.”
A simpler definition that we tend to use? Getting things done.
But I think the more complicated definition is better, because it reveals important nuances of productivity. One of those nuances is that language “being able to…” A key factor of productivity is ability, and there’s a spectrum. I’m able to swim well, but Michael Phelps is able to swim really fast.
Productivity = Effort x Ability
I swam competitively for 12 years, but my ability was never even close to that of Olympic Champion Michael Phelps. On a scale of 1–10, if my swimming ability is a 4, then Phelps’ ability is a 10.
Thus, if we both put forth full effort (10), according to the formula above, my score is 40 to his 100. Do the math — he’d still smoke me in a race even if I tried twice as hard as him.
This example highlights ability as the critical factor of productivity. Elite athletes so hard to defeat because their ability is almost always around 10/10, so when they put in a full 10/10 effort, they will win just about every time. The same concept applies to productivity.
Most of us spend a lot of time working (effort) and too little time improving how we approach work (ability). Even broader than work, I think we do this in life! We spend a lot of time “living” and being “so busy.” But how much time do we spend optimizing our practices, strategizing our routines, planning for our needs, and simply taking care of ourselves?
This article contains rare productivity practices because it focuses on skills and preparation, two things that are done outside of actual work sessions. While these things will always seem like minor details or even inconsequential, they usually bring the highest ROI (Return On Investment)!
Why Skills and Preparation Beat Effort
Ability = Skills x Preparation
Ability can be broken down into skills and preparation. That is, how skilled are you at the task, and how prepared are you to execute? I want to briefly explain why skills and preparation matter more than effort and “in session” productivity tricks. It goes back to the formula. Effort is a variable just like skills and preparation, but they all behave very differently. Here’s the formula.
Productivity = Effort x (Skills x Preparation)
Effort (1–10): Your effort can be 1 on Tuesday and 10 on Wednesday. It can and does vary wildly for various reasons.
Skills (current training level): Your skills are going to be the level to which you’ve trained, no more and no less. I can’t go from a zero on the piano one day to Chopin the next day. That would take years of practice. I also won’t regress quickly if I’ve built up the skill.
Preparation (0–3): The normal score for preparation is one, and that simply means that you’re basically ready and able to perform your skills to your current ability. If you take PEDs, then maybe your preparation goes to two. Cheater! Haha, for real though, caffeine is a PED for productivity. It doesn’t increase your skills, but it does temporarily amplify your energy state and mental acuity, which increases your overall ability. This is one reason I don’t consume caffeine — I don’t want temporary boosts and dependence, I want permanent improvement and independence. In this formula, preparation can go below one, which means it can dramatically affect productivity.
The problem with focusing on effort or PEDs is their temporal nature. Your productivity today might be at the cost of being productive tomorrow (from fatigue or withdrawal). But if you focus on skill-building, you’ll permanently increase your skill score, which will always multiply your efforts, whatever they may be! And if you take a long-term approach to preparation, you’ll multiply your skills and efforts even further!
So the question is, “What are some of the most critical skills and preparation practices for greater productivity?”
Practice #1: Get Excellent Sleep and Nutrition
This is obvious when read in an article, but not always practiced and not always recognized as a factor in real life. Who pursues sleep as a strategy for improving their work? Not many. Who thinks about their lunch in terms of varying fuel quality for their brain this afternoon? Not many.
Based on their behavior, most people think they can short themselves on sleep and eat whatever they want and still perform like champions. They continue to believe it only because they haven’t experienced their true potential. Try improving your sleep quality and length and your diet for awhile and you’ll see the difference. When I stopped eating fast food in college, I stopped falling asleep in class (for the first time in my life).
Here’s a crude example: How do you stop the greatest basketball player in the world? Be it prime Michael Jordan or some mythically good player who has perfect skills, how can you stop them? Easily. Simply withhold water from them for two days. They’ll be so dehydrated and weak that they won’t be able to do much of anything, let alone play basketball. Elite skills + no water = failure.
A severely dehydrated athlete could still provide 10 in effort and have level 10 skills, but without water, their preparation is a 0, which nullifies everything else. (Effort is not a measure of what you can do, it’s a measure of how hard you try, and trying hard with no strength will look no different than not trying.)
Dehydrated Player: Productivity = Effort (10) x (Skills [10] x Preparation [0]) = ZERO (please send him to the hospital)
Sleep and nutrition are the foundational preparation for all of life.
Preparation is a sliding scale, and many people in the world are operating at something like a 0.5 or 0.25 score in their preparation (possibly using caffeine to get closer to one). That means they’re only getting 1/4 or 1/2 the results from their efforts and skills as they should be getting with basic sleep and nutrition! Adding an extra point to preparation goes a lot further than adding a point to effort.
Ben Franklin famously said, “an ounce of prevention is worth a pound of cure.” Similarly, we could say, “An ounce of preparation is worth a pound of caffeine.” It might not translate perfectly with dosage, but you get the point.
Practice #2: Meditate (Focus Practice)
Meditation has a lot of benefits. For productivity, the clear benefit of meditation is improved focus, not through voodoo meditation magic, but because meditation improves your focusing skill. The act of moving your attention from distracted thoughts back to your breaths will prepare you to move your attention back to your work.
Focusing is the single most important skill for being productive. It’s such a simple concept — paying full attention to what you’re doing — but it’s one of those things that’s simple to understand and difficult to master. Any progress made in this area will pay huge dividends throughout your life. Meditation is the best way to directly practice focusing that I’m aware of.
Practice #3: Refine Your Processes
Life is comprised of various processes. Processes within your body, processes of nature, processes of society, and processes of your actions. The cool thing about processes is that they’re reliable. You can typically expect the same result if you follow the same process.
What does this mean for your life? Pay attention to the processes you follow and the results they bring! If your day always seems to start poorly or slowly, then take a close look at your morning routine. That’s the process responsible for those results!
Athletes are yet again a great example because of their various routines (processes) that get them ready to perform. Athletes are some of the most ritualistic people you’ll meet because rituals are processes, and any athlete worth his or her salt must respect the process if they are to succeed.
What processes should I look at?
You don’t necessarily have to look for problem areas. Good processes can be improved further. For example, I used to exercise by alternating basketball and random weight lifting (of all muscle groups), and sessions like that could last up to two hours. I was in pretty good shape, and there was nothing wrong with it, but I wasn’t getting much stronger. When I switched my process to focused, shorter sessions of just weightlifting with targeted muscle groups (push, pull, legs) always followed by a protein shake, results came faster even though I worked out for less time. I improved an already beneficial process, and now think back to the equation. Since this process greatly increased my preparation score, I can apply less effort for better results.
See the distinction here? I could have doubled my workout time or effort under the old process and still been disappointed, because the process was not ideal for my goal of getting stronger. Since I optimized the process, I was able to work less and get better results!
Look at your processes/routines for exercise, eating, grooming, mornings, bedtime, working, and emailing and ask yourself, “Is the process I’m using most optimized for the result I want to achieve?” If you find even one small tweak to improve the process, the beneficial effect will compound!
Practice #4: Set Up Strategic Conditionals (If ____ Happens, Then I Will ____)
Let me give you an example of not doing this that has negatively affected me. I’ve been going to the gym very frequently — about 5 days per week — for full, intense workouts. But I’ve run into three unplanned situations. Here’s how I handled them.
I got a sinus infection [failure].
I was sick, and didn’t work out. I’m okay with not working out while sick, but my definition of “sick” was extremely fuzzy. Because of my vague definition of sick, I skipped working out on a few days that, looking back, I shouldn’t have. One could say a few days missed won’t hurt, but that’s a wrong and dangerous perspective. Every day counts for a lot. Every decision makes a difference. Small choices matter because they set a precedent for the rest of your life.
Am I going to be world class consistent or am I going to meddle in mediocrity because I’m not prepared? In this case, I chose the latter, and I intend to improve upon that! Now I have a clearer idea of what I expect to do depending on how I feel.
If I am feverish, I’m not doing anything.
If I have sniffles but feel I am on the upswing, I expect to walk or do some other kind of light exercise.
If I feel myself again and just have nagging post-sickness symptoms, I will work out as usual.
With a clear plan of “if X, then Y,” sickness won’t derail me for long. If it’s a long-term or complicated illness, I expect myself to define my exercise expectations based on the specific illness.
I drank wine [failure].
After I drink alcohol, I can usually feel it (negatively) on some level the same or next day. Since I had not decided how alcohol would fit into my fitness plans, after drinking, it was easy to delay working out until when I felt “completely fresh.” This caused me to miss a few days that I didn’t want to miss. I was able to exercise, but unable to respond to a spontaneous excuse I hadn’t prepared for.
I injured my pinky finger [success].
Here’s one where I got it right. I jammed my pinky finger playing basketball just yesterday. The top and middle joint have a beautiful purple hue and the finger is swollen. In this state, I am both unable and unwilling to grip significant weight with the finger, due to pain and risk of reinjury. This severely limits my options in the weight room.
I’m not sure when exactly I decided it, but I operate under the expectation that even when I’m injured, I can find a way to exercise. Seeing a girl with a full cast on her leg outwork almost everyone else in the gym on a recent cruise only solidified that view. Injury is rarely an excuse to be completely inactive because of the many ways we can exercise.
After 2.5 hours of basketball yesterday, my legs were jello, my thigh was bruised, and my right hand pinky was out of order. With my upper and lower body needing to rest and heal, I worked my core. Success!
Lastly, consider this: if you have a conditional set up in your mind, then when X happens, not only are you less likely to derail, but you’re more likely to take positive action because you pre-created the intent to act by saying, “if X happens, I will Y.”
Practice #5: Expand Your Definition of Productivity
If you generate good feelings and good health, that’s productive on a lifewide level. That is to say that even the activity I typically badmouth, watching TV, can be productive if you need the break!
Workaholics are practically worshipped in many societies, but they miss out on their true potential. If you’re not well rested, your performance suffers. This is a non-debatable limitation of humans. Some research suggests that of the typical 8-hour workday, people only work for about three hours. Yes, THREE. The human brain is a well-designed machine, and it will take breaks to cool off as needed. In the real world, this means we probably work a lot less than we think we do.
The problem with working your brain too hard is inefficiency. Do you really think the time spent browsing Facebook to burn time is as rejuvenating as lounging on your couch or bed watching your favorite show? Of course not, because one is pretending to work and the other is intentional rest.
The Inefficient Workaholic Cycle
Work hard (100% depletion = zero energy left)
Pretend to keep working as you stalk people on Facebook (10% recovery)
Work exhaustedly with your meager energy
Pretend to keep working as you check your phone (even though you didn’t hear the notification sound)
This desperate attempt to “keep working” often continues because of poor work quality (due to exhaustion). Here’s a better cycle.
The Efficient Work Cycle
Work hard (100% depletion)
Relax just as hard (100% recovery)
Work (100% depletion)
Relax (100% recovery)
Repeat
Work hard. Relax hard. One of my favorite things to do is what I call the “Work and Play Carousel,” in which I’ll alternate an hour or two of work with a similar time of rest and relaxation.
Conclusion
Improving your productivity is rarely a matter of putting in more effort. It’s often a matter of working smarter, building key skills like focusing, preparing yourself for different situations, and simply taking care of yourself. I hope this article helps you tackle your work days in a smarter way!
About the Author
Stephen Guise is the author of three books, including the worldwide bestseller, Mini Habits, which is available in 17 languages. You can learn more about him here.
Monday, January 15, 2018
Northside Family Eye Care´s testimonial
"I have been working with Soledad Tanner for about a year now and I must say my experience with her has exceeded my expectations. Since the very beginning, Ms Tanner has provided me with steady, thoughtful strategies to improve my business profitability as well as educating me in the sometimes complex matters of accounting and key practice metrics for my practice. I highly recommend Soledad Tanner Consulting to take your business to the next level."
Thank you,
Martha Ortega, O.D.
Therapeutic Optometrist
Northside Family Eye Care
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