SMITH an NLP algorithm explained

Google recently published a research paper on a new algorithm called SMITH that it claims outperforms BERT for understanding long queries and long documents. In particular, what makes this new model better is that it is able to understand passages within documents in the same way BERT understands words and sentences, which enables the algorithm to understand longer documents.

On November 3, 2020 I read about a Google algorithm called Smith that claims to outperform BERT.

What is the SMITH Algorithm?

SMITH is a new model for trying to understand entire documents. Models such as BERT are trained to understand words within the context of sentences.

In a very simplified description, the SMITH model is trained to understand passages within the context of the entire document.

While algorithms like BERT are trained on data sets to predict randomly hidden words are from the context within sentences, the SMITH algorithm is trained to predict what the next block of sentences is.

This kind of training helps the algorithm understand larger documents better than the BERT algorithm, according to the researchers.

BERT Algorithm Has Limitations

This is how they present the shortcomings of BERT:

“In recent years, self-attention-based models like Transformers… and BERT …have achieved state-of-the-art performance in the task of text matching. These models, however, are still limited to short text like a few sentences or one paragraph due to the quadratic computational complexity of self-attention with respect to input text length.

In this paper, we address the issue by proposing the Siamese Multi-depth Transformer-based Hierarchical (SMITH) Encoder for long-form document matching. Our model contains several innovations to adapt self-attention models for longer text input.”

According to the researchers, the BERT algorithm is limited to understanding short documents. For a variety of reasons explained in the research paper, BERT is not well suited for understanding long-form documents.

The researchers propose their new algorithm which they say outperforms BERT with longer documents.

They then explain why long documents are difficult:

“…semantic matching between long texts is a more challenging task due to a few reasons:

1) When both texts are long, matching them requires a more thorough understanding of semantic relations including matching patterns between text fragments with long distances;

2) Long documents contain internal structures like sections, passages, and sentences. For human readers, document structure usually plays a key role in content understanding. Similarly, a model also needs to take document structure information into account for better document matching performance;

3) The processing of long texts is more likely to trigger practical issues like out of TPU/GPU memories without careful model design.”

Larger Input Text

BERT is limited to how long documents can be. SMITH, as you will see further down, performs better the longer the document is. This is a known shortcoming with BERT.

This is how they explain it:

“Experimental results on several benchmark data for long-form text matching… show that our proposed SMITH model outperforms the previous state-of-the-art models and increases the maximum input text length from 512 to 2048 when comparing with BERT-based baselines.”

This fact of SMITH being able to do something that BERT is unable to do is what makes the SMITH model intriguing.

The SMITH model doesn’t replace BERT.

The SMITH model supplements BERT by doing the heavy lifting that BERT is unable to do.

The researchers tested it and said:

“Our experimental results on several benchmark datasets for long-form document matching show that our proposed SMITH model outperforms the previous state-of-the-art models including hierarchical attention…, multi-depth attention-based hierarchical recurrent neural network…, and BERT.

Compared to BERT-based baselines, our model is able to increase maximum input text length from 512 to 2048.”

Long to Long Matching

If I am understanding the research paper correctly, the research paper states that the problem of matching long queries to long content has not been adequately explored.

According to the researchers:

“To the best of our knowledge, semantic matching between long document pairs, which has many important applications like news recommendation, related article recommendation, and document clustering, is less explored and needs more research effort.”

Later in the document, they state that there have been some studies that come close to what they are researching.

But overall there appears to be a gap in researching ways to match long queries to long documents. That is the problem the researchers are solving with the SMITH algorithm.

Details of Google’s SMITH

I won’t go deep into the details of the algorithm but I will pick out some general features that communicate a high-level view of what it is.

The document explains that they use a pre-training model that is similar to BERT and many other algorithms.

First, a little background information so the document makes more sense.

Algorithm Pre-training

Pre-training is where an algorithm is trained on a data set. For typical pre-training of these kinds of algorithms, the engineers will mask (hide) random words within sentences. The algorithm tries to predict the masked words.

As an example, if a sentence is written as, “Old McDonald had a ____,” the algorithm when fully trained might predict, “farm” is the missing word.

As the algorithm learns, it eventually becomes optimized to make less mistakes on the training data.

The pre-training is done for the purpose of training the machine to be accurate and make less mistakes.

Here’s what the paper says:

“Inspired by the recent success of language model pre-training methods like BERT, SMITH also adopts the “unsupervised pre-training + fine-tuning” paradigm for the model training.

For the Smith model pre-training, we propose the masked sentence block language modeling task in addition to the original masked word language modeling task used in BERT for long text inputs.”

Blocks of Sentences are Hidden in Pre-training

Here is where the researchers explain a key part of the algorithm, how relations between sentence blocks in a document are used for understanding what a document is about during the pre-training process.

“When the input text becomes long, both relations between words in a sentence block and relations between sentence blocks within a document becomes important for content understanding.

Therefore, we mask both randomly selected words and sentence blocks during model pre-training.”

The researchers next describe in more detail how this algorithm goes above and beyond the BERT algorithm.

What they’re doing is stepping up the training to go beyond word training to take on blocks of sentences.

Here’s how it is described in the research document:

“In addition to the masked word prediction task in BERT, we propose the masked sentence block prediction task to learn the relations between different sentence blocks.”

The SMITH algorithm is trained to predict blocks of sentences. My personal feeling about that is… that’s pretty cool.

This algorithm is learning the relationships between words and then leveling up to learn the context of blocks of sentences and how they relate to each other in a long document.

Section 4.2.2, titled, “Masked Sentence Block Prediction” provides more details on the process (research paper linked below).

Results of SMITH Testing

The researchers noted that SMITH does better with longer text documents.

“The SMITH model which enjoys longer input text lengths compared with other standard self-attention models is a better choice for long document representation learning and matching.”

In the end, the researchers concluded that the SMITH algorithm does better than BERT for long documents.

Why SMITH Research Paper is Important

One of the reasons I prefer reading research papers over patents is that the research papers share details of whether the proposed model does better than existing and state-of-the-art models.

Many research papers conclude by saying that more work needs to be done. To me, that means that the algorithm experiment is promising but likely not ready to be put into a live environment.

A smaller percentage of research papers say that the results outperform the state of the art. These are the research papers that in my opinion are worth paying attention to because they are likelier to make it into Google’s algorithm.


SMITH Outperforms BERT for Long Form Documents

According to the conclusions reached in the research paper, the SMITH model outperforms many models, including BERT, for understanding long content.

“The experimental results on several benchmark datasets show that our proposed SMITH model outperforms previous state-of-the-art Siamese matching models including HAN, SMASH and BERT for long-form document matching.

Moreover, our proposed model increases the maximum input text length from 512 to 2048 when compared with BERT-based baseline methods.”

The research paper authors confidently state that SMITH beats the state of the art for understanding long-form content.


Read the original research paper:

Description of the SMITH Algorithm

Download the SMITH Algorithm PDF Research Paper:

Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching (PDF)


BERT an NLP algorithm explained


Bidirectional Encoded Representation from Transformers 

Google believes progress in natural language understanding, specifically BERT represents “the biggest leap forward in the past five years, and one of the biggest leaps forward in the history of Search” and has been used in every one in ten searches in their search engine.

        This is due to its capability of providing context to the search keywords. In addition to context, it is also capable of solving

  • Named entity determination.

  • Textual entailment next sentence prediction.

  • Coreference resolution.

  • Question answering.

  • Word sense disambiguation.

  • Automatic summarization.

  • Polysemy resolution.

  • Language translation.

How it works::

Previous models and their advancements in NLP

The state of the art models such as ELMo (Embeddings from Language Model) developed by the Allen Institute from University Washington and GPT(Generative Pre Training) from OpenAI made tremendous progress in the area of natural language understanding however both of these models were unable to address the complexity of solving the context of the sentence. In the English language often the target word’s (context word) meaning depends on the second part of the sentence. Since both methods’ approaches were unidirectional meaning they were only able to comprehend from either left to right OR right to left NOT both directions at the same time. Example:

  1. “2019 brazil traveler to the USA need a visa.”  Before BERT this search returned results about U.S. citizens traveling to Brazil. With BERT, search is able to grasp this nuance and know that the very common word “to” actually matters a lot here and returns Tourism and Visitor | US Embassy and Consulates in Brazil.

  2. “do estheticians stand a lot at work.”  Before BERT this search returned results Medical estheticians vs Spa estheticians and completely ignored the word “stand”, with BERT, the search is able to grasp the keyword stand and relate to the physical nature of the work.


BERT: It addresses the above limitation with the bidirectional approach. As shown in the below figure 1,  which differentiate from the other two models.

   Figure 1:  Source: google paper(


Architecture: BERT is a two-stage NLP model as shown in figure 2, the first stage uses unlabeled data to pre-train the model, and the second stage uses labeled data to fine-tune the model. Since it uses both unsupervised learnings (unlabeled data) and supervised learning(labeled data) it is called a hybrid model. It supports transfer learning which means pre-trained models from the first stage can be transferred without modifications to the second stage for fine-tuning. 

Figure 2: Source Google paper(


Both stages use a similar kind of transformer neural network as shown in figure 3. However, the second stage has an additional layer for labeled data validation and varies depending on the task.  Since these are transformer models, it provides high parallelism for robust training.

A typical transformer architecture

Figure 3: source Ashish Vaswani(


BERT at large is built using 24 transformer blocks, 1024 hidden sizes, and  16 attention heads which can support Total Parameters of 340 Million equivalent to 16TPUs 96hurs for training and one TPU for inference. A typical transformer neural network will be shown in figure 3.

Text Preprocessing:

The pre-training corpus consists of  BooksCorpus 800 Million words and English Wikipedia 2,500 Million words. Every input embedding is a combination of 3 embeddings and it is processed as shown in figure 4.

  1. Position Embeddings: BERT learns and uses positional embeddings to express the position of words in a sentence. These are added to overcome the limitation of the Transformer which, unlike an RNN, is not able to capture “sequence” or “order” information

  2. Segment Embeddings: BERT can also take sentence pairs as inputs for tasks (Question-Answering). That’s why it learns a unique embedding for the first and the second sentences to help the model distinguish between them. In the above example, all the tokens marked as EA belong to sentence A (and similarly for EB)

  3. Token Embeddings: These are the embeddings learned for the specific token from the WordPiece token vocabulary

Figure 4:   source: Google paper(

Pre-training Tasks

BERT is pre-trained on two NLP tasks:

  • Masked Language Modeling

 It uses bidirectional MLM(Masked/attention Language Model). MLM builds relationships between words.  MLM means masking 15% of the words randomly and predicting the masked words using the softmax() function. This MLM is called bidirectional since it trains by looking at the left side of the target/masked/attention word and right side of the masked word and also uses the entire sentence to determine the context. The softmax function is an activation function in the output layer of neural network models that predict a multinomial probability distribution.

  • Next Sentence Prediction(NSP)

NSP is very important to BERT which builds the relationship between sentences. NSP is a binary classification task, the data can be easily generated from any corpus by splitting it into sentence pairs. The task is simple. Given two sentences – A and B, BERT has to determine whether B is the actual next sentence that comes after A or if it’s random. Consider that we have a text dataset of 100,000 sentences. So, there will be 50,000 training examples or pairs of sentences as the training data.

  • For 50% of the pairs, the second sentence would actually be the next sentence to the first sentence

  • For the remaining 50% of the pairs, the second sentence would be a random sentence from the corpus

  • BERT would label ‘is next’ for the first case and ‘NotNext’ for the second case


And this is how BERT is able to become a true task-agnostic model. It combines both the Masked Language Model (MLM) and the Next Sentence Prediction (NSP) pre-training tasks.


BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, GLUE 80.5%, MultiNLI 86.7%, SQuAD 93.2%.


How to build Resilience for Small Business Ecommerce

When it comes to operating an ecommerce business, one of the best things you can do is to create safety nets and plans for a more financially resilient business. To practice financial resilience, you need:

  • Agility
  • Diversification
  • Optimization

Building a financially resilient ecommerce business means that your business is agile enough to pivot when changes are needed, you have diverse streams of income so your revenue doesn’t all dry up at once and you can optimize your processes to reduce wasted spending.

A study by Intuit and Quickbooks found that 61 percent of small businesses around the world struggle with cash flow, with 32 percent unable to pay back loans, pay off vendors or pay employee and owner salaries due to cash flow problems.

Don’t be part of that 61 percent. Instead, use these expert tips to help build a more financially resilient ecommerce business that can withstand and rebound from any financial shock.

Strengthen the Foundations of Your Business

The first step towards building a more financially resilient ecommerce business is to do a business review. Just like a house, you want your business to be built upon a strong foundation. Your business review should look at the structure of your business to see if you can make it more efficient and operate more cost-effectively.

What you’re looking for is how to do more with less. Often, the necessary tasks to strengthen the foundations of your business are tasks you already know you need to do, but you just haven’t had time to get them on your to-do list.

To strengthen your ecommerce business foundation, you might look for ways to:

  • Grow your core business.
  • Diversify your target market and offer your product to new customers.
  • Negotiate new terms with your vendors.
  • Scale back on staff to improve cash flow.

Building financial business resilience starts today. It starts before there’s a financial crisis. Building a strong foundation and fixing any cracks that have appeared before the foundation fails is key to being a financially resilient ecommerce business.

Create a Network of Support

When business owners think about how they’d react when facing a crisis, many default to putting their nose to the grindstone and working harder and longer hours to get through it. But, according to expert Justin Nabity, founder of Physicians Thrive and an experienced financial expert certified in CFP®, CLU® and ChFC®, going it alone isn’t the right trajectory.

“You don’t have to face this difficult situation alone; hence, you can always look to seek professional advice,” says Nabity. Most small business owners think they’re on their own, but there are many professional organizations and mentor groups that can offer support to ecommerce businesses.

Look for local groups of ecommerce business owners or entrepreneurs where you can seek out advice and network with others who may face similar problems.

As the ecommerce market grows, so does the number of ecommerce businesses. With more than 27 percent of the global population shopping online, there’s likely to be a group of ecommerce business owners you can network with locally or virtually.

how many people shop online


Build Up Savings

One of the more obvious but still very important ways to build financial resiliency is to build up your savings account. But, you want to make sure you’re saving with a specific target in mind.

According to Courtney Barbee, owner and COO at The Bookkeeper, you should “analyze and determine your fixed expenses (those costs you would have even with zero in sales), and you save up enough money to cover at least six months of those. This provides a good buffer for any downturn in business, increases in costs, etc. Also, since ecommerce generally has low overhead, it’s not an untenable goal.”

You can build up your on-hand cash savings over time by putting away a small percentage of income each month, or build it up quickly by raising working capital. Some ways you might increase your savings include:

  • Sell existing assets.
  • Invest your own capital.
  • Refinance existing loans.
  • Take loans from investors.

To build up your savings, you can convert assets into cash or save up over time.

Plan Ahead

Chris Morgan, credit expert at Credit Help Info, says, “Aiming to have a good financial standing can be another way to build resilience by anticipating stressful events and being ready for them. There is nothing wrong with planning ahead of time.”

One of the biggest cash flow struggles faced by ecommerce businesses is the delay in getting money from your sales marketplace and into your bank account. This delay can cause cash flow problems by restricting how much money you have in your account at any time. By planning ahead, you can build up or start your ecommerce business with a cash reserve that can be the buffer while funds are being transferred from one account to another.

When you plan ahead and have a cash buffer, it means no lost business. A 2019 study by Intuit found that small business owners lose over $43,000 each year by not taking on projects due to cash flow problems. When you plan ahead, you don’t have to worry about cash flow when evaluating whether or not you want to take on a project.

Diversify Supply Chain, Sales Channel and Fulfillment

One of the most important signs of a financially resilient ecommerce business is diversification. Through diversification, you build up multiple ways of doing business so that you’re not relying on just one shipper or warehouse. So, when there is a problem, you already have relationships with other vendors, shippers and warehouses and can pivot more quickly than other businesses.

Diversification minimizes the risk that your business takes on. Strategic diversification means that if there’s a disruption to any part of your ecommerce business’s supply chain, you have a backup plan and redundancies in place so that you can quickly and easily pivot.

You need to create diversification and redundancies into your:

  • Shippers
  • Warehouse
  • Sales channels
  • Product and service offerings
  • Fulfillment operators

Diversification makes your business resilient overall. No matter what happens to the world outside of your ecommerce business, you have a plan in place to solve it and pivot.

Track Income and Expenses

While much of increasing financial resiliency for your ecommerce business is about planning ahead and creating redundancies in your business, there’s also the quotidian, routine tasks. Tracking your income and expenses is the day-to-day management of financial business resilience. But, you can’t just track income and expenses blindly. You need to know your numbers and understand what they mean to your business.

According to Nabity, “Once you plan out for the maximum number of losses you can minimize, coupled with planning out the minimum turnover you shall require, you may come to the realization that things are not as bad as you might have once perceived them to be.”

Tracking your numbers and knowing what your income and expenses should be at different times of the month gives you more room to maneuver if you see a crisis coming. No longer will you be surprised by a problem — you’ll see it coming. Daily management of your business finances and a deep understanding of what your numbers should look like are critical parts of financial resilience.

Make Low-Risk Investments

If you’re looking to grow your on-hand liquid capital for your ecommerce business, one way you can do that is to take on low-risk investments. Morgan suggests that “companies can consider saving and investing funds to assets that are easily convertible to cash and have this as an emergency fund or reserve to be used in this kind of situation.”

Low-risk investments include high-yield savings accounts and bonds. The benefit of low-risk investments is that your emergency cash reserve isn’t just sitting idle in your bank account. Instead, it’s actively growing and benefitting your business while also still being easily accessible if you need it.

Have a Plan for Raising Prices

A topic that many business owners dread: raising prices. Unfortunately, it’s an uncomfortable necessity. Knowing when and how to strategically raise your prices is a forward-thinking way to keep your business financially resilient. If your prices stagnate for too long, you’ll be out of sync with the rest of the market and will harm your business. Be ready and be thinking about when you’ll raise your prices so you can continue to grow your ecommerce business and make it more financially resilient.

Creating a financially resilient ecommerce business requires some strategic thinking, planning ahead and routine business maintenance, but it can help to make your business more resilient overall so that you can weather anything that happens inside and outside your business. Financial resiliency makes your ecommerce business more adaptable and efficient so that you can pivot at a moment’s notice. Deemsoft can automate accounting and bookkeeping with Quikbooks or any other desired platform and part of your support team for creating and monitoring your ecommerce business’s financial resilience.


Hiring a top consultant

“Products are made in a factory but brands are created in the mind.” - WALTER LANDOR

Questions & more Questions?

  1. “Ask them about their previous roles.”
  2. “Discuss the overall cost up front.”
  3. “Make sure to check references.”

These are some suggestions you might have heard when someone gives you unsolicited advice on how to hire the right employees or consultants. Well, all the aforementioned points are true, of course. You should check a consultant’s online reviews, disclose your budget, and take a look at their previous achievements. It’s a no-brainer.

However, in this article, we’ll talk about some aspects of the process of hiring consultants that don’t get discussed quite often.

You Should Go for ‘Top Consultants’, Not ‘Top Consulting Firms’

When a brand name is attached to a product or service, it’s considered okay to charge an insanely high amount to customers. This applies to consultancies as well. When it comes to consultancies, there are just a handful at the top and the rest are mostly unheard of by most people. Not saying that these big names don’t do a great job. But if you’re willing to think outside the status quo and explore other options, you’ll likely find a top consultant for the quarter the price.

The task of hiring the best consultants doesn’t always have to involve the best consultancies. It sounds like an obvious thing, but surprisingly, so many business owners don’t even consider the possibility of looking at some other places when hiring a consultant.

Whether you hire a freelance consultant or one from a top consulting firm, in the end, you’ll likely have only one, or a couple of consultants who actually work with you. Choose this person based on their personal and professional attributes—not just a big brand name.

 The Process of Hiring a Consultant Is Similar to That of Hiring Employees

Howdo you hire a permanent full-time employee if you’re too busy with your day-to-day operations? It’s simple. You could approach a recruitment agency to handle 80% of the hiring process for you. Then you can interview the top candidates and onboard the chosen one.

The same process can be used to hire a freelance consultant. The only difference is that instead of visiting a recruitment agency, you can approach an online consulting platform. These freelance platforms give you access to hundreds of top management consultants at any time. You can even find consultants who have worked for Google, Facebook, and other Fortune 500 companies.

Sometimes, small and medium-sized business owners who >are not used to working with consultants solely rely on their professional network or social media ads when they’re thinking about hiring a consultant. But it doesn’t have to be this way anymore. The advent of online consulting platforms has made it so easy for businesses to find the best consultants in a matter of days.

 There Are Dozens of Niches Consultants Specialize in

Yes, consulting is more than just business strategy advice or accounting—which are a couple of niches that big consulting firms specialize in. In reality, you can, and should, find consultants for your specific business problem. There’s no doubt that generalists can get the job done as well, but why not hire a consultant who has solved the same problems in the same niche before?

For example, if you’re thinking about hiring a consultant to create your company’s weekly newsletter, you should hire an inbound marketing consultant because they specialize in such tasks. Similarly, if you want to run Facebook ads for your next annual sale, it’s recommended that you hire a Facebook ads consultant instead of a generalist marketing consultant who ‘kind of’ knows about social media ads but isn’t very experienced.

That’s why it’s crucial that you define your problem first and then decide which type of expertise and skills you’re looking for in your consultant.

 Your Entire Team Should Be Open to the Idea of Hiring a Consultant

The decision to hire a consultant should be collective. Hiring the best management consultants is easy, but to make the most of this opportunity depends on the whole team. A consultant’s knowledge and experience might not be of any use if they’re not offered proper support and collaboration from internal stakeholders, especially senior management.

Sometimes, consultants are seen as temporary outsiders and kept at a distance by the permanent team. It’s an age-old human tendency to be a bit skeptical of strangers. Before you bring a consultant on board, let your whole team feel included in the decision, so they don’t feel undermined later. Let them know exactly how each employee and the company will benefit because of the consultant.

For instance, if you’re hiring an Agile coach to help you incorporate your company’s new operations process for the first time, discuss with your team how convenient it’ll make their weekly operations. Make the consulting engagement a win-win for everyone.

It’s often believed that the only thing that a business needs to do is hire a consultant, and they’ll take care of all the problems. What no one tells you is this: The company and the consultant need to work as a team. Sometimes, the consultant will lead the situation, but sometimes, they might need some help from the team as well.


Hiring the best consultants has never been easier because of the ubiquity of freelance consulting platforms. It might just take a few clicks and a couple of phone calls before you’re discussing your project with a top consultant. Businesses can now hire some of the best consultants in the world without even approaching the Big Four firms. No matter what your problem is, there’s a consultant out there that specializes in solving them. All you need to do is approach an online consulting platform.

How to Maximize Productivity

As daylight savings approaches and 2021 is well underway,  it’s a good time to think about different ways you can, well, save time. These days, employees are spending more and more time at the office–certainly exceeding the typical 40-hour workweek. However, increasing hours worked does not necessarily translate to increased efficiency.

So, how can leaders and managers improve employee productivity while still saving time? Here are the top 10 things you can do to increase employee efficiency at the office.

1. Don’t Be Afraid to Delegate

While this tip might seem the most obvious, it is often the most difficult to put into practice. We get it–your company is your baby, so you want to have a direct hand in everything that goes on with it. While there is nothing wrong with prioritizing quality (it is what makes a business successful, after all), checking over every small detail yourself rather than delegating can waste everyone’s valuable time.

Instead, give responsibilities to qualified employees, and trust that they will perform the tasks well. This gives your employees the opportunity to gain skills and leadership experience that will ultimately benefit your company. You hired them for a reason, now give them a chance to prove you right.


2. Match Tasks to Skills

Knowing your employees’ skills and behavioral styles is essential for maximizing efficiency. For example, an extroverted, creative, out-of-the-box thinker is probably a great person to pitch ideas to clients. However, they might struggle if they are given a more rule-intensive, detail-oriented task.

Asking your employees to be great at everything just isn’t efficient–instead, before giving an employee an assignment, ask yourself: is this the person best suited to perform this task? If not, find someone else whose skills and styles match your needs.

3. Communicate Effectively

Every manager knows that communication is the key to a productive workforce. Technology has allowed us to contact each other with the mere click of a button (or should we say, tap of a touch screen)–this naturally means that current communication methods are as efficient as possible, right? Not necessarily. A McKinsey study found that emails can take up nearly 28% of an employee’s time. In fact, email was revealed to be the second most time-consuming activity for workers (after their job-specific tasks).

Instead of relying solely on email, try social networking tools (such as Slack) designed for even quicker team communication. You can also encourage your employees to occasionally adopt a more antiquated form of contact…voice-to-voice communication. Having a quick meeting or phone call can settle a matter that might have taken hours of back-and-forth emails.

4. Keep Goals Clear & Focused

You can’t expect employees to be efficient if they don’t have a focused goal to aim for. If a goal is not clearly defined and actually achievable, employees will be less productive. So, try to make sure employees’ assignments are as clear and narrow as possible. Let them know exactly what you expect of them, and tell them specifically what impact this assignment will have.

One way to do this is to make sure your goals are “SMART” – specific, measurable, attainable, realistic, and timely. Before assigning an employee a task, ask yourself if it fits each of these requirements. If not, ask yourself how the task can be tweaked to help your workers stay focused and efficient.

5. Incentivize Employees

One of the best ways to encourage employees to be more efficient is to actually give them a reason to do so. Recognizing your workers for a job well done will make them feel appreciated and encourage them to continue increasing their productivity.

When deciding how to reward efficient employees, make sure you take into account their individual needs or preferences. For example, one employee might appreciate public recognition, while another would prefer a private “thank you.” In addition to simple words of gratitude, here are a few incentives you can try:

  • PTO: Instead of a bonus or raise, you can offer your employees additional paid time off without having to use their vacation or sick time.
  • Take Them Out For a Meal: You can take the team out to lunch, dinner, or happy hour. Maybe even allow them to leave work early to do so.
  • Send a Handwritten Note: Sending a handwritten note shows you recognize the great work your employees have done and that you care enough to put your own personal time into thanking them.
  • Lazy Monday Coupons: Another option is a “Lazy Monday” coupon, which allows employees to arrive late on a Monday morning.
  • Tell Your Boss: If you email the team or team member thanking them for their work, considering copying YOUR boss on the email.
  • Try a Wellness Program: Consider implementing a workplace wellness program to cut down on the number of sick days and reduce your company’s overall health insurance spend.

6. Cut Out the Excess

If possible, try not to give employees smaller, unnecessary tasks when they are focused on a larger goal. Take a look at the team’s routine, and see if there is anything that you can cut to give employees more time to focus on higher-priority assignments.



For example, if employees are asking to write daily reports for their supervisors, but supervisors generally don’t have time to read them, consider cutting the word count requirement. Doing something simply as a formality is wasting valuable time that could be used for accomplishing goals that actually help your company.

7. Train and Develop Employees

Reducing training, or cutting it all together, might seem like a good way to save company time and money (learning on the job is said to be an effective way to train, after all). However, this could ultimately backfire. Forcing employees to learn their jobs on the fly can be extremely inefficient.

So, instead of having workers haphazardly trying to accomplish a task with zero guidance, take the extra day to teach them the necessary skills to do their job. This way, they can set about accomplishing their tasks on their own, and your time won’t be wasted down the road answering simple questions or correcting errors.

Past their original training, encourage continued employee development. Helping them expand their skillsets will build a much more advanced workforce, which will benefit your company in the long run. There are a number of ways you can support employee development: individual coaching, workshops, courses, seminars, shadowing or mentoring, or even just increasing their responsibilities. Offering these opportunities will give employees additional skills that allow them to improve their efficiency and productivity.


8. Embrace Telecommuting

Allowing your employees to work from home might seem inefficient – after all, how can you guarantee that they will still be productive if no one is watching them? However, the reality is quite the opposite (in fact, studies show that people who work from home are 13% more productive than office employees). Letting your employees telecommute will allow them to save time that would otherwise be wasted completely.

For example, say an employee is feeling too ill to come into work (or is simply worried about getting their coworkers sick) but can still be productive. If you don’t allow them to work from home, they will be forced to take a sick day and skip working altogether. Or, forcing your employee to miss an entire day of work if they have to wait for that 2-4 hour period to get their refrigerator fixed, simply isn’t efficient. Instead, allow your employee to work from home so they can maximize what time they do have available.

9. Give Each Other Feedback

There is no hope of increasing employee efficiency if they don’t know they’re being inefficient in the first place. This is why performance reviews are essential – measure your employees’ performance, then hold individual meetings to let them know where they are excelling, and what areas they need to work on.

Increasing employee efficiency isn’t all about what they can do better – some of the responsibility falls on you as well. But just like your employees, you aren’t psychic. So after reviewing your employees, ask them what you could do to help them improve. Maybe they would like a little more guidance on certain tasks, or would prefer a little more room for creative freedom. Asking for feedback not only gives you clear, immediate ways to help your employees improve, but also encourages a culture of open dialogue that will allow for continued development over time.

10. Think Big Picture

Things that might seem like an inefficient use of time to you now, might actually be to your advantage in the long run. So, before vetoing an apparent misuse of time, ask yourself how this could possibly benefit your company.

Investing in HR System now can save your company – and your employees – countless hours down the road. From automated onboarding to payroll that runs itself, embracing HR technology will improve efficiency, reduce frustration, and help your business grow.

By utilizing a number of our efficiency tips, you can be sure that you don’t fall behind and put that extra hour to good, productive use.

Where To Launch Your Startup

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Attracting Great Talent

Why is talent important?

Superior talent is up to eight times more productive

It’s remarkable how much of a productivity kicker an organization gets from top talent. A recent study of more than 600,000 researchers, entertainers, politicians, and athletes found that high performers are 400 percent more productive than average ones.2 Studies of businesses not only show similar results but also reveal that the gap rises with a job’s complexity. In highly complex occupations—the information- and interaction-intensive work of managers, software developers, and the like—high performers are an astounding 800 percent more productive (Exhibit 1).

The relationship between quality of talent and business performance is dramatic.

Suppose your business strategy involves cross-functional initiatives that would take three years to complete. If you took 20 percent of the average talent working on the project and replaced it with great talent, how soon would you achieve the desired impact? If these people were 400 percent more productive, it would take less than two years; if they were 800 percent more productive, it would take less than one. If a competitor used 20 percent more great talent in similar efforts, it would beat you to market even if it started a year or two later.

You get even more remarkable results comparing the productivity of the top and bottom 1 percent. For unskilled and semiskilled jobs, the top 1 percent are three times more productive; for jobs of middling complexity (say, technicians and supervisors), 12 times more. One person in the top 1 percent is worth 12 in the bottom 1 percent. For high-complexity jobs, the differential is so big it can’t be quantified.3

The late Steve Jobs of Apple summed up talent’s importance with this advice: “Go after the cream of the cream. A small team of A+ players can run circles around a giant team of B and C players.”4 Management guru Jim Collins concurred: “… the single biggest constraint on the success of my organization is the ability to get and to hang on to enough of the right people.”5

Great talent is scarce

The term “war for talent” was coined by McKinsey’s Steven Hankin in 1997 and popularized by the book of that name in 2001.6 It refers to the increasingly fierce competition to attract and retain employees at a time when too few workers are available to replace the baby boomers now departing the workforce in advanced economies.

Fast forward to the wake of the Great Recession, and the war for talent turned into the war for jobs. In economies gripped by financial crises, unemployment hit levels not seen since the early 1980s, so there was no shortage of applicants for many openings. When Walmart launched a new Washington, DC, store in 2013, for example, it received 23,000 applications for 600 positions.

It was harder to get entry-level work there than to be accepted by Harvard: 2.6 percent of Walmart applicants made it through, as opposed to 6.1 percent for the Ivy League university.7

Yet this didn’t end the war for talent. In medium- and higher-complexity positions, where stronger performers have an increasingly disproportionate bottom-line impact, the opposite was true. In those uncertain times, gainfully employed talent became less likely to change employers, so people who had an advantage going into the crisis had an even bigger one. Further, pressure to reduce HR costs made it harder to identify and attract the most talented people. Everything suggests that the war for talent will rage on. “Failure to attract and retain top talent” was the number-one issue in the Conference Board’s 2016 survey of global CEOs—before economic growth and competitive intensity (Exhibit 2). In more complex jobs, this will continue to be true as baby boomers (and their long experience) exit the workforce and technology demands more sophisticated skills.

A McKinsey Global Institute study8 suggests that employers in Europe and North America will require 16 million to 18 million more college-educated workers in 2020 than are going to be available. Companies may not be able to fill one in ten roles they need, much less fill them with top talent. Yet in advanced economies, up to 95 million workers could lack the skills required for employment. Developing economies will face a shortfall of 45 million workers with secondary-school educations and vocational training.9

Most companies don’t get it right

Since business leaders know that talent is valuable and scarce, you might assume that they would know how to find it. Not so (Exhibit 3). A whopping 82 percent of companies don’t believe they recruit highly talented people. For companies that do, only 7 percent think they can keep it.10 More alarmingly, only 23 percent of managers and senior executives active on talent-related topics believe their current acquisition and retention strategies will work.11

These leaders aren’t being humble—most companies just aren’t good at this stuff. Gallup reported that in a 2015 survey, more than 50 percent of respondents were “not engaged”; an additional 17.2 percent were “actively disengaged.”12 Related surveys report that 73 percent of employees are “thinking about another job” and that 43 percent were more likely to consider a new one than they had been a year earlier.13

The fact that the Baby Boomers’ decades of knowledge and experience are now leaving the workplace forever makes this state of play more unsettling. At the natural resources giant BP, for example, many of the most senior engineers are called “machine whisperers” because they can keep important, expensive, and temperamental equipment online. If high-quality talent isn’t brought in to replace such people, the results could be catastrophic.

And the scarcer top talent becomes, the more companies that aren’t on their game will find their best people cherry-picked by companies that are. In the future, this will be even more likely, since millennials are far less loyal to their employers than their parents were. The Bureau of Labor Statistics says that workers now stay at each job, on average, for 4.4 years, but the average expected tenure of the youngest workers is about half that.14 People often underestimate the cost of turnover: the more information- and interaction-intensive the job, the greater the threat to productivity when good people leave it, and the more time and money must be invested in searching and onboarding. And if competitors poach your talent, they get an insider’s understanding of your strategies, operations, and culture.

Talent matters, because its high value and scarcity—and the difficulty of replacing it—create huge opportunities when companies get things right. Let’s now turn to how they can do that.

What are the big ideas?

Focus on the 5 percent who deliver 95 percent of the value

Companies go through cycles of initiatives to improve their talent processes. Yet they reap only incremental improvements, and the vast majority of leaders report that their companies neither recruit enough highly talented people nor believe that their current strategies will work.

What do these leaders miss? Let’s consider American football. If you asked people who is the most highly paid player on a team, they would correctly say the quarterback, the key person in the vast majority of plays. People would probably say that the second most highly paid player was the running back or the wide receiver since they work directly with the quarterback to advance the ball. These people are wrong. It’s the relatively unnoticed left tackle, who protects the quarterback from things he can’t see and could injure him.

Some employees disproportionately create or protect value, and not all of them are obvious. A navy, for example, should obviously ensure that it has the best and brightest people commanding fleets of nuclear submarines. Equally, however, it should ensure that it attracts superior talent to the role of the IT-outage engineer, who prevents catastrophes for the crew, the environment, and humanity. In a world of constrained resources, companies should focus their efforts on the few critical areas where the best people have the biggest impact. Start with roles, not processes (which create generic solutions that don’t meaningfully improve results) or specific people (who might help you in particular situations but don’t build institutional muscle).

Picking the right battles isn’t easy—you must understand the true economics of value creation in specific roles. That’s precisely why this can be one of your secret weapons in the war for talent.

Make your offer magnetic—and deliver

Leaders know the term “employee value proposition,” or EVP: what employees get for what they give. “Gives” come in many flavors—time, effort, experience, ideas. “Gets” include tangible rewards, the experience of working in a company, the way its leadership helps employees, and the substance of the work (Exhibit 4). If your EVP is truly stronger than the competition’s, you will attract and retain the best talent. But for three reasons, few companies have EVPs that meaningfully help them win this war:

Not distinctive. A typical human-resources department spends months determining what employees want—a great job, in a great company, with great leaders, and great rewards. HR then says the value proposition should deliver all this, so the EVP resembles that of every business that’s gone through the same process. It’s better for companies to stand out on one dimension while not ignoring the others. Work for Google if you want to face complex challenges, for Virgin if Richard Branson’s leadership stirs you, or for Amgen, if you aspire to “defeat death.”

Not targeted. Although it’s fine to have an overall EVP, what matters most is a winning EVP for the 5 percent of roles that matter most. If data scientists are hugely important, for example, you’ll want an EVP that lets them invent things; offers a clear, rapid career progression; and helps them have a big impact.15

Unreal. An attractive EVP cooked up by HR and pushed through PR used to help secure the best talent. In the long term, however, this was always a losing proposition, since great people would quickly become disillusioned if the reality didn’t measure up. Today, however, talent won’t buy such promises at all. Employees are a more trusted source of information about working conditions than CEOs or HR chiefs.16 The same Internet and social media that help customers investigate product claims do the same thing for EVPs. Sites such as Glassdoor or Job Advisor offer peer ratings and reviews of what it’s really like to work for a company. Your EVP can’t be spin—it has to be distinctive, targeted, and real.

Technology will be the game-changer

Michael Lewis’s book Moneyball17 pits the collective old-time wisdom of baseball players, managers, coaches, scouts, and front offices against rigorous statistical analysis in determining which players to recruit. Analysis wins, changing the game forever. Could the same be true for recruiting top talent?

When the National Bureau of Economic Research looked into this, it pitted humans against computers for more than 300,000 hires in high-turnover jobs at 15 companies. Human experience, instinct, and judgment were soundly defeated: people picked by computers stayed far longer and performed just as well or better.18 This wasn’t the only such finding. University of Minnesota professors analyzed 17 studies and found that hiring algorithms outperform humans by at least 25 percent. “The effect holds in any situation with a large number of candidates, regardless of whether the job is on the front line, in middle management, or (yes) in the C-suite.”19

Many leaders find this hard to stomach, but some companies are abandoning old ideas. The waste company Richfield Management, for example, uses an algorithm to screen applicants for character traits suggesting a tendency to abuse workers’ compensation. Claims have since dropped by 68 percent.20 After Xerox replaced its recruitment screening process with an online test from Evolve, attrition declined by 20 percent.21

HR software systems from Oracle, SAP’s SuccessFactors, and Workday already gather information through sources such as LinkedIn to provide an advanced warnings when top talent may be thinking about jumping ship. At McKinsey, we used machine-learning algorithms to determine the three variables driving 60 percent of the attrition among our managers. Unexpectedly, all three are unrelated to pay, travel, or hours worked.

Although people analytics is a field still in its infancy, it’s gaining speed. In 2016, only 8 percent of companies reported that they were fully capable of using predictive modeling, but that was up from 4 percent in 2015.22 Leaders who don’t implement concrete plans to leverage technology in the war for talent will quickly fall behind. Yet machines alone won’t win it. In 1997, IBM’s Deep Blue computer thrashed grandmaster Gary Kasparov. Today, however, the world’s best chess players are neither computers nor humans, but human teams playing alongside computers.23 That will be true in business, too.

How do I make it happen?

The new leader of a major US public institution had a mandate for change. Her department failed to meet the budget for five years. The press was having a field day with tales of incompetence, inefficiency, and bureaucracy gone mad. Morale was extremely low; key talent was leaving. The leader felt she knew what had to be fixed, but she didn’t have the talent. There was no quick fix—each division had its own approach to recruiting, and all were consumed with their immediate needs. The defectors were mostly the higher performers and specialist talent the organization wanted to keep.

1. Aspire

In the leader’s words, a team was commissioned to “fix the leaky bucket, and fill it with the finest stuff imaginable!” Core members from each division populated a task force to meet the challenge. Division leaders were told they were on the hook. The team first determined the talent requirements for the organization’s five-year plan. Two roles were especially important: general managers and data-analytics specialists. The team then coupled this demand view of talent with a supply view and identified the gaps. Senior leaders gave the team a mandate for bold action.

2. Assess

With the priorities established, the team took a deep dive into the current mess. What did recruits in each target segment care about? How did the institution compare with their other options? Why were people in key roles departing? Which current approaches were and weren’t working? Using interview techniques to get behind superficial answers, the team gathered qualitative data. Quantitative data were generated by predictive analytics algorithms that determine patterns and an analysis of how general managers spent their time.

The organization’s value proposition—the promise of interesting work, on-the-job development, and an attractive, flexible career path—turned out to be on target. However, the reality didn’t live up to it. When recruits called friends hired previously, they heard that the organization had gone “bureau-crazy.” Recruiters knew this, but their incentives were to get people through the door, so they hyped roles to meet short-term goals. Good talent left quickly, while others, happy with the security and relatively high pay, “quit and stayed,” remaining on the payroll but contributing little.

The team found that specialist candidates wanted a different value proposition: deeper technical development, opportunities for special projects, a more relaxed and informal environment, and freedom from administrative tasks.

3. Architect

The working team recommended two discrete career paths, for generalists and specialists. The role of general managers would be adjusted to let them play more of a coaching (rather than a coordination) role. For data analysts, the team proposed more relaxed, informal recruitment events on school campuses and a stronger referral program. Predictive analytics showed that the organization had significant weaknesses for some roles. Its leaders agreed to “segment of one” discussions with the highest performers to understand their issues and fix them quickly.

Analytics suggested that ten vital leaders might be on the verge of leaving. They were engaged to help reinvent the EVP for the general-manager role—an approach that not only produced better answers but also helped to promote retention. Further changes were proposed for the annual succession-planning process (for instance, focusing on pivotal roles) and the recruitment process, to make both more efficient.

4. Act

The leader and top team led from the front—for example, by personally attending the newly overhauled top-talent development programs—to communicate the importance of making the target EVP real and vibrant. She quickly became known for asking two questions in every performance dialogue: “what are your top five to seven priorities?” and “who are your top five to seven most talented leaders?” People learned that there should be a match between the answers. A talent office created to ensure progress reported on key metrics, such as time and cost to hire, as well as acceptance and attrition rates (overall and for key talent). These were studied with as much intensity as operational and financial metrics. To institutionalize transparency, the talent office developed an interactive dashboard with metrics on hiring, quality, fit, and efficiency.

5. Advance

The results appeared quickly: employee engagement shot up and attrition declined, especially among the most recent hires. Acceptance rates started improving, and employees became a powerful recruiting source. HR launched “choose who you want to work with” campaigns and made the most dynamic leaders and specialists “recruiting captains” for key campuses and career fairs.

Eighteen months later, after rising nearly 40 spots in the public sector’s Best Place to Work ranking, the organization found it easier to access talent, especially data scientists. Attrition dropped to historic lows, particularly in critical general-management and specialist roles. As a final sign of success, instead of trumpeting the organization’s downward spiral, headlines announced the bold new agenda and leadership.

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Managing Risk in Bio-Medical Engineering

We live in an ever-changing world where we are forced to deal with uncertainty every day. But how an organization tackles that uncertainty can be a key predictor of its success.

Risk is a necessary part of doing business, and in a world where enormous amounts of data are being processed at increasingly rapid rates, identifying and mitigating risks is a challenge for any company. It is no exception for Bio-Medical in fact it is more than any other field since it stakes human life for its benefits and hence there is no wonder that many contracts and insurance agreements require solid evidence of good risk management practice.
        ISO 31000 provides direction on how companies can integrate risk-based decision making into an organization’s governance, planning, management, reporting, policies, values and culture. It is an open, principles-based system, meaning it enables organizations to apply the principles in the standard to the organizational context.  
        ISO 31000 helps organizations develop a risk management strategy to effectively identify and mitigate risks, thereby enhancing the likelihood of achieving their objectives and increasing the protection of their assets. Its overarching goal is to develop a risk management culture where employees and stakeholders are aware of the importance of monitoring and managing risk. 
Implementing ISO 31000 also helps organizations see both the positive opportunities and negative consequences associated with risk, and allows for more informed, and thus more effective, decision making, namely in the allocation of resources. What’s more, it can be an active component in improving an organization’s governance and, ultimately, its performance.  


The risk management process involves the systematic application of policies, procedures and practices to the activities of communicating and consulting, establishing the context and assessing, treating, monitoring, reviewing, recording and reporting risk.  

  1. Communication and consultation including ,
    • Bring different areas of expertise together for each step of the RM process
    • Ensure different views are considered when defining risk criteria and evaluating risks
    • Provide sufficient information to facilitate risk oversight and decision-making
    • Build a sense of inclusiveness and ownership among those affected by risk
  2. Scope, context, and criteria, including:
    • Define the purpose and scope of risk management activities
    • Identify the external and internal context for the organization
    • Define risk criteria by specifying the acceptable amount and type of risk
    • Define criteria to evaluate the significance of risk and to support decision-making
  3. Risk assessment, including:
    • Risk identification to find, recognize and describe risks that might help or prevent the achievement of objectives and the variety of tangible or intangible consequences
    • Risk analysis of the nature and characteristics of risk, including the level of risk, risk sources, consequences, likelihood, events, scenarios, controls, and their effectiveness
    • Risk evaluation to support decisions by comparing the results of the risk analysis with the established risk criteria to determine the significance of risk
  4. Risk treatment, including:
    • Select the most appropriate risk treatment option(s)
    • Design risk treatment plans specifying how the treatment options will be implemented.
  5. Monitoring and review, including:
    • Improve the quality and effectiveness of process design, implementation, and outcomes
    • Monitor the RM process and its outcomes, with responsibilities clearly defined
    • Plan, gather, and analyze information, recording results, and providing feedback
    • Incorporate the results in performance management, measurement, and reporting activities
  6. Recording and reporting, including:
    • Communicate risk management activities and outcomes across the organization
    • Provide information for decision-making
    • Improve risk management activities
    • Provide risk information and interacting with stakeholders

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