Do your company have People or Resources?

First of all, I would like to make the corporative approach I'll be using to talk about people and resources clear to everyone.

People - Individuals who have feelings, believes, values, preferences, ambitions, passions and a need to be constantly motivated.

Resources - Everything needed to get something done in a project that has a cost and a production rate embedded. Eventually, when human effort is needed, people might be used as resources.

Now that we clearly understand the difference, let's talk about how companies misuse both, people and resources on its workdays.

It is very common to see companies (especially the Brazilian ones) in a rush to hire people to be used as resources in a given project. And worse, most of the times they try to get the most qualified people for the lowest wages. The problem is that this practice entails in dealing with a set of known issues. If you wanna have a strong corporative culture that will grow multidisciplinary, mature and self-managed teams that will boost your company, this is the wrong way. 

You've got to remember that those resources are people. Putting them together in a team expecting productivity and interaction among them just isn't gonna work. Your company must have a desired employee profile to be fulfilled before hiring. You can't just put an inattentive person in a team full of details driven people. What you think will happen? The answer is very simple, both team and person will be frustrated at the very first interaction, and that's your fault! As result, you'll get low-quality deliverables, deadline breaking, high turnover and money loss due to constant training for new employees (if you train them).

Try to hire people that are like-minded to your company's objectives and policies so your teams may have more engagement in their daily tasks having the big picture in mind.

In another hand, even having team like-minded with your company's culture you still can't forget that they're people. They need to be respected, they need to feel safe and motivated. Throwing tons of tasks to be delivered in record time on then just because they've shown their value as a team isn't gonna work either. There is a very sharp line between motivation and limitation and even knowing it is not easy to find out, you must try.

Treat people like people. If your employees are happy, motivated and confident, probably your customers will be too. 

If your company have a resource-centric culture, people that work for you will treat your company as a resource too, that is, it can be replaced at any time.

When your company have a people-centric culture you will see the advantages of it which I can explain in a future article.

5 Important Artificial Intelligence Predictions (For 2019) Everyone Should Read

Original post created by

Bernard Marr

Artificial Intelligence – specifically machine learning and deep learning – was everywhere in 2018 and don’t expect the hype to die down over the next 12 months.

The hype will die eventually of course, and AI will become another consistent thread in the tapestry of our lives, just like the internet, electricity, and combustion did in days of yore. 

But for at least the next year, and probably longer, expect astonishing breakthroughs as well as continued excitement and hyperbole from commentators. 

This is because expectations of the changes to business and society which AI promises (or in some cases threatens) to bring about go beyond anything dreamed up during previous technological revolutions. AI points towards a future where machines not only do all of the physical work, as they have done since the industrial revolution, but also the “thinking” work – planning, strategizing and making decisions.

The jury’s still out on whether this will lead to a glorious utopia, with humans free to spend their lives following more meaningful pursuits, rather than on those which economic necessity dictates they dedicate their time, or to widespread unemployment and social unrest. 

We probably won’t arrive at either of those outcomes in 2019, but it’s a topic which will continue to be hotly debated. In the meantime, here are five things that we can expect to happen:

1. AI increasingly becomes a matter of international politics

2018 has seen major world powers increasingly putting up fences to protect their national interests when it comes to trade and defense. Nowhere has this been more apparent than in the relationship between the world's two AI superpowers, the US and China.

In the face of tariffs and export restrictions on goods and services used to create AI imposed by the US Government, China has stepped up its efforts to become self-reliant when it comes to research and development.

Chinese tech manufacturer Huawei announced plans to develop its own AI processing chips, reducing the need for the country’s booming AI industry to rely on US manufacturers like Intel and Nvidia.

At the same time, Google has faced public criticism for its apparent willingness to do business with Chinese tech companies (many with links to the Chinese government) while withdrawing (after pressure from its employees) from arrangements to work with US government agencies due to concerns its tech may be militarised.

With nationalist politics enjoying a resurgence, there are two apparent dangers here. Firstly, that artificial intelligence technology could be increasingly adopted by authoritarian regimes to restrict freedoms, such as the rights to privacy or free speech. Secondly, that these tensions could compromise the spirit of cooperation between academic and industrial organizations across the world. 

This framework of open collaboration has been instrumental to the rapid development and deployment of AI technology we see taking place today and putting up borders around a nation’s AI development is likely to slow that progress. In particular, it is expected to slow the development of common standards around AI and data, which could greatly increase the usefulness of AI.

2. A Move Towards “Transparent AI”

The adoption of AI across wider society – particularly when it involves dealing with human data – is hindered by the "black box problem." Mostly, its workings seem arcane and unfathomable without a thorough understanding of what it's actually doing.

To achieve its full potential AI needs to be trusted – we need to know what it is doing with our data, why, and how it makes its decisions when it comes to issues that affect our lives. This is often difficult to convey – particularly as what makes AI particularly useful is its ability to draw connections and make inferences which may not be obvious or may even seem counter-intuitive to us.

But building trust in AI systems isn’t just about reassuring the public. Research and business will also benefit from openness which exposes bias in data or algorithms. Reports have even found that companies are sometimes holding back from deploying AI due to fears they may face liabilities in the future if current technology is later judged to be unfair or unethical.

In 2019 we're likely to see an increased emphasis on measures designed to increase the transparency of AI. This year IBM unveiled technology developed to improve the traceability of decisions into its AI OpenScale technology. This concept gives real-time insights into not only what decisions are being made, but how they are being made, drawing connections between data that is used, decision weighting and potential for bias in information.

The General Data Protection Regulation, put into action across Europe this year, gives citizens some protection against decisions which have “legal or other significant” impact on their lives made solely by machines. While it isn’t yet a blisteringly hot political potato, its prominence in public discourse is likely to grow during 2019, further encouraging businesses to work towards transparency.

3. AI and automation drilling deeper into every business

In 2018, companies began to get a firmer grip on the realities of what AI can and can’t do. After spending the previous few years getting their data in order and identifying areas where AI could bring quick rewards, or fail fast, big business is as a whole ready to move ahead with proven initiatives, moving from piloting and soft-launching to global deployment.

In financial services, vast real-time logs of thousands of transactions per second are routinely parsed by machine learning algorithms. Retailers are proficient at grabbing data through till receipts and loyalty programmes and feeding it into AI engines to work out how to get better at selling us things. Manufacturers use predictive technology to know precisely what stresses machinery can be put under and when it is likely to break down or fail.

In 2019 we’ll see growing confidence that this smart, predictive technology, bolstered by learnings it has picked up in its initial deployments, can be rolled out wholesale across all of a business’s operations.

AI will branch out into support functions such as HR or optimizing supply chains, where decisions around logistics, as well as hiring and firing, will become increasingly informed by automation. AI solutions for managing compliance and legal issues are also likely to be increasingly adopted. As these tools will often be fit-for-purpose across a number of organizations, they will increasingly be offered as-a-service, offering smaller businesses a bite of the AI cherry, too.

We’re also likely to see an increase in businesses using their data to generate new revenue streams. Building up big databases of transactions and customer activity within its industry essentially lets any sufficiently data-savvy business begin to “Googlify” itself. Becoming a source of data-as-a-service has been transformational for businesses such as John Deere, which offers analytics based on agricultural data to help farmers grow crops more efficiently. In 2019 more companies will adopt this strategy as they come to understand the value of the information they own.

4. More jobs will be created by AI than will be lost to it.

As I mentioned in my introduction to this post, in the long-term its uncertain if the rise of the machines will lead to human unemployment and social strife, a utopian workless future, or (probably more realistically) something in between.

For the next year, at least, though, it seems it isn’t going to be immediately problematic in this regard. Gartner predicts that by the end of 2019, AI will be creating more jobs than it is taking. While 1.8 million jobs will be lost to automation – with manufacturing in particular singled out as likely to take a hit – 2.3 million will be created. In particular, Gartner's report finds, these could be focused on education, healthcare, and the public sector.

A likely driver for this disparity is the emphasis placed on rolling out AI in an "augmenting" capacity when it comes to deploying it in non-manual jobs. Warehouse workers and retail cashiers have often been replaced wholesale by automated technology. But when it comes to doctors and lawyers, AI service providers have made concerted effort to present their technology as something which can work alongside human professionals, assisting them with repetitive tasks while leaving the "final say" to them.

This means those industries benefit from the growth in human jobs on the technical side – those needed to deploy the technology and train the workforce on using it – while retaining the professionals who carry out the actual work.

For the financial services, the outlook is perhaps slightly grimmer. Some estimates, such as those made by former Citigroup CEO Vikram Pandit in 2017, predict that the sector's human workforce could be 30% smaller within five years. With back-office functions increasingly being managed by machines, we could be well on our way to seeing that come true by the end of next year.

5. AI assistants will become truly useful

AI is genuinely interwoven into our lives now, to the point that most people don't give a second thought to the fact that when they search Google, shop at Amazon or watch Netflix, highly precise, AI-driven predictions are at work to make the experience flow.

A slightly more apparent sense of engagement with robotic intelligence comes about when we interact with AI assistants – Siri, Alexa, or Google Assistant, for example – to help us make sense of the myriad of data sources available to us in the modern world.

In 2019, more of us than ever will use an AI assistant to arrange our calendars, plan our journeys and order a pizza. These services will become increasingly useful as they learn to anticipate our behaviors better and understand our habits.

Data gathered from users allows application designers to understand exactly which features are providing value, and which are underused, perhaps consuming valuable resources (through bandwidth or reporting) which could be better used elsewhere.

As a result, functions which we do want to use AI for – such as ordering taxis and food deliveries, and choosing restaurants to visit – are becoming increasingly streamlined and accessible.

On top of this, AI assistants are designed to become increasingly efficient at understanding their human users, as the natural language algorithms used to encode speech into computer-readable data, and vice versa, is exposed to more and more information about how we communicate.

It's evident that conversations between Alexa or Google Assistant and us can seem very stilted today. However, the rapid acceleration of understanding in this field means that, by the end of 2019, we will be getting used to far more natural and flowing discourse with the machines we share our lives with.

8 PRODUCTIVITY TIPS

People fail to be productive daily, partly because they feel overwhelmed and partly because of the grandest enemy all professionals share – procrastination.

1. Use Lists and More Lists

A to-do list, a memory list, a tasks list, a breaks list, and even a workout schedule list. To be as productive as you can, free up the memory by writing down things. Lists are the best way to externalize the memory.
One of the biggest enemies of business analysts is the memory. Having a job that demands to remember dozens of things on daily basis is exhausting, and you can only manage to do it all for a limited time. Writing things down and organizing them in clear-to-follow lists will aid you in focusing on what needs to be done when it needs to be done.
This won’t take away the tasks you have, but it will certainly help you remember them all, prioritize them, and take a detached and critical look at the problems at hand.

2. Give Nature a Shot

A study by the University of Michigan shows that you can actually improve the productivity by no less than 20% if you just take a walk in the park. Why? Because a few minutes off that busy work schedule can do wonders for improving your memory and help you remain focused on what’s important.
Now the other question arises: why nature? Why not take a break at the coffee shop around the corner or have a walk in a busy, urban environment?
What we can all agree with is, nature has a great, calming effect on our minds. A peaceful break surrounded by nature and nothing that relates to your work and obligations is exactly what your mind needs to remain productive.

3. Daydream

Don’t go daydreaming about the next vacation or your bed at home. When we say daydreaming, we mean let your mind wander. And by this, we mean let your mind do whatever it wants to do.
Your schedules and lists come very handily here. Leave out short periods during the busy day to daydream. Get into a calming stage when you reach that default mode. This should help you solve problems and think of connections you probably wouldn’t consider otherwise.

4. Focus on the Big Things

Every business analyst must deal with small and big problems. However, being as great as you are, your job shouldn’t focus around the small problems. Learn to delegate these and focus on the big ones instead. The problems that can most impact the organization are your first and only priority, so give your maximum to solving those before you go solving anything else.
The idea of getting the small things done first to get fired up for the big ones or reduce the list of tasks is very wrong. It makes no sense to finish small things when the bigger ones are left unsolved.

5. Make Use of Presentations

Presentations are very useful for business analysts. As soon as you start a project, begin with a layout of the analysis presentation.
It might seem counter-intuitive at the beginning, but it is a very productive habit. Such a habit will cut down the turnaround time of the project in half.
How do you do this?
You do this by creating a presentation, a document, or a simple writing on a white piece of paper. The form doesn’t really matter. The thing that matters is to note down and layout the outcomes that may occur from the very beginning, both the good and the bad.
Once you are done doing this, you can start looking at each of the factors to see what you can and should change. Use reasoning and mathematical equations and simply, create a sure starting point before you take the action.

6. Define the Data Requirements

This step comes naturally after the previous one. Once you have the analysis laid out in a comprehensive manner, you will have the data requirements right there in front of you. When you do, you need to:

• Structure the data requirements
  Design the analysis tables instead of making a list of variables. Make a past campaigns table, a customer demographic table, a table for transactions made in the last year, policy changes for bank credits table, etc.
• Collect as much data as you can
  Even if you are unsure about the variables you need, collect them upfront just to be on the safe site. Including some additional variables now is much better and easier than doing so later in your analysis.

7. Make a Reproducible Analysis

No, this is not as simple as it might sound. Any of the work you do might turn out to be less than reproducible, which can turn out to be a big problem afterward. If you are a beginner, perform your copy-paste steps in Excel. If you are advanced in your business, use a command line interface, but with care.
Similarly, a business analyst must be very careful when he works with notebooks. Don’t go changing previous steps if it uses some of the data set that hasn’t been computed yet. Notebooks are an excellent resource, but only if you maintain their flow.

8. Split Your Work and Take Regular Breaks

Everyone works better when rested. We mentioned taking a nature walk, but naturally, you won’t be able to do this all the time. To keep your productivity levels high, you need to do what every other person in the world needs to do – work in chunks and schedule breaks along the way.
Whenever you feel like you are overwhelmed with your work, take a short break. Get a coffee, take a walk, eat some chocolate – whatever makes you relaxed. Then you can back to that big project you are working on and stop when it is time to take another break.

Conclusion

These eight productivity hacks for business analysts are very effective when it comes to boosting the productivity. But, in the end, it all comes down to what works for you. Test them out to see and use the ones you find best for you to keep your analyst juices flowing.