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5 Vital Soft Skills Data Scientists Must Possess in 2021

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Technical skills are overrated, particularly in data science. Many data scientists quickly realize that much of their job challenges aren’t due to what they can or cannot do. Rather, the mentality with which they approach tasks matters a lot.

For instance, a data scientist who has mastered communication will present their insights better than their more (technically) skilled counterpart whose reports are jumbled. Likewise, extrapolating insights from raw data require a huge dose of creativity and critical thinking, both of which are not taught as technical skills but must instead be developed personally.

Other soft skills that are necessary for data scientists include business aptitude, problem-solving, and adaptability.

All of these are time-proof skills that transcend technological innovations. Success in 2021 and beyond as a data scientist will heavily rely on the development of these soft skills.

Critical Thinking

This author defines critical thinking as “the judicious and objective analysis, exploration and evaluation of an issue or a subject in order to form a viable and justifiable judgment.�

Critical thinking is often regarded as the most essential skill in data science.

It makes you well-informed, enhances your judgment, and makes you better equipped to make more effective decisions. As a data scientist, you must be capable of examining the available data from multiple perspectives. To develop critical thinking, do the following:

  • Question your assumptions: as a scientific field, your job is to apply empirical methods to analyzing data and extracting insights. However, the human mind remains subject to all kinds of biases and presuppositions. You must thoroughly interrogate them to hone your reason and avoid decision pitfalls.
  • Engage different perspectives: As social beings, we are drawn to people who act and think like us. But the lack of healthy dissent leads to poor decision-making. Thinking critically means consistently seeking out fresh perspectives. This doesn’t necessarily mean disagreement; it could be as simple as connecting with colleagues from another department in order to understand their outlook.

Communication

The purpose of data analysis is to make informed decisions. And your responsibility as a data scientist includes being able to present your findings in a clear manner to the non-data-scientists who have to make the decisions.

Your non-technical audience needs to know how you reached a specific conclusion, the justification for your methods, the implication of your findings, and why you consider one solution better than the other.

You can make your presentation more effective through storytelling. As Brent Dykes says in his book, Effective Data Storytelling,  “…narratives are more compelling than statistics if your goal is to make an impact on your audience.”

Visuals achieve the same effect; when used right, they help your audience see and understand patterns between scraps of data. Your insights don’t matter unless you can make others understand it and drive them to take the necessary actions.

Problem Solving

A data scientist is like a detective. Both workers investigate the available facts and data to address problems. In one case, the purpose is to solve crimes; on the other, the purpose is to deliver business value.

Data is what we make of it. And a data scientist needs to be resolute at, and equipped for, investigating issues to the root. Project managers love a data scientist who can identify creative solutions to problems.

For instance, discovering that your company’s customers behave in a certain way is different from why they behave so. And even then, the job is most likely not done. You must still use the available data to determine how to make the customers behave differently or to make the company adapt to the customers’ habits.

Data science is a continuous job of evaluating data and weighing options, determining why one approach to fulfilling a goal is better than the other. The consequences of your conclusions could be massive; so you need to get it right, at least based on the data available to you at the time.

Practice makes you a better problem-solver. There are websites that help you learn to tackle various data science challenges with real business impacts.

Business Aptitude

Analyzing data is one thing; contextualizing it to solve real business problems is another. Dr. N. R. Srinivasa Raghavan of Infosys is widely quoted thus: data science is more than just number crunching: it is the application of various skills to solve particular problems in an industry.

Without a good understanding of business processes and operations (such as supply chains, customer service, finance, human resources, logistics), it would be impossible to extrapolate actionable insights.

Data science is a field involving so much theory but has far-reaching practical implications. Therefore, a good data analyst is one that understands the business model and can quickly adapt to various business situations.

How does the business work? How does your company work? What do you know about your industry? How does your company make money? What product/service does your company deliver, and how does that work? What makes your company lose money? Who are your competitors?

These questions, and more, are important to understanding business operations. You can develop this by research. But you first need to possess a keenness for business and understand that data science is not just about Python, SQL and all the technical parts.

Adaptability

Adaptability has to do with how quickly you are able to adjust to new conditions, which may be positive or negative. In this information age, innovation grows at such a rapid pace that it is often difficult to keep up. We are living in a world of possibilities, and what’s new today can become outdated in a few months or years.

In fact, the tools you use for data analysis five years from now may be different from the ones you employ today.

Adaptability is also important for moments of crisis, a time when data scientists come under greater pressure to deliver. Consider the COVID-19 pandemic. The global spread of this virus has disrupted business operations everywhere and altered, perhaps permanently, the course of work and business.

When there is a setback, people seek answers; they want to know exactly what went wrong and how they can move forward.

Today, everyone relies on data. In this world of several unprecedented changes, you must be ready to adjust to the prevailing trends.

Conclusion

Soft skills deal with how you approach data. You may know all the technical bits of data analysis, but a wrong approach almost always leads to wrong results.

More importantly, the technical aspects may change. In five years or a decade, the currently popular data science tools may be entirely out of the limelight, edged by newer advanced tools.

But skills such as critical thinking and problem-solving will endure. Developing these skills early is a great way to secure your career in the future.

Image Credit: pixaby; pexels

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Culture future of work Productivity remote work Small Business Work Work freedom work from home

The Rise of Remote Work and How to Assess Growth and Development

kpi assess growth

COVID-19 has accelerated the rate of remote work adoption globally, but what have we found out? We have discovered that remote work is not bad, after all.

In the past, many companies have been wary about transitioning to telecommuting, even when most of their office operations do not require the physical presence of employees. The main fears expressed by leaders include a possible decline in employee productivity and a lack of clarity about the measurement of employee performance.

The performance of individual employees determines the trajectory of the company. Without reliable methods to assess its employees’ work, it is difficult for any organization to achieve growth and development.

This article addresses the concerns of managers who are hesitant about remote work and those who have already implemented remote work but have trouble with tracking performance for workplace productivity and office growth.

Set Clear Objectives and KPIs to Assess Growth

How do you know if your employees are getting stuff done? The inevitable first step is to define what it means to get stuff down. In business lingua, that means to establish work objectives and set Key Performance Indicators (KPIs). Effective KPIs are aligned with the company’s overall goals and must contribute to the company’s growth and development.

Communicating to employees the metrics by which their performance will be assessed helps them to understand their priorities. Clear expectations, keep employees focused.

Acknowledge Unquantifiable Performance Indicators

In setting KPIs, though, you need to acknowledge that numbers don’t tell the full story. Not to suggest that you should discount the importance of numbers, but you should also be cognizant of the Key Intangible Performance Indicators. Admittedly, working remotely can compound the vagueness of such indicators — leading employees to feel their efforts are not recognized enough.

Some aspects are just not quantifiable, such as leadership, creativity, innovation, organization, and engagement. There are quantifiable indicators that may make us understand employee engagement and organization, but the subjects themselves are indefinite.

Until new ways and means of measuring these intangibles are discovered — company leadership should acknowledge that Key Intangible Performance Indicators exist, and look for ways to recognize employees who go above and beyond.

Support Employee Development

Assessments should be more supportive than they are judgmental. Employees appreciate frequent check-ins when the aim is to keep them on their toes and support them through difficulties. However, the manager that goes around constantly pointing out his team members’ flaws (without thoughtfully helping them overcome their challenges) is only seen as grumpy.

The rules also apply in remote work, where managers (out of fear of losing control) begin acting intrusively by implementing extreme corporate surveillance. Tracking and assessment are not the ends themselves; the goal of tracking employee performance should be to improve team productivity. In essence, remote work management and performance should usher us into a new era of trust, more autonomy, accountability, and team collaboration.

“Nowadays, it is not enough to equip teams with new digital tools for remote collaboration, which many rapidly did when the pandemic began. It’s only the first step,� says Maxime Bouroumeau-Fuseau, co-founder and CTO of Paris-based Digicoop, a worker cooperative behind the work management platform Kantree. “The changing workplace calls for an environment where employees are empowered to take control of their work.

In our experience as a co-op, when employees are given more autonomy and when micromanagement is replaced by collaboration, teams deliver better results while individual employees feel more invested in their work.�

Allow Autonomy

Many people choose to work remotely because they want to feel a greater sense of ownership of their time and schedules. Therefore, even though time tracking is important for many remote teams, it goes without saying that hourly input is not always a good measure of performance.

The true measure of performance is the work outcome. Remote work allows employees to choose their own work hours; what does it matter if an employee works less per hour but still meets targets consistently? Researchers have shown that autonomy increases productivity.

Use the Right Tools and Analytics for KPIs

With physical offices out of the picture, it is the tools that a remote team uses that define the structure of work and operations. There are tools that keep employees accountable and provide actionable insights into how work gets done in the organization.

The rise of remote work has promoted the importance of analytics of everyday work data to ensure that employees are more productive.

The insights gained help team leaders and the management to understand if the organization is meeting set targets of performance and productivity and determine the rate of growth and development. Tools such as Trello, Kantree, Jira, Asana, Microsoft Teams, Slack, etc., are useful for assessing work progression.

Establish a Culture of Accountability

Note, though, that tools are only as effective as the culture in which they are situated. Your team might be using the best tools, but poor communication can derail employees from the main goals of the company.

When there are issues with employee performance, you must be able to recognize if there is a problem with the tools being used or with the management. Accountability should not only be down-up; it should be top-down too. Managers should be accountable to their subordinates and transparent about office dealings.

Conclusion

Going by statistics, remote work actually improves employee productivity and performance. This, in turn, leads to the overall growth of the company. However, this growth must be intentional. Organizations should implement proper (and flexible) assessment models to know when their work is really progressing and when there are problems that must be solved.

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B2B Business Digitization Business Optimization Deal Management Productivity Small Business Startups supply chain

How to Optimize B2B Deal Management to Cut Costs and Losses in 2021

B2B deal management

A lot of companies suffered supply chain disruptions due to COVID-19. Certain experts have described the situation as a Keynesian supply shock, a negative event that triggers aggregate supply shortages with bigger impacts than the prior reduction in labor supply.

There is still a lot of uncertainty in the air, so many businesses still don’t know how to approach the coming months. Though businesses have been undergoing changes, those shifts do not necessarily have a clear direction.

One area of supply chain operations that have undergone only a little change is deal management.

Deals are still handled pretty much in the same way, with the same old tools and strategies. Yet, they get more complicated. This leads to unnecessary additional costs and losses.

A recent study by Enable summarized the views of 100 sales, purchasing, and finance professionals and found that 83% of companies reported supply chain disruptions in some capacity due to COVID-19, and 47% have seen their revenue decrease between 10-80%.

Many businesses are losing millions of dollars each year because complicated deals are handled using outdated techniques.

COVID-19 and Deal Renegotiation

COVID-19 has delivered the biggest shocks to supply chains globally, forcing businesses to make swift changes to adapt to the new reality.

Right now, governments around the world are easing lockdown measures, despite fears of a second wave of the pandemic sweeping through. There is still a lot of precariousness and businesses are under pressure to renegotiate deals.

Renegotiation is inevitable since COVID-19 has altered the conditions upon which most deals were agreed. The existing arrangement puts all parties in a deal at a disadvantage.

Now, the problem is that many businesses would still be using the same poor tools that had consistently put them at a loss, even before COVID-19 was discovered.

Going forward, businesses need to rethink their strategies and pivot to digital for better deal management. Digitized deal management allows businesses to collect more data, gain better insights, and make better decisions when processing deals.

Ultimately, optimizing deal management strengthens your supply chains and even makes your sales team more effective.

Benefits of Optimized Deal Management to Sales Reps

1. Data-Driven Insights

One of the hallmarks of an improperly managed deal is confusion. Following the signing of an agreement, parties must continue to acquire insights into the realities and conditions that affect the deals. For instance, renegotiating deals at this time will require poring over the data of the business impacts of the pandemic.

Optimized deal management allows the sales team to access and properly assess current information on deals.

2. Friction-less Agreement

Deal negotiation involves many (often conflicting) ideas, and as all parties work towards finding common ground, some uniformity is necessary. Effective deal management puts collective principles above personal ideas. This cohesion drives attitudes that would lead to less friction, an important requirement if deals must go through successfully.

The availability of data-driven insights enhances transparency in the process, which, in turn, builds trust. As such, deals are processed faster, for the good of every party.

3. Collaboration

Deal information should be accessible on-demand to all interested parties at any time. This is important both for making critical decisions and for monitoring progress. The world increasingly becomes connected; deal brokers need to capitalize on this to optimize their processes.

According to Accenture, “digital solutions could ‘virtualize’ the entire end-to-end deal management process, perhaps using a web-based portal to bring together a virtual team from multiple areas of the organization.� Collaboration improves the relationship between deal parties. This, in turn, lowers the lifecycle of deals, empowering sales reps to close more deals in shorter times.

4. Accountability

The situation described above, how businesses lose millions due to unclaimed rebates, is an obvious sign of poor deal management. Optimized deal management is necessary for setting better goals and properly implementing factors to monitor progress.

Digitization of Deal Management

Deal management is one area of business that has not fully embraced digitization. Yet, most of its challenges are tied, directly or indirectly, to the use of outdated tools in a rapidly changing world.

For one, data has become the world’s most vital resource. In deal management, having detailed and accurate data is paramount to preliminary research and for maintaining comprehensive visibility of running deals.

Likewise, data is needed for better forecasting. Recounting the words of an old study, “without accurate forecasts, sales managers can expect a big gap between forecasted deals and actual closed-won deals.�

Businesses have far more data to deal with than they did ten years ago, meaning pages of spreadsheets and other paperwork can no longer deliver the right results. Deal management solutions help you to make better, data-driven decisions by giving you real-time analysis and visibility.

The prevailing data management strategy has data spread across various sources: spreadsheets, emails, and physical paperwork. This lack of consistency is what leads companies to make poor decisions and miss out on financial opportunities such as rebates.

Better forecasting with digitized deal management enhances the robustness of supply chains. By accessing relevant data, businesses can improve their risk monitoring. This results in better preparation and better adaptation to changing needs.

Instead of going with assumptions that things will fall into place, businesses can determine that through proper data analysis and subsequently implement methods to adapt their operations to even the worst shocks.

The digitization of deal management reduces dependency on certain key individuals. Due to the severe limitations of paper spreadsheets, usually only a few individuals broker deals and fully understand the ramifications applied.

With a cloud-hosted deal management solution, however, you can create a multi-threaded relationship. This translates into a more effective implementation of deals by boosting collaboration between all parties to the agreement.

Businesses must change their approach to deal with management. It’s no longer business as usual. In fact, while talking about cloud-hosted deal management solutions, there’s already been suggestions on the future role of artificial intelligence in enhancing deal management.

AI will help improve data analytics, automate financial processes, and overcome forecasting challenges with predictive analytics.

Conclusion

In essence, no business can afford to be left behind. Deal negotiation aims to reach an agreement that is profitable for both sides. But if a business persists with outdated tools and approaches to deal management, it wouldn’t be getting the right value for its agreements.

You can avoid losing money in unclaimed rebates and so on by digitizing your deal management to optimize negotiations.

Digitizing deal management helps you to collect detailed data, maintain comprehensive oversight, and make better decisions concerning deal negotiations.

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AI Business Digitization Digitization IoT New Normal Productivity ReadWrite supply chain

Why Digitizing Supply Chain Management Will Lead to Greater Efficiency in the New Normal

digitizing supply chain management

Going by Deloitte’s report on the performance of SMBs in a digital world, the majority of SMBs have not digitized their supply chain management. Since much of the world has gone digital, this means that several SMBs remain backward and struggle with inefficiency in supply management.

The digitization of supply chain operations helps companies to cut costs and save time, leading to overall increased efficiency.

While many supply chain operations would have been hampered by the effects of the COVID-19 pandemic, as businesses reopen, a digital transformation, tailored to each company’s needs and circumstances, is necessary. Digital tools and technologies that would boost supply chain management include automation, IoT, cloud computing, and analytics.

Benefits of Digitizing SCM

1. Real-Time Visibility

According to a survey, 84% of Chief Information Security Officers admitted to the lack of visibility being their biggest challenge. Lack of visibility creates blind spots and lets vulnerability fester until operations break down significantly, and revenue and profit reduce. This begs a reimagination of supply chain management, with the focus being delivering end-to-end visibility in real-time.

Apparently, businesses need to have 70 – 90% visibility on supply chain operations to address key volatility points efficiently. Only digitization makes this possible.

2. Data Integration

Digitizing supply chain management fosters interoperability and eliminates lags that the chain would have experienced otherwise. It also helps to maintain integrity and trust among partners.

In digital supply chain management, the information does not flow linearly, but rather simultaneously, whereby each point’s details are available to every other point.

This results in smoother decision-making and collaboration from planning to execution. The ultimate benefit is the ability to meet customers’ demands responsively. According to E2open, “Collaborating earlier in the product lifecycle reduces development and turnaround time, with a marked increase in fill rates averaging 10% to 15%.�

3. Predictive Maintenance

The advantage of real-time visibility is not just repairing structural deficiencies as soon as they appear, but more in addressing them before they become obvious. Artificial intelligence and analytics particularly come into play here, significantly increasing the accuracy of predictions. In short, a digital supply chain is a predictive supply chain.

Producing too much is often as bad as producing too little. But with advanced technologies, supply chain managers can gain detailed insights into operations and make smart decisions to forestall disruptions. 

4. Cash Flow Improvement

Digitization does not only affect supply chain operations but finance as well. On the one hand, it optimizes operations, which directly helps to preserve and boost cash flow. Research shows that digitizing the supply chain can lower operational costs by up to 50% and increase revenue by 10%.

SCM Digitization Technologies

  • Internet of Things

RFID has been used to track the exact location of logistics goods and materials, rendering barcode scanners and other similar means obsolete. Radio data transfer also eases communication across channels on the chain, such that broader details, including the condition of the assets, can be shared with colleagues.

Bluetooth, NFC tags, and GPS perform similar functions on different scales. And on a higher level, there are autonomous robots. These technologies are transforming inventory monitoring and warehouse management as a whole.

IoT technology in supply chain management is useful for determining the exact location of items in real-time, tracking movement, and monitoring the items’ storage conditions. These ensure safer and smoother delivery and reduce the operational back-and-forth that characterized traditional models of authenticating items.

  • Advanced Analytics

Supply Chain Analytics is an emerging aspect of SCM that uses quantitative data to extrapolate insight and optimize decision-making about the supply chain. Supply chains are complicated, and it is necessary to have an advanced system that helps managers make sense of all the data and information being gathered.

SCA is divided into:

  • Descriptive analytics: ensuring visibility
  • Predictive analytics: projecting future trends
  • Prescriptive analytics: solving problems
  • Cognitive analytics: answering questions by mimicking human reasoning

Take, for example, predictive analytics, which monitors data patterns to determine probable future trends. Predictive analytics helps managers to intelligently forecast future demand by analyzing several variables and how they affect demand. This helps them adjust operations as necessary and ultimately eliminate unnecessary costs associated with over- or under-supplying. And of course, there is the benefit of predictive maintenance.

The use of predictive analytics by supply chain managers increased to 30% in 2019 from 17% in 2017, while 57% of non-users plan to adopt it within the next five years, according to the 2020 MHI Annual Industry Report.

  • Cloud Computing

Accenture describes cloud computing as “the engine that makes supply chains talk to each other.�

Cloud computing allows you to integrate multiple platforms and data sources for seamless operations and flexibility. Rather than hoarding information in silos, thereby hindering collaboration, the cloud uses a network model that makes resources and data available on demand. This heightened responsiveness and agility boost the speed and efficiency of operations.

Also, cloud computing features a usage-based approach that makes them highly scalable. You can quickly meet new demands by ‘plugging in’ more resources and tools. And you can likewise scale down to focus on a particular niche or market segment.

In any case, it is understandable that the bleak state of cloud cybersecurity has made many companies reluctant to move their operations, including SCM, to the cloud. But frankly, this is up to your cloud provider and overall security consciousness. Measures to protect your system from hacking and data loss abound.

  • Automation

Based on a survey of professionals, warehouse automation receives more investment (57%) than any other supply chain technology, including predictive analytics (47%) and IoT (41%).

The adoption of automation in supply chain operations has been on a steady rise in recent years. A Mckinsey report identifies the following three factors as responsible: a growing shortage of labor, an explosion in demand from online retailers, and some intriguing technological advances.

Automating automatable tasks frees up labor for tasks that require strictly human input. But it is one thing to recognize that automation drives efficiency; it is another actually to adopt it. And in reality, most companies still lean towards manual rather than automated operations. This is due to specific challenges, such as fluctuating customer demands. This particularly puts the biggest companies at an unequal advantage.

However, there are many options available when it comes to automation. A business only needs to understand its market to make the right choices concerning automating their work.

Conclusion

Despite the abounding benefits and potentials of digitizing supply chain management, it is not without its obstacles. For instance, in some cases, government regulations are behind in innovation, mandating manual approaches even when there are better, digitized options.

Also, the process of digitizing the supply chain is not a small undertaking and is one that must be carried out in bits. With a sudden overhaul, you risk upsetting your entire business model.

Digitizing supply chain management leads to efficiency, and the biggest companies acknowledge this and act appropriately. According to Gartner, “by 2023, at least 50% of large global companies will be using AI, advanced analytics, and IoT in supply chain operations.� It is the SMBs that must strive to catch up and optimize its supply chain with digital tools.

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