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How Analytics Professionals Can Improve Their Business Acumen Amid This Crisis

How Analytics Professionals Can Improve Their Business Acumen Amid This Crisis

How Analytics Professionals Can Improve Their Business Acumen Amid This Crisis
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Well, it has already been established that learning technical skills, including tools and languages like Python, Hadoop, SQL, and data visualisation, will indeed get you a data science job. However, it is also vital for a data scientist to have business knowledge in order to survive in this competitive landscape. Having a business understanding will not only help data scientists and analytics professionals to know the elements of a business model but will also be valuable for businesses to maximise their returns. With business knowledge, analytics professionals can effectively use the understanding for collecting and interpreting data.

In fact, in a recent event, the global service delivery head of analytics at Wipro, Sohini Mehta has stated that, with so many automated machine learning platforms in the market, most of the jobs have become easy and quite simple to perform. “It no longer needs just the technical expertise, but there is a lot of business knowledge too that comes into play.”

Understanding the business would also involve understanding the customer and their requirements. This would help data scientists to build appropriate solutions that would solve customer’s needs and problems. Along with that, business knowledge would help data scientists and analytics professionals in understanding how decision-making processes happen at all the levels of the business. Therefore, they would be able to provide accurate data for the right kind of problems to create solutions that can deliver tangible value. 



Here are the four ways that can help analytics professions to improve their business acumen.

Aligning your work with other departments of the organisation

Considering data scientists work with data, the majority of them are usually very isolated from the rest of the departments in the organisation. In order to gain business knowledge, data scientists and analytics professionals must align their work with other business units of the company which would not only help them collaborate but will also make them understand how their analytics solutions are used in the business. 

Data scientists need to put constant effort to continually collaborate with other business departments so that different teams can help them analyse the market for making the solution more relevant for end-users. Along with learning the general business aspect, data scientists also require to have industry-specific knowledge which would include the concerns related to regulation, compliance, and company-specific expertise, which would help them understand the revenue model, target audience, and supply chain process. In fact, according to a data scientist of Mondelez International, Saurabh Awasthi once stated to the media, “Technical expertise is essential to be a data scientist. However, it only makes the aspiring data scientist a modeller. ‘Business understanding’ enables that modeller to become a complete data scientist.”

Expand your knowledge base beyond the specific domain

Apart from aligning your work with other departments of the organisation, data scientists, in order to enhance their business knowledge, also need to focus on building their skills beyond their specific domain knowledge. For instance, in order to understand business in a better way, data scientists can also develop their project management skills, which will help them understand how business projects work and how managerial positions handle such perspectives. 

Exploring the administrative roles will help analytics professionals and data scientists to understand the team dynamics as well as how to manage their deadline and reach the goal. Such knowledge would assist in initiating, planning, executing, driving as well as managing a project to expand your base on the business side. Also, for businesses, involving data science in the core business process would help in achieving value delivery in minimal time with better quality. 

Also Read: How Businesses Can Build Trust With Their Remote Analytics Professionals Who Handle Critical Data

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Experiment with new tools and technologies

Alongside, to get more involved in the business, organisations should allow data scientists to experiment with new tools and technologies, which would enable professionals to work with a greater variety of data and explore different business problems. It’s more like a reverse calculation, where data scientists can get a better understanding of the intricate working of businesses by analysing a variety of data as well as working with newer technologies

This empowers data scientists to broaden their range of insights and solutions they are producing for their customers. Some of these new technologies that businesses are currently working with involve robotic process automation, artificial intelligence, cognitive learning and reinforcement learning, and working with these technologies would allow data scientists to explore more business opportunities. Another way could also be attending tech-conferences, which holds many business talks by expert speakers, which could give data scientists the opportunity to learn as well as network with other business leaders. These conferences act as a hub of knowledge which could assist data scientists in building their business acumen skills with the guidance of the best brains of the industry. Such methodologies would also augment data scientists to ideate and manage business projects.

Also Read: Artificial Intelligence Essentials For Business Leaders

Communicate more with business managers

Lastly, one apparent way data scientists can build their business acumen is by communicating with business managers who are actually on the field to handle business processes. This will help data scientists understand the challenges they face in reality, and with that, they can collaborate to create solutions pertaining to the problem. 
Professionals working with data usually work in silos, and that omits the opportunity to broaden their horizon. Instead, they should communicate more to bring business value. Alongside, doing so will also help data scientists to showcase soft skills which will again make them stand out in the crowd. These business managers could also act as a mentor for data scientists’ career progression in managerial roles. Data scientists communicating with decision-makers would also help business managers to understand data better and use it for informed decision-making.

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