5 Ways for Data Scientists to Bring Technology and Business Closer Together
Whether you already have data analysis processes in place and work with data science specialists or are looking for reasons to dip your toes into big data, bridging raw technology and business decisions in your company is of utmost importance. According to Entrepreneur, 65% of business owners admit that big data has affected their decision-making process in a positive way, while the technology is estimated to increase retail operating margins by 60% on the global level.
Whether for in-house management and development or data analysis, implementing data science professionals into your business model can be highly beneficial for your bottom line. Given their versatile and open-ended skillsets however, data scientists can find it difficult to settle into a company and feel as if their expertise is put to good use. With that said, let’s take a look at several actionable ways in which data scientists can contribute to your workflow and effectively bridge the gap between technology and business in doing so.
The Importance of Data Science Nurturing
Before we dive further, it’s worth noting why data science nurturing is important for a company which deals with high volumes of information. Whether you operate as a small business, a startup or a large energy industry enterprise, information in regards to your business’ performance, infrastructural resources and day-to-day productivity will be generated in some capacity.
Modern data scientists are akin to a “Jack of all trades” when it comes to their skillsets and general expertise. From data analysis, visualization and presentation up to project management, business management, and programming, data scientists can bring about transformative changes to your business model if you bring them on board. According to 2019 Data Science Central findings, the industrial sector, which also includes energy-related companies, hires 39% of data scientist professionals, while 40% are employed by the Fortune 500.
Leaders in markets such as IT, FinTech, energy, and industrial development have recognized the advantages of retaining data scientists. Data scientists efficiently gather company data, analyze it and provide timely updates. These updates positively affect business decision making and serve to better the overall client servicing operations across the board. With that in mind, let’s take a look at some of the most influential and applicable instances in which data scientists can build bridges between technology and business in your company.
1. Programming Skills Implementation
We’ve touched on the fact that data scientists in the 21st century lean heavily into programming and database management – but what does this mean in practice? According to Towards Data Science, contemporary data scientists are likely to possess some of, if not all of, the following languages in their repertoire: Java, Python, SQL, C++, R and AWS to name a few.
These can be used to create, manage, analyze and extrapolate data from your business’ generated information pool in the form of specialized databases. Once these databases are created, managed and monitored by your data scientist, they can be utilized by everyone on the office floor after some onboarding and training activities.
This will effectively bridge the gap between raw data and everyday activities which require constant data access, extrapolation, and management which is not something all of your employees will be able to do natively. Thus, data scientists can cut the gap between raw data and everyday activities, creating simple, dedicated applications with specialized uses, directed at streamlining data management across all departments in your company.
2. Machine-Learning Integration
Machine-learning management is another department in which a data scientist could be of use in your business model. They can not only create machine-learning algorithms and AI to automate much of your data analysis and processing activities but also facilitate conversational AI
such as chatbots to be present on your website.
Once machine-learning is successfully integrated into your workflow, the data scientists’ job is to monitor its performance, eliminate bottlenecks and upgrade the existing code to keep up with your industry’s trends. Machine-learning can also be integrated into energy management, resource monitoring, and infrastructure to help you keep a close eye on expenditure, power flow or other variables related to your business.
3. Data Visualization & Standardization
In terms of bridging the gap between business and technology, there is no better way to do so than by visualizing data for colleagues and team members. This type of visualization is extremely useful in industries which don’t rely on concrete products or infrastructure, such as energy resource management, client servicing or business management.
Data scientists can put their visualization and creativity to good work by exploring different ways in which they can present empiric data for less tech-savvy colleagues, B2B partners, and other stakeholders. This can make data scientists respected and accepted as part of the company’s family in a very tangible and actionable way. Most importantly, it will ensure that everyone on the team understands extrapolated data, its role in the current project and how it can be used to better the end-product’s quality for clients and customers across the globe.
4. Cyber-Security Monitoring
Regardless of how local or niche your business model might be, cyber-security risks are a real threat which should be addressed in some capacity. From in-house security software to professional antivirus and anti-malware management, data scientists can be put in charge of your IT to maintain its peak performance. A step toward digital privacy
from online intrusions or leaks is important due to the cloud-based nature of company server information which makes it easy for knowledgeable hackers to gain access to sensitive data.
When it comes to the energy sector, malware and manual hacking can also lead to rerouted power lines, decrease in overall efficiency as well as complete shutdowns of important pipelines and infrastructure in some of the worst cases. In turn, it would cost your company its reputation, future contracts, and revenue in a way in which shouldn’t be risked. Luckily, data scientists are versatile in terms of programming and database maintenance which allows them to manage cyber-security for your company in comprehensive ways.
5. Data Tracking & Analysis
Lastly, you never want to make a blind or misinformed decision when it comes to accepting new contracts, collaborating with international companies or developing new products or services. Data scientists can thus play the role of data analysts and managers, informing you of industry fluctuations, internal resource expenditures, revenue gain expectancies and other variables depending on your instructions and needs.
While decision-making has been briefly mentioned previously, its importance in the overall business model and ongoing company development cannot be overstated. That is why data scientists can be used not only for tracking and analysis but also for more informed goal-setting, data-driven company growth and software-based risk-free testing of said data for your business’ benefit. Over time, this approach to day-to-day company management will allow you to eliminate ineffective bottlenecks in your workflow, upgrade performing sectors in your business model and gain the upper hand in your industry compared to the competition.
Adopting a Data-Centric Mindset (Conclusion)
Given what we now know about data science and the experts which have dedicated their professional careers to the field, it’s easy to see how it can be implemented into a variety of scenarios and industries. Whether as data analytics, programmers, visualization specialists or project managers, data scientists can and should become part of your standard employee roster for a variety of reasons.
Explore the ways in which energy competitors and other big data-heavy businesses have integrated data scientists into their workflow to find the most efficient ways to do so within your own company. If you play your cards right, you may end up creating a disruptive business model thanks to improved data management, customer experience processes and machine-learning AI.
About our Guest Writer
Dorian Martin is a professional writer with years of experience in business-related topics. He likes to attend conferences and keep himself updated with every new release in terms of marketing, business management, and entrepreneurship. He is a senior writer and content editor at BestEssayEducation, as well as a contributor writer to various websites, such as WowGrade and Grab My Essay.
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