Did you know that more than 1.7 MB of Data is created every minute for every person worldwide, according to Harvard? It is, therefore, essential that we investigate such data further. Furthermore, to make sense of all the information, it contains we have to structure it adequately followed by proper and accurate interpretation of the data before publicly revealing it to the people in a meaningful way. These all are tasks that data scientists excel at and bring to the table as employees of a business organization.

Though in the literal sense, you need not be a data scientist to accomplish the same. Still, such distinguishment will be more of a literal meaning than an actual, real and practical one.

Businesses are full of available data, and with the expertise of a data scientist, they can put it into practical use. Some of the ways we can use data are as follows:

Monetize The Available Data Of A Company

The field that affects the finances of a business is naturally the most important to them. Though many companies have indeed started setting up analytics teams to look after everyday operations like:

  • Enhancing supply chains
  • Minimize Costs
  • Make sure sales processes are optimum

But the sad thing is that even in the businesses that have woken up to Data and its immense value, data monetization is a practice they are not familiar with. According to studies, only one in twelve companies using Data is optimally monetizing it as well. And that means that their investments in salaries and tools related to the field are not getting them the revenue they need to sustain the investment in the long term.

Monetization of Data is the next big thing within data science. MIT’s Sloan School of Management in the recent past prepared a report highlighting the use of available data by companies for monetization. Such data monetization is achieved by optimization of internal-facing processes and enhance external-facing processes like client services. Some sectors that stand to benefit significantly from such data monetization are as follows:

  • Fraud detection for financial services and organizations
  • Targeting tourists and retail customers based on geographical location
  • Providing digital advertisers with click-stream insights and smart targeting
  • Using IoT (Internet of Things) Apps For Revenue

The initiative taken by John Deere, a manufacturing company, deserves a special mention in this respect. They gave data analytical tools to estimate things like crop insurance costs, yield forecasts, and managing related risks, which significantly boosted their revenues.

Have The Expertise To Be A Part of Insights-as-a-Service Firms

The insights resulting from data can achieve several things which are simply terrific for businesses. We have mentioned most of them in the paragraphs above. But the point is that very few companies can afford data scientists and the working environment necessary to gather and analyze data and produce insights. As a result, businesses are outsourcing data intelligence tasks leading to an insights-as-a-service segment. Studies indicate that 66% of enterprises outsource a significant (11%-75%) amount of business intelligence applications, according to the predictions of Forrester that this market will grow 2x with increasing traction. Even companies with data science professionals are likely to hire such insights-as-a-service firms to supplement their existing data science abilities.

Be A Part Of The Core Decision Making Team

Data Science has acquired so much popularity for its usefulness that Chief Data Officer (CDO) is a position that a large majority of companies (57%) already have. Such personnel is part of the top management team at corporate organizations. More than half of all CDOs report to their CEO, increasing 40% in just a year. In addition, a whopping 85% of all companies in the US are trying to foster a data-driven internal work culture. Among the typical responsibilities of a CDO or a data scientist includes:

  • Creating a data-driven internal culture
  • Support such a culture
  • Distribute data insights across diverse business lines
  • Find out innovative uses of available data to achieve superior business processes, services, and products.

Become An AI Specialist

AI and Machine Learning have come to be through the emergence of the field of data science. With their capabilities, the latter is almost on steroids when data science combines with them. Indeed, the job portal reports that 75% of all data scientist roles include the terms “AI” and “Machine learning” in the job descriptions. As a result, professionals with AI skills are much in demand which has risen 2x in the last three years.

You Can Help Make Better Products

The better suited a product is for its intended target audience, the more chances it has of succeeding in the market. An analysis is due To achieve the best fit for solving customer problems. This analysis needs to have data as its basis.

Companies should be able to attract their customers towards products. Therefore, they need to develop products that suit customers’ requirements and provide them with guaranteed satisfaction. Therefore, industries require data to build their product in the best possible way and need the advanced analytical tools used by data scientists.

But industries also take into account market trends while developing products that lead to products that are more updated and targeted with their intended audience. With the rapid expansion in the growth of available data, businesses in all kinds of industries are better positioned to create new products employing various innovative strategies. We can cite the example of Airbnb in this context. Airbnb data scientists process and analyze customer data available to the company to meet consumer needs better and offer better premium services.

Utilize Predictive Analytics to Forecast Outcomes

Without a doubt, predictive analytics is one of the most critical functions that Data plays in the functioning of a business organization. Through the advanced tools and tech of predictive analytics, businesses are better placed to use various types and forms of data properly.

The word predictive is pretty accurate as through predictive analytics; statistical data analysis is carried out. Such analysis is done along several machine learning algorithms to predict future outcomes indicated by available historical Data. There are numerous software tools for data scientists to carry out, such as SAS, SPSS, and SAP HANSA.

Businesses stand to benefit in various ways by using predictive analytics, which is as follows:

  • Assessing risks
  • Sales forecasts
  • Customer segmentation
  • Market analysis

Through the use of predictive analytics, businesses can better assess future business events and act accordingly.

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