Outsourcing, Data Science, and Competitive Advantage

The Acquired Capacity to Use New Information

It has become increasingly evident in recent years that the term ‘data science’ has become commonplace in the business world. In a noteworthy article published in the Harvard Business Review in 2023, data scientist was declared ‘the sexiest job of the 21st century’, due to the fact that there is an extreme shortage of professionals with the necessary qualifications. This lack of data scientists is causing disruption in many industries, so it is important to understand what data science is and why it is so important.

Data science is the process of obtaining actionable insights from large volumes of raw data by utilising scientific principles, computer programming, application software, mathematical and statistical analysis. Highly qualified data scientists are in great demand by corporations as they are able to analyse the company’s archives and identify any potential opportunities to increase revenue. This demand is primarily being driven by the increased amount of data available on the internet. As the internet continues to grow, the need for data scientists to interpret and analyse this data is also increasing.

History of the Development of Data Science

In the early 1990s, we saw the start of an incredible growth in the use of the Internet. Within a few years, millions of users were taking advantage of the Internet to communicate with each other via email and to share data across the web. Fast forward to 2023, and the amount of data being sent and received online is truly staggering. According to Domo, a staggering 2.5 quintillion bytes of data is sent over the Internet every single day, and this figure is expected to continue to rise in the years to come.

The world has become increasingly reliant on technology for both leisure and business purposes, with almost every device now functioning as a two-way data conduit. This has enabled companies to collect vast amounts of data from devices such as mobile phones, computers, automobiles, thermometers, telephones and coffee makers as they process and transmit information over the Internet. However, with such a high volume of data, businesses often struggle to keep up with the task of sorting through it all. This is where the expertise of a data scientist can help, as they are able to interpret the data and make sense of it. Unfortunately, there is a shortage of both data scientists and productive data scientists, meaning that much of the data collected by businesses is going unused.

Outsourcing data science to a nearby country

As the demand for data scientists continues to outstrip the supply of professionals in this field, many businesses are exploring innovative ways to overcome this challenge. One approach that is becoming increasingly popular is outsourcing data science tasks to specialist software outsourcing companies. This can enable businesses to access top quality services for a fraction of the price and, in doing so, bypass the competition for data scientist talent.

By partnering with a suitable outsourced data science provider, companies can benefit from fast access to teams with specialised skill sets, industry expertise and the latest data management and compliance technologies. These distributed teams can be relied upon to monitor data for errors and provide regular insights and analysis. This means businesses can be confident they are getting the most out of their data and they don’t have to worry about managing it in-house. Contracting out these services provides access to data that may otherwise have been unavailable.

Data science service providers have a considerable responsibility; simply hiring a few data scientists, asking for the applicable corporate data and using generic analytics methods is not enough to ensure success. To ensure success, the service provider must have a comprehensive understanding of areas such as data integration and processing, big data, corporate data warehousing and business intelligence and analytics.

This article provides an overview of the subfields of data science, outlining how they are related to data science and examining the impact they have on businesses. It aims to provide insights into the various aspects of data science and how these can be utilised to benefit organisations. It also explores the implications of data science for businesses, detailing how it can be used to inform decision-making and improve operational efficiency. Furthermore, it looks at how data science can be leveraged to gain competitive advantages and create new opportunities. In conclusion, this article presents a comprehensive overview of the subfields of data science and their effects on businesses.

  1. Analysis and information gathering for business use.

    In the contemporary corporate landscape, both Business Intelligence (BI) and Business Intelligence Analytics are integral components of success. BI is a descriptive analytics tool, typically utilising software and services, that analyses data to provide an understanding of the current state of the organisation. This is achieved through the use of reports, summaries, charts and graphs. BI is pertinent in providing a historical and current context.

    By contrast, Business Analysis (BA) is intended to be future focused. Utilising historical data, analysts can employ algorithms to forecast the likely and possible outcomes for the organisation. The basis of Business Intelligence is based on verifiable facts and figures, while Business Analysis tends to be more speculative.

    Organisations may find both Business Intelligence (BI) and Business Analytics (BA) to be highly beneficial when it comes to identifying areas of potential cost savings and improvements in operational efficiency. Both organisational strategy and tactical decision-making can benefit from these services, as their capabilities are powered by data science. Furthermore, the reports and forecasts generated by these services are highly contingent upon the effective organisation and management of data.
  2. Warehouses of information for large organisations

    In the modern age of data collection, it is essential for companies to ensure that their data is securely stored and archived. Enterprise Data Warehousing (EDW) plays a pivotal role in data science and business continuity, as it provides a platform for data governance, management and consolidation. EDW enables data from multiple sources to be organised, accessed and presented in a uniform and straightforward manner. Without EDW, data scientists would have to painstakingly track the origin of the data, collate it and combine it with other sources before they can attempt to analyse and report on it.

    When data is stored across multiple servers, networks and even countries, it is often forgotten about, leading to valuable insights and data being wasted. Utilising an Enterprise Data Warehouse (EDW) to bring all data into one place can not only improve business intelligence (BI) and analytics, but also have a major impact on how data science is used within an organisation. When data is scattered over many repositories, it can be difficult to gain a thorough understanding of it, and the introduction of new data sets can completely alter a company’s view of their business.
  3. Massive amounts of data

    The concept, importance and application of ‘big data’ is often misunderstood. Simply put, ‘big data’ is a term used to refer to a collection of data from both digital and traditional sources within and outside of a company that can be used for ongoing analysis and research. Examples of digital data include website analytics, customer behaviour, social media traffic and statistics, whereas more conventional data sources include financial records and inventory counts.

    Big data is composed of both unstructured data and several different data structures. Unstructured data is characterised by its lack of organisation, making it harder to analyse. Examples of text-based data include metadata (data about data) and website traffic statistics. Furthermore, multi-structured data can come in multiple different forms, such as written material and visuals such as images and videos. As businesses increasingly adopt more complex technologies, the amount of multi-structured data is expected to grow significantly.

    Ultimately, it is the availability of large amounts of data that makes data science a viable field. A data scientist’s ability to assess data, identify problems and provide valuable insights to businesses is contingent upon having access to both digital and traditional data sources.
  4. Consolidating and processing data

    The use of Big Data is inextricably linked with data integration and processing. As we have observed, multi-structured data can originate from a variety of sources and can be presented in multiple formats. Without the implementation of data integration systems and processes, companies would be unable to utilise the data they have collected in an effective manner.

    Data integration is the process of combining information from multiple sources, collecting it, and then cleaning and storing it. Once this has been completed, the data can then be processed, which may involve retrieval and classification with the help of a central server or interface. This is a crucial step in providing data scientists with useful data, and businesses can miss out on invaluable insights if they do not integrate and manage their data correctly.

    In order to become a successful data scientist, it is essential to have a comprehensive understanding of related fields such as business intelligence and analytics, corporate data warehousing, big data and data integration and processing. This knowledge and experience will be invaluable in improving the marketability of data science service providers. In terms of the abilities required to become a data scientist, some of the most important include problem-solving, programming and coding, mathematics and statistics, data visualisation, machine learning and data mining. Additionally, having strong communication skills and the ability to work collaboratively with other team members is essential.

Getting where you want to go in data science.

It is well established that data scientists are in high demand and yet there is an alarming lack of them in the employment sector. A report from IBM in 2023 highlighted this issue and predicted the productivity benefits of Big Data may come to a halt due to the increasing concern that the availability of data science and analytics personnel is not keeping up with the demand. On average, it is expected that job postings for data scientists will remain open for 45 days, five days more than the average market norm. This demonstrates the difficulty in recruiting adequate candidates due to the specific prerequisites and expertise needed to fill such roles.

According to research conducted by the University of California, Riverside, only a limited number of American colleges and universities offer either data science degrees or undergraduate data science programmes. In fact, it is estimated that only approximately one-third of institutions within the US provide either option.

Data scientists require a significant amount of expertise beyond holding a degree. They must possess a variety of “hard” talents and technical abilities, such as having a working knowledge of computer programming, software engineering, and machine learning. Furthermore, they must be adept in SQL queries, data munging, and statistical analysis, which involves transforming raw data into a usable form.

Having strong interpersonal and communication skills, sometimes referred to as ‘soft’ skills, is essential for a data scientist’s success. For a company to be successful, it is important that the data interpretations are translated into a form that is accessible and understandable for both employees and customers. In order to do this, data scientists need to be able to explain the significance of the numbers in a way that is easy to comprehend. This requires years of practice and training.

Why do companies consider outsourcing data science?

Outsourcing data science can be a beneficial option for businesses due to the current shortage of qualified data scientists. As the demand for data scientists increases, the supply of professionals who possess the necessary technical and industry knowledge is not adequately meeting this demand. Therefore, businesses may find that outsourcing their data science requirements is a more effective solution than attempting to hire in-house experts.

When companies outsource their data science needs, they can benefit from the expertise of specialised teams in areas such as big data, data integration and processing, corporate data warehousing, business intelligence, and analytics. This can be especially beneficial as businesses may find that all their data science requirements are met by a single provider, allowing them to streamline their operations and focus on core activities.

Given the swiftness of delivery by outsourcing firms, they may not be suitable for organisations who are reluctant to adapt to change. For many businesses, it is more advantageous to hire personnel directly. As previously noted, data science is a multidisciplinary field that requires the usage of numerous tools and types of knowledge. It is likely to be a challenge to source a data science team or even an individual data scientist that possess the necessary skills and experience. Nevertheless, outsourcing is a great option for businesses that are eager to begin leveraging their data promptly.

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