Growing Importance of Data Science

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The Harvard Business Review dubbed Data Science as the ‘sexiest job of the 21st century’. Odd as it might seem to use the words ‘sexy’ and ‘data science’ in the same sentence, one can see why, when one takes into consideration the unimaginably massive amounts of data accumulating in the world today, at various levels and in different forms. More than 2.5 quintillion bytes of data are churned out each day. This data is a wealth of potential which can be enormously useful if tapped and channeled towards the desired outcome. The person who herds that information into organized pens and sorts them into meaningful outlets does indeed then, take on the role of today’s ‘Marlboro Man’.

What is Data Science?
Technically, Data science is the field of applying advanced analytics techniques and scientific principles to mine valuable information from the available data to assist in business decision-making, strategic planning and other uses.

In simpler terms, it is a field of work which combines math and statistics, data mining, software programming, predictive analytics, data engineering, data visualization, artificial intelligence and machine learning with expertise on the specific field to which it is being applied, to reveal insights hidden in an organization’s data, which can be used for enhanced functioning. Data science practitioners use machine learning algorithms on numbers, text, images, video, audio, and more, to create artificial intelligence (AI) systems which will perform tasks that ordinarily require human intelligence. These systems replicate human insights, which analysts and business users can translate into real life business value. These insights can prove invaluable to decision making and strategic planning. They help organizations increase operational efficiency, identify new business opportunities and improve marketing and sales programs, among other benefits. These inputs can lead to an advantage over business rivals in today’s dog eat dog world. Organizations are becoming increasingly reliant on them to interpret data and provide actionable recommendations towards improving business outcomes.

The growing relevance of Data Science
More and more companies are coming to realize the importance of data science. Regardless of the size or type of industry, organizations that wish to remain competitive in today’s age of unlimited information, need to efficiently develop and put in place data science advantages, or risk being made redundant.

Data science is vital in virtually all aspects of business operations and strategies. For example, it provides information about customers which helps companies create stronger marketing campaigns and helps them target advertising to increase product sales. It enables them to create business plans and strategies that are based on informed analysis of customer behaviour, market trends and competition. Without this input, businesses may miss opportunities and make flawed decisions.

Data science is a big asset in managing financial risks, detecting fraudulent transactions and preventing equipment breakdowns in manufacturing plants and other industrial settings. It helps organisations to pre-empt cyber-attacks and other security threats in their IT systems.

Operationally, data science initiatives can optimize management of supply chains, product inventories, distribution networks and customer service. On a more fundamental level, they increase efficiency and reduce costs.

How does the process of data science work?
IBM avers that Data Science typically follows a life cycle which involves –

  1. Data ingestion: This stage involves the collection of data from various sources, using a variety of methods such as manual entry, web scraping and real-time streaming data, from sources including structured data, like customer data, as well as unstructured data like log files, video, audio, pictures, the Internet of Things (IoT), social media etc.
  2. Data storage and data processing: Companies need to harness different types of storage systems for the data which has been collected, for data with different formats and structures. During data storage and processing, data is cleaned, deduplicated, transformed and combined using ETL (extract, transform, load) or other data integration technologies. This data preparation protects data quality while loading into a data warehouse, data lake or other repository.
  3. Data analysis: In this stage, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distribution of values within the data. This data analytics exploration drives hypothesis generation for a/b testing, which is the process of comparing two variations of a variable. Depending on a machine’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalability.
  4. Communicate: Most importantly, insights are presented as reports and other visual projections. These make the insights and their impact on business easier for business analysts and other decision-makers in the organization, to understand. A data science programming language generally includes components for generating visualizations; alternately, data scientists can use dedicated visualization tools.

However, although the process does make data science a scientific endeavour, in corporate enterprises, data science work "will always be most usefully focused on straightforward commercial realities" that can benefit the business. Hence, data scientists should collaborate with business stakeholders on projects throughout the analytics lifecycle.

Who practices data science?
The practitioners of the discipline of data science are ostensibly the data scientists. Data scientists are not however directly responsible for all the processes involved in the data science lifecycle. The process may also include the following people:

  • Data engineer. The responsibilities of a data engineer are to set up data pipelines and assist in preparation of data and deployment of the model.
  • Data analyst. A data analyst’s post is a position for analytics professionals who still need to achieve the experience level and advanced skills that data scientists have.
  • Machine learning engineer. The job of a machine learning engineer involves developing the machine learning models which have to be used for data science applications.
  • Data visualization developer. Data visualization developers work with data scientists to create visualizations and dashboards which present analytics results to the consumer.
  • Data translator. This role is also called an analytics translator, It is an emerging role that serves as a go-between to business units and helps to communicate plan projects and results.
  • Data architect. This specialist designs and supervises the implementation of the basic systems which will be used to manage and store data for analytics uses.

Eventually though, the data scientist has to work in tandem with the data engineers. Data scientists build machine learning models. However, scaling these efforts to a larger level requires more software engineering skills to enable a program to run more quickly.

In order to undertake this level of logical work yet maintain a broad overview, data scientists require computer science and pure science skills beyond those of a typical business analyst or data analyst. The data scientist must also understand the specifics of the business, such as automobile manufacturing, eCommerce, healthcare etc.

In short, a data scientist must be able to:

  • Know enough about the business to ask relevant questions and identify the pain points of the business.
  • Use statistics and computer science, coupled with business acumen, to implement data analysis.
  • Use a wide range of tools and techniques for preparing and extracting data—everything from databases and SQL to data mining and data integration methods.
  • Decipher insights from the deluge of data available, with the aid of predictive analytics and artificial intelligence (AI), including machine learning models, natural language processing, and deep learning.
  • Automate data processing and calculations through innovative programs.
  • Narrate and illustrate stories that lucidly convey the meaning of results to decision-makers and stakeholders at whichever level of technical understanding they need.
  • Explain the application of the results to solving business problems.
  • Work in tandem with other data science team members.

Hence, data scientists must possess a combination of data preparation, machine learning, data mining, predictive modeling, mathematics skills and statistical analysis, as well as experience with coding and algorithms. Aside from these technical skills, a set of softer skills, including business knowledge, curiosity and critical thinking are also an important part of a data scientist’s arsenal. The ability to present data insights and explain their significance in a way that is easy for business users to understand, is another important skill. This involves data storytelling capabilities which will combine data visualization and narrative text in a prepared presentation.

These skills are in high demand, and as a result, many individuals that are breaking into a data science career, have a choice of a variety of data science programs, such as certification programs, data science courses, and degree programs offered by educational institutions.

Data science versus business intelligence
Data Science can often be confused with Business Intelligence (BI) because they both relate to an organization’s data, and analysis of that data, but they do differ in scope.

Business intelligence (BI) is a term for the technology that enables data preparation, data mining, data management, and data visualization. While it sounds similar to data science, business intelligence focuses more on data from the past to influence a course of action, and the insights from BI tools are more descriptive in nature. BI is geared toward static data that is usually structured. Data science on the other hand, typically utilizes dynamic data to determine predictive variables, which are then used to categorize data and to make forecasts

Data science in Business
One of data science's biggest benefits is its ability to empower and facilitate better decision-making. Businesses stand to gain significantly from this and those that invest in it can factor quantifiable, data-based evidence into their decisions. Such data-driven decisions lead to more robust business performances, capital savings and smoother business workflows and processes.

Data science benefits different companies and industries differently. For instance, in customer-facing organizations, data science identifies and refines target audiences. Customer data can be mined by marketing and sales departments, so that conversion rates are improved and personalized marketing campaigns and promotional offers are created resulting in higher sales.

Data science also reduces fraud, facilitates more effective risk management, enables more profitable financial trading, enhances supply chain performance, increases manufacturing uptime, improves patient outcomes and strengthens cybersecurity protections. The most relevant benefit that data science offers, of course, is its ability to analyse data in real-time, as it is generated

Among businesses, Data science is used in different industries such as:

  • Entertainment. Data science enables streaming services to track and analyse what users watch, which helps determine the new TV shows and films they produce. Based on a user’s viewing history, algorithms fuelled by data can also be utilized to create personalized recommendations.
  • Financial services. Banks and credit card companies mine and analyse data to detect fraudulent transactions, manage financial pitfalls in loans and credit lines, and identify upselling opportunities by evaluating customer portfolios.
  • Healthcare. Healthcare providers and hospitals use machine learning models to study previous patient outcomes and automate X-ray analysis to assist doctors in diagnosing illnesses and planning treatments.
  • Manufacturing. Manufacturers can also derive many benefits from data science. For instance, distribution can be optimized and predictive maintenance can be done to detect potential equipment failures in plants before they occur.
  • Retail. Analysing customer behaviour and buying patterns can help retailers to drive personalized product recommendations and target marketing, advertising and promotions. Data science also helps them manage product inventories and their supply chains to keep items in stock.
  • Transportation. Aside from determining the best modes of transport for shipments, delivery companies, logistics services providers and freight carrier companies can use data science to optimize delivery routes and schedules.
  • Travel. Data science algorithms drive variable pricing for flights and hotel rooms. They also aid airlines with flight planning to optimize routes, passenger loads and crew scheduling.

Across different industries such as in employee recruitment and talent acquisition, other data science uses such as cybersecurity, customer service and business process management, are common. Analytics can also measure how effective job postings are, identify common characteristics of the best performers and provide other information which oil the wheels of the hiring process.

Data science applications
Applications created by data scientists which are universally useful but aid businesses in particular, include pattern recognition, predictive modeling, classification, categorization and sentiment analysis, anomaly detection, as well as technologies such as personalization systems, recommendation engines, and artificial intelligence(AI) tools like chatbots and autonomous machines and vehicles.

These applications drive a wide variety of use cases in organizations, including customer analytics, risk management, fraud detection, stock trading, website personalization, cybersecurity, targeted advertising, customer service, predictive maintenance, logistics and supply chain management, image recognition, speech recognition, natural language processing, medical diagnosis etc.

Careers in data science
The amount of data generated and collected by businesses is on an inexorable, upward trend. It logically follows that the need for data scientists is also going to spike. This has sparked a high demand for workers with data science experience or training, and some companies are unable to fill the boom in new openings.

Many universities now offer undergraduate and graduate programs in data science, which can be a direct pathway to such jobs.

People working in other roles can also be re-trained as data scientists. This is a popular option for organizations that have trouble finding experienced data scientists.

Future of data science
As data science becomes even more prevalent in organizations, there is likely to be an increased use of automated machine learning, including those operated by skilled data scientists looking to streamline and accelerate their work.

Machine learning operations, (MLOps), an emerging concept that adapts DevOps practices is developing from software in an effort to better manage deployment, development and maintenance of machine learning models.

Another likely result is that citizen data scientists are expected to take on a bigger role in the analytics process.

Other trends that will affect the work of data scientists in the future, include the increasing focus on explainable AI, which helps people understand how AI and ML models work and how much to trust their findings in making decisions, and a related emphasis on responsible and moral AI principles, designed to ensure that AI technologies are fair, unbiased and transparent.

AI is without a doubt, the driver of technology in the future - the sheepdog which will herd the cattle of technology…. and the iconic cowboy who commands these sheepdogs and in turn the entire migration pattern of technology, is going to be the data scientist.

TLP Team

TLP Team