For every business, data is a significant factor to attain and maintain competitiveness. Competitive edge in businesses can be attained when values are added - and it happens through data harnessing.

Mining large amounts of structured and unstructured data enable business management to make quantifiable and data-driven decisions by:

  • Identifying the potential and patterns,
  • Recognizing market opportunities,
  • Defining and refining goals based on trends,
  • Adopting best practices to -
    • optimize cost
    • improve operational efficiency
    • improve product/services
    • recruit the right talent
  • Making better products,
  • Predicting outcomes,
  • Assessing business decisions,
  • Improving customer experience,
  • Bringing value and revenue,
  • And more.

To abridge, it is beyond the realm of imagination in this newage without information and data. As an acknowledgment, CIOs and CDOs are leveraging data science, machine learning, and artificial intelligence to drive business and attain business competitiveness.

Some of the top industrial sectors using data analytics include E-commerce, Banking, Insurance, Finance markets, Healthcare and pharmaceutical, Energy, FMCG, web analytics, etc.

How industrial sectors are using data science?

Based on a survey in North America and Europe, Forrester reports that 96 percent of respondents are either planning, implementing, or expanding data-driven decisions.

Here are a couple of illustrations picked from Fortune 500 companies:

  • JP Morgan Chase, the premier bank of the world has the largest consumers of data with 3.5 billion users and 150 petabytes of data holding. It uses Hadoop for analyzing data to detect frauds, add value, manage cash, provide insights on credit market trends and improve the public economy.
  • Meta (formerly known as Facebook), the social media leader uses deep learning-the advanced technology in data science for facial recognition and text analysis.
  • Amazon has transformed its e-commerce using data science. It relies on predictive analytics to increase customer satisfaction using the personalized recommendation system. It has an anticipatory shipping model for predicting product purchases and sending products to the nearest warehouse. Moreover, it uses new algorithms to detect fraud sellers and fraudulent purchases.
  • Airbnb, an international hospitality company hosts information, homestays, lodge records, and uses data to provide better search results to its customers. It uses knowledge graphs to match users’ preferences with various parameters for providing ideal lodgings and localities.
  • Spotify, the online music streaming giant has over 100 million users and deals with a massive amount of big data. It uses data science for providing personalized music recommendations.

Apart from these illustrations, several companies use data science. However, many face challenges or failures.

Why do big data projects fail?

Though data science is adding value to business models, there are a few instances of failure. The initiative to bring all patient medical records into a central database by UK National Health Service is described as the ‘biggest IT failure’ which got scrapped after spending USD14.9 billion. Research studies depict that more than 87 percent of data science projects fail to move beyond their preliminary stages.

Some of the reasons for big data failure could be:

  • Complex models: Many amateur data scientists tend to focus on complex models that take away focus from the bigger picture at hand.

  • Lack of business understanding: Amateur data scientists fail to understand the business objective and the solutions may deviate from the mainstream.

  • Lack of experienced data science leader: Having the right talent onboard is critical for project success. Many companies lack skilled leaders.

  • Lack of trust between stakeholders and the data science team : The C-suite team may fail to sit with the team to discuss the primary pain points of the organization.

  • Inappropriate application of big data technologies: Ambiguity in using tools, understanding the strengths and weaknesses of the approach leads to confusion, rework, delayed delivery.

  • Disagreement on enterprise strategy : The big data project must align with the enterprise strategy. The projects must maximize the decision-making of the executives or else they will fail.

  • Lack of diverse subject matter experts: To implement big data effectively, a diverse team comprising engineers, scientists, architects, and IT support is necessary. They should know different verticals like manufacturing, healthcare, and so forth.

  • Poorly designed models: The absence of a visionary data scientist leads to incomplete problem statements, in-depth analysis, and poor models leading to inadequate solutions.

  • Weak stakeholder buy-in: Executives are necessary to own the project and funding. Without proper funding, a project might not continue to reach success.

  • Allowing company bias to form conclusions : Getting into a project with a biased hypothesis will push for early conclusions. They may be inappropriate leading to incorrect decisions.

Moving forward, let us see how to overcome these gaps while executing big data projects.

How to address the data science skill gap?

According to LinkedIn, there will be 11.5 million data science jobs by 2026. And the latest report by Glassdoor indicates that companies looking for data scientists have the highest demand for candidates with three to six years of experience.

In a report from DICE, the demand for data professionals and engineers is estimated to increase by 50 percent YoY, and even more as companies like Amazon, Capital One, and Accenture keep hiring these professionals at such a rapid pace.

At this crossroads, the government can collaborate with institutions and companies to develop projects and promote research on data science. Although data science is a vast subject, organizations can offer training to high-performing professionals, or to employees whose fundamental skills, such as software engineers and BI analysts, are directly relevant to the field.

Even if schools already have a computer science curriculum, it is critical to introduce data science programming languages like R and Python. Students should be provided real-world case studies and shown how to use these tools to make sense of the data sets and draw logical conclusions from them throughout the programming sessions. Furthermore, data science certifications need to be integrated with courses taught by major universities, so students can see how the information they learn in class actually works in the real world.

As a next step in the collaboration between universities and industry, internship opportunities would allow students to get hands-on experience. As students move through this stage, they will gain confidence and develop the correct blend of business acumen, programming skills, and math knowledge required to become a competent data science professional

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