How Data Science is Paving the Way

How Data Science is Paving the Way

The digital age has transformed data from mere operational by-products into a core strategic asset. Businesses across industries now recognise the latent power of data, often likening it to the new oil. However, as with crude oil, raw data’s value is only realised once it is refined and analysed.


The Inherent Value of Data

As Stephen DeAngelis, founder of Data Science firm Enterra Solutions, outlined in a recent blog post on LinkedIn, the question, “What’s the value of my data?” poses a significant challenge for many organisations. BillSchmarzo, Customer AI and Data Innovation Strategist at Dell Technologies and a leading voice in data analytics, emphasises the complexity of this question, suggesting that data’s value cannot be determined in isolation. Instead, its worth is tied to its ability to enhance business outcomes – whether through improving efficiency, reducing costs, optimising operations, or driving innovation.

Many also believe data has usurped people as a company’s most valuable asset. The World Economic Forum’s classification of data as an asset class akin to oil underscores this shift, highlighting the necessity of refining data to extract its true value. This process involves leveraging data science and business analytics, disciplines that transform raw data into actionable insights.

But there may often be a question surrounding this – how is data science any different from Business Analytics? The answer to this lies in their positioning in the company pipeline and their impact on business outcomes. Despite their differences, these disciplines are complementary.

While data science involves using statistical techniques and machine learning to uncover patterns and predict outcomes from unstructured data, business analytics focuses on analysing structured data to inform specific business decisions. Data scientists often delve into exploratory analyses, tackling broad questions with no pre-defined answers. Business analysts, on the other hand, translate these findings into actionable business strategies. The intersection of these roles is where the true power of data is unleashed.

Schmarzo outlines a framework for understanding data valuation that hinges on its application within business contexts. His approach emphasises several key principles:

  • Contextual Integration: Data’s value emerges from its application to specific business and operational use cases.
  • Incremental Analysis: A step-by-step, use-case-driven approach simplifies data valuation and amplifies its economic impact.
  • Focused Utilisation: Organisations often fail not due to a lack of use cases but because they pursue too many, diluting their efforts.

This pragmatic approach suggests that data’s value is dynamically linked to its contextual utility. Each incremental use case not only generates direct insights but also refines the data, enhancing its value for future applications.

The Role of Embedded Analytics

Embedded analytics integrates data analysis and visualisation directly into software applications, allowing users to analyse data within the application itself. This integration helps users identify issues and opportunities in real time without switching between different platforms, offering a seamless window into an application’s data. Often, this integration is white-labelled, meaning it is rebranded to match the host application’s look and feel, making the analytics appear as a native feature. Alternatively, it may be grey-labelled, retaining some branding from the analytics provider. By embedding rather than building analytics, developers can concentrate on enhancing the core product, relying on the analytics partner to provide continual improvements and new features, notes Yellowfin.

For instance, Kodak’s Prinergy software incorporates embedded analytics, enabling users to monitor metrics like ink usage and production trends directly within the application. This functionality helps users forecast resource needs and enhance efficiency without the need for a separate analytics tool. It offers:

  • Sustainable Competitive Advantage: Embedding modern analytics keeps an application ahead of competitors, providing actionable insights and meeting client expectations for integrated analytics.
  • Enhanced Customer Experience: Modern analytics platforms offer advanced features like automated insights, interactive dashboards, and collaborative tools, significantly improving how customers interact with and benefit from your application.
  • New Revenue Streams: Embedded analytics can be monetised through upsells, offering advanced features or additional capabilities as premium options.
  • Faster Time to Market: Partnering with an established analytics provider speeds up the integration process, allowing quicker deployment and revenue generation.

Historically, effective data analysis required the collaboration of domain experts, statisticians, and data engineers – a resource-intensive process. However, advancements in embedded analytics platforms have streamlined this workflow. Tools like the Enterra Autonomous Decision Science (ADS) platform integrate these capabilities, enabling business experts to leverage sophisticated analytics without extensive technical support.

Henry Peter, Co-founder and CTO of Ushur, a customer experience automation firm, advocates for integrating business intelligence with operational workflows. He argues that actionable insights derived from machine learning should inform real-time business operations, driving immediate and strategic actions across the organisation.

The Payoff

Despite the challenges in data valuation, businesses are increasingly investing in advanced analytics. A study by IDC forecasts nearly 13% annual growth in global big data analytics spending until 2025. Several key factors drive this investment:

  • Operational Risk Management: Analytics helps identify and mitigate risks such as fraud, data breaches, and operational failures.
  • Performance Improvement: Data-driven insights enhance productivity and efficiency at both individual and organisational levels.
  • Enhanced Marketing: It provides a granular understanding of customer behaviour, optimising marketing spend and improving ROI.
  • Situational Awareness: Advanced analytics equips businesses to navigate dynamic environments such as in modelling numerous “what if” scenarios for strategic planning.

The strategic value of data lies not in its raw form but in its refined, analysed state. Advanced analytics transforms data into a powerful tool for decision-making, risk management, and operational efficiency. As technology continues to evolve, the ability to seamlessly integrate data science and business analytics into everyday business operations will become a critical differentiator.

Organisations that effectively leverage their data assets will enjoy reduced operational costs, enhanced decision-making capabilities, and accelerated growth. As Dell’s Schmarzo concludes, the continuous refinement and reuse of data and analytics not only generate immediate business value but also create a compounding effect, enhancing future applications and driving sustained economic benefits. Now is the time for businesses to unlock the potential of their data, turning it into a cornerstone of their strategic advantage.


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