Digital Transformation in Practice

 

Digital transformation is an established trend and the catchphrase in boardrooms everywhere. However, while many companies realize the imperative to change, especially with the rise of AI, it’s not always clear precisely what that entails, where to start, how best to implement, and what benefits to expect.

“Digitalization”, or the layering of digital tools and technology (like a new SaaS product) onto existing processes, is often mistaken as transformation. While digitalization can provide immediate value, that value is incremental and soon flattens out. In contrast, companies with a strong “digital DNA”, built through intentional choices and investments around not only technology but also processes and people, are capable of incredible innovation and adaptation. In today’s competitive business environment, this choice can be the difference between market leader and bankruptcy. (See example below.)

Digital Transformation Defined

Digital transformation is the process of facilitating and accelerating an organization’s journey of ever-greater digital maturity with a consistent delivery of business value. To be truly transformative to the business, there must be new possibilities discovered through innovation, with the company growing a “digital DNA”. It is enabled by a set of powerful computational tools, including simulation, image processing, artificial intelligence, and machine learning.

There is much more to digital transformation than technology, and a holistic strategy is crucial for the journey. Addition of technology alone can be readily implemented into existing workflows with limited challenge to managing the change. While this can generate value in R&D, often in increased efficiencies of domain experts (i.e. scientists), the real prize is in enabling scientific discovery that has orders of magnitude for positive impact on the business.

To realize an organization’s full value potential in the digital transformation journey, change must be extended beyond technology to people—their mindsets, skills, behaviors, reinvented processes, and new operating and customer-facing business models. We don’t just want to provide people with a hammer. We also want to teach them architecture and carpentry.

Lessons in Digital Strategy: A Tale of 3 Booksellers

The business of science is complex: Experts have built up methodologies based on decades of intuition and human-centric discovery methods. Raw data may be just partially captured—some of it in non-digital forms—making it difficult to leverage and reinterpret. The transformation process can appear overwhelmingly long and expensive. It is tempting to make incremental changes to existing processes by “layering on” selected digital technologies without changing the underlying operating and business models. However, this only provides marginal improvements. In today’s world, that is not going to keep a company competitive.

For a safer and faster ROI, optimizing current processes is the way to go because doing something different is often riskier and will usually take longer. It will also require a higher level of investment and significant, sustained support from senior management. But if your customers’ expectations are changing or your competitors are going further, optimization alone may not enable an adequate response to opportunities or threats, requiring additional strategies.

Take for example, a tale of three booksellers. In 1995, the World Wide Web was in its infancy, and a compelling battle was starting between the old and new ways of conducting business.

  • Borders, a 24 year old bookstore chain at the time, pursued a non-digital strategy with its traditional brick-and-mortar stores and massive book inventories. The company could not respond quickly to changing customer behavior, began losing money in 2007, and went out of business in 2011.
  • Barnes & Noble optimized its existing operating and business models by layering digital technologies on top of its existing physical stores model. This bought them some time, but competitors, primarily online, gradually eroded the company’s market share, and Barnes & Noble was purchased by a hedge fund in 2019. Since then, their primary goal is to survive Amazon.
  • Amazon pursued true digital transformation. It re-conceptualized mail-order catalogs by selling books entirely on the internet and offered a far bigger selection at significantly lower prices than feasible with physical stores. It started using customer data, the byproduct of online sales, to provide personalized experiences and improve marketing and operations. Any changes to the marketing strategy and online store could be rolled out in hours or days versus years and delivered disproportionately large benefits. The company had groundbreaking data and found new ways to use it. Novel initiatives were tried at frequent intervals. Some succeeded, some did not, but each provided a valuable lesson. Now of course the company is the Goliath of booksellers but has also expanded into all ecommerce, electronic devices, cloud computing, entertainment, healthcare, and much much more.

Borders’ lack of a digital strategy was clearly not conducive to long-term success, an observation that is even truer today. Barnes & Noble’s strategy of digitalization that layered on digital technologies added short- and medium-term incremental value through linear growth. But in both cases, slow feedback loops for providing business insights, inadequate availability and use of data, the inability to respond quickly, and the high cost of failure limited innovation. Linear ROI did not provide protection from exponentially growing competitors.

Who are the equivalents in your industry, and where does your company fit?

Most substantial science-driven companies today were not “born” digital. Adopting digital DNA is key to digital transformation, incorporating elements such as digital infrastructure, data, and tools that are portable across a company’s multiple business units or labs. It also encompasses organizational agility that empowers scientists to use their initiative to solve complex research problems and a pervasive mindset of experimenting, learning, and adapting.

This is the true digital advantage to a science-driven business: Insights driven by real-time data, the capability to act swiftly, and an appetite for fast, inexpensive failures enable rapid innovation and, ultimately, exceptional success.

A Balanced Approach to Risk

For science-driven companies, there is a sweet spot between not taking enough risk like Borders and Barnes & Noble, and going all-in like Amazon, which showed no profit for years. What we call Applied Digital Innovation projects are the key. These are projects that deliver high value while introducing new digital capabilities (skills, software technology, infrastructure), balancing risk and reward, while simultaneously injecting more digital DNA into the organization, enabling future agility and innovation.

 

Enthought | Business Value through Applied Digital Innovation

 

Our experience comes from working with companies and labs that already have a valuable output, such as a specialty material, chemical formulation, or seismic analysis. Knowing that the end result has value mitigates business risk. However, it is not simply a question of converting data to a digital format and collaborating to develop domain-suitable application programming interfaces (APIs) to query and analyze it. Such optimization might increase the value of the scientific process incrementally by 10% to 20%.

It is important to have a strategy that recognizes the potential beyond the quick, measurable benefits on a discrete project. The Enthought approach is to collaborate with our clients to leverage the opportunity provided by each project and take steps to help change their DNA in that area of business.

 

Enthought | Digital Transformation Iterative Loops

 

The starting point is to examine the most value-adding workflows, using the intent to reinvent them, and building the digital infrastructure with improved data quality so it can be curated and used for secondary analyses and discovery. For example, instead of experts laboriously and manually labeling or characterizing data (e.g., silicon chip defects, medical images, or seismic features), automation tools and techniques can do the job faster, more consistently, and on a larger scale. Next, work to redesign those existing processes, which may be people-centric or based on legacy technology, leveraging the best available digital technologies and computational capabilities to help uncover insights connected to rapid action.

As scientists expand their thinking and better understand digital technology capabilities, the advantages of the transformation become more and more apparent. Perhaps simulations could replace physical experiments. Deeper understanding helps formulate an effective digital strategy and reveals new opportunities for innovation worth pursuing. Progress is made one step at a time, taking a series of small wins that lead to the longer-term goal of transforming the company—shortening time to market, improving competitiveness, and boosting revenue along the way.

 

Want to learn more? Contact us to discuss how we can advance your digital transformation goals.

 

Share this article:

Related Content

Revolutionizing Materials R&D with “AI Supermodels”

Learn how AI Supermodels are allowing for faster, more accurate predictions with far fewer data points.

Read More

Digital Transformation vs. Digital Enhancement: A Starting Decision Framework for Technology Initiatives in R&D

Leveraging advanced technology like generative AI through digital transformation (not digital enhancement) is how to get the biggest returns in scientific R&D.

Read More

Digital Transformation in Practice

There is much more to digital transformation than technology, and a holistic strategy is crucial for the journey.

Read More

Leveraging AI for More Efficient Research in BioPharma

In the rapidly-evolving landscape of drug discovery and development, traditional approaches to R&D in biopharma are no longer sufficient. Artificial intelligence (AI) continues to be a...

Read More

Utilizing LLMs Today in Industrial Materials and Chemical R&D

Leveraging large language models (LLMs) in materials science and chemical R&D isn't just a speculative venture for some AI future. There are two primary use...

Read More

Top 10 AI Concepts Every Scientific R&D Leader Should Know

R&D leaders and scientists need a working understanding of key AI concepts so they can more effectively develop future-forward data strategies and lead the charge...

Read More

Why A Data Fabric is Essential for Modern R&D

Scattered and siloed data is one of the top challenges slowing down scientific discovery and innovation today. What every R&D organization needs is a data...

Read More

Jupyter AI Magics Are Not ✨Magic✨

It doesn’t take ✨magic✨ to integrate ChatGPT into your Jupyter workflow. Integrating ChatGPT into your Jupyter workflow doesn’t have to be magic. New tools are…

Read More

Top 5 Takeaways from the American Chemical Society (ACS) 2023 Fall Meeting: R&D Data, Generative AI and More

By Mike Heiber, Ph.D., Materials Informatics Manager Enthought, Materials Science Solutions The American Chemical Society (ACS) is a premier scientific organization with members all over…

Read More

Real Scientists Make Their Own Tools

There’s a long history of scientists who built new tools to enable their discoveries. Tycho Brahe built a quadrant that allowed him to observe the…

Read More