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FROM OUR CHIEF DIGITAL STRATEGIST

websitebuilder@1and1.de — Fri, 10 Jun 2022 16:40:21 GMT

FROM OUR CHIEF DIGITAL STRATEGIST

In this blog, I share highlights from my conversation with 12 technology executives from some of the world's leading enterprises. We covered four main topics to explore how they drive digital transformation across the enterprise.

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Emerging technologies including NFTs, the metaverse, and AR/VR are pushing the boundaries of innovation. But true innovation comes from industrialization, not experimentation. That calls for a foundation of people and processes that can support change. It's why the best technology leaders are actually business leaders with technology in their DNA.

When it comes to disrupting, technology leaders are thinking through what to control centrally and what level of innovation to allow at the edge – closest to where the demand lies. Of course, the challenge of integrating new and existing technologies continues, especially regarding interoperability and industry standards.

What's becoming clear is the distinction between a culture of innovation versus a culture of invention. Innovation brings the best external ecosystem capabilities together, riding on the shoulders of others and then focusing on the differentiated added value. This is instead of reinventing all the component pieces – something that can feel good in the short term but over the long run is non-scalable and can dilute the return on your investment.

Reward yourself
In terms of governance, cybersecurity is always a concern. Many leaders are adopting an "isolate first, investigate later" approach – a change from the "find the problem and fix it" method once considered essential, but which now raises concerns around long-term business viability. The everyday focus is on industrialized blocking and tackling, but emergency patching, zero trust networks, and architectural segmentation – including preparation, reporting, and remediation – are key considerations.

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Think positively
Today, board members see technology as a game-changing business enabler instead of a piece of infrastructure. As this view of technology evolves, so do the responsibilities of technology leaders – which is why they're moving from the server room to the boardroom.

ARTIFICIAL INTELLIGENCE

websitebuilder@1and1.de — Fri, 10 Jun 2022 16:40:21 GMT

There are so many good reasons to communicate with site visitors. Tell them about sales and new products or update them with tips and information.

Enterprises of all sizes are embracing new analytics initiatives at an unprecedented pace, transforming how they interact with customers, employees, and other business partners. And I believe this is just the beginning.

In 2022, the speed of digital innovation will only accelerate. As a result, enterprise leaders will need to take on new roles and responsibilities as the demand for data and analytics continues to grow.

Data and analytics leaders move into the spotlight

Organizations are making considerable investments in data management, from infrastructure to advanced analytics. But tech is only part of the answer. Even the best-in-class data solutions will fail if they are not helping organizations drive meaningful results.

Analytics becomes a crucial skill
But years ago, anyone could crunch the numbers themselves using spreadsheets – a familiar tool they could readily access. Now, back-end tech has become sophisticated and complex. Consequently, fewer people in the business can access and produce insights, which is a considerable problem when analytics is in high demand.
Responsible AI becomes a priority

The challenge of AI implementations at scale? At the outset, it works based on the available data and human understanding. But as new data enters the equation, the models change. If left unchecked, they can create unintended consequences – some obvious and others not yet known.

Take dating apps as an example. Millions of people use online dating apps powered by predictive models, which users train with each swipe left or right.

Leading the way in digital transformation

Technologies like AI will increasingly become part of everyday life. But sustainable transformation can't happen with technology alone. Instead, it will be up to enterprise leaders to maximize the benefits of data and analytics for organizations, employees, and consumers alike.

AUGMENTED INTELLIGENCE

websitebuilder@1and1.de — Fri, 10 Jun 2022 16:40:20 GMT

Across every industry, organizations are investing in artificial intelligence (AI) and machine learning (ML) to unlock business insights from their data. Unfortunately, many leaders struggle to scale their AI/ML models into production across the enterprise.

Worse still, organizations spend precious time and resources monitoring and retraining models. Teams often can't replicate successful ML experiments, and data scientists don't have access to the technical infrastructure they need to innovate.

Build your plan

When you understand all the areas of MLOps maturity, you can develop a questionnaire to evaluate your organization. All questions must carry a weighting that, when collated, will help you establish if your organization is of low, medium, or high maturity – in other words, a laggard, early adopter, or leader. Here's how:

To assess people maturity, question how well you include the necessary data science and business leaders across all stages of the ML project lifecycle
For process maturity, determine how you've organized your data fabric – is it scattered and inaccessible, centralized but with limited access, or centralized and accessible with robust governance? Also, consider how well you connect data insights to business goals. These factors paint a picture of your models' maturity
And finally, to assess technology maturity, look at how your organization approaches data preparation and ML experimentation. You want to see if your organization has a central tool to give the right insight to the right person at the right time – or if you have a siloed approach wherein data scientists use local computers or servers that are difficult to obtain and set up

The answers to all these questions will lead you to your MLOps readiness score. With this data, you can create a visual representation to benchmark externally against competitors or internally across various business functions.

Assess maturity

To assess people maturity, question how well you include the necessary data science and business leaders across all stages of the ML project lifecycle
For process maturity, determine how you've organized your data fabric – is it scattered and inaccessible, centralized but with limited access, or centralized and accessible with robust governance? Also, consider how well you connect data insights to business goals. These factors paint a picture of your models' maturity

Understand maturity

Every organization is at a different stage when it comes to MLOps maturity. Therefore, it's essential to explore how your organization compares to industry averages and benchmarks to understand its potential for scalable ML solutions.

machine learning operations (MLOps )