For the launch article in our Edge Thinkers’ series, we recently sat down with multi-award-winning technology leader and educator Dr Jonathan Reichental, to discuss his take on potential blind spots for global businesses today.

Data governance: The mundane yet vital shortcoming

Jonathan argued the case for two key candidates. In his view, while the first may feel mundane, he believes it is a shortcoming for many businesses today: the absence of formal data governance. Research shows that 83% of business leaders seek to create data-driven organizations (IDC). The benefits of this are well understood, from driving innovation and growth to optimizing decision making and powering AI. But there’s a stark mismatch between aspiration and execution: just 25% of businesses claim to have achieved their aim of being a data-driven organization (IDC). And this gap is not closing.

Creating a data culture

What are businesses missing? “The critical missing ingredient is the creation of a data culture that can execute data ambitions”, says Reichental. “This means a culture where everyday tasks and operations are driven by mindsets and behaviors with access to and use of the best data available. In my experience, companies that can achieve this perform better: they enjoy higher revenues, improved staff retention and reduce all types of data-related risks.”

Three elements of a high-performing data culture:

We discussed that a high performing data culture depends on at least three elements:

1. Data strategy: a long-term plan and vision for the role and value of data across the organization.

2. Data management: the necessary tools and processes to deliver the strategy.

Most companies have these. But the vision set out in the strategy is rarely executed beyond an initial, nicely designed document. And, Jonathan argues, this is largely due to lack of awareness or ignoring the vital role of …

3. Effective data governance.

In his view: “Too often, organizations have a great data strategy and manage their data, but do not govern it. Do they have access to the right data? Is it up to date? Is the data available to the right people, where and when they need it? These sound like simple questions, but they are not being addressed consistently and effectively by the data governance policies of most businesses today.”

Achieving effective data governance

Reichental highlights two parts to effective data governance:

1. Unleashing the full value of data for your business. Two similar companies can have the same data strategy and management but extract very different levels of value from the data they hold. He argues data governance is key to a seamless flow and interchange of data business-wide, optimizing ROI for data infrastructures and driving operational efficiency.

2. Holistic management of data risks. The level of risk and vulnerabilities for organizational data systems is surging in scale and complexity. In Jonathan’s view, most businesses still have not adequately addressed the significant risk their operations face due to poor data security and compliance.

Practical steps for businesses to take

But what to do? “Again, not entirely glamorous, but businesses need to tackle the basics by integrating data governance into their day-to-day operations.” Reichental contends that this means embedding the skill sets, policies and processes that enable seamless data value, transfer, approval and access across data owners and users.

Beyond this, businesses need to begin to see data governance as the foundation for their future success in harnessing technological innovation. AI is the most obvious example. He says:

“I think everyone recognizes the strong relationship between data quality and good AI. If you’re ingesting bad or outdated data or have missing data, it will lead to significant issues in your development of AI solutions. Consistent, effective data governance helps to tackle these challenges.”

In terms of practical steps for businesses, Reichental recommends:

1. Address any data governance knowledge gaps among your key people and leaders. If this is missing or covered informally within your data practice, regroup and engage stakeholders around the long-term value of effective data governance.

2. Don’t start too big. Jonathan states that he has seen businesses roll out data governance too fast and too wide, resulting in enormous bureaucracy and employee push back. Start small, with one or two important datasets and one department. Achieve results, show the value and then expand business wide.

3. Facilitate DataGovOps with automation and collaboration. One of the biggest barriers he has seen for data governance is the perception that it is manually intensive, requiring small armies of data champions to organize and rally people around insights decoded from data sets. AI and automation systems can increasingly do the heavy lifting. Moreover, early adopters of effective DataGovOps will gain competitive edge and unlock excellence in fields where optimal data access and usage is critical.

Quantum technology: The exciting blind spot

The second business blind spot we discussed will feel less mundane. Reichental maintains: “The rise of quantum computing is as exhilarating to contemplate as it is daunting, due to the sheer scale of potential implications. I have little doubt that by 2030, quantum will dominate business conversations and actions—and perhaps even sooner.”

He points out that Microsoft and Amazon’s recent announcements of advances tell us some key things. First, we are seeing breakthroughs. People can debate the degree and implication of these, but we are clearly heading in the right direction. Second, it means big tech companies are investing significantly in people and resources to achieve such advances. This demonstrates that they see real potential for the commercial application of quantum and big rewards.

Reichental argues that if we do manage to stabilize qubits and produce them at scale—two of the biggest challenges right now, then quantum computing will be a very big deal indeed. “The speed and power of computing promised by quantum are hard to grasp, but it will make current capabilities seem glacial – solving calculations and problems in seconds, rather than days or weeks.”

Preparing for quantum computing

However, he also points out that, although awareness of quantum computing is growing among the C-suite, very few businesses are preparing effectively for its arrival and impact. Indeed, he believes we risk seeing a re-run of global businesses’ largely reactive response to Generative AI in 2022. After years of monitoring AI advances, most were caught off-guard by ChatGPT, he contends, and have spent the last few years playing catch up. “At present, it looks like quantum will be the same for most businesses. But even basic readiness will require far more investment, time and understanding.”

Jonathan suggests that a great first step would be to formulate a quantum education program – to evaluate and communicate potential implications to your organization.

But businesses who really want to get ahead of the promise and risk of quantum need to go much further and think differently.

He says: “What has fascinated me in a 30-year career around technology innovation is the extent of unforeseen consequences from new ideas: things that were simply not on our radar. Quantum has the potential to surprise us like that.”

To best reduce uncertainty and surprise, he advises organizations should avoid considering new technologies in isolation. “Seismic shifts have always resulted from a convergence of technologies. This is where you see the most remarkable outcomes. For example, Uber relies on a combination of many different technologies. You take one away and Uber can’t exist.”

Specifically, Reichental says that businesses need to deploy a systematic approach that evaluates the opportunities and impacts of quantum in the context of other technological advances. To achieve this, an interconnected framework is essential to gauge the ripple effect that one technological breakthrough could have on another.

As an example, he points to Quantum Machine Learning (QML), which will turbocharge the power of AI. “Equally, effective data governance will become even more critical, for example, given quantum computing’s potential to unravel modern encryption.”

From the mundane but missed to the exciting but underappreciated, Jonathan’s advice to industry is clear – effective data governance and proactive preparation for quantum are where businesses should be focusing and investing as an immediate priority.

To hear more updates from Jonathan, please visit: www.reichental.com