Are We Safe From the Dangers of AI?

How do you prevent millions in lost revenue from disappearing from your bottom line?  What can you do when existing customers seem to evaporate twice as quickly as your new customers sign on? Where do you turn when the cost of managing your systems is now $12M over budget, and it is only the end of Q2?

Simple, data science.

Our information age is a remarkable time in history, but the overabundance of data is one of the major challenges to commercial enterprise around the globe.  Nearly every transaction in business today results in the capture and subsequent storage of multiple data points. It is no wonder that data center systems spending worldwide is projected to top $225B USD in 2023, a substantial increase from the $75B estimated a decade earlier.

This is why data science is critical to your business. It’s not about nonsensical purposes like the “inter webs” or “ultra dimensional visualization” (I made that one up). It is about making the most of the exabytes of data flowing through your systems today.  Unfortunately, there is no silver bullet solution that will magically turn your data into real, actionable intelligence.  But the good news is that the practice of data science is geared toward solving this challenge. I think it is best if I start with some basics.

What is actionable intelligence? Simply put, it is information in context that allows for a reduction in uncertainty, improved decision making, and business change. It’s what you and I do daily when we decide whether to carry an umbrella based on the weather forecast.  But in business we cull through vast quantities of data to identify the nugget of intelligence our business needs. This is much more difficult than reaching for the galoshes.

The process of optimizing your data begins within your business practices. Data points are collected during the various actions and transactions that make a business move. For example, these can occur during a shift in inventory or the completion of a service call that generates a series of data points. Data for these are collected and stored in your data centers.

Simple… so far.

Next the data points are summarized into reports. These are analyzed to create information, and the information is placed in a business context to develop actionable intelligence. Decisions made on this intelligence can help you drive down costs, reduce risk, and identify new revenue opportunities.  They might even get Taylor Swift to your next big corporate event (maybe not!).

This is the value of data science. Without this process, your technology dollars could be spent storing data that delivers no measurable value. You could spend hundreds of thousands, or even millions, collecting and storing data across your various technology solutions, all for naught without the actionable intelligence developed in an effective data science program. Think about the productive ways you could be using that money instead.

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Amidst the barrage of industry buzzwords and fleeting trends, the core foundation of most organizations—data strategy—often does not make the headlines. Yet, this vital function is where the real story unfolds. If you are reading this, you might wonder, "Why hasn't data strategy been part of all the hype or news cycle recently?” Let me be candid: I have been in the trenches. I have worked with data for supply chain, enterprise resources, manufacturing, with data-at-rest, with data-in-motion, as an ethical hacker, and in solving data visualization challenges. I have led data transformations that scaled mountains and faced the abyss of our own data disasters.

But here is the thing - I have learned what is at stake and how to minimize disasters. And that is invaluable.

Data is not merely numbers or files; it is the lifeblood of your business. Yet, mishandling it can lead to nightmares. Data protection is not just about avoiding fines or preserving your brand image; it is about keeping your company's heartbeat steady. I have sat in those agonizing meetings with teams facing the grim reality of lost data. It is an experience that etches the weight of data responsibility deep into one's soul.

Automation gone wrong is not just about wasted resources—it can derail entire business strategies. I have witnessed automation projects run off the rails, yielding nothing. But from those failures, I learned to spot the pitfalls in the project lifecycle, to understand the nuances, and to respect the fine balance between human and machine. Now, when I talk automation, it is not from a theoretical standpoint but from a place of well-earned scars and celebrated triumphs.

Change Management that ignores the human factor in change can send shockwaves through an organization. I have seen firsthand the turmoil, the unspoken frustrations, and the quiet departures that occur when change is mismanaged. Raw data does not capture the apprehension of a team facing change or the morale impact when a transition goes awry. But I have been there, leading the charge, feeling the resistance, and eventually guiding teams to the light at the end of the tunnel.

Process Mining is not just a technical task—it is a pulse check for your business's operations. If you misread it, the consequences can be catastrophic. I recall when a misalignment in process mining painted a rosy picture, while the company was on the brink of a collapse. But from the ashes of those missteps, I have built a toolkit of wisdom that ensures we are not just skimming the surface, but diving deep, understanding core issues, and engineering sustainable solutions.

I am not here to give you textbook answers. There is a world of difference between reading about a storm and weathering one. In the landscape of data strategy, I have experienced a wide range of situations. So, when I share advice, know it has been forged in the fires of real-world challenges. Every piece of advice and  strategy I suggest is backed by experience, both good and bad.

There is a saying in our industry: "Trust but verify." Well, I am laying my experiences on the table, the good, the bad, and the downright ugly. That is what real-world experience looks like. And that is why, when I talk about data strategy, you should listen.

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Merriam Webster’s Dictionary defines “convergence” as the merging of distinct technologies, industries or devices into a unified whole.

With over 23,000 active patents, Cisco has a rich heritage of unifying technologies to deliver new value to customers and partners. In 2009, for instance, Cisco converged siloed storage devices and protocols into the fabric of a Unified Computing System, reducing cost and improving scale. Today, Cisco continues to bridge technical islands and create innovative solutions.

Observability: Cisco’s new digital convergence frontier

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Observability is one of the strategic pillars that define Cisco’s solution strategy and enables the delivery of quality digital experiences anywhere, every time. Last year, adding to Cisco’s all-in bet on observability, Cisco CFO Scott Herren told the investment community to measure Cisco on a new category, “Application Experience.” Cisco’s last three acquisitions, Epsagon, Replex and Opsani, added resources and features to the observability platform.

At Cisco Live, the annual technology conference in June 2022, Liz Centoni, EVP, Chief Strategy Officer & GM, Applications, unveiled to over 25,000 attendees the new cloud native observability capabilities through AppDynamics Cloud for its full-stack observability solution. She boldly stated that Cisco is all in on observability, with a vision for a standard operating model that views the entire technology stack regardless of location or scale.

AppDynamics Cloud was designed to meet cloud native environment scaling needs and complement existing AppDynamics application performance monitoring. With deep insight into the user experience, full-stack observability is a solution that adds value across the entire business with real-time insights that benefit brand loyalty and revenue.

Cisco’s full-stack observability builds a bridge for Cisco partners and customers from observing the technology to visibility into the businesses’ health. For any business transaction or application the solution sees, it can tag and create real-time dashboards. From revenue waiting in a shopping cart to the number of trades pending to the number of mortgages waiting to be underwritten to oil moving through a pipeline to the number of players in a gaming experience, whatever a business cares about, if information is in the application, full-stack observability can correlate the technology performance within the context of the business result.

Observability across-the-stacks

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Cisco continues to make advancements to achieve its vision for a unified operating model with integrations in every major architecture. With devices across enterprise networking, collaboration, data center and security intelligently reporting in, Cisco can provide insights through in-depth performance analysis on issues like real-time security threats and traffic latency across the entire application stack.

Full-stack observability combines network, compute, storage, security, cloud and application insights and adds business context. Full-stack observability is the new frontier for high-value partner practices and technologists that can elevate and translate beyond the “this server is broken” to “here is what is impacting the business in this way.” Already, some full-stack observability customers see a 40% reduction in severity 1 issues, with some boasting 90%.

This convergence creates new capabilities where the health of technology is viewed in business context. For example, if there is a delay in trading, it impacts revenue. If there is a slow server in a gaming experience, it causes users to switch games. If there is a mobile application delivering a poor experience on a specific page or interaction because of a confusing interface, the abandoned page or interaction may be identified. These are tangible and measurable business impacts. Full-stack observability allows customers and partners to observe beyond the technology performance to offer insights into business performance, pinpointing issues in real-time.

Today, most network, security, infrastructure, storage, application, cloud and business operations teams are generally siloed with separate niche tools that often don’t easily share information. Now, Cisco and its rich ecosystem of partners are all in on delivering the next era of convergence full-stack observability makes possible.