Drowning in Data
Organisations are inundated with data from a myriad of sources. Data is generated from customer, vendor and client interactions, from device sensors, software and application telemetry—and the list goes on. Data is the heart of today’s digital enterprise and having a data strategy is critical to being successful. Companies often start with technology solutions to unlock the value concealed in their data, but the humans tasked with applying that tech can quickly become overwhelmed. That’s why savvy organisations are accelerating the use of artificial intelligence (AI) and machine-learning (ML) technologies in an attempt to harness their data more effectively and create new revenue streams, improve customer experiences, augment employees’ intelligence, and operate more efficiently. According to McKinsey & Company’s global survey on AI, 50 percent of survey respondents report that their companies have adopted AI in at least one business function.1
Enterprises are well aware of the potential benefits of AI and ML. New use cases continue to emerge that promise to create agility and resiliency in the organisation. However, organisations are failing to realise value from AI, deep learning, and other machine-learning technologies. Why is it such a struggle?
The biggest challenge is the sheer size and scope of datasets; they are massive. And in many cases, they change in real time. Until recently, most companies were dealing with manageable amounts of data and deploying a relatively small number of models in isolation. That trend has shifted as organisations embed decision automation into a wide range of applications creating new technical challenges that come from building and deploying ML based systems. Projects frequently stall out when attempts are made to scale up to realise the full potential of AI and ML. In fact, research from IDC indicates that around 28% of AI/ML initiatives fail.2
Company-wide adoption faces several other hurdles to success:
- Lack of senior-level buy-in: Leadership may not view AI/ML initiatives as realistic strategies or leaders have been burned by failed attempts in the past.
- Organisational silos: ML initiatives often operate in isolation from each other, making it difficult to align workflows between disparate teams, systems and datasets.
- Technical debt: ML projects include a broad array of technologies, tools and frameworks. Legacy systems and technical debt can slow transformation.
- Models are imperfect: ML models are trained on data from a snapshot in time; as data changes the models must be frequently retrained and fine-tuned.
For organisations that have adopted a DevOps culture, many of these challenges are familiar. Similar to DevOps transformations, successful ML adoption requires a holistic cultural and mindset shift, wherein every corner of the organisation understands how a data-driven approach will move the company forward. Enter Machine Learning Operations (MLOps). Creating an environment where this culture will thrive doesn’t happen overnight. It starts with being cloud smart. Thinking strategically about how your organisation will consume cloud services will enable business agility. Then taking the right platform-based approach so your businesses can enable MLOps to realise the true value of AI and ML at scale.
Machine learning operations (MLOps) enables smooth collaboration and communication between data science and IT teams to deploy, run and train ML models at scale.
Put Data to Work for Your Organisation
In an era where data is the catalyst for change, organisations are leveraging MLOps, data-driven development, and AI tools to create value, generate new revenue streams, and improve customer experiences. In the next evolution of the cloud journey the focus turns to building the platforms that will enable data to work for the organisation.
Data has evolved from being viewed as a supporting asset into a mission-critical component of success. A well-devised data strategy is crucial to maximise the value of enterprise data. Machine-learning technology can create considerable competitive advantage by:
- Improving visibility and uncovering more insights from data
- Improving efficiency of external and internal processes (i.e., optimising your value stream and operations)
- Enabling a better understanding of your customers by anticipating their needs, allowing you to serve them better
- Reducing costs considerably
From global mining operations that deploy sensors to track maintenance schedules on massive machines to financial institutions using ML models to identify fraudulent transactions in real time, the potential applications of ML in your data strategy and value stream are nearly limitless. The consumers of your data, whether employees or customers, want the fastest and most reliable access to that data. They want data now, and they want repeatable, dependable ways to extract intelligence from it, so they can act on it.
Cloud technologies enable organisations to accelerate the consumption of data products while building long-term resiliency. Hybrid cloud and multicloud environments are the prevailing architecture on which many organisations are building their business. But it’s not just about the technology—it’s more important to understand why and how to leverage the technology. In fact, starting with the technology and dismissing the need for a solid data strategy frequently stalls the project, and in some cases leads to outright failure. Businesses have been embracing the modern cloud for nearly 20 years, yet they still fail to consider several key aspects of cloud adoption.
- Cost – Buyers often completely underestimate the cost. They’re enticed by the ultimate savings potential of the cloud, but initial costs can still be high.
- Security – Data breaches are always a concern, but disjointed messaging and lack of alignment between IT teams, line of business, security teams, and leadership are often the biggest barrier to adoption.
- Licensing – Many buyers are caught off guard by complicated licensing terms and how that can impact other software agreements.
- Planning – Regardless of the cloud solution, during modernisation efforts you still must leverage legacy systems. Organisations often jump in and act without formalising a strategic roadmap.
Thinking cloud smart and insight-driven enables your business to leverage multiple cloud philosophies.
Cloud Agnostic
The organisation is thinking strategically with a focus on building a sustainable and automated technology foundation. You’re preparing the business to consume cloud services from anywhere.
Cloud Appropriate
Your hybrid infrastructure is flexible and allows portability as new service offerings come to market. The business has flexibility without needing to rearchitect the entire network. You have the freedom to choose the cloud services that best meet your needs and deliver the intended outcomes.
Thinking cloud smart and insight-driven enables your business to leverage multiple cloud philosophies.
Cloud Agnostic
The organisation is thinking strategically with a focus on building a sustainable and automated technology foundation. You’re preparing the business to consume cloud services from anywhere.
Cloud Appropriate
Your hybrid infrastructure is flexible and allows portability as new service offerings come to market. The business has flexibility without needing to rearchitect the entire network. You have the freedom to choose the cloud services that best meet your needs and deliver the intended outcomes.
Abraham Lincoln is credited with saying, “If I had six hours to chop down a tree, I’d spend the first four hours sharpening the axe.” Whether Lincoln actually spoke these words, the point is worth considering. Approaching the cloud without a plan and more importantly failing to take the right strategic approach is the biggest mistake organisations make. The company frequently starts their journey by selecting a platform. They’re seduced by the features and think it will fix all their problems. In some cases, that might even seem like the right approach—until it’s too late. For instance, say marketing chooses a product because the API fully integrates with their CRM platform. Or the IT department selects a product based on the capacity for virtual servers or the product’s ability to scale containers. Both groups identified benefits that deliver value, but without a holistic approach, they’ll never capitalise on the potential of MLOps. Instead of choosing a technology based on features and benefits, think about your end customer and work backwards.
Start with the end in mind
- Start with a focus on the outcomes: What do you want to achieve or provide for your end customer?
- Next, think about the processes: What do you need to enable the desired outcomes?
- Lastly, determine the platform: What platforms or products will enable our process?
Starting with a plan and building a roadmap will help you construct operational value streams and enablement runways that will serve as the foundation for achieving true business and delivery agility.
TEKsystems’ Tips
- Plan twice. Act once:
Think about what specifically you are trying to deliver to your customers. Once you identify the outcomes, then—and only then—build your roadmap and activate your plan. - Embrace experimentation:
Cultivate a culture that thrives on experimenting. Measure the results, assess the outcomes, then learn, iterate and quickly move on. - Think holistically:
MLOps must be an evolution across the entire organisation, it cannot be thought of as a specialty or adopted by individual functions. - Consciously consider architecture:
Your architecture should be intentionally tailored to make downstream efforts of MLOps easier and more efficient. - Define your vision:
The path to optimising your organisation with MLOps is a journey—it won’t happen overnight. But a well-defined vision will help breakdown the silos and connect the dots so you can optimise the organisation. - Be mindful of model drifting:
In the ML world, models are constantly changing. Consider a model trained pre-COVID and then applying that model today. The parameters feeding the model have changed dramatically. Your models must be frequently fine-tuned.
Sources
- The State of Navigating AI in 2020, McKinsey & Company
- AI Strategies View 2020: Executive Summary, IDC
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