Digital transformation. Everyone has their own ideas about what digital transformation means, so I decided to look up a few definitions.
Gartner: “Digital transformation can refer to anything from IT modernization (for example, cloud computing), to digital optimization, to the invention of new digital business models.”
CIO blog post: “Digital transformation is a foundational change in how an organization delivers value to its customers.”
Accenture: “Digital transformation is the process by which companies embed technologies across their businesses to drive fundamental change.”
In reflecting on these definitions, I particularly like how Gartner highlights legacy modernization as a common component of such initiatives, noting that digital transformation can be more about digitization than transformation. A major goal of these projects is cost reduction; it’s not sexy, it’s pragmatic. Finding opportunities for monetary savings offers the benefit of reducing costs, but more importantly, it enables a reallocation of budgets towards innovation projects.
Cost savings opportunities
Let’s look at some examples for savings potential.
- Reduce tech debt: Tech debt is a constant challenge—trying to move faster and deliver enhancements in a constantly evolving market is difficult. The key here is prioritization, iteration, and execution excellence. Avoid rework wherever possible.
- Replace legacy: It’s hard to avoid having “legacy” systems/applications or versions since technology advancements are moving so fast these days. Certain technologies require skills to maintain them that are harder to find, which is a further risk as those resources move towards retirement age. Beware of technology that is not strategic to your long-term plans and look to update that quickly.
- Optimize automation: AI and machine learning (ML) are now the key terms here, but RPA (Robotic Process Automation) still has its place in driving efficiency throughout the enterprise. For example, I’ve seen great success automating repetitive processes, such as reconciliations and loan application processing.
- Reduce compliance costs: Compliance is a cost of doing business, but how much of that cost is somewhat in your control? Look for opportunities to reduce redundancy and automate tasks. Try to piggy-back with strategic, revenue-driving projects to accomplish compliance but also to get more advances. For example, we have some customers using their data platform originally established for compliance initiatives to drive new use cases. These data lakes house much of the data needed to also support other use cases. Leveraging the shared platform serves multiple objectives and can be more cost effective.
Strategies to maximize impact
Just as there are multiple initiatives, there are multiple levers that can be used to realize these cost savings. Options that come forward immediately in most organizations include outsourcing everything, squeezing vendors, renegotiating contracts and of course, reducing head count. Any of these might be viable and strategic, but be sure to look holistically and strategically assess the longer-term goals. For example, outsourcing everything typically does not reduce costs in my experience as the time managing the outsourced environment can be quite costly.
- Look for redundancy: My advice is to look holistically at the approaches and ask tough questions of your organization. Redundancy is often a culprit in organizations. We maintain the same information in multiple places because it is used across the enterprise. And of course, these siloes all need to be maintained. We see this consistently in the data platform/data storage space.
Replacing redundant data storage is a clear opportunity in this category. Look for redundancy and assess cost savings using consolidation. Challenge providers to prove the solution that is the most efficient. Here’s a sample story from one of our European customers realizing success in consolidation.
- Turn it off: Another potential area of cost containment is what I’ll call “lingering” systems. If the plan is to retire a system, assess why you are investing in it. Compliance requirements are often the reason for maintaining something, but eventually you have to avoid the compliance cost and redirect that budget. If you’re not turning it off, you’re not planning correctly. Understand your strategy—what features are meant to be turned off or available elsewhere to retire an application. Then make it happen. This might take redistributing resources in the meantime, but look for opportunities to avoid the next cycle of mandatory compliance updates and make this your hard deadline.
We see this in the area of operational databases and legacy data warehouses. Our customers are realizing substantial savings by retiring older, legacy solutions. The consolidation of vendor management and streamlining skills/expertise also offers soft costs savings above and beyond what is shown on an invoice.
- Turn it on: The options for efficiency gains using automation run across the enterprise and can affect underwriting, regulatory reporting, financial crime prevention, improved trading, the customer call center, and so on. While all these areas can have projects underway simultaneously to leverage machine learning and AI, look for efficiencies while doing so.
We have a customer who started the data lake with a focus on regulatory compliance. Then they quickly realized it has much of the data they need to perform accelerated mortgage approvals, payments monitoring, and more. You don’t need to start from scratch for a new requirement and can move faster. A proven provider can also accelerate projects by eliminating RFPs and the subsequent vetting process of a new supplier.
Look for accelerators to reduce costs
Cloudera offers various tools that can help you accelerate your data and AI initiatives in the above categories. Two specific areas that I talk a lot about with customers include Universal Data Distribution (UDD) and AMPs.
Universal Data Distribution: This is a concept that can help you incrementally move forward on initiative by enabling data to be collected from any location and reside in any location for analytics to be performed. This page describes UDD in more detail.
AMPs: Applied ML Prototypes (AMPs) are machine learning (ML) projects that can be deployed with one click directly from Cloudera Machine Learning. AMPs enable data scientists to go from an idea to a fully working ML use case in a fraction of the time. It provides an end-to-end framework for building, deploying, and monitoring business-ready ML applications instantly. You can get more information and review the available AMPS here.
If you are interested in learning more or discussing any of these initiatives, please reach out to me. Also, you can read the latest about how Cloudera was named a 2022 Customers’ Choice for Cloud DBMS on Gartner® Peer Insights™ here.