Most IT leaders know that IT costs run around 3% of revenue for an average company. The legacy CIO will see their job as keeping the IT costs around that number. But… did you know that technology investment is actually INCREASING year over year? The reason it isn’t visible in the “3%” is that technology investment lives in the business, not in a generic IT cost. The most inspiring tech leaders see their job not as maintaining a cost-center, but as ushering revenue production and operational savings through technology across the business. The legacy tech leader thinks of IT as the delivery of servers, infrastructure, end user computing, etc. The inspiring tech leader thinks of technology as way the business delivers on its core mission, creating successful customers and building revenue for the company. For some organizations that mentality would require a shift from how IT has been traditionally been understood or managed. In others, it’s the expectation from day 1. The inspiring tech leader demands to fulfill the modern understanding of the role and if not, finds an organization that will.
Traditional IT Budgets as Percentage of Revenue by Sector (source, WSJ/Deloitte) … but NOW… (as Paul Harvey said) for the REST OF THE STORY.
Traditional IT Budgeting is Archaic
The traditional IT budget was in staff, infrastructure, maintenance, capital projects, etc. Does this still need to exist to an extent? Yes, because some infrastructure will continue to exist independent of profit-centers. That said, as technology continues to evolve, we’ll see the direct alignment between revenue and cost of cloud capabilities. A recent manufacturing customer of mine had traditionally been budgeting to support their infrastructure and productivity… until they decided to create a connected product. That connected product scaled to millions of customers and as it did, the cost of cloud increased along with it. Did the business see this as a cost to keep within the 3%? No, they saw it as a cost that would scale with the revenue. In a sense, the management of this scenario brings a much higher degree of scrutiny onto the costs that technology provides, since it now lives with all the other direct costs associated with bringing a product to market. This would have been much more difficult in a traditional infrastructure that purchased and depreciated hardware costs over a long period. The relationship would be more abstract. In this case we have a clear association between costs and product… costs and revenue.
Data as an Example of Revenue
In another example, we can apply the same scenario to data. In legacy environments, much is made of enterprise data warehouses and building an a complicated system of data movement to achieve eventual outcomes. The challenge with these warehouses is they often were challenged at their most basic goal… deliver value to the business that is meaningful to the bottom line. The cloud has increased the agility of data. This isn’t to say that “big data” scenarios aren’t necessary, but what the cloud has achieved is a reduction to the time to value in data. Further, it has brought data closer to decisions which drive outcomes. This is because modern data environments move beyond human interpretation and strive to apply data to specific scenarios using machine learning that progresses through maturity steps quickly to engage transformative outcomes. Think about the data maturity in steps, with level 0 requiring more interpretation from humans and level 5 achieving value the fastest and most proactively:
The breakdown can also be understood like this:
- Level 0: Humans interpret dis-unified data to make decisions
- Level 1: Humans interpret data to make decisions, but slowly
- Level 2: Humans interpretation is accelerated by visualization and cloud
- Level 3: Machine learning predicts outcome to accelerate human decision
- Level 4: Machine learning proposes course of action based on prediction
- Level 5: Machine answers “what-if” for strategic decisions based on prediction
In the scope of data maturity, you can see how the critical path is attached to not just visualizing data, but accelerating the ability for decisions to be made based on a proposed choice, or a strategic direction. The improvements here are immense, as optimizations tend to result in real, substantive difference in the operational position of a company or the value delivered to its customers. For instance, companies that target improving supply chain optimization often increase their inventory efficiency by 30% with machine learning. That can be many millions of optimized inventory in warehouses and purchase allocation.
Demand & Inventory optimization as a targeted example with ROI:
The goal of data is not to just make it available. It is to actively seek out, identify ROI, build products around the ROI, and then prove the results were achieved. The best data organizations are built not around infrastructure, but instead around value attainment. They operate almost like an investment firm. They identify (or help enable the identification of) opportunities that the business should fund, then seek to prove it. This is a serious departure from the legacy approach of IT, maintaining data warehouses and talking about reports. The best in data focus on outcomes and delivering accelerated value.
The best technology leaders will be the best technology investors. They place bets with the business where technology resources will be applied and integrate those bets into the way the business executes on its mission. This isn’t an aspirational future, this is the future. The successful companies know how to turn technology muscle into mission-oriented outcomes for the organization. They also know how to manage those initiatives through a return-oriented project portfolio management center. The investments are sometimes short term, sometimes long term, but they have clear value and outcomes attached the can be measured and proven.
Resist the desire to control every innovation tightly. The best organizations grow innovation as a culture, not just a program. It’s not about YOU… it’s about the company and its mission.
The best technology leaders are also willing to take reasonable risks and to experiment. To be an investor doesn’t mean everything is figured out. The best leaders are willing to explore, find, and create opportunity where it doesn’t exist before. It means the best leaders create ideation factories, enable innovation across the business, and drive the best ideas into fully realized solutions. They avoid being blockers to innovation and enable activity across the organization that results in exploration. There is a saying that “100% of nothing is still nothing”. The best leaders need to drive engagement and excitement that turns investment into reality. The investment is in mentality, time, and engineering to make ideas happen and to support those who do.
Let’s commit to being leaders of business. We need to understand the mission or our organizations and being change agents to push the mission forward in its continual evolution toward our customer’s needs.