Industrializing digital investments with the right toolset
We live in a world of collaboration – human to human, connecting expertise and intelligence in real-time across functions and organizations; device to human, enabling human decision making with machine-based intelligence, and device to device, for reduced human error and seamless interactions. The right toolset can simplify the act of collaboration.
In our previous two articles, we presented our perspectives on how to scale your digital investments by aligning internal IT and OT organizations, and how to reinvent skillset for digital@scale. We outlined three key considerations for organizational alignment to support the new digital future:
1) Mindset Change—A new way of thinking and organizing to effectively deliver against business objectives
2) Skillset Change—Aligned skills and capabilities to support critical business priorities
3) Toolset Change—Supporting tools, standards and processes to enable faster delivery and support collaboration across technology and business groups
In our first article, we explored mindset change with two traditional organizational models heavily leveraged in asset heavy industries such as discrete manufacturing, energy, process, and mining, and the need for leadership to consider tradeoffs across centralized control for standardization and individual site autonomy for delivering against specific business priorities.
The second article explained skillset change required to enable digital@scale including the rise of storytellers to bridge the connect between technologists and business executives; how the core technology skills like Data Engineering, IT Architecture, User Experience and Project Management have evolved across the ‘four walls’ to give way to ecosystem wide collaboration; the new age digital leaders with different attributes and ways of working to foster a cultural shift in the organization; and finally the changing expectations from traditional technology leaders to enable broader technology-business relationship development, faster time-to market, and the ability to fail fast, adapt and recover.
In this article we explore the need for investing in the right toolset to support digital@scale. Without effective collaboration in action, the value from digital investments get limited to small islands of success versus an enterprise wide opportunity. We see three key areas for reimagining toolsets to bridge the gap and support the Digital future:
1) Robust architecture to support industrialization with localization
Industry reports suggest two-thirds of every digital transformation project fails, when trying to build digital systems on top of existing legacy architecture, limiting value creation. Achieving scale benefits from digital investments, requires a re-design of enterprise architecture to be agnostic, flexible and scalable to accommodate future needs and adjust to changing scenarios.
The core principle of architecture modernization is to develop a templated design that enables easy industrialization across the organization, at the same time, providing the flexibility for accommodating localization requirements. Consider the case of a machine learning expert developing an algorithm on recovery optimization at one of the mine sites. In order to scale up this algorithm for cross-site coverage, and enable enterprise level insights and analytics, we need an architecture that is built on the following concepts:
- Platform design for seamless data integration across analytical and enterprise systems
- Open API framework that enables data exchange across the four walls of the enterprise
- Reference architecture patterns for a “post-premise” world of cloud integration to enable scale with growth
- “Intelligence at the edge” with sensors, devices and machines talking to each other
- End-to-end multi-cloud integration with an enterprise platform
- Templated architecture with localized design patterns to address the uniqueness of every site and / or function
Ultimately, the decision between re-platforming versus extending existing core, is primarily driven by the trade-offs between the degree of change needed (how deep is ‘legacy’?) and requirements for localization.
2) Customer back approach to technology investments
Allocation of investments and technology budgets are changing. The industry is moving towards an Operating spend model with increased investments in cloud / ‘as-a-service’ model, and greater flexibility in budget spend. While traditionally the technology costs have been measured by %-points of total costs, the Digital future will require measurements by customer value generation.
Most organizations have not yet adopted this changed mindset for technology investment allocations primarily because it requires an understanding of how to determine the true customer value generated from digital investments. Customer back approach has transformed the way we think about technology investments.
Start from the top by answering four critical questions: Who is the customer? What is the customer problem or opportunity? What are the key customer benefits? How will we execute this opportunity? A clear understanding of the customer value can then be linked to the business capability and the associated technology components. Prioritization of investments should then be driven by the customer value generated and the need of the hour!
Another critical element is the ability to dynamically allocate funding. This requires a shift of budget allocations from traditional cost heads (e.g. labor, service, hardware) to the management of solution portfolios driven by your customer value proposition.
Budget allocation percentages should be based on the value delivered by these solution portfolios, with each portfolio including cost of labor, hardware or software needed to support the solution. Fixed annual/ bi-annual budgeting cycles should be replaced by on-going shorter cycles with quarterly or potentially monthly adjustments.
To respond to these trends, organizations need to require complete transparency to technology costs to allow faster decision-making. This requires a tool-based approach to track IT costs by each solution portfolio. This enables an outcome driven model to find and remove inefficiencies in current cost structures, and also allows the business greater visibility to their technology costs and help partner on ideas to generate customer value.
3) Intelligent operational processes
The biggest hindrance to rapid decision making in the digital world, is archaic, and often disparate, operational processes that make it extremely difficult to collaborate. Most organizations continue to focus on standards driven rigid operational processes, with automation, leading to incremental cost optimization but limited success in improving faster response.
Consider the simple example of an application issue at one of the sites. In most organizations, the site operations leads continue to work with local teams to resolve the issue working with the OEM/ software vendor. The details of the incidents are logged in local systems for tracking, with limited discipline in logging all / or any details. This leads to the formation of islands of support teams at each site adding redundancy in the technology support roles and often higher costs. There is no centralized visibility to common application issues, resolutions and path forward to improve the landscape or share learnings with other sites or central team.
The world is moving towards “intelligent” processes – for example, predictive incident management engine that analyses real-time metrics to predict the root causes and trigger operational tasks, thereby improving overall value to technology users. Another example is a natural language processing (NLP) crawler that can preemptively scan for incidents and problems and trigger actions.
The complexity in current operational processes is even more pronounced in capacity forecasting and provisioning. With the advent of cloud technology, there is a greater need for control over your forecasting and provisioning processes to ensure you only pay for what you use. The discipline to quickly turn-up environments for testing, decommissioning environments once work is done, and forecasting expected capacity needs for the organization is critical to managing costs. None of this can be done without investments in intelligent automation and developing process rigor with approval workflows.
The technology organizations have been focusing on automation for the last several years and continue to invest in greater degree of automation to reduce human error. The advent of digital is forcing organizations to simplify the underlying processes being automated and reduce manual interventions with intelligent machine processing.
In summary, a robust architecture, dynamic customer-value-based investment and intelligent processes are pillars for digital scale at the speed required for transformative impact. Before an organization finalizes its investment plans in digital, it is critical to invest in the right toolset to reduce all bottlenecks to value realization.