Educational Interview With DEfactoED On Big Company Transformation Challenges And How To Address Their Weakness In Capturing Value Through Analytics
We recently provided input to DEfactoEd on state of the art analytics and the challenges big organizations face building out their digital nervous systems. We focussed on Transformation Analytics as a way to enable and accelerate the transitional outcomes.
DEfactoED have kindly allowed us to publish an excerpt of the content we provided.
DEFACTO EXPERT PERSPECTIVES
MARK WALLER ON DIGITAL TRANSFORMATION
Introduction
Many established traditional businesses are really struggling to get value and results from their new technology investments. They are often disappointed and disillusioned with the outcomes they are seeing. One key aspect of the problem is the void in data around “the new” to inform critical transformational change in the face of customer demand and game changing competition from born-digital companies.
Agile transformational initiatives reallocate scarce resources to experiment around new products and processes to drive growth and, where successful, rapidly scale them. Part of the experiment and scale process is to understand the impact on revenue and costs of a potential change. This requires data. However, businesses — especially established ones — frequently lack the technology, processes and capabilities to capture and curate this data in a way that’s meaningful. Whilst a business is in this data void they are flying blind in unchartered territory. What is needed is “Transformation Analytics” curated from new and expanded data sets and the change in mindset and culture required to use these tools to win against born-digital companies.
The challenge for traditional businesses
Traditional businesses have a lot of things to deal with that born-digital business do not. They have legacy mindsets, cultures and protocols which drives the view: ‘we have some new technology, lets replace legacy systems with the same philosophy and approach we built them with’. The net result is: “new technology plus old ways of working equals expensive old way of working”. The need to deliver results from existing brown-field “cash cow” business models adds further complexity when combined with green-field innovation initiatives.
The likes of Amazon by contrast do not have brown-field issues of this nature and have the data sets, culture and systems to continually innovate through experimentation and rapid scaling. Traditional businesses lack the data and analytical capability to innovate in such an informed way and effectively manage agile experimentation and rapid scaling in the same way.
Primarily the challenge for established businesses is the mindset and technology shift required between the legacy and digital worlds. This covers every aspect of a business’s activities: the way it is structured and governed, the way decisions are made, and the way success is measured and rewarded. It requires a structural shift from functional, vertical, hierarchical control to horizontal collaborative networks. It also requires an operational shift from long term static plans to sense and respond cycles in order to achieve profitable growth in a world that demands the mass customization of micro market segments.
As such, the old conceptual models of the 1990’s such as: corporate 5-year plans, annual budgets and rigid programs to deliver predefined “Target Operating Models” or performance improvement outcomes no longer apply. In today’s rapidly changing and uncertain world it no longer makes sense to lock down plans and resources in this way.
Organizations have to learn to work with chaos and complexity rather than attempting to eliminate it through standardization and simplification. The capability of managing with ambiguity and finding the data (structured and unstructured) to make sense of it needs to be embedded as a core competency. This requires an approach which is networked, open and flexible, rather than hierarchical, siloed and rigid.
Given this, today’s businesses need to pursue an agile philosophy to help them progress through the unknown, conducting experiments to test and learn, and enabling them to make informed choices about what to scale and what to cancel. This is a constant process of change, improvement, experimentation and innovation, similar to the Formula 1 marginal gains within season model in motor racing, or the concept of Dev-ops in the IT sphere.
Whereas the ERP systems implemented by most organizations enabled process and data standardisation, today the imperative is to be much more responsive to the front-line and engage with the value chains and customers that form the networked ecosystem. This calls for different systems and processes operating at a much faster pace, providing the near real-time ‘Situational awareness’ needed to understand what is happening in the market place and inform the actions required.
This points to the crucial significance of data analytics as the core driver of much of the change that is taking place. Data is the foundation on which technology and business works. Many of the advances that we are seeing from new digital technologies are predicated upon a robust and authoritative decision-relevant data set, and the ability to make use of it through insightful analysis.
However, for many traditional businesses accessing the right data and curating it effectively is a challenge. In a world where access to expertise in data is becoming the core competency of a modern enterprise, traditional businesses are frequently lagging behind.
At the same time, the providers of systems, services and advice to the business also need to update their mental models in terms as what is relevant to the challenges of the new digital economy. But many of these organizations are also stuck in a legacy mindset particularly in terms of how best to make the digital transition. They too need to understand that the new world requires new ways of thinking, new ways of working and new technology enablers. Too often they are still unquestioningly applying old-world solutions to new world problems.
Embracing digital
In response to these challenges, which place data at the heart of the changes taking place, some traditional businesses are now starting to recognise that they need to adopt an approach to embracing new digital technology which is centered on content organized around critical decisions. Legacy Enterprise systems are typically not well geared to do this, leading to a proliferation of disconnected temporary point solutions that make the enterprise decision support problem worse rather than better.
The right technology needs to be deployed to enable data to be captured, curated and consumed in multiple management forums so it informs and shapes the key decisions when they need to be made. In addition, the scope and application of data needs to be broadened in order to properly inform key events. For example, to be able to influence buyer behavior by way of recommendations or automate service requests. While some organizations are starting to recognize this, they are still struggling to deal with it at the necessary pace, scope and scale.
Furthermore, many traditional businesses are still failing to adequately utilise the data they already have in support of fact-based insight driven decision making. A good start point is to identify critical problem areas where there are known pain points that for whatever reason are proving too difficult to solve using conventional data tools and techniques and use this as an opportunity to pilot and experiment new methods and tools. Such experimentation and scaling will also help identify the skills required and the type of advice and support needed as internal capability is being built.
‘Transformation Analytics’
Transformation Analytics is a class of analytics that helps businesses transform. It addresses the “white space” where emerging issues and opportunities are not supported by legacy reporting. The approach collects, curates and explores data sets to help track the changes and the progress being made, and helps inform the forward steps that should be taken and the future innovations that should be pursued. Transformation Analytics provides the situational awareness of where the enterprise is at any point in time on the continuous transformation journey. It raises the understanding of where the business needs to get to and creates the context and insight for continuous innovation and sustainable profitable growth.
In the area of brown and green field innovation It provides the evidence to support which experiments are working, which need to be adjusted, which are ready to scale, and which need to be curtailed. In the area of legacy business, it provides the insights to determine which products, processes and business models need to be retired, which need to be rejuvenated and which can continue as-is.
Transformation Analytics provides predictions and simulations of how customer demand, revenue growth and costs will change on an ongoing transformational journey, helping organizations balance supply, demand, profitability and customer delight.
Transformational Analytics captures the data necessary to understand and run agile innovation experiments. A truly data driven company will work out how to get the data required before they start to experiment and scale. The analytics inform where agile innovation experiments should take place, help define what constitutes success, and shape the optimal way to scale so that the innovation becomes part of business as usual. They help senior leadership allocate scarce resources towards the best places to experiment and scale around both brownfield and greenfield innovation opportunities. And the analytics provide insights about the critical success factors for future innovation and scaling.
Moreover, Transformation Analytics provides situational awareness to enable the Transformation journey to be planned in terms of where the business is, where it needs to be, and how best to get there. It’s the permanent inflight GPS that provides the real time information to optimize or reset the journey on the fly. Without this data the journey is a matter of guesswork and most guessing in a changing environment is dangerously inaccurate!
Transformational Analytics acts as a bridge between the legacy business and the emerging business, between the present world and future state, between immediate operational decisions and decisions around transformational initiatives in support of a journey or campaign of continuous change. Moreover, it provides a way for established businesses to build the data-related capabilities that are essential for success in the digital world, as they introduce, develop, and evolve their Transformation Analytics solutions to help inform their digital journeys.
Transformational Analytics helps organizations make sense of the current reality, the potential future state, and the path towards it. It supports the constant churn of new products, processes and business models by providing insight and a greater level of control through ambiguity. It is eessential for every organisation that needs to strike the balance between today’s business and tomorrows.
