Digital transformation projects are, at their heart, about data: about using data, new and old, to do business differently, either with customers or employees. Most such projects begin, reasonably enough, by settling on how to collect, store, and present that data. But for true digital transformation based on information, the most critical stages or dimensions of transformation are arguably data value development and data value realization. IDC’s model of information transformation positions these in the context of information transformation, itself a key component of digital business transformation. And this framework makes it clear that any organization seeking effective digital transformation needs to consider these two stages fully.
The CIO is in a unique position to ensure that the business is positioned to do this, being at the interface between lines of business units, which understand where the value is required and what it can do to transform the business, and the IT function, which understands how to gather, store, and transform the data to make it useful.
The CIO needs to engage in frequent dialogue with business unit leaders – particularly CDOs and CMOs, and of course the CEO – about business goals and to understand and communicate the value that new sources and volumes of data can provide to the business.
The CIO also needs to drive the cross-company dialogue about deriving value from information, where these two worlds collide, at all levels, ensuring that staff inside and outside IT are communicating effectively with each other and working toward congruent goals.
Value development is perhaps the Cinderella topic of information transformation, often associated primarily with the necessary but unsexy topics of program management and data quality management. These, however, are fundamental to effective digital transformation: garbage in, garbage out is an old adage, but it still applies. It’s even more difficult to ensure quality in the age of Big Data when volumes are high and increasing exponentially, where data lineage becomes ever harder to ascertain and yet where fuzzy data is often “good enough,” perhaps due to new processing techniques and evolving data science.
And this brings us to a key point. Value development also covers the leading-edge element of data science, i.e., the consideration and development of the correct analytical approaches to use: algorithms and advanced techniques, such as predictive analytics or cognitive approaches and particularly machine learning, an approach whose potential is vast and only just starting to be tapped. Analysis is at the heart of information transformation, and this requires the right data, the right tools, the right talent – and the right links between lines of business and IT.
As an example of how this can be done, consider Caterpillar, which initiated its first Data Innovation Lab in February 2015. The goal is to help drive data-based progress for the company and its customers, dealers, suppliers, employees, and shareholders, and Caterpillar makes clear that while collecting data is foundational, what’s critical to drive transformation is data analysis: “uncovering ideas and connections, transforming 0s and 1s into solutions that catalyze change,” and to “experiment with new types of data to drive process and product advances.” Its mission is explicitly to bring together resources “from key fields for the company’s research and development efforts, including engineering, computer science, operations research, and statistics, to drive innovative development through data analysis.”
Schematic view of a data innovation center: roles and responsibilities
For the CIO, working to create effective value development can be a challenging task, as it can require new and specialized tools and/or skills as well as rethinking the linkage between information and business. It requires considering the entire information architecture, platform, and skill base, as ERP transitions to the cloud and new data sources come online, from sources such as social media and the Internet of Things, as well as higher value data from internal systems, as techniques and platforms for analyzing business data in real time become increasingly feasible and widespread.
Furthermore, as cognitive systems continue their rapid entry into the business environment from rarefied experiments or large-scale consumer apps, then skills and knowledge to bring advanced machine learning techniques will be required to handle tasks as diverse as period-end financial forecasting, financial fraud detection, voice and facial recognition for security, insurance claim processing, logistics optimization, and medical diagnostics.
So this illustrates well why value development is a topic for the CIO: who else would understand the range of issues from data quality through data foundations to advanced analytics skillsets? To illustrate: Caterpillar’s Data Innovation Lab, set up in conjunction with the University of Illinois, will include staff from fields including data science, computer science, and engineering.
To make this happen, the CIO and the whole IT function will need to drive and adopt a mature and holistic approach to information governance and talent management and a transformative approach to process, and to engage effectively with the business in communicating the role of information as a platform for the business’ digital transformation goals.
IDC believes that the information strategy, and with that its data discovery initiatives, is a critical area of investment as organizations evolve their digital transformation road map. In order to understand the maturity of the organization as it relates to digital transformation, we recommend that CIOs leverage IDC’s MaturityScape Snapshot to help identify the key areas to focus on.