Despite what any technology company says, digital transformation is not something you can buy. But it will force you to spend.
A digital transformation is something that you do and keep doing and is highly customized to your company. It enables all important decisions — whether made by a person, a team or an algorithm — to be informed by data and context.
Following Data Through 4 Phases of Transformation
A digital transformation may encompass hundreds of discrete changes in process, behavior and technology. But as companies progress through the various stages of a digital transformation, their use of data and digital assets changes.
In my research I have tracked the use of data and how it changes as organizations create increasingly sophisticated digital processes and analytics.
This high-level digital transformation maturity model will help put these changes in context:
♦ Phase 1 — Experimentation: understanding requirements of a digital transformation and the enabling technologies
♦ Phase 2 — Implementation: developing and delivering the first data-driven use cases
♦ Phase 3 — Expansion: expanding to multiple lines of business and multiple use cases
♦ Phase 4 — Optimization: establishing new digital processes, advanced analytics, and real-time intelligence as the “new normal”
Discovery and Rationalization
During the experimentation phase, the main drivers of the transition — CMOs, CIOs, CISOs, etc. — will wish to inventory all digital assets, tools, technologies and expertise available to them.
In terms of the data, that inventory is built from a process of discovery usually undertaken by central IT with participation from all relevant lines of business. The number and variety of data sources varies widely depending on company size and industry.
Early on, it may only be a handful of sources, including CRM systems, customer databases, clickstream data and data warehouses. Later in the process, a medium-sized enterprise might identify dozens, and a data-rich financial services company may have hundreds or thousands of sources.
Once an inventory of available data sources is complete, the next step is to extract that data and use it to populate a data lake. Modern data platforms such as Apache Hadoop or commercial platforms now provide basically limitless scale and capacity for ingesting data and creating just such a repository. So, at this point if you follow the data into the data lake, you will see large volumes of structured data (from databases and data warehouses) and unstructured data (from everywhere else) stored in their original formats.
Once your digital assets are discovered and extracted, they must be reviewed and rationalized by your data wonks (data scientists, data engineers, statisticians, BI experts and quants). This can be a highly revealing exercise that will significantly shape the process of creating digital processes.
Proof and Potency
During the implementation phase, the focus will mainly be on establishing and integrating new digital processes, and then proving their efficacy.
The characteristic shrinking of development and deployment cycles by agile businesses has their executives looking for quick ROI and a measurable proof of progress by the implementation teams. These teams will find that populating a data lake is really step zero in a digital transformation. The use cases and new analytics that are fed by that data lake are the true measure of progress.
The focus on building a data lake is largely due to the fact that the traditional data warehouse is the wrong metaphor, the wrong technology and the wrong price point to be the central data repository for digital processes.
The creation of dynamic digital processes — whether highly personalized marketing, effective fraud detection or real-time responsiveness in operational or analytical dashboards — requires a dynamic data platform more akin to a data lake than a data warehouse. This is not to denigrate the usefulness of the data warehouse, but rather to follow where the data leads: to flexibility and variances in structure, speed, scale and context.
As BI and development teams begin to see exciting new use cases by this expanded data landscape, they also begin to look beyond the borders of their own data sources to external data sources, data brokers and the expanding universe of data for hire.
Momentum and Fluency
During the expansion phase, each individual use case success serves as an individual instrument in the digital orchestra you are looking to assemble.
In this case, there is an expanded commitment of IT staff, budget, training, hardware, executive sponsorship and a permanent commitment to operate as a digital business. As use cases are expanded, key stakeholder organizations like marketing, IT, security ops and sales ops are now defined and measured with data-driven metrics.
Another indicator of this phase is a steadily expanding collection of data sets.
Organizations that employ a capable Chief Data Officer and top quality data scientists and data engineers now have specialized agents who focus full time on gaining advantage from available data. These agents will also look beyond homegrown data sets to a much larger universe of data — such as public data sets, demographics, climate, geospatial, social and specialized industry data sets.
One of the most striking outcomes of a late-stage transformation is that economic metrics such as ROI, costs savings, return on marketing spend and savings from fraud detection are objectively measured by the data.
Stunning examples of digital businesses in action — think Amazon recommendations, Prama analytics at TransUnion, Amex Offers from American Express — are raising the high water mark on digital transformation. They have become fluent in developing the right type of questions, the right way to ask and the right data to get the answers and make the decisions they face.
At this level, all key lines of business have the data and the tools they need. IT departments become the curators and caretakers of the digital footprint.
Companies at the top level of sophistication of digital processes are at the top of the food chain, and not because they are necessarily smarter than the rest of us. It is more that they started earlier, and learned first and fastest. Now it's time for the rest of us to catch up.