SGI Canada

Cal Rosen, Chief Data Officer at SGI Canada, is based in Toronto, Canada. Cal began his data and analytics journey more than 25 years ago defining and building data warehouses and prototypes for the Telco industry in the US and Canada with Teradata Industry Consulting. He has since led data and analytics consulting practices for PwC and Cap Gemini Ernst & Young in addition to starting and successfully running his own consulting business – Action Info Consulting. Cal has successfully delivered end-to-end programs and projects in multiple industries across North America (e.g., Communications, Retail, Financial & Insurance, Energy & Utilities, Transportation, Healthcare, Natural Resources, Gaming and Public Sector). More recently, Cal has held executive leadership roles in the nascent Data Office’s for two of Canada’s largest International Banks helping to define, author and roll out their data mandates, their data strategies as well as the entirety of their data programs. As a data and analytics thought leader, passionate evangelist, and author, Cal is a sought after speaker and panelist having delivered sessions in countless data and analytics conferences.

Having defined and developed far reaching data and analytics (D&A) strategies and programs as a Management Consultant and Executive in over 40 large North American and Global organizations in numerous industry sectors, I have seen a lot and learned even more about achieving the D&A holy grail.

There is a widely held belief that a given organization is different than their competitors, but when it comes to D&A, many have more in common than theybelieve. A common outcome for every organization is that enhanced D&A capabilities will stimulate proactive business decisions and actions via the effective dissemination of the right data, with the right level of detail, to the right person, at the right time.

While the ingredients for D&A follows broadly shared and often discussed CSF’s and guiding principles, the success of any emerging D&A program lies in the variability of the recipe. In general, the differences are rooted in where you are starting from (current state being what you have done and where you have been), organizational culture (one of; steady as she goes followers, toe in the water measured innovators, or aggressive bronco riders), capacity to change, and the appetite (pace and funding) to get there. The view from the top in securing executive sponsorship and consensus for “what good looks like” is another key consideration.

Data & Analytics Management Framework

One of the foundations of success is establishing a Data & Analytics Management Framework (DMF). A DMF documents and describes the tenets of the D&A program for the effective governance, management, and utilization of data assets. The DMF outlines the relationship between stakeholders, people, process, and technology to govern the delivery of company-wide data capabilities to support privacy and consent, consistent and trusted insight development, and evidence-based decision making. Each dimension of the DMF defines and describes the essential ingredients (both the what and the how) behind the D&A program including the target state, the operating model and business engagement.

While some organizations embed portions of the DMF within their D&A strategy, a D&A strategy is not the same as a DMF. A DMF is realized incrementally via a D&A strategy and roadmap that lay out the program to incrementally build out the target state. The D&A strategy describes the priorities, guiding principles, actions and the roadmap (order of projects/initiatives) you're pursuing to reach the target state. The DMF defines what you have accomplished when you have delivered the D&A strategy. It represents both the what and the how of your D&A program. 

In other words, the DMF answers the question, “What capabilities make up the data and analytics program?” Conversely, a D&A strategy should outline where and when individual DMF capabilities will be developed and delivered. The D&A strategy needs to be built in concert with the framework. To help the businesses reach their target state and to ensure alignment across the organization, it is important for the D&A strategy to define the strategic themes of the organization’s D&A program. The D&A strategy and roadmap answer the question, “How & when will the DMF capabilities be realized?”.

Effective D&A strategies and roadmaps are written with due consideration to the following principles:

• Innovative: as in the world of investing, diversification across multiple fronts in an orchestrated pattern across tactical, strategic and incubator initiatives.

• Practical: aD&A strategy should be pragmatic and well-grounded in recognizing and accounting for the inherent capacity for change within your organization (business and technology).

• Aligned: As an adjunct to the business strategy, a D&A strategy articulates near-term quick wins and illustrates a long-term target state that closely follows and is fully aligned with the stated business direction (whatever that may be).

D&A strategies and roadmaps evolve over time, but the DMF stands as the book of record for describing the entirety of the D&A program.

The third and final leg of the stool is the Data Policy. The Data Policy contains declarative statements that establish the guidelines and standards to guide employee behaviour in the use of D&A to support business objectives across the Company. The Data Policy also defines key roles and responsibilities for those accountable for data management to ensure that data is managed appropriately as a strategic asset to optimize business value, reduce risk and maintain the trust of all stakeholder parties. 

 ​D&A strategies and roadmaps evolve over time, but the DMF stands as the book of record for describing the entirety of the D&A program 

In summary, the DMF describes an operating model for effective data asset utilization and management, the D&A Strategy describes the actions in how to build out the DMF, and the Data Policy measures compliance with stated intentions through declarative statements.

Making Data Work at Scale

Regardless of where you are in your data journey (be it ideation, initiation, renewal, transitional, growth), the nature of D&A suggests that getting ahead and remaining there requires the collective organization to be comfortable operating in a state of perpetual evolution. A key operating principle is to be nimble enough to evolve at-scale while maintaining currency and alignment with the business strategy.

Human nature dictates that change cannot happen overnight.

Among the many challenges to overcome include how to change the organizational culture into one that is data-driven. So, what does that mean? When kicking-off a large-scale D&A program, it is critical to identify and engage key stakeholders, i.e., individuals that will be directly involved in, impacted by or influential to the program. They can be internal or external participants, as well as partners and/or agents of the business. By understanding the key stakeholders, their level of support towards the program, and their unique set of circumstances, program leadership can effectively prioritize elements of the program scope and realize capabilities in the most impactful manner. Change Management of your stakeholder community is critical to adoption at-scale and long-term program success.

Top 6 Critical Success Factors

The following is a list of observations and learnings based on my earlier work in the form of a top 6 critical success factors (in random order except for #1) for all successful D&A programs:

6. Upon initiation of the D&A program, clearly articulate what each phase of the program will look like when you’re done 

• Beginning with the end in mind allows stakeholders to focus on how the target state environment will sustain long-term impact and growth at-scale.

• Lesson learned: D&A priorities must be defined by and aligned with the business strategy, i.e., business need drives the agenda.

5. Maintain close line of sight with the business early and often

Showcase the ‘art of the possible’ and continuously engage.

• Stay focused on building business-centric impactful use cases and solutions.

• Lesson learned: large projects (lift and shift, renewal, or modernization) take time and can be slow and time consuming. I often describe this as constructing a 50-story condo building from scratch. It takes time to dig deep enough to install the footings, pour the foundation and build out the parking garage. After many months, there is little to show other than a lot of dirt moved around in a large hole in the ground. Putting up a design centre next to the construction site is an ideal way to engage the business and work together to showcase the target state while delivering quick-wins along the way.

4. Design and build your target states data structures with consumption in mind 

• Develop an intuitive and integrated semantic business-term based single version of truth.

• Using an operational system-based data model (including data naming conventions) as your solution foundation is not viable.

• Lesson learned: this is closely tied to a CSF that follows but speaks to how easy it will be for business end-users to engage with and understand corporate data to realize the promise of unencumbered data democratization.

3. Emphasize data management processes and practices (i.e., data governance, metadata, and data quality)

• Broad success depends on removing the age-old cloak of mysticism that hangs over corporate data in so many organizations.

• Manage, define, and assess data consistently and continuously.

• According to Andrew Ng, computer scientist and technology entrepreneur, “Data is food for AI…Many data scientists have their own ways to clean data but what we don’t have is a systematic mental framework for doing it. The model and the code for many applications are basically a solved problem. Now that the models have advanced to a certain point, we’ve got to make the data work as well…consistency of data is paramount.” 

• Lesson learned: effective management of data begins and ends with the business in the middle. Develop simple ways to describe to the business what it is, why it is important (aka what is in it for them), and how it will be delivered.

2. Over deliver on Change Management

• Enable changing business behaviours by making it easy and habit forming to access and use data and tools thereby driving innovation.

• According to the New Vantage Partners 2022 Data and AI Leadership - Fortune 1000 Executive Survey, “Cultural impediments remain the greatest barrier to organizations becoming data driven, with 91.9% identifying this as the great challenge.”

• Lesson learned: there a multiple D&A change battle fields to be addressed in winning over the business community, including a formal multi-track Data Literacy program, defined Data Office operations (aka rules of engagement) and communications, end-user gala events (drop-in sessions, solution showcases, competitions, and seminars), and fostering an end-user community across lines of business.

…and finally, without a doubt, the number one critical success factor:

1. Focus on Adoption, as this is the only true measure of success.

• Addressing ever changing business demands by rapidly enabling new D&A capabilities will result in innovation at scale.

• Lesson learned:you could do everything right including rolling out advanced technology and tools, preparing the business community for change, and managing data proactively but if you fail to achieve wide-scale adoption, you have missed the mark and under delivered.

D&A solutions at-scale are rapidly emerging as a mission-critical business capability that can impact and, in many cases, determine your future success. Making your D&A program real with the ability to produce repeatable value at-scale is highly dependent on the organizations resolve and preparedness to stay the course (working through the eventual stages of impatience and disillusionment) as well as the experience of the program leadership and the team on the floor.