How to Create a Company Data Driven Operating Model
- A data driven organization embraces and insists upon insight driven decision making. Decisions that impact the customer experience or company objectives must be made with insights, and not intuition or subjective guesswork.
- A data driven operating model (DDOM) transforms data into actionable insights that improve customer interactions and decision making.
- A DDOM treats company data like a company product. In fact, business analytics leaders often advise their data is more valuable than their products.
Data Driven Models Make Companies Smarter
A DDOM is a business model which operationalizes data to produce fact-based insights for decision making. It leverages data to build better products or services, deliver greater customer value, improve staff productivity, outflank competitors, lower costs and earn more revenues and profits.
Most of all, a DDOM insists that all decision making is based on data, facts and insights. Companies that successfully implement this model make better decisions, which is one of only four sustainable competitive advantages.
A Data Driven Operating Framework
Johnny Grow's specialty is building business growth models. We learned long ago that revenue growth is accelerated when built on a data driven model.
However, many companies find data both abstract and overwhelming. They struggle to operationalize the process of converting data to actionable insights that yield measurable and predicted business outcomes. That's where a framework can help.
Below is the Johnny Grow DDOM framework with the essential components to make this competitive advantage a reality.
When data transformation is operationalized into a streamlined process with clear objectives, business use cases, supporting metrics and the delivery of actionable insights to customer facing staff and decision makers, it only makes sense that the staff and managers make better and faster decisions that more directly contribute to the company’s performance objectives and priorities.
Here is some supporting information for each of the components.
Most companies recognize the business imperative to be or become customer centric.
Customer centric companies understand how to apply customer strategies such as customer engagement, customer relationship management or customer experience management to improve customer satisfaction and in turn drive revenue growth.
Data driven objectives vary by business growth goals and operational maturity, but some common examples include using data to identify what factors most produce customer satisfaction, areas of dissatisfaction, most attractive target markets, optimal product pricing, highest impact performance improvement areas, greatest upsell opportunities or new revenue streams. The one constant among these objectives is the transformation of customer data to answer questions that drive both customer satisfaction and company priorities such as revenue growth.
Customer objectives change during their purchase journey so smart companies start by mapping customer journeys and identifying the customer insights needed at each stage.
Each customer purchase stage must be aligned with the seller's sales process and supported with personas, methods and metrics to achieve both customer and company goals.
This requires identifying the data assets to know what is most important to each customer at each buy cycle stage (i.e., their pain or objectives), how to achieve those goals (i.e., processes or best practices) and measure progress (i.e., metrics and outcomes). Use cases are used to identify the sources and uses of data for these data assets.
Objectives and use cases are many and varied, so must be prioritized. The best method to identify the highest impact and most important data use cases is a Design Thinking workshop. A key to design thinking success is to use a multi-disciplinary team (i.e., users, managers, SMEs, stakeholder and IT) organized around the business objective and not focused on data or technology.
The top priority use cases (called Hills in design thinking terms) are memorialized as agile user stories so that they can be estimated, weighted and put into a product backlog or sprint plan for execution.
Performance metrics must link data to measurable business impact. In fact, the inability to link data to realized business outcomes impairs decision making and will doom any DDOM effort.
In this step, the most essential metrics are identified to measure the use case progress or completion. The underlying data is identified so it can be sourced, transformed and displayed in a dashboard or other business intelligence visualization.
It's also important to designate an owner for each metric. This person ensures the metric is the most essential to achieve the objective and use case and is responsible for achieving the objective. This is an iterative effort that is improved with collaboration among a cross-organizational team and executive stakeholder.
Good metrics are delivered as actionable customer insights (i.e., role-based, real-time and highly visual) and as part of a broader closed loop reporting process.
Each use case in the prior step specifies a goal and benefit for a role or persona. Data transformation in the CRM system is needed to connect the dots between data and insights and deliver information assets to the people that can use them.
It's a technical exercise that harvests data, often from multiple tables or silos, creates a staging location or data model, transforms the data into information assets and delivers insights in a way they are most actionable. Experienced data and CRM architects create these data assets at the lowest level of granularity so they are extensible and may be combined with other data assets. They also categorize assets in a library so they can be managed, curated and easily accessed.
Customer data is the fuel that powers business growth. CRM systems are normally the customer system of record so using ETL (extract, transform and load) or similar analytics tools in your CRM application is generally the preferred route.
Sometimes the data at hand is not enough to produce insights that satisfy the use case objective. In this scenario, you will need to append the customer records with a CRM data enrichment process.
Data achieves its potential when insights are used by staff to improve customer experiences or make more informed decisions. This final activity includes two parts. The first step is to deliver the right insights to the right person in the right form at the right time.
Many times, insights are distributed to CRM dashboards or reports, but they can also be delivered with push-based guided recommendations, on-demand with self service knowledgebases or portals that support interactive queries or Natural Language Processing with Q&A response.
The second step is for the recipient to use the information as intended, or as modified if desired. This step is the final realization of the DDOM and is much less about technology and more about culture. Insights can prescribe actions but its people that effectuate positive change.
Replacing intuition with data for decision making starts at the top with executives and managers that lead by example. They should also require all financial and customer facing decisions to be anchored in data. They should make data-based decisions the norm and not the exception. As legendary detective Sherlock Holmes told his assistant, "It is a capital mistake to theorize before one has data."
Only when the company culture reinforces data-driven, fact-based and objective decision making and discourages gut-based, trial and error and subjective decision making will users and managers deliver the last mile of the DDOM.
Some years ago, a smart guy named William Edwards Deming mandated to his staff, "In God we trust, all others must provide data." That perfectly describes the culture to support a DDOM.