With so many companies competing to win the digital transformation race and capitalize on their data assets, it is crucial to keep in mind the reasons for data deployment success and failure.
The primary success factor is having a solid enterprise data strategy in line with the digital transformation roadmap and the skill to successfully operationalize this data strategy along the way. Failure, on the other hand, often comes from a lack of an end-to-end data strategy that is mapped to business value, miscommunication or misunderstanding of the strategy across the organization, and lack of cohesion among teams on how to implement the strategy.
To avoid the pitfalls, let’s break down the keys to success.
What Should a Data Strategy Actually Include?
Every enterprise’s data strategy will look a bit different, but in essence, a solid data strategy should address the following:
- Define how the enterprise data will help the company meet business objectives this year, next year, and over the business strategy horizon.
- Outline the methodology of how the enterprise will deliver on the data vision, taking account of the organization’s data governance maturity curve and what can realistically be accomplished over a given period of time.
- Establish a roadmap for executing and operationalizing the strategy, including milestones and deliverables, that is prioritized based on the business objectives.
- Define clear success parameters to measure whether the deployment is tracking to the plan or falling behind.
- Create and socialize detailed narratives that describe the changes the enterprise needs to make and the incremental value realized across processes, workforce, tool use, and business intelligence applications that will maximize the overall value of the enterprise data.
- Outline talent and acumen gaps to support the digital data-driven enterprise and a plan to either develop them or source them.
- Establish a process to cultivate and reinforce the culture of a ‘data-centric organization’, starting with leaders who will act as change champions to evangelize and educate functional stakeholders on the value of the data strategy and vision.
The Lasting Value of Having a Data Strategy
Data strategy takes a holistic view of the enterprise’s use of data throughout its operations to identify where and how data enables the various operational capabilities and competitive advantages, as well as focus areas for improvement to close gaps caused by poor, incomplete, or disconnected data that impairs operations.
For example, part of the strategy will include developing a taxonomy and classification of key data objects across master data, functional data, and transactional data to understand data quality and data use by various groups or domains, and to establish clear ownership and methods to drive standardization for efficiencies and operational improvements.
Furthermore, it will set forth governance for the data lifecycle through identification of which applications use which data elements, what are the systems of record or origination for key data objects, how those are supported by internal and external databases, how and where the data flows across the organization, and the various use cases and consumers (internal/external) of the data elements.
Data strategy will also define how the organization can best procure, use and integrate third-party data acquired from data brokers or affiliates to ensure internal and external sources are complementary and aligned in driving value.
Along the way, these activities will bring further clarity to the integration architecture and the security framework since, to understand data flows, it is critical to understand the transmission methods across the technology landscape.
Finally, a data strategy creates a mechanism to map and govern the data taxonomy for reporting, analytics, and business intelligence tools to ensure completeness and accuracy of the information enabling those capabilities.
How and When to Transition from Strategy to Deployment
Having defined a robust enterprise data strategy, it’s time to realize your data vision. No need to boil the ocean and make it overwhelming. Rather, deliver on your strategy bit by bit where there is already clearly defined objectives and control mechanisms in place.
Start by identifying key existing or planned strategic initiatives and programs and then incorporate the enterprise data strategy (e.g. ERP deployment, MDM or PLM system implementation, analytics platform implementation, Middleware implementation, Data Warehousing/Datalake development, Data Quality assessment, System/Application retirement, M&A activity, or corporate reorganization, etc.).
Sequence and timing are key: you’ll want to build the roadmap of the strategic initiatives and review how each deployment can support your data vision.
Your Checklist for Creating a Data Strategy Document
Of course, you’ll need to formalize your data strategy into a data strategy document and playbook that organizes processes and methodologies for each key data activity and helps to ensure a consistent and disciplined deployment approach along the enterprise data transformation roadmap. While this is not an exhaustive list, and each topic warrants its own deep dive, it’s a complete table of contents that will drive your strategy creation activities.
- Draft data governance framework and methodology
- Perform data governance maturity assessment
- Establish data standards definition and documentation approach (including policies and regulatory requirements)
- Agree on data organization definition and security (roles and responsibilities)
- Document data flow and lineage and origin to archive strategy (create/update/obsolete/archive)
- Provide current state technical architecture landscape
- Perform inventory of data resources (databases, content management, catalogs)
- Develop data architecture strategy (scope, ETL/ELT approach, querying, presentation/analytics, standards, security, etc.)
- Develop future state architecture map as part of the deployment scope
- Define data migration strategy and methodology (source, target, load method, etc.)
- Develop data migration load plan (load timing by data object)
- Outline data validation approach including quality and compliance requirements (Quality Plan)
- Define satellite systems and integrations scope related to master data (PLM, MDM, Supplier Management, etc.)
- Identify business rules and error handling
- Develop data readiness plan across people, processes, and technology
- Define data quality strategy and methodology (data profiling approach, active/passive quality checks, DQT rule book, DQ application)
- Develop data quality rule build plan
- Identify data quality metrics reporting per load cycle and post-go-live
- Outline data cleansing reporting and approach
- Define data analytics lifecycle and approach
- Perform analytics and reporting tool selection to meet business needs
- Perform readiness and risk assessment including critical success factors
- Identify and monitor technical usage metrics
How to Structure Your ERP Data Deployment Team
Any plan is only as effective as the ability to execute it. Having a data strategy deployment orchestrator and a deployment team that understands the data strategy is key.
The data deployment team should encompass the end-to-end data scope of the project and needs to be cross-functional including members of both business and technical functions. Their objective is to drive the data activities across functions and groups, with a holistic view across the domains. End-to-end command of data across functions not only helps with driving accountability but also with identifying inter-dependencies between the data team and the other workstreams. The more teams work together, the more effective the data strategy implementation will be.
Depending on the scope and complexity of the program, the team could have individual leads for each horizontal sub-workstreams (PMO, Standards, Data Quality, OCM, etc.) or one individual lead across many sub-workstreams. An often overlooked recommendation is to have an individual leader who can orchestrate the different functions, workstreams, and individuals with a strong grasp of the overall data strategy deployment roadmap and playbook.
The key intent of this deployment team structure is to ensure there are no gaps in the data scope or delivery capabilities required by the data strategy and deployment playbook. Attempting to deploy your data strategy in silos, inconsistently, or with limited resource capabilities will only delay results, frustrate the end users, and drive cost overruns.
The importance of data strategy and how that strategy can be effectively operationalized in an integrated fashion with other business and technology initiatives is critical in today’s business environment. With an enterprise data strategy in place, data will no longer be a tangled gnarl of information but can become a key tool to effectively propel your organization’s digital maturity.