How Business and IT Should Partner to Build a Data Strategy
Any functioning company has a vision, goals, and direction. A successful company follows a strategy – set forth by its executives – to realize its vision, achieve its goals, and determine its direction. Executing executive strategy requires cross-functional collaboration amongst teams across the organization.
Having a data strategy is crucial to achieving your greater business goals. To set attainable and tangible business objectives, you need to utilize data. Your IT department will implement and oversee the technologies powering your data strategy; therefore, business processes and goals must factor in IT resources and capabilities. Your organization’s business unit should work closely with your IT team to build an effective data strategy.
Utilizing Data Analysis to Pursue Strategies
When it comes to business goals, each goal should have a quantitative KPI or metric that is extracted from data and analytics. Of course, this is easier said than done! Even the simplest of business questions can spawn dozens of data threads needed for analysis. For example, if you are looking to track business profitability, you’ll have to look at key components such as revenue, COGS, and overhead. Each of these data sources must be broken down even further to fully understand the several metrics that make up the greater question of profitability. The ultimate goal of having a data strategy is to define all the inputs required to calculate key metrics and incorporate them into your production processes.
Your Data Strategy Checklist
Before engaging in any data management project, it’s important to outline your to-do list. Below is a checklist with the foundational steps to building out a data strategy:
- Scope the project (what business problem are you trying to solve?)
- Generate a work breakdown structure for strategy.
- Create a timeline with milestones.
- Define teams and individual role definitions.
- Determine how you collaborate with others.
- Schedule meetings (kick-off, status, sprint planning, and steering meetings).
- Begin research of your technology stack.
- Budget for your data strategy project.
- Kick off with the team!
Let’s Get Started!
As you work toward building a data strategy, there are four deliverables you should focus on: a current state understanding, a future state architecture, a gap analysis, and a road map.
Current State Understanding
Your data strategy should reinforce and advance your overall business strategy, which refers to the processes you use to operate and improve your business. To grow, you must establish your baseline to measure your growth. To gain an understanding of your current state, review your current data architecture, data flows, and source systems by:
- Collecting existing diagrams and analyzing existing discovery
- Verifying source systems (you have more than you think!)
- Diagramming and visualizing
- Having a logical model (what are the existing business domains?)
Future State Architecture
Once you’ve established a baseline, imagine where you’d like the future state architecture. Your future state architecture should support the data platform at your company, including a logical model of the data warehouse and technology decisions. To determine your future state, define your data threads, which requires you to identify your source systems and domains.
Gap Analysis
What changes need to happen to move your company from the current state to the desired future state? Establish your technical framework and identify the gaps. This framework should be built for moving, curating, and delivering data to your business use cases.
Road Map
Roading mapping is the most time-consuming and the most important deliverable. Your data strategy roadmap will outline how and when to implement your data vision and mission. This will help you manage change and ensure that IT goals are aligned with business goals. A well-thought-out roadmap reveals true dependencies so you can adjust as priorities shift. There are several important steps to building out a road map, which we will cover below.
Step 1: Build and estimate a backlog.
Your backlog should be based on your intended future state and what business deliverables you want to generate.
Questions to answer:
- What data threads need to be engineered?
- How are you going to deliver the inputs to the artifact?
- How are you going to productionalize the result to create the action?
Step 2: Define your vision and interim goals.
Before you can embark on any project, you’ll need to establish your end goals and the goals along the way.
Questions to answer:
- What does success look like?
- What priorities should you target?
Step 3: Identify your KPIs.
Determine the data and metrics that will be the most useful for operational and strategic decisions.
Questions to answer:
- Who needs what information?
- How valuable is that information to guide your decisions?
Step 4: Understand everything about your data.
This means understanding your sources of data, data relationships, and data quality issues.
Questions to answer:
- What data are you sourcing from?
- What data issues can be resolved by code versus process changes?
- How can you map this data to a new, clean, holistic data model for reporting and analytics?
Step 5: Recommend a selection of technologies and tools.
Technologies and tools will be the means to your end, so be sure to conduct a thorough evaluation process.
Questions to answer:
- What architecture considerations are required to best use your technology stack and ecosystem?
- What orchestration tools will you need?
- What additional tools will you need to consider in the future as the platform matures?
Step 6: Design a high-level solution architecture.
Use this architecture on your data marts, leading to quicker access to data and faster insights.
Questions to answer:
- How should raw data, data collection, data storage, and access be structured for ease of use?
- How frequently – and with what method – should this data be refreshed?
- Who will own and support this in the future?
Step 7: Generate a specific backlog of tasks for implementing o9 development.
Questions to answer:
- How will you build out the end-to-end foundation?
- How will you coordinate with groups to gain the most immediate benefit?
Step 8: Develop a go-forward plan with a budget, milestones, and a timeline.
These components of your roadmap will make your future state a reality.
Questions to answer:
- When can you complete this project?
- How will you allocate the work?
- How much will it all cost?
- What internal and external resources are needed to put everything into action?
A Timeline of Your Data Strategy Road Map
At 2nd Watch, our approach is based on a 5- to 7-month data strategy timeline. Our data experts and consultants think about implementing a data strategy in phases.
Phase 1: Foundation and process – 3 to 4 months
- Set up your ETL and audit framework.
- Ingest your primary data set into a persistent storage layer.
- Engineer a business model for Phase 1 scope.
- Create a data dictionary, source to target, and support documentation for scaling the platform.
- Develop business use cases based on Phase 1 scope.
- Establish a strategic foundation for ongoing maturity and add future data sources.
- Assign a data engineer and architect to support model development.
Phase 2: Development analytics – 2 to 3 months
- Ingest your primary data sources for development.
- Add integration dataflows to increase and expand the business layer for Phase 2 scope.
- Identify SMEs and data champions to drive QA and identify new report requirements.
Phases 3 and 4: Secondary sources
- Ingest all other data sources to supplement data warehouse development.
- Add integration dataflows to increase and expand the business layer for Phase 3 and 4 scope.
- Integrate new dataflows into existing reporting and create opportunities for new audiences and enterprise level roll-ups.
Phase 5 and beyond: Support
- Continue to model data for Phases 5 and beyond.
- Expand advanced analytics capabilities for predictive and clustering models.
- Eliminate downstream dependencies on legacy systems to allow sunsetting applications.
- Integrate additional data consumers and data applications for expanded functionality.
2nd Watch’s consultants work with your team to understand your business goals and design a tech strategy to ensure success throughout the organization. From planning to technology selection to user adoption, our team’s data advisory services enhance business optimization via data-driven insights for the long haul. Contact us today to learn how 2nd Watch can build a modern data foundation for your business intelligence and predictive analytics to ensure your organization can make better decisions now and in the future.
