Many companies have declared that they will move aggressively to the cloud to be positioned for AI. They view AI as the next frontier of their digital transformation. One of the key points these companies understand is that a simple “lift & shift” will be insufficient. They need to transform and modernize their entire approach to data – how it is collected, stored, cleansed, transported, and governed.
Successful organizations will begin by launching an initiative to develop a comprehensive data strategy. They know that a piecemeal approach to data transformation will create more of the data siloes that have been plaguing them for years. Siloes that slow customer service, hinder self-service, reduce the value of their analytics efforts and clearly prohibit them from getting the full value of AI.
But … where to start? Very few companies have conducted a thorough data strategy. How do you organize such a project? Who are the stakeholders? What are the tasks to perform? What do the deliverables look like? How do you manage such a project. And, perhaps most importantly – how can the resulting strategy be complete but implementable in steps, how can it be thorough but flexible?
Infinitive has the answer – our Data Strategy Methodology. This methodology is the result of many data strategies performed for our clients over the years. All the lessons learned from those projects are included. All the best practices are incorporated.
Best of all – it’s free. Download it here.
Data Maturity: What state is your data in today?
The first stage of the IDSM methodology is a data maturity assessment. How close is your company to being data-centric? The key output is a data vision for the future. A unified vision or “North Star” is defined during this phase. Sometimes this North Star is business oriented, sometimes it is technology oriented. For example, First Republic Bank has as its mission, “To provide extraordinary service, in an extraordinary way.” Another large bank stated (in 2019) that by 2014 they would have transformed their data and moved to the cloud to, “take full advantage of the benefits provided by artificial intelligence.”
Data Strategy: What will the future look like?
The Infinitive Data Strategy Methodology (IDSM) starts at the beginning – what are the business challenges and opportunities facing the company. This means examining topics such as:
- Expanding into adjacent markets
- Extreme personalization
- Automation
- Using AI to avoid customer bad debt
- New competitors unencumbered by legacy operations
- The changing demographics of the customer base
The data strategy phase creates the reference architecture that will exist at the end of the data transformation effort.
Roadmap and Product Lifecycle: What and when will the phases of work in the data transformation occur?
The tasks required to implement the next generation data architecture at the company are defined in this phase. Quick wins are identified. Once the strategy is defined, we then need to build a road map to implement it. Early phases of the roadmap will be quick wins and foundational data capabilities. These efforts should be carefully incorporated into other ongoing efforts to minimize disruption to the business.
Operating Model: How will the project scheduled in the last phase be conducted?
Program and data governance structures are defined. How will differing opinions on the details of the data transformation be resolved? How will the users of the new data architecture be represented in the data transformation project? The operating model is focused on both governance and enablement. Well-structured governance is needed to ensure priorities are clear and balanced with ongoing IT initiatives to ensure proper allocation of budgets and IT staff. The operating model should enable the organization through support structures that grow skills and help development teams and establishing clear guidelines for building out data capabilities. The key output of this phase is a center of excellence (with staffing defined) for the conduct of the coming data transformation work.
Infinitive’s Data Transformation Methodology provides a proven approach to conducting a complete data transformation project. It starts with a data strategy, then progresses to building the infrastructure foundation for the new data architecture, scaling the infrastructure, and migrating the data, and building a data-driven culture. By following this methodology, enterprises can benefit from the many real-world data transformation projects successfully conducted by Infinitive over the years.
Download the methodology.
