Data strategy is pivotal in your digital transformation journey. Whether you are at the early stage of digital transformation or have already gone through a few phases, your data strategy should continue to evolve.
In this age of fourth-generation customer relationship management (CRM) and experience management (XM) software, the need for a suitable data strategy with an underlying framework is immense. The scope of experience management is not limited to customers and prospects but applies to all stakeholders. The importance of insights is higher than ever to help understand causalities and establish wisdom-based operations to interface with all stakeholders. In a wisdom-based operation, contextual data helps answer “why” alongside “how” and “what.” Thus, for timely and accurate insights, there is no better alternative than a well-planned data strategy.
What should this framework be? According to the Harvard Business Review’s (HBR) May-June 2017 edition, the framework needs to be based on a single source of truth (SSOT) and a multiple version of truth (MVOT) architecture. A “data lake” platform would be the main component due to its capability of storing a virtually unlimited amount of structured and unstructured data from myriad sources in almost every format possible.
Rigorous or flexible data management?
Each has its pros and cons, since the data-management orientation of SSOT is rigorous and geared to optimized data mining, normalization, storage, and access, which ensures data safekeeping, discretion, reliability, quality, regulatory compliance, and control. MVOT’s data-management orientation, on the other hand, is flexible and optimizes data analytics, modeling, visualization, conversion, and fortification that aims for a competitive edge in positioning and profitability.
During the development of your data strategy, looking back at your prior strategy helps you understand the challenges, lessons, and implications given past experience. A historical view will also help you define the present purpose of having the right data strategy, considering your industry or niche. This also helps determine the most promising ways to address any foreseeable challenges. When you are at the point where you will have to choose the right mix of SSOT and MVOT, two key considerations come into play:
- The maturity of the organization in the digital transformation spectrum
- The purposes perceived by the leaders along with the organization’s vision
The right balance
If you are starting from square one or in the process of bringing stability to current processes, you should probably consider SSOT and shift towards MVOT over time. While a perfect balance might not be possible or ideal all the time, decisions based on internal strengths and weaknesses and market signals would be appropriate. Usually, organizations dealing with sensitive information tend to rely on SSOT, whereas end-user or consumer-facing organizations would go with a mix of SSOT and MVOT, leaning more towards MVOT. Retailers usually fall into this category, for instance. Seeing through the lenses that are key to your business and aligning your strategy periodically will help during your execution plan. Your plan should incorporate timing and sequencing of related initiatives as well as suitable resource allocation.
Whether it is to deliver a better experience, strengthen trust, increase satisfaction with choices, or even make process improvements, the importance of an effective data strategy is paramount, along with the right choice of technology and underlying services from partners. The outcome of an effective data strategy is not necessarily an exhaustive list of KPIs. The right set of measures and correlations encompassing an end-to-end outlook helps identify the operational elements influencing end-user experiences directly or indirectly across all channels. Identifying the right set of measures is crucial to keep your reports simple and meaningful, although it can be challenging at times. Aiming for easily auditable and understandable reports could be a good first step in your data-strategy journey.
For more on this topic, please read the “Enterprise Data Strategy” series.