Information is at the core of the performance of any organization, but managing the ever more complex data flows and greater data volumes created by the digital economy requires a data management strategy. An organization can only implement its data management strategy if it has an excellent understanding of its data handling processes. Identifying and resolving issues in its data management process performance is the first step for any organization in realizing its data management strategy.
Organizations run on information. It is not an exaggeration to state that information is more important for an organization’s survival than even cash. An organization that is flooded with cash will eventually hit the rocks if it is unable to manage its business-critical information in such a way that the correct information is available to the right users in a timely manner. Furthermore, it is not enough that the correct information is available at the right time, but it also has to be sufficiently accurate.
Like cash, information flows in and out of an organization in the form of various data streams, and the trend is that the flow of data will become more complex as data is generated, received and modified using various devices and applications. A continuously changing world and the unstoppable evolution of technology mean that data streams are by no means static but rather change constantly, which adds its own level of complexity.
Just as an organization needs to have processes to manage its cash flow and, thereby, ensure its liquidity remains at a safe level, not only now but in the foreseeable future, there also need to be processes in place that adequately manage the organization’s data in such a way that the availability of the information required by the core and support processes is guaranteed at all times. This is easier said than done, because an organization can define what incoming data flows it is able to capture but it has limited resources for influencing when and if the data is produced and sent. One thing an organization can influence, however, is how its incoming data is produced and how it handles the data it receives, which is where a data management strategy is essential.
Starting point: data management strategy
A viable data management strategy is the starting point for an organization to take control of its data management processes. A data management strategy defines the long-term commitments that must be made to ensure that the appropriate process are in place and maintained to support the capture and handling of the data required for the organization to outperform its competition in delivering its core products and services to its clients. The owners of the data management processes need to be involved in the formulation of the data management strategy, which needs to be championed by top management.
Strategic planning starts from an understanding of the current situation and a vision for the target. If an organization does not have a clear picture of the state of its data management at the process level, it needs to carry out a thorough analysis by identifying its inbound as well as outbound data streams and analyzing the data management processes that manage the data in between. The result of such an analysis is an assessment of the maturity of the organization’s data management capabilities and a realistic basis for determining the vision of what the organization wants its data management capabilities to look like in three to five years’ time. The journey from the current situation to the target defined in the vision requires not only financial investments but also the commitment of the whole organization starting from management and the owners of the data management processes. The vision itself must be set at a realistic level; nothing is worse than setting unachievable goals, which inevitably leads to wasted resources and disenchantment.
Development roadmap as a guideline
Any journey requires a set of directions. The guideline for the data management strategy should be a development roadmap that acknowledges the organization’s current data management maturity level and defines the actions, investments and learning processes required to achieve the target maturity level. The roadmap must include clear and achievable milestones that serve as checkpoints to ensure that the organization is on the right track but that also empower the stakeholders involved in the change process.
Information is power, and the success of the data management strategy requires change management, in which information is the critical element. The stakeholders whose commitment is essential to the success of the strategy need to be kept in the loop through clear and structured communication. From the very start of the implementation, there must be a clear communication plan that involves all the data management processes involved, because at the end of the day, the relationship between strategy and processes is mutual; strategic decisions influence the performance of processes, and the success or failure of a strategy can be measured by the performance of the related processes.