The evolving data management plan
This requires the DMP to define the overall strategy of how an organisation manages its data and how it will put that data to work to fuel decisions and shape the company's direction. Additionally, the DMP must assist in overcoming silos by encouraging an environment of collaboration.
To this end, it must define the methodologies and standards that all employees must adhere to in relation to the likes of what data is collected, who has access, what can it be used for, and how it is protected.
Such a plan is a collaborative effort that includes all stakeholders. As such, it will be an organic document that evolves as technologies change and compliance requirements evolve. Fundamentally, this plan will steer the company to more consistently manage the data it collects from various channels.
Key drivers
Underpinning the DMP is the need to be compliant with current regulations. However, keeping track with this rapidly-moving environment requires a dedicated data protection officer (or officers) that will oversee the data activities, and stay abreast of the regulations as well as the potential impact they may have on the organisation.
As part of this, the company must make sure all data activities are auditable, traceable, and as transparent as possible. Having proper record protection and well-defined (and compliant) data retention policies are essential. A DMP is not a once-off process but one that must be continuously monitored and assessed to ensure that it aligns with market requirements.
Companies should consider trying to automate the auditing, compliance, and reporting process wherever possible to free up its employees to focus on delivering more strategic value to the organisation.
Trendspotting
Keeping track of trends is, therefore, an important part of assessing the relevance of the DMP. Already, data-driven enterprises put an increased priority on matters relating to data protection compliance and regulations.
Moreover, machine learning (ML) and artificial intelligence (AI) are contributing to a data-first scenario where analytics can be managed using sophisticated algorithms. The Internet of Things has already become mainstream and is seeing significantly increased data generation that is fed into the business. ML and AI can help reduce the burden on human resources and provide employees with the information required to make strategic insights faster than before.
Another trend worth noting is that of DataOps and how it can be used to quickly derive value from this vast amount of data being collected. This focuses on cultivating data management practices and processes that improve the speed and accuracy of analytics. It includes data access, quality control, automation, integration, and, ultimately, model deployment and management.
Hybrid data management solutions (as a result of cloud adoption) are also starting to play an increasing role in the DMP of an organisation. Part of their success can be attributed to the increasing adoption of open source technologies that is seeing data migrating into open source formats. No longer will DMP be driven by closed and proprietary systems and data. Instead, a more open environment will drive better business value.
Changing practices
Thanks to the advent of big data and the sheer volume of data being created from various sources, it has become increasingly difficult to manage enterprise data using legacy DM practices and infrastructure. As a result, data management solutions needed to become more agile.
This agile-centricity will permeate everything from data designs and delivery through to the continuous maintenance needed to keep pace with changing business needs. Data is growing and the DMP will be a fundamental guide in this competitive landscape as businesses look for more innovative ways to differentiate themselves.
