The journey towards a data-driven organization involves adaptation to an operating model including people and process as a data management capability enabler, combined with technology and system investments. Such self-sustaining capabilities need to be established to operate data management solutions and continuously generate sustainable value from the enterprise’s data assets.
Definition of a data management operating model includes financing model, incentive structure, data asset ownership and accountability, as well as valuation framework as it relates to quality measures. Listed below are key considerations chartering and executing the new operating model:
- Executive sponsorship for organizational and investment decisions
- Prioritization framework for strategic initiatives
- Data governance and governance body in action
- Financing & investment structure
- Span of control and autonomy
Implementation of data management operating model is technology agnostic and focuses on the self-sustaining capabilities that improve and mature over time when technology and data continue to evolve. Kenway brings tailored implementation approaches based on unique requirements and challenges for each company and use case. Clients’ current state and desired end goal is well studied to put forth the best recommendations for the client, hence each engagement is treated uniquely with a clear understanding of budget, timeline and strategic requirements.
What is Data Management?
Technological improvements have driven down the cost of implementing business applications across industries and verticals, thus increasing competitive pressures by reducing operational costs through automation and faster communication. Organizations have been forced to respond by building digital systems and adopting cloud-based SaaS applications, ultimately creating a data-rich environment. However, this has also left data siloed in multiple systems, with different attributes and details supporting a variety of use-cases spread across organizations’ networks.
Data Management is the practice of building a foundational framework for ingesting, cleaning, organizing, and maintaining integral data for your organization. It can be thought of as the capabilities necessary to capture data value using information to drive effective decision making and capital allocation. It should support your data’s lifecycle from creation and maintenance, to archiving and destruction. There are various components that make up an effective Data Management practice, including data storage, data cleanup, data mastering, and data security. The diagram below showcases some of the key components.
Why Data Management?
Improving Data Management capabilities will have an immediate impact by giving resources faster access to clean, trustworthy data. Understanding data lineage allows you to solve data quality issues at the source. A robust mastering framework ensures data-driven insights can be shared across the organization in a repeatable way – enabling trend identification and linking initiatives to measurable business outcomes (KPIs).
Data engineering techniques combined with data architecture can be an effective way to reduce costs by optimizing the physical storage of data and reducing duplication. Thinking of data quality as something that can be enforced via system and process design, as opposed to something that can be addressed after a database or data platform implementation has concluded, leads to more successful outcomes and higher quality analytics from the organization..
Some benefits of implementing a Data Management framework include:
Kenway's Data Management Framework
Adoption of an effective Data Management framework enables the enforcement and implementation of your organization’s Data Governance standards and policies. It works symbiotically with Data Governance in that the policies founded through the governance process should inform the rules by which data is transmitted and/or stored in your data warehouse. It is critical that the Data Management framework establish a feedback loop allowing data stewards to continuously curate and maintain high-quality data. This feedback loop will inform the need for additional Data Governance policies and standards as the platform and data matures (e.g., identifying additional cleaning rules as new data comes in).
A solid data foundation is also a critical requirement to enable a business analytics platform to deliver essential data insights to your organization’s business. Having an effective Data Management framework in place will start the foundation for your data to truly become the heartbeat of your organization. It will enable you to get your data faster and more accurately, so it’s “analytics” ready. This will then enable your leadership team to confidently make crucial business decisions.
Along with curating datasets for consumption by the analytics platform, Data Management can also be effective in synchronizing core operational systems so that the data is consistent across your organization on-premises and cloud systems. Standardizing pipelines and services helps reduce complexity and costs when point-to-point interfaces are required. The diagram below showcases the interrelationships between all of these elements of your information landscape.
Get to Know Our Experts
As an organization, we are driven by our Guiding Principles. This means acting with integrity and communicating effectively to provide our clients with the highest quality of consulting services. Our experts embody these principles in everything they do, and have combined experience working with and for Fortune 500 companies in regulatory, compliance and technology capacities, and preparing these clients for the road ahead.
As an expert in Kenway’s Data Management practice, Patrick has helped numerous organizations successfully navigate their Data Management, Data Engineering, and Business Intelligence challenges. Particularly well-versed in the strategic value of an organization’s data, Patrick has helped lead, architect and implement data solutions that allow companies to successfully navigate their data journey. Prior to Kenway, Patrick spent his career working as a data consultant, with a focus on Digital Innovation and Data Strategies.
Byron focuses on leading engagements that help clients make better use of their data, in alignment with their corporate strategy and goals. His data solutions have spanned the entire journey from data discovery & profiling, to ingestion & ELT processes, to data modeling & architecture, to data visualization & analytics. He focuses not only on elegant solutions but also adoption by end users and delivering without any technical baggage. Before joining Kenway, Byron worked at a large South African financial services provider. He holds an MBA and MSBA from Arizona State University in Data & Business Analytics.
“A successful Data Management solution should enable the business to optimize both its short-term decision making and its long-term strategic thinking.”
“Any data engagement should start with a thorough understanding of how it will aid the business strategy and objectives. Then ample time should be given to understanding data structure, and allowing analytics (regardless of the tool) to follow on easily and seamlessly.”