Data Strategy, the master plan or blueprint for confronting day-to-day business challenges and meeting pre-defined business goals using data, has gained significant traction. More companies are seeing the connection between following Data Strategy best practices and boosting profit.
Gartner estimates that 50% of financial planning and analytics leaders will take charge of developing and refining their Data Strategy. Moreover, corporate annual reports refer to data nearly 80% more often than they did in 2017.
Consequently, the mandate for a well-articulated and executed enterprise Data Strategy developed from best practices will continue with the intent of increasing revenue. To achieve this goal, leaders must mold a Data Strategy into a pattern that enables timely decision-making among all employees, business partners, and customers.
Below are five essential Data Strategy best practices to get you started.
Best Practice 1: Enable Business Goals Based on Current Requirements
Any Data Strategy must integrate with the business strategy, as illustrated by Global Data Strategy’s diagram, conceptualized and presented by managing director Donna Burbank:
Through this alignment, data strategies help enterprises find new opportunities, create efficiencies, integrate data from disparate sources for unique business insight, and support organizational change.
Aligning a Data Strategy can be tricky, as 80% of an enterprise’s data consists of redundant, obsolete, and trivial information. This unorganized data obscures information needed for well-constructed data strategies.
Instead, as a best practice, get to the why, how, and what processes need to input, keep, transform, and transport data in an organized state, so it can be used to serve the enterprise. To proceed, do a data discovery process, learning about locations, purposes, and metadata surrounding enterprise-wide data and how it does or does not fit business requirements. Include data discovery of third parties, public sources, and other external data to understand the current marketplace better.
Finally, develop a Data Strategy roadmap, a step-by-step guide to transforming a business from its current state into one that meets business objectives. Use this roadmap to help communicate and sell the vision describing how the Data Strategy will meet business requirements and succeed.
Best Practice 2: Coordinate Business Units to Unify Data Processes
Standardizing data processes across an organization without interrupting or interfering with a business unit’s value remains a significant challenge. Typically, departments have individualized systems for using, distributing, and producing data. Getting groups to modify their procedures to integrate with other teams requires a cultural change.
So, as a best practice, choose a Data Strategy that supports a good change management plan and allows internal teams to drive procedural evolutions. In addition, plan team communications about issues and concerns as data activities evolve, minimizing resistance and encouraging departments to problem-solve.
For example, the United States Transportation Command, or USTRANSCOM, handled its data journey by creating a business glossary for peer review, and unified terms with a common lexicon. With a shared vocabulary, business units efficiently identified critical data sources, tables, elements, and data profiles to manage jointly.
To address siloed data, consider tactics using data orchestration, the collection and organization of data from numerous data storage points for accessibility and efficient processing. By guiding data development and delivery, data strategies that include data orchestration synergize DataOps across business units, potentially giving the enterprise better Data Quality.
Best Practice 3: Implement Data Governance to Share Data and Comply with Privacy Rules
When unifying data processes to coordinate business units, companies must place structures and resources to support the Data Strategy. Doing so requires formalizing policies and procedures for sharing data and respecting privacy through the best practice of Data Governance.
Through Data Governance, companies ensure that the correct data flows efficiently to the right resources at the right time. Peter Aiken, professor of information systems at VCU and founder of Anything Awesome, shows how Data Strategy and Data Governance work together:
The Data Strategy, aligned with the organizational strategy, informs what data assets would better support Data Governance’s success, including securing data and allowing data access. Conversely, Data Governance provides feedback on how well the Data Strategy works for the company when its policies are implemented. Consequently, either the Data Strategy or Data Governance guidance may change.
External requirements, enforced by state and national laws, are central in this feedback between Data Strategy and Governance. Data Governance activities toward compliance inform any changes needed to the Data Strategy and the business strategy.
Enterprises must handle regulatory nuances better. To do so, organizations must recraft their Data Strategy toward a more holistic and agile approach as a best practice.
Ensuring data asset security will also be emphasized in good data strategies. According to a data security governance survey, 81% of executives chose to protect all of the organization’s data assets, a more defensive approach. In comparison, 58% selected a more forward-looking approach, reducing time to insights/analytics through appropriate data access policies.
Best Practice 4: Define Metrics Based on Business Objectives
As a best practice, check on Data Strategy’s data-enabled business goals, uniform data processes, and Data Governance when using a systematic approach to define metrics and thresholds. Also, choose measurements that best inform progress and minimize obstacles according to the Data Strategy roadmap developed earlier to enable business goals.
Consider a Data Strategy that first removes constraints toward business objectives and reports progress with those tasks. Peter Aiken provides an iterative process where leaders can identify and move toward alleviating limitations based on defining a hindrance through metrics.
Start small by focusing on one or two critical constraints and set up those metrics. In addition, evaluate Data Strategy components, including data integration, metadata, storage, security, commerce, and governance, and set thresholds of what numbers mean good enough.
Plan for shifting what constraints to use and how they get measured with some iterations. Data Management must evolve its collection of practices, concepts, and processes dedicated to leveraging data assets to meet marketplace changes.
In addition, review the Data Strategy when looking at metrics based on business objectives to see that they are current. Based on this review, companies can assess whether to update the Data Strategy or the metrics types and collection to understand business objectives progress better.
Best Practice 5: Define Measurable Metrics Related to Data Policy Implementations
While metrics based on business objectives inform whether a Data Strategy has achieved a desired result, data policy implemented on the ground may deviate. For example, sometimes a change happens in the business context, such as introducing new technology. Then, business activities adapt independently of the Data Strategy’s guidance.
Conversely, company employees, clients, and customers may need to be more consistent with the existing Data Strategy policy in their implementation. Then they need additional training to handle or use data.
In that case, the company would need to address any misunderstanding. Defining measurable metrics related to data policy implementations considers reasons for unexpected deployments. Furthermore, they can be compared to metrics based on business objectives to see how much has changed in the Data Strategy handling.
As a best practice, get an inside view of data policy implementations through data observability, a method of monitoring and analyzing the health of an organization’s data and data systems. DataOps teams leverage automated tools to evaluate data behaviors, which can provide a quick snapshot of Data Strategy effectiveness.
Where automation does not make sense for a few critical metrics, have the people implementing the data policies track them. For either automated or manual approaches, improve metrics related to data policy implementations through iterations, as mentioned in best practice No. 4 above.
Best practices give organizations tools to develop and use a well-articulated and executed Data Strategy. They include setting up data-enabled business goals, standardizing data processes across business units, and implementing Data Governance to handle data access and security. Additionally, metrics should be set up that tie business objectives to their data strategies and observe data policy implementations.
Most importantly, treat Data Strategy development and operationalization adaptably to evolve strategies that work and address limitations. In a quickly changing business environment with massive amounts of information, a Data Strategy must be aligned with the business’s current needs. That way, employees, business partners, and customers can make timely decisions that positively impact enterprise profitability.
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