Thanks to advancements in Big Data and the Internet of Things (IoT), businesses have an influx of data and tools to visualize information from across their organization. Companies that leverage this data properly can optimize their performance and achieve a competitive advantage.
However, there is a glaring gap in this path to optimized performance. Analytics are only as accurate and valuable as the data being used. Unfortunately, in many cases, data management practices struggle to keep up with the high velocity of data and growing demands of real-time analytics. In turn, data quality suffers, trust in the data is compromised, and analytics slow to a crawl.
Choosing a data management structure: Waterfall vs Agile
Most data management strategies operate using traditional waterfall structures. These are linear, direct workflows. However, specifications and goals often change over the lifespan of a project. Because of this, data teams are only successful if they can adapt to new requirements and shifting priorities. Waterfall data management is not flexible enough for this, so many data strategists have started employing Agile methodology instead.
Precise, real-time analytics require a constant feedback loop and frequent revisions to established data structures. Agile methodologies break up big projects into small, manageable components, which allows for lean, fast-paced, and high-quality iterations. User feedback and test-driven development are key components of the methodology, which results in a cleaner, faster delivery.
Benefits of Agile in master data management
When it comes to data management and database development, an Agile approach leads to:
- Better quality data
- Faster-paced updates and project delivery
- Programs that are more responsive to analytics needs and big data demands
- Collaborative environment
Agile: Streamlining development
An Agile approach to data management sounds great in theory, but how can these methods be effectively applied in the real world? How can data management processes and workflows be revised to support an Agile approach?
Here are 5 ways to move toward an Agile data management environment:
Data agility requires speed—and automation increases speed. First, identify areas where automated functions can be implemented quickly and provide immediate improvement. Data validation is one example where automation can have direct benefit. As data flows into the database, automated validation rules can verify data points and identify duplicate records. Rather than writing separate validation rules for each of 100 datasets, look for ways where a single validation rule can cover multiple tables. This type of automation moves tedious, repetitive tasks away from valuable human resources, freeing up their time for more strategic work.
Finding ways to simplify data and database structure improves efficiency and agility. For example, to reduce confusion and repetition, developers should standardize naming conventions across the organization and ensure that fields with the same name always have the same definition. By committing to a naming convention and not overloading field names with multiple meanings, teams can reduce training times for their users while also significantly decreasing the amount of documentation they need to maintain. This allows developers to focus on writing code.
An essential component of simplifying a database is documenting standards. Agile methods often neglect documentation in order to achieve maximum velocity. In the case of data management, it is better to focus on creating the right documentation. In Agile terms, the team can specify agreed-upon documentation requirements in its “definition of done”—meaning the team commits to not move to a new project until the current project has been created. The upfront work of creating this company-wide documentation will go a long way to drive down training and maintenance costs, reduce technical debt, and ensure that the data team is working cohesively.
Large database initiatives should be broken up into smaller projects that have 1-2 manageable, deliverable goals per month. Like successful Agile approaches in software development, these smaller initiatives should be executed by small, highly-skilled teams. Moving away from the long, static project structure means that data teams can be more responsive to user feedback and manage the addition of new data sources or changes to database structure in a timely way.
5. Prioritize data governance
Consider how Agile methods will affect the over-arching data governance best practices within your organization. How is consistency achieved across all data functions? How will data resources be affected? Data governance will define how Agile approaches affect the rest of the organization, from data protection to database architecture and content management.
High-quality data is imperative for a well-functioning business intelligence program. In order to capture, validate, and incorporate Big Data into central databases—and keep pace with real-time analytics—Agile database management is essential.
To ease into an Agile environment, start with small initiatives and cherry pick easy wins first to build momentum and determine best practices. Within an organization, create partnerships between business teams and data teams to develop and lead short-term and long-term strategies. Consider using consultants who are skilled and experienced in Agile database management to develop and drive the initiative and to set up a clear roadmap and timeline.
Taking an Agile approach to data management will have a positive ripple effect in your organization; agility leads to higher data quality which leads to more precise analytics and then to better, focused, data-driven business decisions.
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Jill Reber is a nationally recognized expert on data privacy—particularly GDPR, CCPA, and other data protection laws—and has spoken on the topic at conferences sponsored by American Banker, International In-House Counsel Journal, and other national and international organizations.