Improve data quality with an Agile approach to data management
 
 

Improve data quality with an Agile approach to data management

How accurate are the data visualizations and business intelligence (BI) reports that your company depends on for daily business insight and strategy? In our increasingly data-driven world, companies who can leverage data and analytics to optimize performance achieve a competitive advantage. Businesses have an influx of big data and a multitude of impressive analytics tools to choose from that can help visualize data from every angle and perspective possible.

 

There is a glaring gap in this path to optimized performance, though. Analytics are only as accurate and valuable as your data is high-quality and usable. Unfortunately, in many cases, data management practices are struggling to keep up with the high velocity of data and the growing demands of real-time analytics. In turn, data quality suffers, trust in the data becomes compromised, and analytical reporting efforts are slowed to a crawl.

 

Agile vs waterfall

Most data management strategies still operate in traditional, waterfall structures. These linear, deliberate, and rigid workflows, although stable, do not allow for the flexibility and responsiveness necessary in data-driven business environments. Agile organization recognize that specifications and goals do not remain unchanged from conception through delivery. Successful data teams must be able to adapt to changes in requirements and shifting priorities. Precise, real-time analytics require a constant feedback loop and frequent tweaks and improvements to established data structures.

 

In the quest to become more adaptable, data strategists are moving towards the Agile approach that has been so successful in software development. Agile methodologies break up big projects into small, manageable components. This allows for lean, fast-paced and high-quality iterations that thrive on user feedback and test-driven development.

 

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 answer those questions and move toward a more agile data management environment:

 

1. Automate

Data agility means speed. Speed is better achieved when functions are automated. 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. These types of automation move tedious, repetitive tasks away from valuable human resources, freeing up their time for more strategic work.

 

2. Simplify

Seeking out ways to simplify data and database structure will go a long way in improving 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 meaning, teams can reduce training times for their users while also significantly reducing the amount of documentation they need to maintain. This allows your developers to focus less time on documentation and more time on writing code, which goes a long way towards making your organization more agile.

 

3. Document

An essential component of simplifying a database is documenting standards. It can be argued that agile methods often tend to neglect documentation in order to achieve maximum velocity. In the case of data management, though, it is better to focus on creating the right documentation and focusing only what is otherwise not covered by common sense and existing naming conventions. In agile terms, the team can specify agreed-upon documentation requirements in the team’s “definition of done” – meaning the team commits to not move onto 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 in a cohesive unit now and in the long term.

 

4. Restructure

Large database initiatives should be broken up into smaller projects having 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 focused on high-quality output. 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 manner.

 

5. Prioritize Data Governance

It is also important to consider how implementing Agile methods will affect the over-arching data governance best practices within an organization. How is consistency achieved across all data functions? How will data resources be affected? A strong focus on data governance will help define how Agile approaches will penetrate everything from data security to database architecture and content management.

 

High-quality data is imperative for a well-functioning business intelligence program. In order to keep pace with the fast-changing data needs of real-time analytics while capturing, validating, and incorporating valuable big data into central databases, 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 for 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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