Data management and governance best practices
Data management and governance best practices for creating a 360° view of your business
If your role involves overseeing multiple groups or departments, you’re probably uncomfortably familiar with this scenario: You meet with one team in the morning to discuss customer data from their part of the company. In the afternoon, you’re reviewing team two’s data, and while it makes sense standing on its own, something isn’t jiving with what you saw that morning. It’s disconcerting to say the least, and could point to some major problems with how your organization manages, processes, and interprets data.
The importance of data management can’t be understated. Data should be accessible and actionable, meaning that it’s structured in a way that you can actually use it to make decisions about your business and better support your customers. If your organization is struggling, it may be time to rethink your processes and infrastructure, and implement a well-defined data governance strategy and architecture to manage it.
Reasons you might need a new data strategy:
Discordant data across the organization:
Data can get messy, and many organizations struggle with incohesive data structures within departments and across the business. Data is duplicated across systems, which can be inefficient to maintain and create confusion if there are discrepancies. This tangle of data can lead to different groups pulling data and coming to different conclusions (at best). Without a well-planned data structure and data governance policy, it’s unlikely that your entire organization is using the best information to reach your strategy goals.
Inefficient use of resources:
Waiting is the antithesis of efficiency. If you and your team must wait for other people or departments to provide information from the applications they manage, or to crunch numbers for you, you’re wasting time. The same goes for reinventing the wheel with ad hoc querying or reporting on data that’s so complicated or convoluted that you need to wait for someone else to explain it. It’s a waste of your time, and it’s probably a waste of someone else’s, too.
Too much data for traditional data management processes:
Spreadsheets can only handle so many columns and rows before they start collapsing under the strain of processing, and they have limited flexibility. Bogged down systems are a waste of time, and they can inhibit your organization from taking advantage of historical data that could be used to tell the story of your business’s past – and help you guide its future.
What do we mean by data governance and system integration?
Data governance is a strong, comprehensive operating model tying together people, data management, and the systems and architecture that store your data. At its most basic, it is a decision-making framework for all data-related matters.
System or data integration is the process of pulling together data from various applications to create a cohesive picture that tells the story of your business. This can be accomplished through various avenues, including real-time data pulls between applications with robust system integration processes, standard ETL practices in a data warehouse to house historic and current data from multiple sources, or in some cases, an advanced analytics platform that unifies, cleanses, and interprets the data before reporting.
The story of your data -- benefits of data and system integration
Not surprisingly, the benefits of integration directly mirror the reasons why you might need a new data management strategy.
Reliable source of truth
Thoughtfully integrating and cleansing data spanning multiple systems helps create a more cohesive view of your information, and ensures data consistency across your organization. It eliminates dual maintenance, relying on origin systems of record. Ultimately, creating a “single source of truth” not only reflects having a source system of record, but also about integrating your data in a way that allows for strategic reporting and analytics, with actionable, easily consumable data.
Actionable data, when you need it
The ability to freely explore data and visualizations, without boundaries or restrictions (outside of necessary regulatory and security safeguards) is a huge benefit for the streamlined functioning of your business. Not only does self-service reporting eliminate wait times, but it provides accurate data to the right people for empowering informed business decisions when they need it. This should be the ultimate goal of any data management and analytics system.
Create robust data to use for analytics and strategic planning
By compiling all your data from various enterprise and departmental channels in one location, you can get a full view of your business and your customers. This enables trend analysis and supports strategic planning, and allows the different departments in your business to function better. For example, with comprehensive information from marketing, sales, finance and support, your teams can tailor communications and efforts to individual customers, increasing the likelihood of repeat business and increasing customer satisfaction and retention.
Next steps? Establishing a data governance program and creating data infrastructure
Frequently technology initiatives start with business problems, and getting to the root of those problems starts with questions.
What are the business problems and pain points that you need solutions for?
What questions do you need your data to answer?
What metrics will get to the root of your problems, and allow you to track success and failures?
What are the best tools for the job?
Your data governance strategy should be created around your specific needs (you shouldn’t try to bend your data strategy to your questions after the fact). You can establish a data governance program by stepping through a defined process (outlined below). At the end, you should fully understand the needs and implications of your data context, quality, hygiene, and privacy needs, and how to store your data (from both an infrastructure and operational perspective).
Data integration: the end game
The ultimate goal of your data integration strategy isn’t to create a bunch of rules and setup well-organized data. It’s about establishing a platform for a data strategy that can help you grow your business. It’s about moving towards timely and actionable analytics that answers your questions, and guides you in your strategic decisions with predictive technology. It’s about aligning people, processes, and tools so you truly understand your customers and can create the best experience possible.
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