Top 10 Data Management Principles

To stay on the cutting edge, your business needs to be making smarter, faster decisions than the competition. The only way to do this is to leverage your most valuable asset - data - better than your competitors. A significant determinant of an organization's ability to use their data is their data management infrastructure. Just like a physical warehouse, a disorganized, badly optimized or poorly supported physical warehouse has the potential to cost the company more than it brings in in value, so it's critical to ensure that the foundation of all data-driven decisions at your organization is strong.

If you're looking to make your business a market leader, you need to make sure you have embraced these Top 10 Data Management Principles.
1

Start with the right model



Step one of a good data management system is a good data management plan. Without a clear understanding of your organization's data needs and current capabilities, any attempts to rectify shortcomings in the system will be misguided and potentially wasteful. Take the time to build a comprehensive data model that clearly lays out how data is created, imported, used, and moved around your organization.

Use tools such as this Data Management Questionnaire to assess your existing data architecture and data management processes.
2

Make it all flow



With your data model completed, visualize the flow of data around your system, clearly labelling repositories and the processes by which data is moved from one to the other. An accurate data flow diagram can immediately reveal inefficiencies, bottlenecks and double-ups in your system, helping to better focus remodelling efforts.
3

Build for the data you have



It's a truism that as much of 80% of business data is in an unstructured or raw format. While the individual amount differs from organization to organization, it's safe to say that if you're only working with your structured data, you're missing out on a significant number of potential insights.
Building a data management system that's able to work equally effortlessly with structured, unstructured, and raw data is absolutely essential for any business looking to maximize the value of clickstream or transaction data.
4

Consider alternatives to data warehouse structures



Break with the idea of a single, vulnerable, and expensive data center or the traditional monolithic, highly centralised data warehouse structures. A new data architecture utilizing next-generation technologies can pay for itself. The likes of a Logical Data Warehouse allow greater flexibility in how and where data is stored across the network, streamline data-sourcing activities, dramatically cuts operational costs, and extends and enhances your operations business-wide.
5

Choose flexible architecture



Don't build a data warehouse system that doesn't suit your organization now or in the future. Hand-in-hand with taking a more flexible approach to where you data is stored is looking at new methods of storing it. The age where every business has to own their server hardware lock, stock, and barrel is truly over, but that equally doesn't mean you have to fully commit to the cloud. Examine your options carefully in light of operational and regulatory requirements and make the right choice knowing that your data warehousing system is ready for the cloud, the onsite server, or a hybrid system.
6

Automate your data quality and exception management



To get precise, actionable insights, you need to start with clean, well-organised data, however the proliferation of traditional data warehouses and data lakes creates an endless pool of incorrect and duplicated data. Review your existing database management and data quality methodologies and consider how these may be putting your business at risk. There are a multitude of products and services that offer data quality management principles but are accompanied by high costs and extensive resource time. The key is to finding a solution that automates data quality and exception management, and the only way to do that is through implementing an integrated data fabric solution so the business-led approach to data management and consolidation is applied across every single connected data source, be it metadata, data dictionary, structured, or unstructured.
7

Align your security processes to your technology



The evolution of shared infrastructure, data management systems, and cloud technologies, brings with it an increase in tools and processes that organizations must secure. Data governance and data privacy must be taken in to consideration with any existing and future data architecture without restricting or decreasing the potential value of the business' data. Technologies that connect to data in varied and remote sources as opposed to moving the data are prime for organization's with even the most extreme data governance, risk, compliance and security requirements.
8

Build data projects with the right models from the start



Reclaim valuable time and ensure that you're always building solutions from the best possible materials by automating data cleansing and organization. Using the correct business rules and application templates for your data project can make or break its success. Pre-Built industry models like those in Zetaris Archetype, provide tried-and-tested application templates designed and benchmarked for your specific industry and dramatically reduces the cost and time to insight, allowing you to make decisions when they need to be made.
9

Make your data agile and accessible



One of the most important considerations is how to create a data-centric view across your environment, from endpoints, storage, and data management to data quality management, applications, and services, while maintaining data security, data governance and framework throughout your organization. Look at technologies such as Enterprise Data Fabric that creates an integration layer that allows end-users seamless access to all data on the network, regardless of the location or the nature of the repository. It allows you to quickly and precisely pull insights from your data without waiting for lengthy transfers to complete.
10

Ensure data management is adopted enterprise-wide



Companies that lack an expansive and easily accessible data architecture decrease the likelihood that the whole-of-company will adhere to specific data management processes. Without easy and timely access to real-time data, teams across the organization may create and store the data they need in siloed repositories that vary in depth, breadth, and formatting, isolating data, increasing data duplication and costs, and a detriment to the quality and value of the organization's data.
Data Analytics Insight Marketing
Stephanie Collis
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