Many organizations buy and implement new modern technology solutions in the hope that their data problems will be magically solved overnight, but they quickly find that technology alone is not enough. Other organizations adopt an entire modern data architecture with the same hope, filled with optimism in their buzz-worthy new stack—blessed by all the hottest tech blogs with echoes of big data, data streams, data pipelines, data lakes, data warehouses, data lake houses, data vaults, data meshes, and on and on. They too find that their problems remain because a modern data architecture is only one part of a modern approach to data management. Some organizations lift their legacy data stack and drop it onto a cloud platform as-is and dust their hands in accomplishment, only to find that their problems did not dissolve in the cloud.Data modernization initiatives often fail because modern data problems require more than a change to technology; they need to modernize their entire approach to data management.What is Data Management?Data management is the way you carry out your data strategy. Data management is the actual work done throughout the data lifecycle to create valuable information repeatably and at scale.What is Modern Data Management?A modern approach to data management is one that solves the modern data problems an organization faces today and allows for adaptation to future and known modern data problems outside of their current challenges.Why Should You Adopt a Modern Approach to Data Management?Modern data management allows you to turn data into your organization’s strongest asset. The volume and complexity of data continues to grow, and so does the demand from business users who require access to the data for better, more complex analytics. Enabling your business users with this data will empower your business to thrive in today’s marketplace.What Does it Take to Have A Successful Approach to Modern Data Management?A successful approach to modern data management requires leadership, data principles, quality assurance, governance, and architecture.1.) Data Requires Leadership:Data programs and projects are challenging, and they often fail. They require everyone to be on the same page—the technical teams, the business, and the stakeholders—due to the high likelihood that priorities and focus will change over time. Leadership should supply an organization with vision and a comprehensive strategy that incorporates people, processes, technologies, and data to drive successful outcomes. Leadership helps prioritize what needs to be done first to build momentum, helps keep focus when distractions arise, and helps remove barriers to success when they inevitably come up.How Do You Get Leadership Buy-in For Your Data Management Program? Tightly integrate your business strategy and initiatives with your data management activities. Define clear goals and outcomes from the investment, which can only be established by leadership.2.) People and Processes Benefit from Data Principles:Data principles enable data consumers to be empowered to make decisions. In my years in the data and analytics industry, I have found that there are hundreds or even thousands of decisions that go into making a data-driven decision-making tool and successfully incorporating it into a workflow. It is impossible to predict and to plan all these decisions ahead of time. It is equally difficult to explain the nuances of every decision to stakeholders, as they are often not equipped with the vocabulary or experience to guide technical teams to make the best decision for an organization.How Do You Select Data Principles? When you have a clearly defined set of data principles, it makes decision-making easier because people can point to how each decision aligns with established principles to build consensus around the best path forward. Some examples of principles include:Data will be of high-qualityData will respect persons and will be handled ethicallyEach subject area will have a single source of truthReuse patterns and frameworks to limit rework and promote consistency3.) Quality Data Mitigates Risk: A modern approach to data management is like a balanced financial portfolio. You need to balance your strategy between opportunity and risk mitigation. Securing your data assets from internal and external threats is a critical part of data management—no one wants their organization to end up on the news as the next data breach. A less talked about risk is data quality. A misinformed decision in an organization can have huge cost implications.How Do You Ensure Data Quality? You need a strategy for data quality. How you manage reference data, master data, and metadata makes a dramatic difference in the reliability and quality of your data. If you are not managing these types of data in your organization, you are carrying unmanaged risk.See our blog, “Why Data Quality Can’t Be Fixed with Technology” to learn how to build a strategy to improve data quality 4.) Data Governance Needs to be Realistic and Practical:There is too much data in most organizations today to be able to centrally govern every data asset equally in an enterprise data warehouse. Many organizations that try to do this end up with dozens of departmental workaround solutions because the central org moves too slowly and cannot keep up with the pace of business. On the other hand, big data solutions that live by the mantra of “ingest it now, we’ll let others figure out what to do with it later” have become unruly and left a trail of unfulfilled promises in the enterprise.How Do You Implement a Realistic Data Governance Program? Start by cataloging and classifying data assets to decide what is critical and deserves strong governance, and what can be replicated centrally without integrating or predetermining business rules. Not all data needs the same level of governance, and certainly organizations do not have unlimited budgets to do so. Defining levels of governance for assets can help clarify priorities and build a roadmap for future enhancements without stalling out innovation in the organization.5.) Data Architecture is the Blueprint for Success:A modern data architecture is one that supports large volumes of data while being highly performant and cost efficient. It supports diverse types of data, including relational, semi-structured, and unstructured data. It enables diverse types of analytical workloads, from dashboarding and reporting to advanced analytics to real-time and streaming. It should allow for elevated levels of governance as in a data warehouse yet be flexible enough to ingest data prior to defining how it connects to the larger organization as in a data lake.Customer Spotlight: Trucking Company, SEFL, modernizes its data architecture to gain immediate insights into sales performance, pricing, and claims reportingHow Do You Create a Modern Data Architecture? Start by documenting the current architecture of your data estate. Knowing where you are today will help you define where you want to go in the future. Then consider the business requirements and technical requirements for your different use cases. Most data architectures benefit from a data warehouse and a persistent staging layer, while others will benefit from having less-latent pathways to data. Knowing your requirements will help you understand the necessity of each component to define a comprehensive target architecture.A modern data management approach will take your organization one step closer to turning your data into your organization’s strongest asset.