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Many organizations are getting on the movement of Data Management. Is it really a necessity? Having over 200 applications, 2000 plus data sources, and more than 400,000 spreadsheets, it is very overwhelming how much information there is within an organization. The demands are increasing to become more data-driven, connecting different data sources with centralized dashboards. We have become a data-driven, data-focused society. However, the infrastructure, strategy, skillset and data cleanliness are not there to support this demand. With the proper approaches, the potential for data to support decision-making is remarkable.
Nevertheless, poor data quality and the inability to connect information are holding us back from reaching the full potential of a complete picture. Garbage in - garbage out. The issue of data quality grows in importance as we strive to make decisions on funding and operation in near real-time. While software and solutions exist to help monitor and improve the quality of structured (formatted) data, the real solution is a significant, organization-wide commitment to treating data as a valuable asset. In practice, this is difficult to achieve and requires extraordinary discipline and leadership support. That does not even talk about 80-90 percent of unstructured data.
So, as you can see it becomes imperative to put together a data management strategy. However, you cannot do this alone. This cannot be owned by IT. Business entities own the data. IT can provide assistance and guidance but this must be a team effort.
The transformational insights that executives are constantly seeking to leverage can be unlocked with a data strategy that makes high-quality, well-integrated, trustworthy, relevant data readily available to the business users who need it.
The strategic use of data can enable governments to provide higher-quality services. Direct resources appropriately and harness opportunities to improve impact. Make better evidence-informed decisions and better understand the impact of programs so that funds can be directed to where they are most likely to deliver the best results. Maintain legitimacy and credibility in an increasingly complex society. A data strategy would help protect citizens from the misuse of their data.
Create effective data management by first understanding and aligning to the business and its data consumption needs. Data management is not one size fits all. Cut through the noise related to data management, and create a strategy, and process that is right for your organization.
Therefore, what is data management - it is a roadmap for using, connecting, and maintaining data. This roadmap ensures that all the activities surrounding data management from collection to collaboration work together effectively and efficiently to be as useful as possible and easy to govern. With a data management strategy in place, you can avoid some of these common data challenges:
• Incompatible, duplicate, or missing data from undocumented or inconsistently documented sources
• Manual entries to convert or add information to multiple systems
• Siloed projects that use the same data, yet duplicate the efforts and costs associated with that data
• Data activities that consume time and resources but do not contribute to overall business objectives
• Using a copy of a copy and maintaining information outside the originating application
Performing effective data management requires a multi-faceted approach that includes investments in people, processes, and technology.
“With the proper approaches, the potential for data to support decision-making is remarkable”
Put a strategy in place to ensure data is available, accessible, well integrated, secured, of acceptable quality, and suitably visualized to fuel organization-wide decision-making. Start treating data as a strategic and organizational asset. Launch a data management practice that builds a common understanding of the customer valued data in each division/department. Make data management part of the digital strategy.
Many components are included under the umbrella of data management, all working in concert to:
• Deliver data and allow it to support the data appetites of the business
• Successfully support data through its lifecycle
• Ensure it is appropriately treated as it flows through the organization’s environment
The different phases of a data strategy should include:
• Analyze/verify organizational strategies and requirements - current and future. If you do not let your business, objectives inform your data management strategy, you could waste valuable time and resources collecting, storing, and analyzing the wrong types of data. It is usually helpful to ask questions like:
o What are your organization’s overall objectives?
o What data is needed to meet these objectives?
o What types of insights and information are required to make progress against these initiatives?
• Identify requirements for data - what information is needed to support strategies and overall objectives. Do we have the information and who owns it?
• Current state analysis - data architecture. Identify the applications and data sources. What information is stored and in what format? How frequently is the information updated - retire old systems. Standardize the build and optimization of your data environment.
• Create strong data processes. How will you clean and transform raw data to prepare it for analysis? How will you identify incomplete or disparate data? What will be the guidelines for naming data, documenting lineage, and adding metadata to increase discoverability? What integrations and connections are needed to eliminate manual entries and data silos.
• Security and risk management - How do you handle confidential information. What is the retention policy? How do you clean-out old data? How do you protect your information?
• Governance and policies - Set up data standards. Establish and sustain an enabling framework that drives the use of data. Identify data stewards with the business entity.
• Communicate,plan and build awareness. Keep the organization informed during this data journey. Ensure staff is aware and provide training to manage data and generate analysis outcomes. Change management is crucial
• Reporting - business intelligence. Which teams or departments need the ability to collaborate. How can you make access to data and analysis easier for the end-user? How will you communicate any data insights?
It is overwhelming if you are trying to manage all the data for the organization at once. Focus on 2 -3 objectives and highlight the value of increased data usage, visibility, and predictability. If you can prove value in this effort, you will increase buy-in throughout the organization. The end result, better quality data - greater data access - new opportunities for leveraging data - more reliable and robust reporting.