Tips on How To Write a Data Management Plan

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    Many perceive digital transformation as a source of new challenges. But digital transformation failure can fall behind competitors and reduce business performance. A data management plan can help kick-start the digital transformation process and make it a success.

    How to use data to improve efficiency?

    Data can be divided into “passive” and “active” in every organization. The goal of digital transformation is to transform data from “passive” to “active”, that is, to a working tool that can and should be managed. Only then can data bring real value and benefit to the business.

    In every company, inefficient IT activities that slow down business processes and bring only expenses can be turned into a helpful tool. This tool becomes part of business processes, converts data into understandable numbers, and generates revenue.

    Passive data is usually unstructured, of unknown quality, and obtained due to non-transparent or complex transformations. Information is not connected or coordinated with each other, and a narrow circle of specialists is responsible for its storage, use, and adaptation. At the same time, the rest of the company’s employees do not have access to the data or do not understand it.

    In contrast, active data is structured, well described, measurable, understandable, and, most importantly, usable by everyone involved in the digital process. They are derived from transparent, open transformations, making it possible to trace the entire path from data source to recipient. An example would be using a data governance system that allows a business to understand what data it owns, how it relates to each other, and who uses it.

    One of the main goals of converting data into an asset is to make sure that data is democratized. We mean available to all employees of the company under security policies.

    What are the key steps in data pipeline transformation?

    A typical data pipeline for almost any data process consists of the following steps: 

    • Generation of an idea
    • Idea testing on a limited volume of data
    • Modeling, during which information is embedded into existing models 
    • Development of a new data model and validation of the goal
    • Conducting automated tests
    • Deployment 

    While previously in vogue were disparate modeling tools for business processes, software, and data, now it seems more promising to convert all data into centralized and linked models. There is also a steady trend towards the active use of external data sources and their incorporation into existing organizational models, which are then used in data monetization cases.

    Today the trend is the transition from imperative to declarative development of data warehouses. In the first scenario, it was necessary to specify a sequence of actions to achieve a result, and the repositories behaved as prescribed by their creators. The second scenario implies the use of a fundamentally different approach. 

    In the declarative development of data warehouses, any data pipeline can be described in the form of modules. Their relationships, transformation rules, and embedded tools will generate the code that performs the final data transformations. It becomes possible to describe the problem and expected result, but without explaining how to achieve it.`

    Another trend is to move from manual monitoring, testing, uploading, and modeling in MS Excel to the use of advanced Software Engineering techniques – versioning, code review, and so on. Every part of the data pipeline is put into a single repository.

    The final element of data pipeline transformation is using the “Data Pipeline as Code” concept. It implies eliminating many manual steps from the process and ensuring a smooth, automated flow of data from one phase of the pipeline to the next. During the development phase, it is also essential to lay down the ability to automatically run tests, check data quality and description, and automate the installation and assembly of the entire data network into a coherent pipeline.

    Today’s data management requirements

    Everything in Tech has its requirements, and data management isn`t an exception. These requirements are essential to ensure everything works properly. The primary data management requirements are:

    • Comprehensive data security. This does not only mean information security in the form of protection against cyber-attacks and insider attacks. It also means mandatory compliance with regulatory requirements, relevant legislation, and industry standards.
    • Cloud-oriented or multi-cloud solutions. This is an opportunity for data to be more accessible. Cloud solutions enable data and resources to be distributed, managed, and handled from multiple geographic locations. This approach also increases data security and the flexibility of all company systems.
    • Provision of DataOps and AIOps processes and integration into existing DevSecOps processes. The main task of DevOps is to provide the business with working software. The mission of DataOps is to give the company actual working data. The task of digital transformation is to build DataOps into existing DevOps processes or implement new ones.

    You must meet these requirements. This guarantees no problems with data management.

    How to determine the maturity of your company’s data management system?

    There are two ways to determine the maturity of a company, a quick and an accurate one. They will help you understand what changes need to be made to processes within the company.

    The fast way involves self-assessment based on publicly available techniques. You can use the American method CMMI (Capability Maturity Model Integration). These methods already have checklists, each point of which helps to understand the maturity stage of your company. Such techniques also help to understand which processes in the company are effective and which are not.

    The second method is precise. This is a comprehensive audit involving industry experts or an auditing company. This method is more expensive and more time-consuming.

    Implementing a data management plan into a digitalization strategy

    A data management plan describes how an organization or business operates with data. Writing a data management plan formalizes your management structure and operations. 

    It also ensures that everyone is on the same page, so everyone understands everyone, all connections are established, and your goals will be achieved.

    To build a data management plan into a digitalization strategy, you need:

    • Assess the current maturity of your company
    • Form business unit strategies and IT strategy, digital transformation strategy
    • Conduct a GAP analysis and identify priority areas for the organization based on what gaps in the data pipeline have been identified
    • Form step-by-step plans to improve the identified gaps, obtain a resource estimate on the implementation of the plans
    • Capture and defend the data management strategy or include it as a block in the digital transformation strategy

    These transformations will result in forming a plan that considers the organization’s development strategy, an action plan, and resource constraints. The company will understand how much effort and money it will spend. At the same time, the plan’s implementation will be supported by key stakeholders from the company’s management and business units.

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    Andrew Wilson is a skilled writer working for an essay writing service with experience in content marketing, technologies, digital marketing, branding strategies, and marketing trends. All this helps him to deliver professional articles to an audience and build a strong feedback rate through the readers.