What do you do if your data management is disorganized and unproductive?

1 Assess your current situation

In order to improve your data management, the first step is to assess your current situation and identify the root causes of your problems. Common issues that can lead to disorganized and unproductive data management include a lack of clear data governance policies and roles, inconsistent data definitions and standards, poor data quality and accuracy, data silos and duplication, inadequate data security and backup, and outdated or incompatible data tools and platforms. To evaluate the quality, relevance, completeness, and usability of your data, as well as the risks and gaps in your data management, you can conduct a data audit. This systematic review of your data sources, processes, and outputs can be done through various methods and tools such as interviews, surveys, checklists, data profiling, data mapping, and data lineage.

2 Define your data goals and strategy

The second step to improve your data management is to define your data goals and strategy, which should be in line with your business objectives and needs. You should determine what you want to achieve with your data, how you will measure your progress and success, and what actions you need to take to improve your data management. For example, you could aim to increase customer satisfaction and retention through better data quality and personalization, or enhance operational efficiency by automating data workflows and eliminating data silos. Additionally, you can reduce costs and risks by optimizing data storage and security, while also supporting innovation and growth through enabling data analytics and insights. To define your data goals and strategy, you can use various frameworks and tools such as SMART goals, SWOT analysis, a data strategy canvas, or a data roadmap.

3 Implement data governance and standards

The third step to improve your data management is to implement data governance and standards, which are the rules and guidelines that govern how you collect, store, access, and use your data. These standards can help you ensure that your data is consistent, reliable, secure, and compliant with regulations and best practices. To implement data governance and standards, you could use a data governance framework, create a data governance committee, utilize data governance software, or utilize data quality tools. Examples of these standards include assigning roles and responsibilities such as data owners, stewards, analysts, and users; creating policies and procedures such as data quality, privacy, security, and retention; defining metadata such as data dictionaries, glossaries, and catalogs; and establishing formats and models such as data types, schemas, and structures.

5 Analyze and visualize your data

The fifth step to improve your data management is to analyze and visualize your data, which involves applying statistical, mathematical, or logical techniques to your data to uncover patterns, trends, correlations, or anomalies and displaying your data in graphical or interactive forms to communicate your findings and insights. Data analysis and visualization can help you gain a more comprehensive understanding of your data, generate useful insights, and support decision making. Descriptive analysis is the process of summarizing and describing your data using metrics like mean, median, mode, standard deviation, or frequency. Exploratory analysis involves exploring and discovering your data using techniques like filtering, sorting, grouping, or clustering. Inferential analysis tests hypotheses and makes predictions based on your data using techniques such as regression, classification, or machine learning. Data visualization creates charts, graphs, maps, dashboards, or reports to display your data in an attractive way. To analyze and visualize your data effectively, you can use various methods and tools such as data analysis software, data visualization software or business intelligence software.

6 Monitor and improve your data management

The sixth and final step to improve your data management is to monitor and improve your data performance and outcomes. Doing so will help ensure that your data management is aligned with your data goals and strategy, and that your data is delivering value and benefits to your business. Examples of data monitoring and improvement include metrics and indicators, such as data quality, accuracy, completeness, timeliness, or usability; feedback and reviews, such as audits, surveys, interviews, or reports; and improvement actions, like cleansing, enrichment, validation, or optimization. To monitor and improve your data management, you can use various methods and tools like data quality software, a data dashboard, a data scorecard, or a data improvement plan.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?