Look into any mainstream industry and, sure enough, market-leaders have something in common: they are all invested in becoming data-driven. The ability to make sense of and act upon data insights is now a differentiating factor, enabling businesses to make better-informed decisions more quickly.
The majority of organisations – from small ‘kitchen table’ businesses to Fortune 1000 giants – are gathering and acting upon growing supplies of data. But the success to which companies are leveraging data depends on the effectiveness of their ability to manage it.
Data management is the practice of collecting, organising, storing and maintaining an organisation’s data so it can be effectively analysed to help inform business decisions. We have written about how to manage data effectively previously. In this blog, we will be discussing what is a data management strategy, how to develop a data management plan, and why it is necessary to take an organised approach to data as a business today.
Why is data management important to an organisation?
Data management is a crucial process for making sense of the growing quantities of data created and consumed by organisations today. Effective data management enables people and departments across the organisation to source insights, when they need them.
Successful data management makes data more:
Visible: Data is easy to find, navigate and analyse in order for people across the organisation to do their jobs more effectively.
Reliable: Errors are reduced by establishing policies and processes, so the organisation can have confidence in the data they are analysing.
Secure: Data is protected from loss, theft and cybersecurity breaches. Information is backed up and retrievable.
Scalable: Organisations can scale data usage when required. Processes are easy to repeat, to avoid conducting the same work time and time again.
What is a data management strategy?
A data management strategy is an organisation’s roadmap for using data to achieve its business goals, ensuring that all aspects of data management – from collection to security – are working effectively in a way that benefits the organisation as much as possible. Considerations may include how data is stored, consumed and processed; how data is controlled, monitored and protected; and how it is categorised and standardised in line with data classification and quality frameworks.
A sound data management strategy maximises the use of available data for business intelligence while ensuring it is simple to govern. It should ultimately help an organisation get the best benefits from its data assets, ensuring that:
- Data is compatible, unduplicated, accountable and consistent.
- Separate projects are not duplicating efforts or costs associated with data.
- Time and resources are not consumed by data activities that do not benefit the organisation.
How to develop a data management plan
A comprehensive data management plan provides the requisite groundwork from which to develop an effective and ongoing data management strategy. Below are steps that every organisation should take in order to develop a data management plan.
1. Carry out a data audit
Unmonitored data sources can be used to breach defences. Break down data by type such as emails, statistics and financial information, and by source such as private data centres, third-party applications and customer credit cards.
Create flowcharts to show the paths the data takes in and out of the organisation.
2. Specify your organisation’s data requirements
Organisations should have a specific understanding on the types of data they will possess, its storage requirements, and how it will be protected.
What will be your database or data warehousing requirements, and will they be scalable with your organisation’s growth ambitions? Where and how long will your data be stored and backed up?
3. Identify your documentation requirements
How will your data management processes be recorded and documented? Would someone new to your organisation be able to access the information they need to work within the same frameworks and policies?
Consider how your approach to documentation can make conducting your data management strategy more achievable and intuitive.
4. Establish data quality control
Understand how you can ensure the quality of your data, and add to or amend it as necessary. Can this process be automated or transferred to a cloud environment?
5. Define your data policies
The information uncovered in your data management plan will help to shape your organisation’s data management policies, which will dictate how data is handled by everybody within the organisation to ensure it remains secure, consistent and effective.
6. Describe how data will be reported
A lot of your organisation’s data will be used to make strategic business decisions. How can you make the process of reporting as efficient as possible, to ensure data insights are available when required in a digestible format?
7. Create roles and responsibilities
Assign roles and responsibilities to everybody involved in the data management process, from collection, data entry, quality control, storage, backups, reporting, documentation and more.
Ensure these roles and permissions are recorded, and that a team ensures compliance of policies and procedures, and is responsible for reviewing them regularly.
8. Set a budget for your data strategy
What tools or software do you need to implement your data management strategy? Do you need to make additional hiring decisions? Do you need to acquire any particular licence or certifications?
Once you have acted upon these considerations, you should have a clear idea of your data management plan, which will inform your data management strategy. This may change over time as you discover shortcomings or oversights, but it will provide a solid base from which to embark.
A sound, well-considered approach to data management will become increasingly important as the volume and variety of data collected by organisations continues to grow, and becomes ever more valuable. Good data management will make your organisation more productive and more cost-efficient. It will reduce security risks and lead to more accurate decision-making in a competitive digital marketplace.