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6 Best Practices for Effective Data Management

6 Best Practices for Effective Data Management

Effective Data Management

Gartner says that by 2021, enterprises using a cohesive strategy incorporating data hubs, lakes and warehouses will support 30% more use cases than competitors.

Today, data-driven enterprises are collecting huge amounts of data about customers, employees and the industry – that’s being used to improve decision making, increase efficiency, enhance customer experience, and drive digital transformation. Big data is evolving so quickly that data and analytics leaders are deploying a combination of data warehouses, data lakes and data hubs to manage the flood of complex data.

An effective data management solution will not only improve the quality of your data, but also help you get maximum value from it. When combined with an effective data management strategy that’s tailored to the needs of your business, it can help you make better business decisions, and drive growth. Data and analytics leaders looking for effective data management and better analytics must follow these six best practices:

1. Have a Holistic Approach

Today, organizations use many applications and data is scattered across the web, social media, support tickets, ERP and CRM systems. Unifying data across these systems and functions gives business leaders a complete view of the business and gives the necessary insights to optimize processes. Having a holistic view of data aligns everyone with the organization’s goals, and frees up teams to focus on more important tasks.

2. Provide Better Data Access

Easy access to business data is critical for success as it allows you to move quickly and make informed decisions. Getting actionable insights from your data is possible only when the right data is delivered to the right person at the right time. Each employee should have access according to their role, rather than having blanket policies that restrict or allow data access without taking into account the requirements of the role.

3. Maintain Data Quality

Having accurate data gives you valuable insights into your business. On the other hand, poor data quality can result in inaccurate reports and lead to wrong decisions. As the number and variety of data sources increase, there is a higher possibility for errors and missing data. Examine your data thoroughly for any inaccuracies, standardize data formats, and eliminate redundancies by cleansing your data.

4. Ensure Data Governance

Create a data governance framework to ensure that critical data is effectively managed and protected. This includes having processes in place for data collection, data standardization and ensuring that the right person has access to critical data at the right time. Having an effective data governance strategy can result in increased productivity and better insights.

5. Prioritize Data Security

Ensure that your company doesn’t fall victim to a data breach and endanger your information. Older systems can be especially vulnerable to external cyber threats as outdated interfaces are easier to exploit and may not be compatible with newer upgrades. In this case, you should consider migrating to a modern big data system that includes state-of-the-art data security features. Any effective data management strategy must take data security into account and ensure compliance with local data security regulations. Finally, plan a course of action in case there is a data breach in your company. Ideally, you should be able to prevent this but it is good to have a data security strategy in place.

6. Develop a Data Recovery Plan

Almost every company today is data-driven, and losing your data can be very damaging for your business. Any disruption in a company’s data flow can render an organization completely incapable to do business. Therefore, it’s important for you to have a data recovery strategy in place in case of an accident. You need a backup and recovery plan that explains clearly how often different types of data need to be backed up, and what steps must be taken for data recovery if a disaster occurs.

 

Applications of Big Data