Data management is a key part of any organisation that collects, stores and manages data, especially with the new GDPR guidelines that governs how data must be managed across the whole of Europe. But data management is a much more in-depth principle than just ensuring you adhere to the GDPR.
It also relates to the policies and processes an individual company has in place to ensure everyone is onboard when it comes to getting the best insights into the business, so employees across all departments can use information (and we're not just talking about personal data) to achieve company goals.
Data management also relates to the lifecycle of data and how it moves through an organisation, whether customer-related insights, legacy data that can be used for predictive analytics to see how previous events or actions have impacted a business's running. It covers many steps of the data cycle including acquiring, validating, storing, protecting, and processing data in order to ensure availability, consistency, and relevance of the data for an organisation's users.
As touched upon above, good data management should help ensure compliance to regulations like the GDPR and the UK's own data laws, ensuring a company's approach to using data is legally sound and closing the doors to penalties likely to arise if they're not compliant.
Data management is also becoming a more critical issue for businesses due to the sheer quantity of data we all produce. More and more data means storage management is more of a challenge, as well as sorting out relevant data from duplicates, dark data' and old files. Finding out how to overcome these problems is all part of the digital disruption that virtually all industries are facing.
How is data managed?
The most common way of managing data is by using a master data file; this is known as Master Data Management (MDM). This file defines an asset and its data properties so as to remove vague or rival data policies and give an organisation complete control over its data. This master data is normally managed from a single location.
But data management is about more than just the technology used. For data to be managed effectively and efficiently, it needs to align with the company's business strategy and for there to be a clear idea of what data the company needs to be able to move forward.
What are the best practices in data management?
When managing data to gain business insights, it's best to start by deciding on a business question you want an answer to, then pulling in the data needed to answer that question. Next, consider how that data will be organised and who will be managing it.
Organisations should judiciously consider their data collection procedures and documentation before collecting data. Using data templates should ensure only relevant, usable data will be collected.
Data should also be subject to quality control. This could include double-checking manually-entered data using quality level flags for signifying potential problems, checking format consistency, and including data cleansing methods.
The data should be documented to describe its context, information and parameters, as well as identifying staff who can best use the data. Documentation also involves creating wide-ranging metadata tags to enable users to discover and use the data.
When archiving data, organisations should use a repository that supports data discovery, access, and distribution. Note that with data archiving, there are regulations and policies that need to be taken into consideration.
Advanced analytics are needed as well. The challenge most organisations face is how to best use analytics and integrate it with the business process. Integrating analytics into business processes will ensure a better degree of success in data management projects.
What are the benefits of data management?
Data management is just the initial step towards controlling the large structured and unstructured data volumes that deluge organisations each day. By using data management best practices, organisations can exploit their data and gain insights to make it more useful.
One benefit is improved compliance, because organisations managing their data get greater transparency into their business processes and where their information resides, allowing them to delete or secure data where appropriate. Another benefit is that by monitoring and logging data, firms can find any anomalies that need further investigation, hence improving their security measures.
Effective data management can also help in reducing errors, because the master data provides one single version of truth for all an organisation's most important information. That eliminates the possibility of relying on incorrect information, which ensures any applications built on the master data are more likely to be accurate and effective.
It can also enhance customer engagement and loyalty, because marketing teams can gain insights from the data to tailor services to customers and personalise their interactions.
Because of the storage costs involved with large amounts of unusable or unnecessary data, effective data management can help get costs under control, as well as make the time spent on data collection and sorting much easier for analysts.
Data management can even increase an organisation's revenue, by using that data to optimise business processes and eliminate inefficiencies.
