A modern business has access to an almost endless stream of data. With so much access to that data, those businesses can take advantage of intelligent insights that make business decisions far easier to make. The challenge is managing that data.
We live and work in a digital economy. Now, data is simply another form of capital. Not only does that data have long-term implications when it comes to competitive strategy development, but it can also be the key to brand growth.
If you’re collecting data but your management of it is subpar, then your business will be less efficient than it should be.
The importance of data management
Data is a tool that needs to be viewed as a corporate asset. Using data in the right way can ensure that you can make better decisions about a business. It can be used to dramatically enhance the impact of marketing campaigns, and can even reduce business costs and boost revenue.
Without a more effective data management strategy, businesses are hindering their potential. That’s because that data can be lost easily, leading to data silos that are only accessible to a limited number of employees. That then leads to inconsistent data reports and, in a worst-case scenario, incorrect findings that are based on ineffective use of that data.
Smart data management is also critical in these days of regulatory compliance. Everything from data privacy to data protection needs to be folded into the data management process. As businesses can collect increasingly large and varied data volumes, managing that data is a growing challenge that needs to be tackled.
The risks and challenges of data management
Efficient and proactive data management is one of the most important tools in your business growth toolkit. By being able to analyze data at scale, a business can gain vital insights that will help to provide real value to customers and clients.
The larger a business, the more important data management becomes. As people in different departments require different data sets, they also need to be able to access the data they need, when they need it. Too many organizations fail to make this intuitive and straightforward.
The result can be halted workflows, less efficient team members, and faulty data reports that can hinder potential.
A siloed data architecture makes it much more difficult to manage and integrate data in a more coordinated way. Even in a well-designed data habitat, there will often be many databases and more than one data system. Analysts and data scientists need to be able to access the data they need if the business wants the insights that matter.
Far too many businesses and tech experts claim that a move to a cloud system will eradicate all potential issues with data management and accessibility. While it’s true that cloud platforms make data management easier, it does present its own challenges. Data needs to be moved to its new location, and there is a cost to cloud platforms that need to be monitored closely.
Data management vs Data Governance
There are a lot of misconceptions that arise when discussing the differences between data management and data governance. The key differences are that:
- Data governance is about building the best procedures and policies around your data
- Data management is enacting those policies and procedures to better collect and use that data to make more informed business decisions
The fact is that data management and data governance are integratable. Data governance is simply the cross-organizational process that allows a business to set data policy and to define ownership of that data and how it is used.
It all ties into Business Intelligence (BI). This is often confused with business analytics or even data analytics, but BI is a lot more than that. It’s a deep dive into the decision-making, operational processes, and the overall business structure of an organization.
Data-driven business intelligence simply drives revenue and in-house efficiency. This means that organizations can use that BI to maintain or increase competitive advantage.
Data governance and data management are founded on the principles of DAMA International’s Guide to the Data Management Body of Knowledge (DAMA-DMBOK2). That states that data governance is:
The exercise of authority, control, and shared decision-making (planning, monitoring, and enforcement) over the management of data assets.
What that means is that data governance is the core element of data management, and is the element that ties together the main disciplines of data management.
Included in data governance is the notion of master data management (MDM). While this comes under the umbrella of data management it’s a lot more focused and fine-tuned. MDM relates to the processes that a business uses to organize, localize, and ultimately enrich existing master data. It’s based on the rules utilized by the sales and marketing teams, as well as the overall operations strategies being used by the business.
Of course, security is of paramount importance. Data security is, traditionally, a responsibility of IT teams. That’s no longer the case, and a well-designed data governance and data management strategy ensures that the ownership of data is allocated. The ‘data owners’ are then in place to more easily (and safely) make decisions about data access.
The tasks and roles of data management
Data management means tackling a range of duties and tasks while employing a diverse skill set. For smaller businesses that lack big-budget resources, a single worker or small team may be responsible for a variety of roles and tasks. That can make data management much more challenging.
Larger brands with access to teams can generally afford to allocate specific tasks to different people. That’s where data management professionals come in. If you want to manage your data more effectively, you need to ensure that you have some or all of the following:
- Data architect
- Database administrator
- Data modelers
- Data analysts
- Data integration developer
- Data engineer
- Data steward
- Data analyst
Of course, in smaller teams, one person will need to a) understand what these roles are, and b) take responsibility for more than one role. As a business grows, steps should be taken to integrate new team members with those specialist skills into the data team.
What are the benefits of data management?
While there are lots of things that can go wrong with an inefficient data management strategy, the benefits that come with an efficient strategy are hard to beat. One of the reasons why data management has become so critical in the last five years is simply because those benefits can transform the future of a brand.
Bad data mismanagement can quickly lead to big errors and the damage that those errors can lead to. By prioritizing efficiency and accuracy through the use of the right architecture and use of tools/resources, error occurrence can be largely eliminated.
When data is now viewed as a vital business asset, making mistakes through poor data management can be fatal for even the most established brand
When you have a data setup that is correctly and sensibly managed and updated, and accessibility is straightforward for all that need it, efficiency is the result. If you instead have data that is mismanaged it can quickly become inaccurate or prone to mistakes. That quickly leads to wasted time, a waste of resources, and a ripple effect that can slow down an entire organization.
When it comes to data there are a lot of risks. That doesn’t mean simple mistakes such as assessing the wrong analytics and finding faulty results. From consumer data protection to regional legislation and regulatory compliance such as GDPR, your data needs to be kept safe and secure.
Data security is one of the most important elements of effective data management. With so many instances of cybercrime, and with data as one of the targets of those bad actors, protecting your data is more critical than ever. A mismanaged data management system will only increase the potential for security breaches, data theft, or data loss.
A robust data management system backed up by intuitive architecture makes business data more visible for those that need it. That leads to a more organized and structured working protocol. In turn, that more effective data management means that scaling data can be kept up to date.
An evolving data management system is vital if brands are to cope with the growing data volumes of today. That means keeping up with the newest possibilities of analytics, understanding compliance needs, and establishing data management best processes.
Only by doing so can the business of today keep up with the needs of their target audiences, keep ahead of their competitors, and grow their business.