We talk a lot about data security and the threats posed by attacks from hackers and other actors. The risk posed is real and should be taken seriously. However, there is another weak link when it comes to securing the data that makes a business tick - and that’s you. Whether you are an employer, employee, or a stakeholder, the importance of understanding how to handle data is critical.
Data breaches often stem from an inadvertent event, like data being accidentally exposed or card details leaked to the wrong hands. Complacency, lack of diligence, or simply not knowing best practices can lead to serious failures in data handling.
The potential ramifications of these failures to businesses are enormous, and it includes even minor infractions. An e-commerce business, for example, can find itself banned from the Amazon platform if it is found to be in breach of stringent data policies. Such measures can destroy otherwise profitable businesses.
Taking it Beyond Data Compliance
Adhering to data regulations and laws set by governments worldwide should be viewed as the absolute minimum requirement in managing and handling data. Though increasingly strict, government regulations focus on setting up a legal framework for companies to take responsibility for personal data. The EU’s GDPR, for instance, primarily focuses on the information of customers rather than data relevant to business operations.
Therefore, individual businesses need to take it beyond data compliance and treat data management for what it is - the digital cash for the digital economy.
People who carelessly spend money, not securing it in banks and investments, tend to crash and burn fast. The same principle can be applied to companies that do not handle the data with the care and attention it needs. Data breaches occur, which results in sensitive information being leaked and compromising any competitive advantage. Not to mention the exodus of customers and fines that will likely follow.
Having a coherent data strategy supported by robust processes and advanced tools will go a long way to securing the data that fuels the APIs of businesses.
The strategy, processes, and tools can all be wasted if these common mistakes are not averted. The importance of data management is nothing new, yet many companies are still failing to control their data. Given the potential price of failure outlined in the previous section, the rewards of reducing those errors could be invaluable.
1. Overlooking Security
The possibilities data can bring to businesses are endless, and it can be tempting to be relaxed about security for the sake of innovation. For many teams, it can be frustrating to work around access controls and being unable to share data freely - but these measures are there for a reason: Security.
The balance between access and security is a challenge many businesses need to contend with. However, securing the data is essential for the survival of companies, and there are ways for teams to collaborate and share information securely.
A security-led approach will reduce the risk of data spilling out into the public domain, whether it’s intentional or not. Leaks from within the company are as much as a risk from external threats such as hackers.
The reality is the digital landscape is continually evolving, and data science is moving with it. One of the biggest mistakes a company can make is believing it is 100% secure. The resulting complacency would lead to costly lapses in security and greater vulnerability to data leaks.
Additionally, data can easily be duplicated through the tools and applications a typical company uses. Therefore, teams must follow best practices on data and maintain constant vigilance on how the data is being used.
3. Poor Data Collection
The first step of data management is the collection of data when it enters the organization. There are many entry points, including tools and APIs. Managing the data from these platforms is an essential component of a strong data strategy.
Businesses often make the mistake of simply absorbing all the data that comes into the system, which includes irrelevant and wasteful information. This brings two key drawbacks:
- Overburdened systems: For tools and programs to work efficiently, it’s critical to control the level of data that it handles. With too much information coming in, programs can crash, leading to potentially severe disruption of business operations.
- Inaccurate data: Another critical aspect of digitized businesses is the accuracy of data. Without any control over the data coming in, companies will have an inaccurate picture of operations, hindering any progress towards growth.
Having a stringent set of processes to manage data collection will prove a robust foundation for companies to build on. To do so, businesses should follow these steps:
- Identify key data: The first stage is to identify the relevant data from each process. For example, when the customer enters billing details, focus on the information needed to make the transaction happen. Nothing more.
- Tracking tools: Being able to track data in real-time will allow teams to monitor the information coming in continuously. An example could be tracking web traffic, understanding the users’ journey, and using that data to improve APIs and other web applications.
- Analysis: With the right data coming in and the irrelevant data being filtered out, businesses will be able to carry out accurate data analysis.
- Implement: Successful data collection requires standardization of its approach. It’s essential for companies to communicate the processes to different teams and implement the overall strategy.
4. Not Matching Data Management with Business Strategy
The purpose of data management is to ensure the data can be used to support business operations in a way that does not compromise privacy and security. Yet, many companies fail to comprehensively tie in the data strategy with business objectives, resulting in collecting data that are irrelevant and missing important data.
As digitization takes hold, data is an integral part of any business strategy. To stay ahead of the curve (and competitors), companies need to understand their core objectives and how data can support achieving those objectives.
5. Simply Storing Data
Before the AI revolution, the capacity to use data was somewhat limited. It could be used to monitor email campaigns, sale conversion rates, and not much else. However, the Internet of Things (IoT) era now fully underway, data is proving to be the fuel of business and innovation.
Failing to utilize the data proactively and leaving it in silos will cost companies millions, if not billions of dollars, in missed opportunities.
Having a dedicated team in place to manage the data and condition the data so it can be used to support business operations, make technological advances, and ultimately fuel growth.
Avoid Data Complacency
The age of Big Data is here, and it will stay. Managed well, data can lead the digital transformation of businesses, ushering in an era of innovation. Poorly managed data strategies, however, could see companies drown under the weight of bad, unusable data.
As a business, it’s essential not to stand still at a time where data is fuelling the next industrial revolution. The five mistakes listed in this post have one common connection - complacency. By being proactive with the data and making it work, companies will succeed in the digital era.