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Data management refers to the processes your organization has in place to collect data, store it, organize it, and, most importantly, protect it. Good management involves the effective use of in-house and cloud-based IT systems that can assist you in every aspect of how you handle data.
That can include analytical software or a top-selling application that can help your company make data-driven decisions, as well as robust security measures that protect your data from cyberattacks and being stolen.
Cybercrime is a major threat, with some 70% of small businesses unprepared for a cyberattack. Of course, security is just one aspect of good data management, but it’s an important one.
Many mistakes in data management crop up time and again, so let’s look at seven of the most common.
1. Not creating a governance framework
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It is a good call to look at software programs and automated systems (such as FME) that will help with analytics, security, and so on. It’s also wise to consider cloud-based storage to increase security and ensure data is accessible from anywhere. But who’s overseeing your data strategies and ensuring they not only work well but are kept up to date?
You need to implement some form of governance framework and assign staff members to be part of it. You could choose, if your organization is large enough, to have a dedicated manager whose role is to oversee your data management processes.
Having a framework in place means each step of your data management has oversight and problems can be addressed and rectified quickly.
2. Not carrying out quality checks
Data is data, right? Wrong! The data you use must be of the highest quality. If any of it isn’t, it can have an adverse effect on the decisions you make based on that data. This need for quality applies to any type of data you utilize and any area you use it in.
Regular quality checks and audits are part of a good governance process. You should look closely at how you collect data and the type of data you’re focusing on. This may be collected via webforms or your inbound call center service, for example.
Comparing current or new data sets to historical ones is one way of maintaining quality. If there are major changes or discrepancies, look to see if these are natural or if the quality has degraded.
3. Using the wrong technology
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Gone are the days of relying on manual management. With the amount of data most organizations handle, the need for technology has never been greater. Many businesses rush into data management and the intricacies involved without considering the tech they have and need. Having inadequate resources means poor data management.
Audit the current systems and tools in your organization to see if they meet your needs. If not, look at what you require and the options currently on the market. Will any staff need training with new tools? If so, factor that in. Also, does your enterprise VoIP provider offer integrations with the tools you use?
By ensuring you have the right data management tools and tech, you guarantee good quality and governance.
4. Poor scheduling
A lot of organizations rush into big data and data management without considering what it entails and what’s required (e.g. the aforementioned technology). If this applies to you, you need to have a well-thought-out plan and a realistic timetable for implementation.
Poor scheduling can lead to an overrun on associated costs. Therefore, it is important that your governance staff draw up a plan that considers all aspects of implementing a new management architecture. This should include vital factors such as costs, training needs, and a realistic timetable for implementation and activation.
5. Keeping redundant data
One thing many companies fail to consider is the type of data they collect and whether it has any relevance to their business. Perhaps more importantly, they have no strategy for ‘retiring’ data. What that means is your organization could be storing and managing a lot of data that’s redundant and of no use to you.
Consider two tactics here. Firstly, ensure the data you keep is relevant and of use, and discard any that’s not. Secondly, set a regular recycling schedule to look at what you’re storing. For example, if a customer has moved house, there’s little point in keeping their old address. If data is no longer useful, it’s time to discard it.
6. Lackluster security
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While data privacy laws can vary, regulation in many sectors is tight, particularly when it comes to areas like financial services or healthcare. A mistake many companies make is to move to a big data model without considering the levels of risk they’ll face or taking steps to mitigate this and ensure compliance with relevant laws and regulations.
Risk mitigation should be a major consideration if you’re planning a move to a big data model. This applies to any type of business, from those with an omnichannel retail setup to healthcare providers.
You need to consider if and what laws and regulations apply and what rights users have when it comes to accessing data you hold on them. You should be regularly auditing your security measures and risk mitigation policies to ensure they’re up to date and working as needed.
7. An overreliance on IT teams
There can be a misconception that data management should be solely handled by IT. While this may seem like a logical allocation of responsibility, IT staff don’t necessarily understand the different business processes you’ll be utilizing the data for. This could potentially lead to issues.
Your governance team (or committee) should include staff members from different areas of your business. Ideally, it should be headed by someone from management who has a broader understanding of what you want to achieve. This means you can take a balanced approach to data management and avoid problems down the line.
The Takeaway
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If you’re moving to using big data in your business, avoiding these mistakes is crucial. Consider educating all your staff on new processes, tools, and systems. You may want to create an informational webinar that helps with this. Ensuring everyone in your company is on board with data management will contribute to its success.
You have multiple data-driven business decisions to make every day. If you want to know how to become an affiliate marketer, you’ll look at the relevant data to decide if affiliate marketing suits your business. In this digital era, data is the lifeblood of your company, and you need to look after it.
From helping you consider the cost of DocuSign to deciding on a marketing budget, data can drive your business forward and empower you to make informed decisions. Without it, you would be stranded in a vacuum where it would be difficult to do business or make any moves that weren’t based on guesswork. Take control today!
FME & Data Management
Save time and money with FME today. FME is a data integration platform with comprehensive support for spatial data. Automations are a big feature on the FME platform. Great workflows may begin on desktop, but FME Server Automations take them to the next level. There are many FME uses. ‘In the Nick of Time: Automating Wildfire Threat Assessments’ and ‘Automating an Auction with FME Server’ illustrate FME capabilities.
FME Server is the enterprise integration platform designed to save your organization time and money while increasing productivity. Automations are at the heart of that solution.
Automate with FME now. Spend more time using data, and less time fighting it. Learn more about Automations on FME or check out the FME Accelerator. Get a quick run down on how FME can help you manage your data better with this free online workshop.
Grace Lau
Grace Lau is the Director of Growth Content at Dialpad, an AI-powered cloud VoIP apps and communication platform for better and easier team collaboration. She has over 10 years of experience in content writing and strategy. Currently, she is responsible for leading branded and editorial content strategies and partnering with SEO and Ops teams to build and nurture content. Grace Lau also published articles for domains such as Agency Vista and IoT For All.