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Data is becoming increasingly important in the modern business landscapes. Our ability to collect, collate, and sort through data from a number of sources has enabled a level of insight and data-based decision making that has simply never existed before. From marketing to sales to internal workflow, big data can help us sharpen and improve the business in a wide variety of ways.
However, this doesn’t mean that it’s always helpful. Mistakes with big data can result in inefficiency, incorrect conclusions, and even security risks. For that reason, we’ll look at mistakes you want to avoid when dealing with data.
Neglecting your data security
Big data is all about aggregating it, collecting it from different sources and storing it all in the same place. THis makes it easier to sort, to compare, and to put together to make more complete insights than any one dataset could offer. However, it also means that all of your business’s (and perhaps your customers’) valuable data is in one place. A breach of a big data system could be devastating to your business. Protecting your company from data privacy violations is essential. This means installing top-of-the-line security software, ensuring your employees have the appropriate level of access, and teaching them data security to make sure they don’t inadvertently leave a vulnerability that a criminal could expose and exploit.
Jumping the gun on big data
It can be tempting to jump off from the word go and try and build a big data system from the start. However, that’s an easy way to get overwhelmed by the volume of data and the different formats that it can take. If your team hasn’t worked with data collection and analysis, then start by working with a smaller number of data sources. When they get used to them, start incorporating more. The more practice with smaller projects, the better you will be able to predict the kind of efforts and tools needed for bigger projects, as well. Ensure your team is going through the training and big data courses they need, too.
Getting into data without considering the business needs
Yes, big data can help you make all kinds of insights and can improve decision making across the board. However, simply diving into your data without any kind of goal, any question you want the data to answer, or problem to solve, is going to end up in a lot of wasted time and money. When you start investing in big data, look at your business needs, not simply in investing as much as you can into your data infrastructure. Otherwise, you can experience ballooning tech costs that you can’t later justify because you didn’t have a clear reason to make that investment in the first place. Always start with the business perspective.
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Treating all data as the same
Surely, data can’t lie, can it? The short answer is no, but it can be gathered and used incorrectly. For instance, when it comes to manual data entry, you must have measures to double-check any data to avoid the risk of human error. Data can become out of data, as well, so your team must be able to see the relevance of data and when it was collected to know if it’s going to be of any use to them. Furthermore, duplicate data can lead to datasets becoming highly inaccurate, since there may more entries that make a certain trend seem much more prevalent than it really is. Ensure your team is focused on checking for and removing bad data.
Not scaling your tools to your needs
As your big data efforts grow, so too will the complexity of managing and organizing that data. This is important, as mentioned, to ensure that you’re using good quality data for your insights. When you’re moving to new projects, make sure that you also use a database compare tool to see if you can find database systems that allow you to work more efficiently and make it easier for your team to find the data they need when they need it. If you are moving to new systems, then ensure you have a migration plan fully laid out so you don’t lose any data or run into roadblocks in the middle of it, too.
Make sure all teams are involved in big data efforts
Your data analysts are going to do the brunt of the work when it comes to managing data and developing insights by reading it. However, while the data and analytics team will play the key role in extracting the data, it should be your IT team who is helping to set up the systems that store said data. Furthermore, teams who are relevant to the data should work with the analysis team to offer context where it might be needed. For instance, a member from the sales team may be able to address questions about sales data that helps the others come to more complete conclusion and insights.
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Investing too much in predictive analysis
With enough data, you can see what the future has in store for your business. That’s the big promise of predictive analysis. Many companies already do some kinds of predictive analysis, already, such as with cash flow projections. However, blind reliance on that predictive analysis can leave you vulnerable if your expected outcome doesn’t come to pass. To use a cash flow example, if you think your sales are going to spike, you might make more investments in the business because of it. However, if that spike doesn’t come to pass, then you will have spent money you can’t afford to spend. Good predictive analysis is about managing your expectations and having a Plan B in case those predictions are off.
Relying too much on machine learning
Make no mistake, machine learning and AI is becoming increasingly impressive and very helpful when it comes to managing, organizing, and finding data that is relevant to business questions. However, it’s important not to rely on machine learning when it comes to solving problems that aren’t based entirely in hardware. Data scientists are necessary as well, adding the human learning element that can pick out the unpredictabilities and see how the data interfaces with the real-world counterparts that it represents. Understand that big data is a tool to help with problem-solving, not a solution in and of itself. Besides having the tech to manage the data, you need the data analysts and scientists to make good use of it, too.
Not translating your data
Data analysts might talk a big game about what big data can do. However, if they’re not able to communicate the results, insights, and answers they find from it, then you won’t be able to make good use of it. C-suite members, management, and team leaders have to understand the conclusions drawn from big data as well. Not only does this mean you must focus on the ability of your data team to communicate effectively, it also means that you should make use of tools such as data visualization software. The data and analysis teams have to be able to present data in ways that are understandable and actionable. Otherwise, their solutions and advice will never be implemented.
Your data could help you transform your business, but you must put a series of best practices in place to ensure as much. The tips above are just the start, make sure your team is constantly looking for ways to improve their approach to data.