Data is a powerful tool, offering cost savings through improved efficiency and insight into what customers want.
For example, tools like Motionloft use data to determine the busiest times for your business so you can better determine staffing needs and coordinate schedules, while tools like HubSpot track your spending data to help you use budgetary resources more wisely.
In addition, by analyzing the purchase behavior of your customers, you can find out what’s selling and offer them more of what they want. And in an era when your online presence is critical, you can use data to monitor and improve it. A boost in online visibility could mean a boost in customers as well.
Clearly, data is extremely useful. But with all of its possibilities, it can also be overwhelming for those who aren’t sure where to get started. Here’s how to begin using data to your company’s advantage.
1. Store it safely.
It’s frustrating going through the hard, expensive process of collecting data, only to lose it. Plus, lost data can forfeit your clients’ trust, cost you money, or even bankrupt your business. In fact, 70 percent of small firms close their doors after a data loss incident, and 94 percent of all companies that suffer severe data loss aren’t able to recover. So when you collect data, you have to ensure it’s safe from both loss (though system failure, human error, etc.) and hacking — but still accessible to your team. Be vigilant about backing up data; use firewalls, spam filters, and permission controls to help combat cybersecurity risks.
You should also take care when disposing of hardware that holds your data. As Fredrik Forslund, vice president of cloud and data erasure at Blancco, explains, “Selling old hardware via an online marketplace might feel like a good option, but in reality it creates a serious risk of exposing dangerous levels of personal data.” Have a plan for disposing of used drives without risking data being left behind. To be sure sensitive data won’t be recovered, physically destroying the drives is likely your best bet.
2. Clean it.
The first step in cleaning your data is identifying the right tool for the job. Excel is best when you have fewer than 1 million data points, there’s a logical pattern for cleaning the data, and you need to do so quickly. Python or another scripting language may be better if the pattern required for cleaning the data doesn’t easily work with Excel functions or you need to document your process as you go. Comb through the data to make sure that what you’re looking at makes sense. For instance, if you’re examining monthly salary data for blue-collar workers and the maximum value is $1,000,000, that should be a red flag that something is incorrect. Are there any mistakes or typos? Is everything in its correct column and field?
Once you’ve assessed any housekeeping issues with your data, think about how you want to correct them. Some fixes will be obvious (e.g., changing “M4ary” to “Mary” in the name field), but not always. Data redundancy issues, for example, can be complex to address — so much so that database administrators often view a certain amount of redundancy as acceptable. Remember to refer to the original source of the data if you need to. If you have questions, the raw data can clear it up.
3. Augment some of the analysis with tech.
If you’re collecting data you don’t know what to do with, you’re not alone. To take one just example, consider the manufacturing industry. “Manufacturers produce massive amounts of data, but it must be manually analyzed to find ways of improving the supply chain. As a result, most of this data sits idle,” explains Ali Hassan R., co-founder and CEO of AI supply chain company ThroughPut Inc. Not only can technology help automate data collection, but tech can also aid in analysis, thereby making the process less overwhelming.
According to one study of the sector, advanced analytics such as predictive maintenance or yield-energy-throughput can improve earnings before interest, taxes, depreciation, and amortization by as much as 4 to 10 percent. In fact, augmented analytics — which uses machine learning and AI to change how analytics content is produced and communicated — is among the top technology trends that Gartner predicts will cause disruption over the next three to five years. Gartner Research Vice President Rita Sallam warns that “data and analytics leaders must examine the potential business impact of these trends and adjust business models and operations accordingly, or risk losing competitive advantage to those who do.”
If keeping your competitive edge seems like a good idea, you’ll want data and advanced analytics on your side. By storing, protecting, and cleaning data properly — as well as using the right technology to analyze it — not only will data no longer be overwhelming, but you’ll also be harnessing it to the best of its tremendous capabilities.
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