6 best practices for using data to set yearly targets
Raw, unfiltered data can be a goldmine for businesses looking to expand their knowledge of the average consumer. However, the data has to be legible first, and this practice takes work.
When there's a lot of data floating around, such as in the Internet of Things, the task can seem too daunting to complete. Regardless, people find a way to profit from the data insights they receive every day.
Using the Internet of Things (IoT) to the best of the company's ability can sometimes seem like its own art form, or a talent people can only acquire through arduous practice. Understanding data isn't an art, but rather a science. There are ways to understand data without getting too overwhelmed at the notion or somehow collapsing the business around the information.
Using the data to reach annual goals is not only possible, but could be easier than other alternatives.
1. Use Artificial Intelligence
Going through data points one by one is doable, but tedious and overwhelming if there's a lot of data to sift through for a deadline. Bringing in machine learning and artificial intelligence, or AI, to process the data for the company can cut out the redundancy. The benefits of using AI can lead to collecting more information, while also analyzing the data much faster than a human could.
Of course, the age-old rebuttal of machines taking jobs away could pertain to this situation if there was a strong market of individuals working in this sector. Using data is still quite a new concept in the business world, and machines to sift through the information couldn't have come at a better time. While some may suffer from the loss, their expertise could be much more valuable in other departments.
2. Data Governance
Data governance is all about getting better data while sharing the wealth with everyone in the company. Essentially, governance is about focusing on the availability, usability, security and integration of the data. Newer businesses, especially, can implement data governance so the whole industry can use the same data.
Governance allows the company to reduce cost and improve security while also giving better compliance overall. The data has more quality and insight to implement, so projects have a much lower risk of failure.
3. Goals Before Data
Having a lot of data is ideal, especially after everything is organized and feasible for use. However, having a lot of data about the opposite topic of what companies are trying to promote can be extremely detrimental. People who work with data must know their company's goals before digging through data and the IoT for information to use.
Setting up target goals for the company to follow can help bring the ideal kind of data into focus. Learning how to calculate target goals and distribute jobs accordingly can help everyone get on the same page.
4. Plan Cybersecurity
Accumulated data is useless if a hacker steals everything. Keeping the data safe and tamper-proof should be one of the business' top priorities. No company owner wants their information compromised, but the business may take an even larger blow if the culprits take off with their consumers' information, too.
Having a qualified IT department is an essential step to having robust cybersecurity protocols. Every company using data should have an IT department already in place, but making them better can't hurt, either. The business should allow IT to hinder new types of data sourcing while managing unstructured data.
5. Easy Access
For the sake of security, larger companies should limit which team members can view and access their stored data. However, employees who do need access to the data shouldn't have to go through hoops to get in. Staff members should be able to pull from data at a moment's notice.
Another crucial reason for better access is to ensure the data stays relevant. If the data is stored away in hiding for too long, the information may get forgotten and put aside at critical times. When the information does come out of hiding, if it ever does, the matter may have already passed, leading to missed opportunities.
6. Keep Data Organized
Disorganized data is one of the main reasons information can get lost between the cracks, and there are many ways for data to become poorly organized. If a classification is wrong or ownership is out of place, the information may quickly lose relevancy or never get used at all. Knowing where everything is and how it got there keeps a business running like a well-oiled machine.
IT departments also have to keep data as clean as possible, which means keeping up on classifications as new information gets introduced. Ensuring someone in the company is accountable for the organization of the data can also help at least one pair of eyes stay on the information. While solely using AI to keep track of everything may be tempting, relying entirely on machines for such valuable information may not always be the best bet.
Data and Target GoalsKeeping business goals in mind while looking up information in the IoT can help initially sorting through all of the data available. Before the information can be useful, AI has to classify the data, people go behind the AI to make everything's correct and someone has to understand the data to put into context.
The entire process sounds extremely tedious merely to get a few steps closer to a milestone. However, those few steps might as well be leaps and bounds in front of competitors who aren't using data from the IoT or other sources to fuel their strategies.