data analysis principles

7 Principles Of Data Analytics That All Employees Should Learn

“Big Data”, “data analytics”, “data” – people from all industries and professions within business use these buzzwords like teenagers using new slang.

They certainly are necessary now making big decisions but since the meaning of these terms can be abstract, many people aren’t really sure what “good” data analysis entails.

With that said, there are several principles of data analytics that can serve as a guide if the science and techniques still cause confusion.

7 Essential Principles of Data Analytics

Although there are dozens, maybe hundreds of principles out there, we’ve handpicked seven which will serve anyone on their data analytics quest, whether they’re familiar with it or completely unversed.

1. Provide the right interfaces for users to consume the data

Data analysis has become a new normal yet it can seem completely foreign to those who aren’t data scientists. That means there’s a need for dashboards and displays which present data in a digestible form for those who aren’t well versed in extracting insights from strange charts and symbols.

The opposite can be true as well. A dashboard that doesn’t speak the language of those who regularly read data can oversimplify the process needed to extract meaningful insights. For example, data analysts would work best with an SQL interface while a data scientist would need an interface that uses R language.

One of the key principles for data analytics is flexibility. In other words, it’s important to have a tool that allows for varying displays to meet the demands and skillsets of the various people interpreting the data.

2. Commitment to defining expected results

A major source of confusion when running data analysis is knowing what to look for in the first place. Many businesses are diving into data without having any clear goals or expected results. For data analysis to have any real purpose, there must be a standard or threshold (ie. whether it’s for consistent billing or data entry accuracy).

With such a target in place, you can then monitor or investigate how information is being entered, or whether certain KPIs are meeting those initial standards. And more importantly, it’s vital for companies to set clear goals in terms of what they want to find and eliminate (ie. duplicate billings, unused vacation time).

3. Everyone can be an analyst

True, an employee in charge of HR matters can’t compete with a BSc graduate who lives, breathes and sleeps analytics. But everyone in this day and age has to interpret data to some extent. For example, simply running a report to find discrepancies with how invoice numbers are entered is a form of data analysis.

Someone has to take ownership of those figures, and it’s not likely going to be a top-level data analyst or scientist, but someone in an accounts payable or receivable department. So what really matters is not your credentials, but rather, the relevance of data to one’s specific position. After all, your company’s data presents information that virtually all members of your organization can glean insights from.

4. Data analysis only matters if it’s meaningful & actionable

Data should actually say something and allow you to take action based on what it tells you. For example, knowing what your average payables outstanding is during the summer months may be an interesting yet an unactionable piece of data. But if you notice that your account payable department makes payments during times when payments normally wouldn’t be processed, you could then proactively look into why this is happening.

This relates to the first three points above, #2 especially, where the objective is to look for problems you can actually fix, rather than just interesting pieces of information. A simple way to avoid the latter is to set analytic standards, run reports to monitor those standards and then find the discrepancies. This will ensure you are looking at specifics rather than just aimlessly scanning through data.

5. The four V’s of big data

A more conceptual lesson to learn about data analysis is the four V’s of data analysis. The four V’s stand for volume, variety, velocity and veracity. Volume simply refers to the amount or scale of data. Variety refers to the different types of data flowing in, while velocity speaks to the speed and flow at which data comes through.

Finally, there’s veracity, which refers to the uncertainty of data or its quality (is it reliable/unreliable? Is it accurate?). Understanding these four V’s and how they interact serves as a pillar on how to evaluate your data.

6. Choose Data Tools Wisely

Understandably, not everyone utilizing a data analysis tool will have the time or the experience to interpret mountains of information. Therefore, the tool itself should process and present datasets in one convenient location and in an easy-to-digest format.

It’s vital to have a tool that offers the best of many worlds. In other words, a tool that is fast, user-friendly, scalable and with real-time reporting abilities. A solution that meets all four criteria will save your business time, effort and resources.

7. Collect as often as possible

Last but not least, there’s the issue of collecting data. One reason why data analysis often seems overwhelming comes from the fact that business owners and staff run data reports too infrequently.

Proactively running regular reports clears the backlog of data that needs processing so that you’re looking solely at current info – not the additional burden of data from weeks or months prior. More importantly, regular reporting gives you an opportunity to spot errors, fraud signals and other threats long before major damage or profit losses occur.  

Observing the Principles Of Data Analytics

Data analysis is a complicated, nuanced and diverse science. Without guidelines and frameworks in place, it becomes very easy to get lost in a sea of data by not knowing what to look or why to look for it.

By keeping these seven principles in mind, you’ll stay on the path to finding actionable and measurable data. Data analysis is not just about finding interesting facts – what matters most is what you do with the results!.

Our GLAnalytics solution continues to help businesses in all industries meet all seven data analytics principles with a user-friendly, fast processing and cost-effective approach. Get in touch with us today to learn how GLAnalytics will do the same for you!

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