Data is everywhere. It’s constantly collected, analyzed, and used by businesses of every shape, size, and description. However, not all data is created equal. As a business owner, you must determine the essential key performance indicators (KPIs) that will help you grow.
That’s the end goal of data analytics: To drill down through the crust to find the well of usable and actionable information. And it’s those unique KPIs and parameters that will help your company find the edge it needs over your competitors.
What is Data Analytics?
It’s important we first remove the jargon and buzzwords so we can focus on what matters. The definition is right there in the name:
Assembling all of the possible information at your disposal and then sorting through it in hopes of finding the “Ah-ha! Moment” that helps you make the right decision for your company.
There are four primary types of analytics: descriptive, diagnostic, predictive, and prescriptive. We like to use the Gartner Maturity Model as a teaching tool.
Even a one-person startup can take advantage of data analytics. Say you wanted to make handmade masks and sell them to your neighbors at a community center. After selling them for a month, you’ve collected 4 weeks of data.
Descriptive analytics tells you “What happened?” It answers questions like:
- How much money did I make?
- What masks did I sell the most of?
- Who did I sell the most masks to?
In terms of sales, you made $20 of profit the first week, $50 of profit the second week, $100 the third week, and $10 the fourth.
Diagnostic analytics tells you “Why did it happen?”
- In the third week, I sold superhero-style masks mostly at an annual back-to-school event for parents.
- With the fourth week, the cost of the superhero mask material shot up because of a shortage in supply, which caused my supply of popular masks to dwindle.
Predictive analytics tells you “What will happen?”
- Most students are already in school now, so they may not need additional superhero masks in the upcoming weeks.
- Additional analysis is hard because you only have 1 month of data.
- If you had years of data (hopefully not, because that’s a long time to be wearing masks), then you could identify seasonal trends and more.
Prescriptive analytics help you determine “How can we make it happen?
- You need enough data collected over long stretches of time to make this come to life.
- This combines the analysis of the first three levels so you can actually impact the course of events, instead of merely responding to what happens.
In fact, business leaders have engaged in analytics for centuries. You just have more data and better computational tools available to you now than ever before. Thus, when you think about it in such terms, data analytics begins to just feel logical and natural, which hopefully reduces the feelings of being overwhelmed.
What are Data Science and Big Data?
Don’t get intimidated by these terms. As we’ve discussed before, data science and big data are complementary concepts:
- Big Data: gathering the information from as many relevant sources as possible
- Data Science: studying the information by cataloging it into buckets for future study
You need data science to make sense of big data, and then you have to analyze that data to figure out how you can use it to help your business grow.
Ultimately, effective data analytics for your business comes down to how you treat your data.
#1 The Quality of Your Data Matters
Businesses collect a lot of data from their current customers, and they pay a ton to receive data on prospective ones. But if you’re not really looking into the information or if you don’t have a good way to make sense of it, you’re wasting time, money, and resources.
A common barrier to entry is the “cleanliness” of your data. It is difficult to get valuable insight without first making sure that your data makes sense and is consistent. It’s also useful to identify gaps in your data, especially if you want to combine data from two or more different systems for more insight.
In short, business analytics provide insight into what you’re doing right AND wrong so that you can do more of one and less of the others. By understanding your data, you can apply it to your business goals. This could be to increase your net profits, lower costs, conduct more efficient projects, or whatever makes sense to your business objectives.
#2 Begin with What Makes Sense
If you’ve never used data analytics for your business, don’t fret! Instead, focus on doing one thing at a time with your data. This is definitely a situation where dipping your toes in the water is more beneficial than jumping into the deep end before you learn how to swim.
If your company has no prior background, experience, or capabilities with analyzing data, your best entry point will be to connect your data to a visualization tool.
- Start by cleaning some of the data up, which typically means ensuring all your data is formatted properly.
- Next, plug it into a report — even the most basic Excel spreadsheet will do.
- From there, you can examine trends of what has happened so you can determine what might happen in the future.
It’s that simple.
For a young company that wants to start off on the right foot with your data, we recommend using descriptive analytics as your foundation. Not only do you lack the necessary first-person data for deeper forms of analytics, but you need to confirm you know what’s happening with the fresh data you are collecting. From there, you can progress along the experience curve as your company and data mature.
#3 Set Clear Goals for Your Data
It’s essential that your company set helpful goals about how you want to create, use, and interpret data. These goals should match where you want your various departments to be in the future so you can start moving toward them.
Let’s return to our mask-selling example earlier. Based on the data we collected and the descriptive analytics we performed, we could pursue the following goals:
- Increasing overall items sold
- Increasing gross sales
- Increasing sales of top-performing masks
- Bringing up sales of low-performing products
- Identifying your core audience
- Diversifying your customer base
As you collect more data, you could also begin deeper analytical modeling with goals such as:
- Calculating the best times for future sales
- Determining when and how to offer discounts for high and low performers
- When you should launch new products
You must find ways to crack open your business data so you can set the goals that push toward fresh growth.
#4 Use Data with Wisdom and Purpose
Whether or not they’ve taken advantage of business analytics yet, older and more mature companies still have a ton of available data. If your business fits that description, you should start along the path of developing out descriptive and diagnostic reports and insights. From there, you can move toward predicting what will happen in the future, and even figuring out how to make it happen.
Having access to all the first- and third-party data in the world won’t help your business if you don’t do anything with it. You must develop clear tactics, strategies, and objectives for how you want to achieve your goals and make your business operations come to life.
Data Analytics Will Transform Your Business
The real benefit of data analytics is it pushes your company to be proactive instead of reactive. By giving you specific lines of sight into your data from the parameters that make the most sense to your business, you can use data to your tactical advantage to make forward-thinking decisions. When you put business analytics into action, your company takes its collected information out of a vacuum, adds real-world context, and develops a clear course of action for the next weeks, months, and years.
If your business wants to take a big step up in performance, consider EAG 1Source. We offer extensive back-office solutions that include working with data analytics experts who can extract value from your data and set you up for success. Contact us today for a consultation.