Companies have never had so much power in their hands: for the first time ever, they have incomparable amounts of data to use as they see fit to make informed decisions. But the amount of data they have is only increasing; companies will have to continue harnessing that data to ensure that they are making the best possible decisions for their team. And how can they do that? What’s the best way to use data to make good decisions? How can data be processed safely? We’ll cover these questions and much more in this post.
Data Analytics: What is it?
Simply put, data analytics collects raw data and turns it into valuable information. This means that those who have the data make sense of it and analyze it to make the best possible decisions for the business. There are four types of data analytics: predictive data analytics, prescriptive data analytics, diagnostic data analytics, and descriptive data analytics. Let’s see how they differ:
Predictive data analytics: this is the most common type of data analytics, used to identify trends, correlations, and causation and is made up of two specific sections: predictive modeling and statistical modeling.
Predictive modeling: a campaign could look at average demographics to anticipate campaign results.
Statistical modeling: statistical modeling would then be used to make sense of the data and draw conclusions.
Prescriptive data analytics: here, big data and artificial intelligence come together to predict outcomes and identify future actions, using both optimization and random testing.
Optimization: using optimization, data analysts can find specific areas that need improvement and work on that, keeping quality actions in place and just correcting areas that can be improved upon.
Random testing: this can be used to create new, previously untried combinations to see if something unexpected happens.
Diagnostic data analytics: the third type of data analytics is definitely the least exciting, but is extremely important for improving; diagnostic data analytics looks at past results to make decisions using discover and alerts and query and drill downs.
Discover and alerts: these preset alerts help you anticipate problems before they occur, using past data to flag potentially disruptive situations.
Query and drill downs: these further break down data, explaining why something occurred using data.
Descriptive data analytics: the other forms of data analytics would be useless without descriptive data analytics; having important data is great, but only if it can be properly presented using ad hoc reporting or canned reports.
Ad hoc reporting: these reports are created on the fly to address a specific issue and help provide in-depth data to fix a problem.
Canned reports: this pre-designed report is regularly issued, such as a monthly or weekly report, to give an overview of a specific topic.
Benefits of data analytics
Despite the most commonly understood benefit of data analytics being using high-quality data to make the best decision, data analytics actually offers many advantages that are of great value to your company. Let’s discuss.
Personalize the customer experience
By collecting data from lots of different channels, you’ll be able to gather more and more information on your clients, helping personalize the customer experience even further. More information and data on customer behavior can make all the difference when it comes to making profits.
Assist in large-scale business decision-making
If the company has been witnessing financial losses or decreased productivity, data analytics can be used to analyze both past and future data to draw conclusions. Are certain employees performing better than others? Is the company’s remote work policy hurting overall productivity? Looking at company-wide data can help influence business decisions.
Data can show where you need to improve or where efficiency and productivity are limited; seeing the data of how each part of the business is working can help you identify problem areas and use data to make high-quality decisions, especially in sectors where lots of change is experienced, such as retail (demand varies significantly season to season).
Prevent risks and tackle issues
Preventative data analytics comes into play here and is a great option for companies that are looking to avoid the potential risks of customer or employee theft, uncollected receivables, employee safety, and legal liability. Using predictive models, companies can predict future issues and address them before they happen. And when problems do arise, analysis of past decisions can suggest the correct way to handle them.
As more and more companies use data to make decisions, the risks of cybersecurity are growing and becoming increasingly present. Using data of previous breaches or cyberattacks, corporate cybersecurity teams can see what went wrong, stop the attack, and take action to avoid a similar problem in the future.
Data in business: how does it help?
To make smart decisions, you must use data. And as the market moves at an incredibly fast pace, data can help you:
Determine who your customers are
Decide how to reach your customers
Determine your specific market
Let you know what’s currently happening in the market
Suggest future predictions
Sounds like a lot, right? Well, it is. Data analytics can be the difference between making a smart business decision and a damaging one. After all, with proof of why something is working, you’re better equipped to make future decisions as well. Now that we’ve covered what data analytics is and its different kinds, let’s discuss how to run a quality data analysis in just five steps.
How to Use Data to Make Decisions
When a big decision is coming up, you might be torn on how to make it. Do you look at past data? Or future predictions? What about your gut feeling? It can be tough at the moment, but if you use data to make decisions, you’ll be better off and in just five steps, you can do just that.
Step 1: Know what you want
Okay, this seems like an obvious step, but it’s quite important: you can’t reach your ideal solution if you don't know what you want! Before you take any further action, make sure you create a clear vision of what you want to achieve with this decision together with your company’s future vision. If you’re unsure, take a look at your company’s yearly goals and key results to help guide you.
Step 2: Find data sources
Now that you have your vision clearly defined, it’s time to find places from which to extract data. After all, with just a vision and no data, you won’t be able to make a smart decision. To collect data, you have to decide the metrics you want to explore, such as the gross profit margin, return on investment, productivity, total number of customers, and recurring revenue. To then collect your data, reporting tools such as Microsoft’s Power BI let you collect data from multiple sources to have a global picture of your company’s status.
Step 3: Organize the data
Collecting data is great, but only if you know how to use it. In fact, the data you collect is actually useless unless you know how to put it into use. Once you’ve collected your data, make sure it’s clearly displayed and visualized so that data analysts can draw smart conclusions from the data. Executive dashboards present in the data in such a way that it’s clear and easy to read.
Step 4: Analyze the data
You’ve gathered your data and displayed it in a way that makes it clear to everyone: now what? Well, it’s time to actually analyze your data and draw conclusions that will guide your decision making process. Use both research that you’ve gathered yourself and from external sources to make the best possible decision.
Step 5: Draw conclusions
As you analyze your data, you’ll start to draw conclusions, but it’s important to make this an entirely separate step so that your conclusions stand on their own. While drawing conclusions, ask yourself the following questions:
What conclusion am I drawing that I already had?
What conclusions are new?
How can I use this new information to meet business goals?
The answers to these questions will help you successfully make your decisions and then bam! You’ve completed your data analysis.
Your Future in Data Analytics
Have we convinced you that data analytics is the career choice for you?! After all, the US Bureau of Labor Statistics predicts that the demand for data analytics roles will grow by 23% from 2021 to 2031, much, much faster than the 5% increase in other industries. We thought so–at Ironhack, we offer a Data Analytics Bootcamp to prepare you to jump into the field, teaching you everything you need to know to be a competitive candidate for entry-level data analytics jobs in practically any sector. And once you’ve got a bit of experience under your belt, the world is your oyster: you can choose to specialize in a specific field or advance in your preferred role. With highly-competitive salaries and lots of room for growth, there’s no better choice.
Check out our Data Analytics Bootcamp today and dive into tech–you’ll thank us later!