"The currency of the digital age is to turn data into information, and information into insight,” says Carly Fiorina, the former CEO of HP.
According to Forbes, 2.5 quintillion bytes of data is created each day at our current pace, but that pace is only accelerating with the growth of the Internet of Things (IoT).
Over the last two years alone 90 percent of the data in the world was generated.
The distinction between business intelligence and data analytics is simple: Business Intelligence is how information is graphically displayed to show key information to the right person at the right time. Data Analytics is how you go about creating and gathering the information for users to get that data in the first place.
You have all sorts of information from clients, employees, transaction histories, and more. The term “data analytics" refers to your use of that data. Once you've analyzed your data, you can use that information to update your processes, adjust business approaches, and make other changes that will help your company. Often, people who think of data analytics imagine big corporations. However, data analytics isn't just for those big businesses. Small and medium-sized businesses can use data analytics to their advantage. Data analytics for small businesses is crucial. The key is having the right data analytics tools.
So, how do these things actually work? It starts with a query. You query your business's database and then collect the relevant information. With that information, you can look at the patterns and make your business decisions. With data analytics and business intelligence, you can figure out how to maximize profits, save money on overhead costs, and make your company as efficient as possible. The uses are almost endless. The more experience you gain, the more creative you can get with your data. Of course, this process takes a lot of work and persistence. That's where data analytics software comes in.
Before you choose your software, take some time to explore data analytics a little further. If you've read the above paragraphs, then you know that data analytics comes in different categories. You've seen some of those categories up close. The next few paragraphs will help you go even further in your exploration. Once you've made sense of all the buzzwords, you can make the most of your self-service analytics tools.
If data analytics is your end product, data analysis is the effort that you put in to get that end product. When you use data analysis, you apply a systematic approach to your data, and then you look at that data from lots of different angles. This often involves using a self-service analytics platform or other self-service analytics tools.
Sometimes, companies dive into big data analytics when making business decisions. Big data analytics works almost exactly like ordinary analytics. Like ordinary analytics, it involves analyzing and using data to make decisions for your business. As the name implies, however, it involves working with "big" data. So, what makes data big? The difference is in the amount of data and in the complexity of that data. If you pull data from your own company, you have regular data. If you pull data from country-wide statistics, then you have big data. You can use big data analytics to enhance your business practices. For example, you might look at a country's purchasing habits and use that information to adjust your marketing campaigns. You might analyze your target demographic's social media choices and then make your own social media choices based on that information. Once again, you have almost limitless possibilities. The more you work with big data analytics, the more you will discover alternate uses.
When you use data analytics software, you can mine your data quickly. Data software can accomplish in a few seconds what may take a human hours to do on his or her own. Software can help you achieve quick data preparation, which gets your data ready for mining and grouping. Business owners love data analytics software. The Business Application Research Center found that data prep software consistently exceeded business owners’ high expectations. Your company likely generates data from several sources. With the right software, you can query any of those sources to get the information that you want.
Businesses use data analytics of all sizes for different reasons. According to Forbes, here are just a few of those business use cases:
As we said, businesses use data analytics for plenty of other reasons, too. We've only listed the most common ones. With the right data mining opportunities, the benefits are virtually endless.
You have lots of different options when you pursue data analytics solutions. Your choice will depend on your own needs and preferences. Remember that not all data solutions are the same. Consider your own business uses for data software, and choose the solution that will help you manage those uses efficiently.
Now, let’s take a closer look at the different types of data analytics. Once you understand the different types, you can take advantage of your self-service data analytics or any other tools you may choose to use.
A lot of data analytics begin with descriptive analytics. You can use descriptive analytics in your first stage of data gathering. Descriptive analytics uses historical data or data that concerns specific events. This is the simplest form of data. It merely tells you whether or not something happened, but it doesn't go any deeper than that. Your descriptive analytics will help you piece together a full idea of the events in question. Like with other types of data gathering, you can organize this information and notice consistent patterns. Companies often use descriptive analytics as a foundation for further data analysis. In fact, you can't perform other types of analytics without descriptive analytics. Once you gather your initial information, you have a springboard into even more data strategies. The path you take after your descriptive analytics is up to you. With time, you'll figure out how to forge those paths. With the right software, those choices become much easier.
Predictive analytics takes diagnostic analytics one step further. You start with the why information provided by diagnostic analytics. Then, you use that information to recreate certain conditions and make predictions based on your recreations. For example, let’s go back to the hypothetical retail company that we mentioned in the last paragraph. This company used diagnostic analytics to find out why they experienced a surge in sales. They guessed that they probably experienced the surge because they gave away door prizes on the first day of summer vacation. With predictive analytics, this company might try a similar promotion during spring break or another holiday that results in school closures. They’ll make predictions based on their previous data collection. With these methods, that store is using predictive analytics.
If descriptive analytics give you the what, then diagnostic analytics give you the why. Diagnostic analytics is a more advanced form of analytics than the descriptive method. The term “diagnostic” implies that something went wrong, but that’s not always the case. True, companies can use diagnostic analytics to find out why a problem occurred. However, companies can also use diagnostic analytics to examine things that went well. For example, if a retail company has an unexplained surge in sales, the owner might use a funnel 3D tool to find out why.
Prescriptive analytics is the final and most advanced stage of data analysis. With prescriptive analysis, a company’s researchers will take all of the information that they’ve learned from the previous analytical stages. From there, they’ll determine the best course of action for the future. Let’s return to the aforementioned retail store. They’ve determined that the timing of their giveaways did indeed cause the surge in sales. Prescriptive analytics will help them decide when to use more giveaways in the future. We’ve used a very simple example, but prescriptive analytics can often involve more factors. With prescriptive analytics, companies can examine all of those factors and choices and figure out the best way to move forward.
Once again, when you choose your data analytics software, you want to make sure that you're getting the best option for yourself and your company. So, how do you know that you're making the right decision? You're already making a great start by learning all about different types of analytics. Deepening your knowledge will give you a better sense of what you need. Additionally, you can ask the following questions when you choose your data analytics software.
We already explored business intelligence above. However, now that we've explored the different facets of data analytics let's take this opportunity to dive more deeply into business intelligence.
Data analytics gives you a lot of tools that you can use to help your company. Business intelligence is the use of all of those tools. When you practice business intelligence, you use your available methods, software, and strategies to keep an eye on your own company and how it performs in the broader context of the business world. What sort of tools and methods can you use? Just like with broader data analytics, you have all kinds of options. Take a look at the paragraphs below to get a broader idea of the routes you might take with your business intelligence.
The term “business intelligence system” refers to the tools that you use to gather business intelligence. In the above paragraph, we described business intelligence as the use of tools and methods to help your company perform well. Business intelligence systems, on the other hand, are the tools themselves. You'll notice a lot of overlap in these terms if you do further research. Essentially, business intelligence is like the act of gardening. It's the process of helping something grow and change. Business intelligence systems are like the spades and backhoes you would use while tending that garden. Data-gathering software is an example of a business intelligence system. When people mention business intelligence systems, they're generally referring to software.
Business intelligence strategy is precisely what it sounds like: the development of a business intelligence method. This process can involve a lot of different steps. For example, you might monitor the current business intelligence trends using pivot tables or gauges. You might also develop a strategy that includes regular reporting, intelligence meetings, and other strategic moves. Data analysis beginners often become overwhelmed by all of their options. If you're new to the world of business intelligence, don't get discouraged. With some practice, you'll eventually develop a strategy that works for you.
The term “business intelligence solutions” is generally used interchangeably with “business intelligence systems.” In fact, you may often read those terms together: “business intelligence solutions and systems.” In any case, “solution” usually means “software” in this context. Again, it refers to the tools that you use to gather and use your data. Sometimes, the word “solutions” refers to the specific tools you can use within your software. A text box, bound image, or geopoint map can be a solution.
Now that you've taken a deep dive into business intelligence let's look at the different types of business intelligence that you can use.
A business intelligence dashboard gives you a simple way to visualize your data. A dashboard puts all of your relevant data onto a single screen. You can organize your data into clearly-defined blocks. Dashboard information can include things like key performance indicators, information for specific dates, and more. KPI dashboards, in particular, let you see right away whether or not your business is hitting the right marks. Dashboards come with a lot of advantages. For example, the simple organization lets you access your information quickly. Not all dashboards use the organization you see above. Other dashboards may use tools like choropleth map, range filter, combo box, or or list box. If you prefer branches over boxes, then the tree view will work well. A web view works well for those who like to notice connections. You can choose how you organize your dashboard. You can also share that information with other people who may need it. If you control the dashboard, you get to choose how and when you share. The right software, for example, will display relevant information on a mobile phone just as easily as it would on a desktop.
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Visualizations, as you might have guessed, are business intelligence tools that let you visualize your data. Examples include pies, a chart, a treemap, a scatter chart, and other visual aids. Visualizations help you make the most of your data. Business intelligence is all about noticing patterns, and visualizations are the easiest way to help you see those patterns. As a result, you'll make faster, more informed decisions. Furthermore, when you want to share your data with others and discuss that data, visualizations make the process easy and straightforward.
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Data reporting is one of the best features you can get from your data software. This feature will save you a lot of time. You can set your software to keep track of specific data points. Then, you’ll receive automated reports on that data. Data reporting is perfect for those who want to track specific data over a long period of time. Reporting also helps you share your data with those who need the information. Often, that data reporting comes in the form of visualizations. You can organize those reports however you’d like, too. Whether you want to see that information in a grid table, bubble map, pie map, or other form, the right software will organize your information in whatever way works best for you.
We mentioned predictive analytics above, but let’s look at it in the context of business intelligence. As we mentioned, predictive analytics involves learning why a set of data turned out a certain way. As a type of business intelligence, predictive analytics uses tools like reporting and visualizations to help its end users make the best choices.
Data mining is the act of using your data to generate even more data. As the word "mining" suggests, you're essentially "digging" for insights. When you use data mining, you look at large portions of data. From there, you dig deeper into that data to learn about its specific points. In business intelligence, this means looking at your own large sets of data to find smaller patterns.
Looking for Data Analytics & Business Intelligence tools that will take your company to the next level? Have any questions about data analytics and how they can help you? Then get in touch with us at ChristianSteven Software today. We’d be happy to talk with you.
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