Today’s world is becoming increasingly digital, which is both a blessing and a curse. On the one hand, we can build apps, connect tools, acquire new customers, and drive revenue with just a laptop and an internet connection. On the other hand, there is data - vast amounts of data piling up about everything we and our customers do every day, all for us to discover.
If we can better understand that data, we’ll know more not just about our businesses, but also about our customers - the way they behave, shop, and interact with us. And the starting point to understanding this wealth of information is data intelligence.
Data intelligence is the process of getting meaningful information from large datasets. This is usually done through a number of different tools, that allow you to gather the data in one place, clean it, apply different analyses, and then perform data visualization. In short, it’s data about data or metadata.
Data intelligence allows businesses to become more data-driven, efficient, and in tune with their customers, as well as understand the key metrics and KPIs regarding their products or services. With artificial intelligence and machine learning, data intelligence is becoming more accessible and cheaper for businesses of all sizes.
Moreover, data intelligence helps companies answer important questions about their data, such as where it comes from, what the data quality is, what the best use cases are for that data, and many others.
Here is an example situation: an e-commerce company uses data intelligence to extract purchase data from the past year. Through data analysis, they create datasets that explain:
These data insights provide massive business value and thanks to modern data platforms, they’re relatively easy to find and use. Data intelligence principles are the same, no matter if the data is unstructured (in a data lake) or structured (in a data warehouse).
If you’re just thinking about getting started with data intelligence, it may seem like a cumbersome process. However, big data is useless if it is not properly managed, cleaned, and analyzed.
Here are some of the main benefits to keep in mind.
If you want to become a data-driven business, staying on top of what you do as a business and how your customers interact with you is crucial. By collecting and analyzing data from various datasets, you can more accurately predict what the future holds and what you need to do to decrease churn, increase customer satisfaction, etc. And thanks to modern BI and data visualization apps, you no longer need to have a data science expert on board.
Data for business intelligence can show you what you can expect based on the analysis of your previous efforts. For example, you can take a look into your busiest months in terms of orders and accurately predict when you will need a few sets of extra hands in sales and customer support.
Today’s data analytics platforms allow you to automate the way you aggregate, store, analyze and visualize data. This means that you don’t have to wait for days or weeks to find out what’s happening with your app or your customers. You get the data instantly thanks to proper data management and you can act in time.
Data analysis can tell you many things - for example, how many of your customers switched from a competitor to you and precisely why they did it. It can also show you the main items your offer is lacking and that would make your customers consider a competitor. These are just some of the many ways you can stay ahead of the curve by doing nothing more than reviewing your existing data catalog.
The two terms are often lumped together and sometimes used interchangeably, and it’s important to know the difference between them if you want to get useful results out the data you have available.
Data governance happens before data analysis - it entails the criteria for selecting the most relevant data for your business. In other words, data scientists have immense pools of data and determining which data assets matter is crucial.
In short, data governance means finding out:
In other words, data governance helps you determine what the best data points are, what you’re going to do with them, and who the responsible persons are for that data.
There are three main types of data intelligence that you should be familiar with.
Descriptive data intelligence describes what is going on with your data and assesses business performance. Think of a healthcare app that needs curated data about the patients with the most app use during the year.
Predictive data intelligence uses readily available data to make predictions about future performance. For example, a marketplace app offering predictive data about the best types of customers that should bring in the most revenue in the future.
For example, you can take a look at dashboards made in the data intelligence tools such as Cumul.io to get a good idea of what the future looks like.
Diagnostic data intelligence takes a deep dive into your data sources to find out what has gone wrong and determine why it’s happening. For example, you have a dating app that has a high churn rate a month after signup. This type of data can help you make informed decisions more quickly.
For example, we have a customer who uses analytics dashboards to track the changes in electricity and water consumption in buildings. That way, they can immediately spot leaks and problems instead of waiting for the bill at the end of the month to find out that there was a problem.
Data intelligence is the starting point of every good digital transformation. It can help your business make data-driven decisions, eliminate inefficiencies, forge better relationships with customers and make additional revenue.
And once your data is in order, you need to visualize it to understand it more quickly. Or even better, show it to your customers in the form of an embedded dashboard in your product.
Does this sound like something your product needs? Sign up today for your free trial of Cumul.io and start visualizing your data!
