Data is, without any doubt, the most powerful tool in a marketer’s arsenal. It has a proven track record for generating leads, predicting business trends, or providing insights that lead to better IT help desk services. And when it comes to data, there’s one topic in the market that we can easily call the hottest these days. That is the topic of data discovery.
Why is data discovery important, you wonder?
A data-driven business is 23 times more likely to attract new customers. The competition sure knows this. Today, 86% of insight-driven companies act on data intelligence continuously to boost their outcomes – and it works!
But, how can one use data to influence the decision-making process?
Data flows everywhere around us. You can find it in online systems, traditional transactional systems, social media channels, etc. It’s right there at our fingertips, but with so much information, we’d drown in data unless we use a smart way to detect the important information, analyze and use it.
The total volume of data was calculated at 64.2 zettabytes in 2020. By 2025, these numbers are estimated to grow to 181 zettabytes. If you feel like there’s too much information right now, only imagine what will happen in a few years.
This is where data discovery comes into the picture – and why it’s such a buzzing topic among marketers today. In this article, we’ll discuss it in detail and give you some tips on how to implement it.
Understanding Data Discovery
Data discovery is the collection and analysis of data from different sources. It’s a process that consists of carefully executed steps with the goal of finding useful information that can be used to improve a business strategy. Often linked to business intelligence, data discovery informs the decisions of companies by bringing together large chunks of data from siloed sources and extracting the important information.
To put it simply, when you implement a data discovery strategy, you are finding, cleansing, and preparing tons of data to use for your business. Data discovery helps make your data user-friendly and clean and basically – ready to use. The process can be separated into 3 main categories:
- Collection and preparation of data
- Visual analysis
- Advanced guided analytics of the data
The Benefits of Data Discovery
Data discovery comes with many benefits that expand beyond providing insights. Let’s go through them here:
1. Improved Compliance and Risk Management
As the collected data volume grows, compliance has moved to the top of the list of companies’ priorities. When you deal with large amounts of data, it gets harder to remain compliant. The only way to ensure this is the case is to assess and keep track of the data you collect and use. Osano’s article on what is data discovery lists compliance as one of the main reasons to implement one – and they aren’t wrong. With the use of this tool, you can run data processing impact assessments to continuously fulfill the privacy laws requirements.
2. A Big-Picture View of Data
Companies today juggle endless data, making it impossible to combine it and decide on what to use. Data discovery gives companies a big-picture view of the data they possess, allowing them to easily combine it in their analysis and decision-making.
3. User-Friendly Data Analysis
Thanks to data discovery, you can obtain data analysis that is user-friendly and understandable to all, including experts, sales teams, stakeholders, investors, and customers.
4. Automated Data Classification
Unless you opt for manual data discovery, which is a highly demanding action for most businesses, you can use a variety of tools to automatically collect and classify data based on context. You can also apply specific customization to the data being collected to make sure it is stored and categorized properly.
Some Use Cases of Data Discovery
Businesses use data discovery to achieve the following:
- Compliance with data protection laws
- Insights into business relationships
- Lead generation
- Investment opportunities
- Fraud detection
- Social media analysis
Let’s take a look at the most frequent use cases of data discovery.
1. Data Discovery for Compliance
Being compliant with privacy laws and other data protection regulations is vital for the survival of a business these days. Countries around the world have been tweaking and strengthening their laws regarding data collection and usage a lot these past couple of years. If you aren’t compliant with the regulations, you can lose a fortune on the hefty fees, not to mention ruin your reputation on the market.
2. Data Discovery for Lead Generation
When you map the data visually and analyze it, you can find insights that were otherwise hard to recognize. Businesses today use data discovery to find information about the targeted audience, and they use it to make important decisions for lead generation.
3. Data Discovery for Investment Opportunities
These days, companies compete in who will collect and better analyze data. They do this because data provides them with the most valuable insights about the market – investment opportunities. Whether they do this manually or resort to data discovery tools, information collected from customers and various other sources can generate the most promising investment signals.
The Process of Data Discovery: How to Implement one
Now that we’ve discussed the why, let’s move on to the actionable part i.e., the how. Below you’ll find the main steps of data discovery and learn how to implement one for your business.
Step 1: Preparation
Smart data discovery always starts with some preparation. For starters, you must identify the needs for data discovery i.e., what you want to achieve with it. This will tell you what type of data you need to collect for the goal you have in mind.
For example, if you are looking to learn more about the customer to improve your marketing strategy, you’ll need data such as customer communication, previous purchases, customer behavior, etc. The places to look for it would include your website, social media channels, and also the Web in general.
Without preparation, the data you collect will be endless and as such, convoluted. This will make it impossible to find any relevancy in the information you obtain or make any specific decisions.
Today, many software solutions can gather and prepare data for you in a short time, as well as categorize it based on your instructions. This way, you can collect a large quantity of data from relevant sources, combine and integrate it. We call this data crunching.
Step 2: Data Visualization
At this point, you should already have data collected and turned into a readable format. What comes next is the visualization stage or the visual data discovery. Also known as data mapping, this step refers to the display of ready data in a visual format.
One of the best features of data discovery tools is dashboard analysis. You should have in front of you a variety of visual formats including maps, graphs, and charts.
Visualization is a proven tool when it comes to easy decision-making. The data segmented and organized in a graph or a chart is a convenient platform that experts can use to make decisions.
Step 3: Data analysis
Once the mapping and visualization steps are complete, the last part is the analysis. Thanks to those graphs and charts, experts can now summarize the most important information and organize it into a readable format.
In most cases, this format takes the form of a summary or a spreadsheet. Experts use the data obtained and the visual formats to create descriptions. The descriptions and short and emphasize the key finding from the data discovery process.
Think of these as short stories, but stories that contain nothing other than the most valuable insights for your business. The descriptions pinpoint the most important data from information gathered from multiple sources.
It’s when you get to this point that you know what steps to take to improve your business.
Final Tip: Record Learnings and Repeat the Steps
Just like with other analysis processes, data discovery is an iterative process. It’s not something you do once and get it over with. Information for any business changes all the time, and what you learned yesterday might not apply a month – or a year from now.
Knowing this, you should record your learnings from the process and iterate. This is not a one-off task. If you want to use the potential of data and improve your business in the future, you should commit to continuous data collection, analysis, and interpretation.
When done properly, data discovery can give your business a competitive edge, as well as show you the mistakes you’re making right now.
Data is and will remain one of the strongest tools you have to grow in the industry, and while using it requires time and effort, it will all pay off if you do it right!