The Complete Data Discovery Process for Better Data Decisions

Is your company thinking of implementing AI tools to be able to work faster and with better insights? Or perhaps your team has decided to move to the cloud because it’s a more scalable and less pricey option? Both are smart moves, but without a clear understanding of your data, these projects can easily go off track.

Having solid data infrastructure is all about making the right decisions about where your data lives, how well you understand it, and how you use it. That’s where data discovery plays a crucial role — it’s the process of exploring, organizing, and analyzing your data to find what’s useful and what’s missing. 

CHI Software is here to help you understand why getting your data right from the start is critical to project success. We’ll also share tips from data engineering experts on how to create a robust data discovery life cycle while avoiding common mistakes that slow down data-driven growth.

Article Highlights 

• Data exploration techniques make your data accessible, so that you aren’t sorting through spreadsheets struggling to figure out where information is coming from;
• Relying solely on the tech team or data scientists isn’t enough – business input is just as important. Your business goals should guide the data discovery process from the very beginning, so that technical experts can focus on the insights that truly matter for your company;
• You can use Power BI, Tableau, Google Looker Studio, or Kibana to make insights actionable and easy to understand. 

How Data Discovery Drives Project Success

You don’t need to go as far as making major tech transformations or overhauling your entire data governance system to see the importance of the discovery process. Let’s see what you can change for the better right from the start.

Clearer Priorities

What is the foundation of your company’s plans today? How long does it take to gather and analyze all the important data to make a decision? We start with these questions because we know they are business pain points, and CHI Software has seen how exploring the right data can solve them.

Having a grasp on your data discovery life cycle means being able to quickly explore all data from every source available to your business, research and identify the main data sources, and verify their reliability. 

In an ideal scenario, you want to be able to seamlessly and continuously view all the factors that influence your daily decisions – like traffic, channels, and ad leads.

Reducing Risks

Imagine a financial application that lets a sync error go unnoticed, leading to outdated customer data, which then causes a failure in reporting or regulatory compliance.

Proactively reviewing your data can reveal critical errors and compliance concerns before they escalate – and that’s why it’s essential to maintain control over data quality in order to avoid acting on false or outdated information.

Smarter Tooling Decisions

Understanding your data according to volume, type, and arrival speed is the first step toward making it useful for your technical team. This understanding will help you select the best tools and systems for your current tasks. 

For example, if you know that you have a large amount of rapidly changing data, then your technical team need not waste time thinking about what type of storage to use – they should know right away that the best option here will be cloud storage combined with fast processing tools. 

Getting Everyone on the Same Page

It’s common for business and tech teams to speak different languages. One team might talk about “customer segments,” while the other says “user groups”, but both are discussing the same thing. A structured data review helps align these perspectives. 

Collaboration becomes easier when everyone accesses the same data – and it’s clear and well-organized. Handoffs are smoother, misunderstandings are minimized, and the whole project moves forward faster.

However, data discovery is a process, and you need to build your practices correctly to get maximum results. 

Data Discovery Steps You Can’t Skip

Here is the list of essential steps that can help you save time and reduce risks when creating a data discovery solution.

Step 1. Set Goals

Before you start working with data, clearly define what you are trying to achieve. Your team may choose intentions like: 

• Reduce monthly operating costs by 10% within 4 months by automating manual reporting tasks;
• Fix 95% of data errors across departments within 60 days using a new data quality framework;
• Boost upsell revenue by 10% over the next 90 days by identifying high-value customer segments.

With a clear goal in mind, you will know what information you want to see, and your tech team will be able to create a data engineering strategy much faster.

Step 2. Identify Data

You can’t improve what you don’t know you have. So, the next step is setting up a data inventory process to find out what information you are storing and where. Your sources could be a CRM platform, website analytics, sales reports, or tools supported by big data services.

Data source identification directly impacts which of the following data discovery methods you’ll choose.

What Manual Work with Data Looks Like

When you explore your data manually or with minimal tooling, the process often includes:

1. Collecting data from different places (a CRM, website, Excel files, etc.),
2. Checking what each file or system contains,
3. Removing duplicates, fixing errors, and unifying formats,
4. Creating visual reports like graphs or charts to spot trends.

For small businesses or teams with limited data, manual methods of data discovery can work – but they tend to take greater time and effort. Bear in mind that you also need someone who understands the data well to be involved in the process – because they will be able to spot mistakes or inconsistencies no one else can.

Data Clarity at Scale with Automation

Automated tools are the best choice if your company uses multiple data systems, needs reliable information right away, or is lacking a dedicated data expert.

Here’s how automation tools work with your data:

1. Scanning all your systems to find data;
2. Flagging data risks (missing values, duplicates, outdated information);
3. Unifying data from different formats and systems;
4. Classifying data based on type, sensitivity, and usage;
5. Suggesting patterns or insights using AI;
6. Mapping data using metadata to show where it comes from and how it’s used;

Step 3. Prepare Your Data

Providing data visibility is your next big step: your team should be able to find and use data they need, without spending hours searching. 

1. Start with data cataloging. A data catalog organizes all your datasets in one searchable location, including all the necessary details about them;
2. Define relationships between datasets to avoid data silos, which are one of the most costly mistakes a business can make with its data. Connect all the available datasets to see the broadest picture of your business processes;
3. Set data access rules so only the right people can safely view and work with the right data. 

Step 4. Explore Your Data

Once you have high-quality and organized data; what’s next? Follow tested by CHI Software approach to unlock insights:

• Browse your reports and look for trends. Power BI or Tableau creates dashboards and reports that are easy to read and understand; 
• Spot unusual behavior to prevent problems and notice opportunities. You can use Splunk or Datadog for anomaly detection and alerting on unusual patterns;
• Track progress over time, across teams, products, or regions. Sisense will help you compare KPIs, using interactive dashboards;
• Summarize the data in simple terms: what is happening and why is it important? We recommend using ThoughtSpot, Zoho Analytics or Narrative Science Quill;
• Link insights to specific actions – for example, adjusting a marketing campaign or changing how teams work.

Step 5. Generate and Share Insights

Next you will want to share all the insights you have found, using visual tools to help your team understand the data:

• Looker to explore data and create customized reports for different departments;
• Google Data Studio to create charts and dashboards from multiple data sources;
• Heat map tools such as Hotjar to visualize user interactions with your website;
• Miro or Lucidchart to map out workflows and action plans based on the data you collect.

And finally, as your business evolves, so do your needs. Our advice is simple: make it a habit to review your data regularly, revisit your business objectives, and refine the tools and methods you use.

Common Pitfalls Companies Face During Data Discovery

Just like any other business process, getting to know your data has its hurdles. In six years of working with data, CHI Software’s experts have seen and tackled all sorts of roadblocks. 

Pitfall	Tip from CHI Software
Overlooking data mapping	Use Collibra or Apache Atlas to track data sources.
Ignoring unstructured data	Implement NLP tools like AWS Comprehend or Google NLP to analyze unstructured data.
Forgetting to refresh your data	Refresh your data regularly, automate this process with Apache Kafka, AWS Glue, or Fivetran.
Skipping scalability planning	Choose сloud-based platforms дшлу Snowflake, BigQuery, or Databricks that scale with your data.

Overlooking Data Mapping

Data mapping means having a clear map of where each piece of your data comes from, where it goes, and how it changes along the way. If you skip this step, you might face:

• Losing track of sensitive information;
• Breaking privacy laws (like GDPR) without realizing it;
• Failing an audit because you can’t prove where your data came from;
• Having trouble with error correction, since you don’t know what was the cause.

To avoid all these pitfalls, make sure you use reliable tools like Collibra or Apache Atlas to track data sources and keep records of their origin.

Ignoring Unstructured Data

Don’t underestimate unstructured data: patterns unearthed from your chat logs with customers or reviews can significantly expand your team’s view of the current business situation.

Natural language processing (NLP) models and sentiment analysis of customer reviews, social media, or support logs using AWS Comprehend and Google Cloud NLP can help you uncover valuable patterns and trends.

Forgetting to Refresh Your Data

Outdated data can mislead your strategy. If you launch a new product, you need to make sure that all the data about it and customers’ reactions to it will be included in your dashboards right from the start.

So, do refresh your data sources regularly. It will be easier to monitor your data if you automate the updating process with tools like Apache Kafka, AWS Glue, or Fivetran. 

Skipping Scalability Planning

As you explore and organize your data, always keep your business growth in focus. When systems can’t grow with your business, your ability to explore data slows down or stops completely. 

Choose tools that scale with your data. The best options here may be cloud-based platforms Snowflake, BigQuery, or Databricks, which are built to handle large growing datasets and allow you to adjust computing power on demand. 

How Data Discovery Empowers AI, Cloud Migration, and Analytics

Understanding your data is the foundation of almost any project, from big data processing to migrating to the cloud. At CHI Software, we have seen first-hand what happens when companies skip this step: they usually face disappointing results and high costs.

Providing AI with Clean and Structured Data

AI can only learn from your data – it’s the foundation for everything. That’s why your information needs to be clean, labeled, and relevant to your goals. However, many companies jump hastily into new AI projects without understanding their resources, which can lead to problems later down the line.

For example, CHI Software helped a retail company clean up years of fragmented records on sales. The predictive model we developed eventually delivered 30% better accuracy, but only after we laid the groundwork with proper data assessment and structuring.

Preventing Cloud Mess

Moving to the cloud brings scalability and speed – but imagine the mess that poor-quality or unorganized data can create. Because you pay for cloud storage by usage you could end up paying for that mess.

Before migrating, taking a closer look at your data allows you to:

• Determine what to move and what to keep;
• Create a data catalog for a smoother transition;
• Plan cloud storage based on actual usage and access patterns.

We use cloud tools such as AWS Glue, Google BigQuery, and Azure Data Factory to create pipelines that collect, organize, and prepare the exact data you need.

Getting Consistent Numbers for Analytics

Dashboards and analytics are only as good as the data behind them. Without clarity upfront, you risk acting on numbers that don’t add up.

At CHI Software, we use several tried-and-true strategies that make your analytics faster and easier to use:

• Setting up automatic data validation using Great Expectations;
• Centralizing data sources using ETL tools Talend, Airbyte, or Apache NiFi;
• Creating role-based dashboards using Tableau, Power BI, or Looker, based on the data that is central to your goals.

Conclusion 

Data discovery helps you see what information you work with, understand your customer’s sentiment, and answer any business questions that may arise. 

We’ve seen across projects that meaningful data insights aren’t reserved for analysts: when the whole team is aligned, outcomes improve dramatically. With proper preparation, the right tools, and a clear focus, companies can navigate their data more effectively and make stronger, insight-driven decisions.

If you want to feel confident at every step, it’s a good idea to team up with a reliable tech partner like CHI Software. We’ll guide you through the process, handle all the technical work, launch the project, monitor its progress, and make sure your team knows how to work with the new solution.

FAQs

• What are the early signs that we need a formal data discovery process?

  Pay attention to the following issues:
  - Delays in decision-making because no one is sure what data is accurate or relevant;
  - Repeated errors in reports or conflicting KPIs between departments;.
  - Teams relying on assumptions rather than data when planning campaigns or changes;
  - You are collecting information, but aren't making use of it.

• Can we skip data discovery if we already have data analysts and engineers on board?

  Not really. Even companies with strong technical teams risk overlooking critical insights if they don’t understand their data. Analysts and engineers may work with what is available rather than what is relevant or of high quality.
  
  A structured discovery workflow ensures:
  - Alignment between business goals and available data;
  - A clear understanding of data gaps, duplicates, and inconsistencies;
  - Efficient use of technical time and tools.

• How much time and budget should we realistically allocate to data discovery with CHI Software?

  We can determine the exact development time and budget only after understanding the size of your company, its data landscape and goals, but here are some general guidelines for you to know:
  
  Timeline: CHI Software can develop a small and medium-sized solution with limited data sources in two-four weeks, while large projects with complex data environments can take up to 12 weeks.
  
  Costs: The discovery starter pack includes an audit of existing data and a basic discovery plan, starting at USD 5,000. The full discovery with multiple sources and integrations starts at USD 25,000.

• What are the risks of starting AI or cloud projects without proper data discovery?

  Skipping the data discovery step can lead to:
  - Training artificial intelligence on poor-quality or biased data, resulting in inaccurate results;
  - Wasted cloud storage costs due to the transfer of excess or irrelevant data;
  - Inconsistent analytics results that confuse teams and delay decision-making;
  - Lost business insights hidden in unstructured or underutilized data.

• How can external experts like CHI Software improve our data discovery process compared to internal teams?

  Working with CHI Software offers:
  - Identification of blind spots that your teams may miss;
  - Use of data discovery techniques and tools proven by own experience;
  - Application of cross-industry knowledge gained in retail, fintech, healthcare, and other industries;
  - Deep research with a focus on your specific priorities.