Data Discovery Explained

Learn data discovery basics like what it is, challenges, benefits, and best practices with MicroStrategy’s guide to data discovery.


Definition: What is Data Discovery?

Data discovery is the collection and analysis of data from various sources to gain insight from hidden patterns and trends. It is the first step in fully harnessing an organization’s data to inform critical business decisions. Through the data discovery process, data is gathered, combined, and analyzed in a sequence of steps. The goal is to make messy and scattered data clean, understandable, and user-friendly.

What are the Benefits of Data Discovery?

Data discovery provides a framework for firms to unlock and act upon the insights contained within their data. It transforms messy and unstructured data to facilitate and enhance its analysis. Data discovery allows firms to:

Gather Actionable Insights

From KPIs to trends and distributions, the data discovery process instantly unlocks essential information within unstructured data. Data discovery takes complex data and structures it in ways which allow users to visualize and comprehend the information within it.

Save Time

While analytical tools require data to follow a specific format, data is rarely stored to match this requirement. Data discovery aggregates and formats data from various sources and different structures to facilitate its analysis. This process provides analysts with the right data in the right format.

Scale Data Across Teams

Data is versatile and often contains information that can be used in several different analyses. Departments or users can leverage the same data in different ways to create unique insights. Data discovery facilitates this process and provides all users with a single version of the truth.

Clean and Reuse Data

Data analysis is a continuous process. As new data is collected, current data needs to be cleaned, stored, and made available for future use. Data discovery leverages both new and past data so it can be reliably reused at scale.

What are the Challenges of Data Discovery?

Successful data discovery relies on complete, accurate, manageable, and consistent data. Therefore, the major challenges in data discovery come from the collection, storage, and management of data.


Volume describes the enormous quantity of data created and stored, which can hamper analyses and introduce bias. Data discovery must overcome this challenge with strong data governance and capable technology.


As the number of data sources continue to soar, the increasing variety of formats presents a challenge in presenting data consistently. Successful data discovery requires strong technical skills to gather and clean data so it’s ready to be analyzed and consumed.

Data Velocity

Velocity is the speed at which data is created. Data discovery becomes a challenge as the rate of data creation grows by the day. New data must be continuously and correctly added to the repository to ensure timely insights.


Data must remain consistent across an organization so everyone within it is on the same page. Inconsistencies can result in poor decisions based on invalid or out-of-date data. It’s critical there be a single version of the truth as data is edited, pulled, and analyzed on a regular basis.

Data Management

Mismanaged data introduces several hurtles into the data discovery process. Data collected and stored inaccurately, illogically, or inappropriately can introduce errors into an analysis without the user’s knowledge. While issues of data management are often created far before analysis takes place, they pose serious hurdles within the data discovery process.

5 Steps of Data Discovery

Data discovery is a process that can unlock the value contained within data. It requires a significant investment in time, energy, and capital to perform correctly. Data enters an analysis unstructured and unusable. Through the data discovery process, raw data is captured and transformed to generate actionable insights and recommendations, informing both daily and long-term strategic decisions. While the details of each firm’s processes will look different based on the tools at their disposal, the following five steps should guide the data discovery process to leverage the full value of their data.

1. Connect and Blend Data

The first step in the data discovery process is to gather the right data in one place. Data, scattered across many sources, must be placed in a single area where analysis can take place. An operations analyst who wants to consider how weather trends might influence sales needs to blend weather data with sales data from the organization’s CRM. While independently stored, the data from these sources needs to be combined and treated as one.

2. Cleanse and prepare data

Raw data imported from different sources can rarely be analyzed as-is. Data needs to be cleaned and structured in ways that facilitate reliable and robust analysis. In survey analyses, marketing researchers must break down free-response answers to catch mistakes and categorize responses. A respondent who misspells their state or uses the abbreviation will need to be normalized for reliable analysis.

3. Share data

With data constructed and free from redundant or unneeded information, it must be shared with others in the organization. Even though this data is the single version of the truth, it can be leveraged in different ways. From personal perspectives, individuals can approach data from distinct paths and create unique insights. While a statistician and data scientist will analyze different aspects of data, they will each provide their own interpretation and analysis of it.

4. Analyze and generate insights

Individuals can read, analyze, and create value from validated data when there is a single distributed version of the data. Common tools include distributional analysis, predictive models, and market basket analysis. It is important to understand the type of insights generated by different analytical tools. A hospital might use a distributional analysis to better schedule their emergency room staff to coincide with patient surges, while a supermarket might use a market basket analyses to update their layout and promote product patterns.

5. Visualize Insights

Insights need to be communicated once they are found, and visualizations allow users to easily do this. A heat map of year-over-year sales quickly highlights regions that need greater attention. A scatter plot of sales by advertising budget allows marketing managers to easily comprehend the trend and allocate their advertising budget based on the period’s sales goals.

Data Discovery Tools Compared


Excel is the base model for data discovery. Its capabilities allow users to pull, prepare, and analyze data within one document. While it can perform all required functions, many tasks require the manual manipulation of data. The manual and non-customizable nature of the platform severely limits the depth of analysis which can be conducted. For basic KPIs and distributions, Excel is a great tool. However, it’s best to use a more advanced analytical tool if deeper analysis is required.


Favored by statisticians, R requires a more specialized skill set. While the platform is the least user-friendly on this list, it can be extremely useful. Since it is an open-source platform, R can explore, edit, manipulate, analyze, and visualize data in nearly every possible way. While the platform has powerful capabilities, its steep learning curve is prohibitive for most. The success of analysis in R is heavily dependent on your previous experience and knowledge of the R coding language.


With MicroStrategy, users can easily access, blend, and analyze data. From spreadsheets to big data sources like Hadoop, nearly any type of data source is accessible through the software. MicroStrategy easily links to data and automatically formats it with built-in data wrangling and parsing tools. Everyday users can benefit from automatic recommendations, while data scientists can leverage the full breadth of its capabilities.

MicroStrategy makes it easy to seamlessly share and distribute data throughout any organization. Users can upload dossiers to a central library where others can access, view, and analyze the data. This allows organizations to operate under a single version of the truth, because the data can be verified across all stages of the data discovery process.

With over 300 out-of-the-box analytical functions and a built-in integration to R, MicroStrategy offers users an extensive variety of tools. These functions can be leveraged by anyone across the organization to perform statistical calculations, forecast metrics, and develop predictive models.

An extensive library of visualizations and an intuitive drag-and-drop interface allow users to easily build dossiers to tell their story through data. Users can also customize their visualization to highlight insights or simply better represent their brand.


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