Every day around the world, leaders rely on immense reams of data, distilled down into a clear, easy-to-understand format, for intelligent insights and analysis that inform a variety of key business decisions.
And we are truly in the Golden Age of data. With the rise of artificial intelligence (AI), state-of-the-art data collection and analysis software and huge advances in data collection, businesses have a deep wealth of data at their fingertips.
But simply possessing this data isn’t enough to offer any significant benefits for businesses. Instead, industry leaders must rely on smart data discovery to identify and analyze the insights, meanings, patterns, trends and more that inform the important and profitable decisions that successful business requires.
In this piece, we examine what exactly data discovery is, its various types and the processes required to extract these insights. Let’s get started.
What is data discovery?
Data discovery is the process of extracting meaningful patterns from data. This is achieved by collecting data from a wide variety of sources and then applying advanced analytics to it to identify specific patterns or themes.
Many businesses have a number of disparate, often siloed data sources in their possession. While they may serve some small function within their own right, the real value lies in bringing these sources together and analyzing them to glean deeper patterns and insights.
The data discovery process combines these individual data sources to provide businesses with a big-picture view of their data. Such a view offers greater insights into the available data, which in turn creates more informed decisions.
This process typically involves identifying connecting multiple sources of data, followed by connecting, cleaning and preparing the data before deeper, more-advanced analysis.
The importance of data discovery has soared in recent years. Spurred on by the increase in remote working, the creation of new data is set to grow to more than 180 zettabytes by 2025, and virtually every business collects vast amounts of data, whether from customers, suppliers, or other sources.
But while businesses have increasingly large amounts of it, data on its own is not enough to provide them with a real, noticeable impact. They need smart, tangible insights that can be extracted from this data in order to make meaningful changes within their business—insights that data discovery provides.
- Data discovery enables businesses from virtually every industry to reveal valuable insights, patterns and trends from a variety of data sources. These insights help make business leaders more agile in their day-to-day operations, assisting them in making better decisions that impact the company.
- These insights are available to all. Regardless of whether you have the necessary experience or expertise in IT or data, data discovery empowers virtually any company leader to take a deep dive into their business, its customers, its employees or any other element to extract valuable and, crucially, actionable insights.
- One of the first requirements before analyzing a business’ data is cleaning and preparing it. As a result, data discovery offers the added benefit of mitigating the impact of any dirty data that might otherwise disrupt your business analyses.
- AI and ML have permeated virtually every aspect of our lives, and data discovery is no exception. AI and ML have transformed the process of data discovery, facilitating deeper, complex and more advanced analyses for even more rewarding business insights.
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Why Is data discovery important?
In a time when virtually every business creates, receives or relies upon a rapidly growing amount of data from a plethora of different sources, data discovery is all but essential.
From third-party analytics or global trends to internal sales figures or year-on-year reports, businesses have never had such a vast and wide variety of data available to them.
But the merit of data doesn’t lie in simply possessing it—it lies in the intelligent extraction of the patterns, themes and trends that are buried within it.
Just as eggs, milk and flour don’t make pancakes until mixed and cooked, nor do individual, siloed data sources reveal any useful meaning unless they have gone through the process of data discovery.
Once these insights have been revealed, they can then be analyzed to make a business smarter, more agile and more cost-effective.
And in a competitive business landscape, these insights are vital. Data discovery generates powerful intelligence that can be actioned across a business, with benefits including everything from significant cost savings, reduced overheads, increased revenue, identification of new markets and more.
Types of data discovery
Data discovery is a diverse and multi-faceted practice, and as such there are a myriad of different types that lie within it, combining a variety of different analytical and modeling approaches.
Each different approach offers different merits—and drawbacks—but the two most common approaches to data discovery are: manual and smart.
As the name suggests, manual data discovery is the manual preparation, processing and analysis of data by a human being skilled in data management. Prior to the advent of AI and ML, these data specialists would be required to undertake all the intricate processes of data discovery by hand, without the benefit of automation.
Smart data discovery, on the other hand, is the opposite of this. Smart data discovery relies on dedicated AI/ML software to bring agility and automation to the entire process, from collation and cleaning of data to the final advanced analysis. Naturally, this typically offers faster, more accurate results than manual data discovery.
How is data discovered?
The process of data discovery can be sorted into six defined steps:
- Identify your needs
- Combine your data sources
- Clean the data
- Visualize the data
- Analyze the data
- Record results
However, different approaches to data discovery will naturally require different processes within them, with a variety of tools, tactics and end goals employed throughout.
But despite these different approaches, one thing remains the same: data discovery is always iterative. By repeating the process, businesses can gather, analyze and distill their results for improved, more accurate insights over time.
It’s worth noting that the emergent role of Data Product Manager is very relevant to this data discovery process, as this role is concerned with the ownership of data, so getting a clear picture of the tracking plan/catalog is essential. Learn more about the core issues facing Data Product Managers.
The first step of data discovery is to clearly identify and delineate the goals of the process. Every successful data discovery process needs a defined purpose to help guide the task and act as something to work towards.
Knowing your ultimate purpose also helps you identify what kind of data you need to process and analyze, helping finesse your approach. While it’s important to keep an open mind in this regard, having a clear purpose helps set out your path and prevents time wasted on superfluous data sets.
This purpose varies according to each business’ different needs, industry and goals. But possible examples include the identification of a new target market in a growing retail company, for instance, or the reduction of ingredient wastage in a food manufacturing and processing factory.
Once you have identified your purpose, you then need to collate and combine all your data from your various sources.
Most businesses have a wide variety of data sources, from email marketing metrics and records of customer interactions to supply chain demand or product histories. By collating all these disparate sources together, companies are able to build a complete picture of their data landscape.
Indeed, successful data discovery relies on the intelligent combination and integration of multiple data sources. A single data source is not enough on its own to provide businesses with the clear and comprehensive insights they need. As such, this step, also known as ‘data crunching’, is essential.
Next in the process comes the cleaning and preparation of the data. Often the most labor-intensive stage in the process, this task helps companies get a clearer picture of the insights that lie within the data by removing unnecessary and extraneous details.
Cleaning the data often involves the use of dedicated and automated tools to improve the overall quality of the data. These tools can unify a variety of data formats, identify null values, disregard outliers and standardize the quality of data across the board.
Without this, the data will be too dense and complex for analysts to discover any real business insights. It also makes the final analysis and visualization of the data quicker and easier.
Once the data has been sufficiently cleaned and prepared into a readable format, the next step is to actually visualize the data.
Data visualization involves transforming the prepared data into a visual format that is convenient and easy to understand. Charts, maps, graphs, diagrams—these are all examples of effective data visualization methods that help businesses better understand their data and glean meaningful insights.
With data visualization complete, now is the time for a deep and meaningful analysis of the data. The end goal of this analysis is to create a summary of the data into a concise, easy-to-read format, with any insights, patterns and trends highlighted for convenience.
Analysis builds on the implications within the visualization, highlighting key statistics, points of interest and noteworthy trends that business leaders should know. It is often described as bringing out the ‘story’ hidden within the data, and is perhaps the most important of these six steps.
Finally, once the analysis is complete, the entire process starts again.
Like most data analysis, data discovery is an iterative process. With so much data being created virtually all the time, businesses need a near-constant approach to their data discovery to get maximum value from their data.
While this iterative process can seem—and often is—a laborious process, the rewards and benefits of data discovery far outweigh the time and effort required to achieve it.
How Snowplow helps companies with data discovery
Snowplow is a behavioral data analytics tool which collects the most granular and accurate data and sends it to your cloud storage location – i.e. data warehouse or lake – as part of the Modern Data Stack. A behavioral ‘event’ might be anything from clicking on a link to moving place in a call center line.
Traditionally, analysts avoided behavioral data, as it can be notoriously difficult to maintain, and therefore ‘discover’ and effectively use. These analysts often steered towards easier-to-use types of data, like financial transactions, which are easily organized and discovered, but which are no way near as predictive as behavioral data.
For evidence of how effective behavioral data is, we just need to look to top-end tech products like AirBnB or Spotify, which provide a personalized customer experience based on their previous interactions. But these companies have effectively unlimited resources to meticulously catalog and analyse these digitial interactions. Snowplow is unlocking the power of behavioral data for a new, far broader, audience, and we’ve already seen staggering results across data teams and businesses.