With enterprises storing years of data that sprawl thousands of databases, what do you do when your database is missing key data? How do you get separate databases to talk to each other for data analysis? More importantly, how do you even figure out which databases and data sets should communicate to produce more valuable insights? Enter data discovery, the process of diving deep into all of your data locations and pooling data resources together from those disparate systems to show new threads of meaning across your data.
Think of data discovery as a precursor to data visualization. Without the knowledge of which data locations and data points should be brought together, you can’t create a compelling visual to represent relationships in your data.
See below to learn about data discovery and some of the data discovery tools that can help you to accomplish your goals:
Diving into data discovery
See more: What is Raw Data?
There are several key pieces of the data discovery process:
- Data exploration: This practice is nearly identical to data discovery, but it functions as a broader approach. While data discovery focuses on answering specific questions or solving known data problems in your search, data exploration typically happens before you actually know what questions need answers. The initial steps of data exploration help you see what’s available to work with in your different data sets, which gives you the context to ask the right questions during data discovery.
- Data preparation: Data preparation is the process of organizing and handing over raw data for discovery and analysis. The practice can be done manually, but in big data settings, tools to extract, transfer, and load (ETL), as well as for data warehousing and data visualization, may be necessary to prepare the data for use.
- Smart data discovery: This type of discovery is performed when artificial intelligence (AI) combs through your data to discover patterns and get them ready for visualization. Many companies are using smart data discovery not only for its business intelligence (BI) potential, but also because it requires fewer data scientists to have expertise in data discovery procedures.
Customer and behavioral analysis
Customer and behavioral analysis usually require a large amount of data from different data locations, since you’ll want to know everything from their purchase history to their customer service inquiries, basic demographics, and online behaviors associated with your brand. To truly assess trends in customer behavior, you can use data discovery to find all relevant customer data across company databases.
Full life cycle of data
Consider a complicated corporate process like supply chain management. Your data moves through several points in a supply chain life cycle, such as raw material production and delivery, manufacturing, quality assurance, and delivery. As products move through the life cycle, their value and most important attributes may change, and the data can be stored in separate databases.
Separating these details complicates data analysts’ abilities to see how the supply chain is performing as a whole. Through data discovery, you can look at all of these data locations, knowing what questions you need to answer to better understand the supply chain. Data discovery gives you the ability to view every puzzle piece as a part of the whole.
Enabling data visualization
Data visualization is the practice that summarizes data in a way that’s easily digestible and understood, which benefits both data professionals and non-data pros. But how do you know which pieces of data you want to visualize and how you can connect them? Data discovery forces you to ask these questions and retrieve the data you’ll need to create meaningful visualizations.
See more: What is Data Visualization?
Business intelligence initiatives
The practice of data discovery helps data analysts to dive into various pools of business intelligence data, armed with specific goals about the data that they want to discover. With a data discovery mindset and data discovery tools, businesses can maximize their BI data to compare themselves against competitors and setting goals.
Impactful predictive analytics
Predictive analytics give companies valuable insight into how they’re performing now and what they may need to do to improve future metrics. Predictive analytics often might pull data from one database or source, but data discovery offers the opportunity to find and use more integrated and holistic data for predictive analytics.
Companies rely on data discovery tools to streamline the discovery process and gain greater data visibility, such as in these cases below:
“We found Altair Monarch to be a very promising and efficient data preparation solution that we use with a variety of report formats that encompass but aren’t just restricted to ASCII files, PDF, spool, text and XML formats. We can easily access data from sales reports, balance sheets and other logs. With Altair Monarch’s range of pre-built data functions, we are able to quickly select and automatically convert messy, dark and semi-structured files into more structured data that we use for further analytical purposes. What’s even better is that models built in Monarch can be exported into common BI and other analytics platforms. This also makes reporting and visualization very simple – because we are able to export data to other platforms after blending and enriching our extracted data with Monarch’s click-based interface that doesn’t demand advanced coding knowledge.” -Project director in the services industry, review of Altair Monarch at Gartner Peer Insights
“Very versatile and user friendly software that allows you to deploy results quickly, on the fly even. Data transparency and business efficiency is improved tremendously, without the need for an extensive training program or course. On the job is the best way to learn using it, figuring problems out with the aid of the community page and stackoverflow, and if all else fails there are committed consultancies that can sit with you and work out complex business needs, from which you will gain another level of understanding of the software onto which you can build further. We use this software not only for data analytics, but also for data browsing and data management, creating whole data portals for all disciplines in the business.” -Data scientist in the energy industry, review of TIBCO Spotfire at Gartner Peer Insights
The global data discovery market has grown over the years, from $4.53 billion in 2016 to a projected $23.34 billion by 2025, according to Kenneth Research.
Much of this growth is coming from the services and health care industries.
Data discovery software makers
These are some of the top data discovery software providers, most of which offer other data management tools:
- TIBCO Spotfire
- Qlik Sense
- Dark Web ID
- Altair Monarch
- Alteryx Connect