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Data is a valuable asset for almost every organization. It offers a competitive advantage, drives every big idea, and informs every critical decision. But as the volume of data the world generates grows year on year, how can businesses keep pace and leverage these modern data assets for their own benefits?
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Business intelligence (BI) encompasses the methods and tools businesses use to convert historical data into insights they can use to make everyday decisions. As such, BI involves a range of methodologies, including:
This list isn’t exhaustive. However, it exemplifies how many different elements contribute to business intelligence. It is a holistic strategy to help businesses make informed decisions based on past data.
How does this work in practice? A typical scenario might look like this.
This is just one of many use cases for BI. We’ll discuss more later. But for now, let’s take a look at data discovery.
Data discovery is the process of classifying and exploring siloed data stores to detect patterns and uncover insights. Think of it as arranging a chaotic mess of tiles into a mosaic.
Businesses routinely battle with disparate data siloed across databases, data warehouses, and external sources like social media sites. Data discovery helps analysts collate this data and then use it to discover actionable insights that may not be immediately obvious.
They can even do this proactively to identify and address concerning trends before they become a problem.
How might data discovery work in practice? Here’s a common 5-step process.
Many of the workflows in steps two, three, and even four can be automated today with AI applications. This takes a lot of the tedious, difficult work out of data discovery, meaning even those without IT skills can begin discovering insights from the data at hand.
Data discovery and business intelligence are both about attempting to use data assets to help businesses make improved, data-led decisions. But there are some differentiators.
BI usually aims to answer predefined questions. For example, a business leader may want to answer a query like, ‘Are we meeting our KPI for customer retention this year?’ They will then ask an expert to draw historical information from the organization’s data warehouse.
The expert will access the organization’s internal warehouse to gather the information they need. They will then present this information in a standardized report so that the organization's management can determine whether the company has met its business outcomes.
Data discovery is much more free-form. It’s about exploring data sets to uncover insights that may not be obvious.
The analyst dives into the collated data set with fewer preconceptions about what they hope to find. The goal is to explore the data and find trends and patterns—not necessarily to answer a specific query. This approach helps businesses discover risks and opportunities proactively. In contrast, BI is more prescriptive and based on historical data.
Business intelligence usually leverages structured data sources stored in databases (like spreadsheets) and data warehouses (central data repositories). This data is collected and stored in a standardized format to ensure data quality and accessibility, making it simpler for analysts to generate consistent reports.
While data discovery can and does involve structured stores, it can also analyze a broader spectrum of custom data types, such as semi-structured and unstructured on-premises and cloud data. It can incorporate multimedia files, social media content, and external data sources like public databases and industry reports.
And, crucially, it doesn’t require a centralized data warehouse. Many tools can classify and organize data in situ.
Navigating BI tools requires a specific skill set, as does interpreting complex records. These tools also typically take months to create because of the need for enterprise data. The reports are typically broader and more prescriptive, providing high-level overviews supported by metrics. As such, BI is utilized by expert data scientists, such as data engineers and analysts.
Data discovery tools are easy to use and offer guided advanced analytics. Many leverage artificial intelligence and automatically categorize and standardize raw data. This opens the door for all business users to utilize data discovery in their workflows, even those without IT expertise.
For instance, a marketing team could collate and explore social media data to decide which content types produce the best results.
BI still encompasses data exploration, but the scope of analysis is more limited. Analysts usually already know which datasets and metrics to extract before they begin to interact with the information. That’s a benefit when selecting financial metrics to answer specific questions but a drawback when there might be multiple versions of the truth.
Data discovery is much more open. It takes an exploratory approach rather than relying on predefined data to make evidence-based decisions. If someone discovers a unique trend, they can pursue the lead dynamically to find out more.
This is largely because data discovery isn't tied to the organization's data warehouse, meaning users have more freedom to discover hypotheses and find creative solutions.
Business intelligence utilizes standardized report templates. These reports are designed to be repeatable and easily quantifiable, such as when tracking quarterly sales figures against sales forecasts. Data can then be handpicked from the organization’s data catalog to support this theory.
With data discovery, reports aren't always considered the end goal. Instead, the goal is more open-ended and down to the discovered data. Don't get us wrong. Insights from data discovery are often still visualized to make them easier to understand. But this isn't an essential part of the data discovery strategy—more an optional extra.
It’s worth noting that data discovery and business intelligence, while different, aren’t mutually exclusive.
Going back to the ‘sales figures versus sales forecasts’ BI report, for example. If the company determined there was a disparity between the expected sales and the actual sales, they may use data discovery to determine why this is the case.
Once they’ve explored the data sets to glean insights, they can then use BI once again to collate the findings in a visual report. If the report raises more questions and hypotheses, the data discovery process can begin again.
You can see how the two methods can link together in a feedback loop to provide ongoing insights and data visualizations to business data leaders. We suggest using both. That said, each strategy has its ideal use cases. Let's explore some of those now.
Data discovery and business intelligence each serve different purposes. It isn’t a ‘one or the other’ situation, but we recommend selecting your method based on the task at hand. Let’s look at common use cases for each.
BI works best when it comes to tracking a specific metric or answering a question based on readily available data. It is more prescriptive and single-minded than data discovery.
This isn’t necessarily a bad thing, though. In fact, BI is better than data discovery for several use cases.
Data discovery is an ongoing process. It does not necessarily require a motive. This open-ended problem-solving means data discovery has several excellent use cases. Let's detail a few now.
Data discovery and business intelligence are both essential aspects of an organization’s data strategy. BI is best for answering predefined questions and monitoring trends over time, while data discovery is suited to exploration. It uncovers insights and trends you otherwise may have missed.
However, both solutions stem from the same need—to drive better decision-making and create more opportunities for businesses to use their data to grow.
Our number one piece of advice? Use a platform to help you manage and automate data cleaning, data classification, and curation practice. When done manually, these processes take a long time. A platform will make these tedious jobs much easier.
For instance, using a platform with ML and AI capabilities will take much of the hassle out of data cataloging and classification. Aside from that, it’ll also help you comply with all of the rules and regulations surrounding data handling.
Above all, it’ll save you time. That’s time you can then spend discovering insights and drawing value from your data to make better decisions and grow your business.
With RecordPoint, you can find and manage your data wherever it lies.
Our machine-learning model will automatically classify and standardize your structured and unstructured data sets. It will automatically remove any redundant, obsolete, or trivial datasets, leaving behind only the information you need to make better business decisions. You can then access your data directly from the platform without needing to catalog your information manually.
For business intelligence? Our automated data reporting feature will offer a comprehensive view of core metrics and organizational risk based on all of your data stores. With over 900 connection opportunities, you can even integrate our platform with your preferred BI solution, whether that be Power BI, Tableau, Qlik, or Looker.
Want to utilize data discovery? Our manage-in-place features let you search for data and explore where it lies rather than hunting through siloed stores. You’ll save all the hassle of manual exploration—giving you more time to analyze your data for valuable insights.
Why trust us? We’re the only SaaS records management platform to complete a formal third-party IRAP assessment. That means we’ve proven our commitment to data management, security, and compliance.
Want to learn more? Schedule a demo today and discover why we’re the trusted data solution for dozens of heavily regulated organizations.
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Protect your customers and your business with
the Data Trust Platform.