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5 Keys to Real-Time Analytics

The value analytics brings to a business is inversely related to the time it takes to create said analysis. In a traditional world of quarterly lookbacks, an analyst’s output may be interesting, but its ability to drive real relevant change is hindered by time and effort. The fundamentals that were once present may have all changed.

This is why real-time analytics are a breakthrough for a business. If you can take the insights from analytics, and present them in a way where executive teams can act on them more quickly, you are shifting the paradigm to a place where businesses can proactively drive their own destiny, rather than just reflect on it.

Accurate, well-formatted data is essential for real-time analytics. With structured data, teams can solve problems and be proactive about making decisions. But since all datasets grow, in both size and complexity, businesses must invest time and resources to achieve and maintain said structure. For this reason, it’s wise to know what hurdles stand in the way of real-time analytics, along with the best practices for transcending them.

We’ve compiled five key tips for you on how to achieve real-time analytics.

  1. Data Silos

As the number of SaaS applications ascends ever upward, so does the cloud data nested within them. Today’s customers interact with your business across many touchpoints. They chat with sales, file support tickets, fill out request forms, attend webinars. Traditional extract, transform, load (ETL) tools spread data across systems, which makes feeding reports, analytics, and business intelligence (BI) tools with real-time data more challenging.

Tip: Centralize data silos into a single database. This way, all departments report from one system, not many. Rather than depend on IT to run bulk extracts or wait for developers to learn new APIs for SaaS applications, research more modern tools that let you connect to your SaaS applications, authenticate via their APIs, and replicate all the raw data automatically.  

  1. Data Quality

Simply combining data is not enough. Your data must be accurate and current as well. Upon centralizing data, you are likely to encounter thousands of duplicates, conflicts, inconsistency in formats. Data quality is too poor for combining into one central system.

Tip: While data prep is the linchpin of any business committed to real-time analytics, the challenge of extracting, transforming, and loading high quality data has heralded a next generation of ETL. These fast, self-service tools prioritize the most recently updated data in the event of a conflict. To amend inconsistencies in formats, they employ type translation, where all data types are translated into a common format that a human can understand in analysis. A date stored as a Unix number is automatically converted to UTC. Deals written as alphanumeric hashes get translated into everyday language, like ‘Prospect’ or ‘Won’. Automated tools will also eliminate duplicate records. With de-duplication keys, they match like records — like contacts on email address, or companies by company name — between multiple datasets. If you have 30,000 duplicates, such tools save a ton of time, ensuring your data isn’t days, weeks, or months old. So your data is analytics-ready, and thus can be used in real-time.

  1. Preserving Object Relationships

Real-time analytics requires much more than unifying data and improving data quality. Remember that contacts have relationships to other objects that you’ll want access for real-time reporting. So what do you do if you have two or more records in different systems that relate to the same object (e.g. a person, company, etc.)? Bereft some predetermined common identifier or primary key across data silos, how do you match like records across systems, preserve object relationships, and not hit a dead end on your road to real-time analytics?

Tip: Tools that automate your data pipeline will save you a ton of time in this step. First, they’ll define how one field relates to another field for a given object — Events, Companies, Contacts, Deals, Engagements, Forms, Owners, Broadcast Messages. Some will even let you customize the mappings. Either way, you can skip having to conjure definitions and support for all of the key objects that you plan to carry over.

  1. Unifying Multiple Data Models into a Single Schema

There’s more to real-time analytics than a unified dataset of well-formatted data with their object relationships preserved. Indeed, you’ll also want to consider that every database has its own unique data model, or schema. One system might pin data to a lead; another across leads, contacts, and accounts; while yet another structures data around users. That is, you can map relationships all you want, but when it comes time to analyze or visualize that data, you’ll still be stuck with a morass of data models, which impinges on joining tables, and makes queries technically arcane.

Tip: Data transformation is the act of interpreting these unique data models and turning them into a single, universal schema. Once again, automated data pipelines seem poised to invoke ETL’s impending extinction. For with these tools, not only do multiple data models collapse into a common schema, but if you change your mappings, that new schema will update on its own accord.

  1. Warehousing

Automation has taken you this far: you’ve connected your SaaS applications, matched records, mapped their relationships, and merged every major object into a single database. Your formats are standardized, many data models transformed into a universal schema. Now how to access that data? Where is it stored, backed up? Will it update automatically? Can you connect it to any dashboard or BI tool? Do you need to invest in hardware?

Tip: Fortunately, you now stop overthinking this step of the equation. For at the push of a button you can now get a SQL warehouse, populated with all the data from your SaaS applications, updated and maintained automatically. With a cloud data warehouse, there’s no hardware to worry about. Just feed your fused database to tools like Tableau, Microsoft Power BI, Amazon Quicksight, Metabase and more. Query or visualize and get insights in minutes.

Although these hurdles seem like a lot of work to overcome, it’s now easier than ever to eliminate data prep thanks to an automated data pipeline. What this enables is for teams to finally close the loop on reporting data across all applications. From a single view of customers internally, you can improve the customer experience externally, generating more profitable and successful customers that connect your investments to outcomes.