Introduction to unified data

Unified data is when a company merges its many fragmented data sources into one, single central view. Unified data provides a more complete and accurate picture of a company’s data, but unifying the data is far from simple. To tie data sources together, companies need a system to unite them, such as an analytics platform.

Why is unified data so great?

Companies strive to unify their data because, by default, most data is inaccessible. It’s often scattered throughout the company and divided into information silos among business units and teams. Without a central way to manage data, businesses can’t make informed decisions. Marketing teams can’t accurately measure demand for their product. Product teams can’t fully understand their customer journey, and analytics teams, which are often tasked with breaking down information silos, can’t provide accurate business intelligence to leadership. When companies are able to unify their data, they make all of their business units more productive. But unifying data can pose a tremendous organizational challenge, as well as an engineering one.

The challenge of creating unified data

The technologies that businesses use to store data are highly fragmented. There are tens of thousands of hardware and software providers that each have their own vernacular, programming languages, syntaxes, and practices. On-premise storage servers may not be able to speak to cloud-hosted business intelligence tools which can’t access virtualized servers. Conventions like application programming interfaces (APIs) can connect systems, but don’t always offer enough functionality. Additionally, not all data is the same. There’s big data, thick data, and structured, unstructured, and multi-structured data. Some systems can only process certain types of data, and each dataset can vary wildly. It is little wonder that 85 percent of companies strive to be data-driven yet only 37 percent claim to be successful at using their data. Most data ecosystems rival the United Nations in complexity. Every application speaks slightly different dialects and they require translators to communicate. Businesses that succeed at unifying their data, are better able to plan, budget, forecast, and build products. For unification, many businesses turn to analytics platforms.

“Businesses that succeed at unifying their data, are better able to plan, budget, forecast, and build products. For unification, many businesses turn to analytics platforms.”

Team analyzing unified data of its users from multiple sources

How can analytics platforms help with unified data?

Analytics platforms are purpose-built to capture, store, and analyze data from a variety of sources. They are, by definition, tools for unifying data. Most offer pre-built integrations to common systems and universal APIs for less common ones. They allow enterprises to tie their ERP, CRM, web applications, marketing systems, customer applications, and data partners together to view the data from one interface. The best analytics platforms have highly intuitive interfaces that are designed to mask the complexity of the underlying data architecture. They use dashboards to help users visualize their data. Some platforms offer machine learning algorithms to simplify and automate the process of analysis. Brands can use an analytics platform to knit data from across silos, business units, and teams together and provide everyone access. The more individuals within a business that are data-informed, the better. At e-signature provider DocuSign, the product team gave over 100 individuals across the business access to its Mixpanel instance so that the data science team wouldn’t serve as an insight bottleneck.

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How to create a unified data ecosystem

Analytics platforms each have their own quirks. Some are designed to be highly accessible but lack advanced features for manipulating data. Others are designed to handle massively complex datasets but suffer from what’s known as featuritis–a confusing interface with too many features. Some platforms strike a balance of high functionality and high usability. When teams evaluate analytics platforms, they should find the one that best fits their current and future needs. Teams can examine analytics platforms based on these factors:

  • Integrations: Can the platform integrate with most data sources?
  • Performance: Does the platform have a high storage capacity?
  • Reliability: Does the platform guarantee access?
  • Availability: Does the platform guarantee uptime?
  • Latency: Can users access data in near real-time?
  • Concurrency: Can the platform use faster, non-relational query techniques?
  • Compliance: Is the platform data center compliant?
  • Innovation: Is the company constantly improving its product?

In addition to features, it’s important for teams to consider process issues like internal data governance and quality. Pieces of legislation like the European Union’s GDPR are forcing many companies into an era greater data transparency. Customers increasingly demand to know what data businesses collect on them, and what they use it for. Businesses need to ensure that they are being transparent with user data. At the same time, data breaches are increasingly common. Any teams seeking to unify their data must also consider the potential danger of making it easier for hackers to access. To keep themselves safe, teams can publish internal data governance guidelines and make sure their partners are compliant and can customer data secure.

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