Rethinking Data Unification: 3 Tips to Overcome Barriers

Data Unification

At this point, everyone knows that big data analytics is no longer “the next big thing” – it’s the current big thing. McKinsey estimates that only 30% of the potential value locked in data and analytics has been realized by companies worldwide – with siloed data presenting the main challenge.

Data unification struggles typically result from one or more of these common barriers:

  • POINT-TO-POINT INTEGRATIONS

Many companies combat data silos with point-to-point integrations, cobbling together a small number of relevant data feeds at an aggregate level. To address new questions or business requirements, additional point-to-point integrations are required. Over time, a highly complex web of integrations forms, becoming a nightmare to manage and update.

What can you do?

Centralize your enterprise data on a single, data/system-agnostic platform. Having all data in a central location allows single-step management and control applied across all access points. The result is an efficient enterprise deployment of a single version of the truth, at the data-level. With all data available in totality and granularity, no bounds are placed on the scope of analytic questions that can be asked of the data.

 

  • AGGREGATED DATA PREVENTS AD-HOC ANALYSIS

Aggregating or cubing serves well under a well-defined specification and meets today’s reporting requirements; however, the data in the cubes isn’t detailed or flexible enough to provide answers to any new questions that may arise.

What can you do?

Load all of the data first – without pre-aggregation or transformation. By loading the data AS IS, you can avoid costly logic changes by storing data in its original form, which makes it quick and easy to restructure based on analytical needs or new data. With this approach, businesses can quickly and easily ask the questions that could yield the greatest competitive edge.

 

  • SYSTEMS BUILT FOR EXPERTS

Expert-focused technologies require programming or intensive data modeling and semantic layer design. This forces the business users to become heavily dependent on these limited resources and results in a complex, back-and-forth process between business and IT.

What can you do?

Employ an integrated technology that serves both end-users and IT professionals. Look for a solution that utilizes a single, underlying language to support all needs. No need to have a team of people with varied skill sets to maintain multiple pieces of technology – you can have cross-trainable resources across both business and IT. With a big data solution that has interfaces tailored to all user types and skills, the business can use one tool to acquire, manage, and explore all data. 

 

DOES YOUR COMPANY FACE ANY OF THESE DATA UNIFICATION BARRIERS?

In today’s competitive landscape, a unified view of data is critical to ensuring a company’s future growth. Download this whitepaper to learn how to overcome barriers to data unification and achieve powerful new business insights by:

  • Making all the data available for analysis
  • Enabling immediate analysis
  • Building granular-level hybrid data structures
  • Scaling across the business
  • Allowing for straightforward updating of data structures and mappings