You need data to run your business. When done right, your data guides stakeholders to critical business insights that allow you to make decisions and move forward with confidence. When done wrong, however, you wonder what part of the puzzle you’re missing, if another direction is better, and how anyone could get answers from the mountains of data you’re storing. It’s clear that analytics are becoming a lifeline for any organization, especially with rapidly-changing business environments. Going beyond dashboards, leaders are increasingly relying on data products such as custom apps and workflow automations to make critical decisions that impact short-term revenues and long-term growth for enterprises. To achieve these outcomes with their data, businesses are increasingly relying on Data integration. According to Gartner, Data integration is “the consistent access and delivery of data across the spectrum of data subject areas and data structure types in the enterprise to meet the data consumption requirements of all applications and business processes.” Data integration works in harmony with business operations and supports an agile business intelligence model. But getting to the right data is often clunky, restrictive, and hard to manage. So, how can your company efficiently integrate its data so it supports business users?
The difficulty in leveraging your data
To understand how to get there, let’s first look at why it’s been so complicated to leverage all your data. Your company likely has data integrations and pipelines in place to support using data analytics to answer business questions, discover relationships and correlations, and predict outcomes across key areas of your business. But building modern analytics for any enterprise is becoming more challenging. Legacy systems are growing increasingly outdated. Many of these systems support the core technological infrastructure for every aspect of a business, from product development to customer support. Because these systems support so many critical business functions, they have been duct taped together and have often been manually jerry-rigged into integrating with other applications. This makes it difficult to scale operations or change how the data is stored and shared. Companies that have focused on digital transformation and moving to the cloud have often been hampered by working with these legacy systems and end up transferring the duct-taped methodology for storage into the cloud.
Sometimes, despite millions of dollars spent on data warehousing and adding tools like online analytical processing (OLAP) to that warehouse, it means the data becomes even less accessible.
Often, there can be a rigid semantic layer that allows for fast processing on some specific data questions but doesn’t allow users to bring in additional data, variants, or dimensions. This can mean there are bottlenecks at every stage of the process from ETL (extract, transform, and load) pipelines to access rights to what kinds of data can be combined for analysis.
It becomes clear that as your company looks to modernize and become more digital and agile, the key factor to your success is how data is integrated, how it is stored, how it flows, and how it’s accessed throughout the organization.
Your company may have several ways they’re supporting data integration.
