Thirty-five years ago, Robert Waterman mentioned in In Search of Excellence that companies were “data-rich and information poor”. Not a lot has changed since then.
For sure organizations are “data richer”, having immense, varied data at their disposal. But they are still poor in terms of the information they have access to, even as CIOs and other business leaders have implemented multiple strategies aimed at using and exploiting data. Most still struggle to build data into their business strategies and use insights to deliver the value promised to customers and grow revenue. There are a plethora of reasons, from poor data-driven processes to culture. Solving these issues enables companies to unleash the power of data and use it to drive the business forward.
How companies onboard customer data is invariably one of the biggest reasons. Apparently, data onboarding presents a challenge for businesses aiming to become “information-rich”. In this blog post, we’ll find out reasons why data onboarding is challenging and how they can be resolved.
Data Onboarding Roadblocks
Data Silos
Whether owing to departmental differences in applications investments or M&A, data typically resides in silos. Oftentimes, data will be duplicated in distributed data marts to respond to particular user demands, which makes it challenging to consolidate that data.
Moreso, these data silos have varying formats and standards that fuel problems further. Statistically speaking, organizations find it difficult to onboard data from third-party vendors, customers, or public data—in that case, analysts have no control over the data structure and norms used in the data, requiring them to decipher elemental data to make it consistent across sources for successful integration.
Errors and Discrepancies in Data
Inconsistencies and errors are buried within all datasets, such as an age value of 230 years, invalid zip code or data format. When the volume of datasets is large, spotting these errors is difficult. And even if a company onboards this data, it is bound to deliver inaccurate insights which could lead to poor decision-making and value generation.
Increasing Data Volume and Formats
The volume and formats of data in most organizations have exploded, but with more data comes an increased demand to onboard, integrate, and use that data. It’s a challenge for companies to onboard such highly complex, bi-directional data. Not only does this take a lot of time, sometimes even months, but also put a lot of pressure on IT.
Undue Pressure on IT Teams
In the majority of organizations, it’s the job of IT users to onboard data from customers. That takes a lot of their time and effort. Most IT integrators take six to 12 weeks of calendar time to create data onboarding connections, while customers wait to receive the desired value. Consequently, IT teams struggle to focus on more important tasks that could slow down innovation and growth in the company.
Overcome Roadblocks Using a Self-service Approach
Companies that can reimagine their approach through self-service will transform data onboarding.
Self-service integration enables even non-technical business users to onboard complex customer data in minutes instead of months. It empowers non-techies to access different types of data streams as well as customer entities using dashboards and intuitive screens and create data onboarding connections by only pointing and clicking through easy screens. As a result, customers do not have to wait and they can realize value sooner.
At the same time, IT is freed to focus on governance and more high-value tasks. That means, they no longer get bogged by the integration tedium.
The use of machine learning and artificial intelligence and end-to-end encrypted environments allow users to get rid of errors and inconsistencies from data, keeping the authenticity and security of data intact. Companies can, ultimately, securely exchange data across partner ecosystems, thus improving the ease of doing business and growing revenue.