Data Onboarding: Overcoming the Challenge

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Modernizing data onboarding will enable enterprises to deliver greater value and bottom-line impact than they ever thought possible.

Every mid-large enterprise is challenged with managing terabytes of data, and most struggle (not so silently) with how to effectively derive more value from what is an ever-growing pile of data. Our research with IDC shows that, in the months leading up to the pandemic and during the early months of lockdown, many organizations increased their data loads by introducing new external data (40%), new internal data (45%), and new data types (45%). Unfortunately, much of this data is going to waste gathering digital dust.  As many as 68% of organizations say they fail to leverage the majority of data available to them, according to IDC. Proper data onboarding can help.

Why is this?  Among the multiple factors limiting an organization’s ability to harness real-time insights and ROI from their data are complications posed by data onboarding.

Even when organizations have modern analytics in place to create usable and impactful insights from their data, data onboarding roadblocks thwart those analyses from creating real value.

See also: 6 Data Management Tips for Small Business Owners

One of the main sticking points is a struggle with uniting online and offline sources in a cogent and consistent way. This is a huge hindrance in the ability to leverage analytics-ready data in near real-time, which is crucial for the modern enterprise to effectively compete.

Failure to capture and ready information for analysis can hold companies back from creating truly personalized and seamless customer experiences that support retention.

There are, however, practical steps that businesses can take to overcome these obstacles and improve data onboarding – and ultimately empower organizations to take full advantage of their data.

Watch those format limitations

Format is extremely important to data onboarding. If data is in a proprietary format, users cannot expect to simply integrate it for analysis without conversion. Leveraging flat files whenever possible can simplify the procedure, but the data must still be transformed into an analytics-ready state.

There are also sector-specific challenges, particularly for banks, insurance companies, and other businesses that possess highly sensitive customer information. This data is often stored and coded in silos to create a higher level of exclusivity and security to match governance measures.  Understanding how to safely unleash these data types, which can extend to other core operational data such as SAP, is crucial to driving more value from all the organization’s data.

Equally important is data delivery. While some firms have modernized their data pipelines to accelerate and automate delivery at scale, others are still overly reliant on legacy systems and mainframes. Relying primarily on labor-intensive processes for transactional data slows down availability and analysis and can never keep up with the speed of business decision-making.

Reduce manual methods

It is widely understood that siloed data hurts organizational success, and many business leaders recognize the need to modernize accessibility, where possible through the cloud, modern data warehouses, or data lakes. But they must remember that transmission is just as important as destination. Many are still relying on brittle ETL (extract, transform, load) processes. Traditional ETL is slow, working in batch mode, which takes a significant amount of time to process and is not fit for purpose in the modern enterprise. It also often requires manual programming to map data sources to targets that impose an even greater query workload burden on the production systems of these much-needed data sources.

Instead, organizations should look to deliver their data as the pulse of the business that can be accessed at any time, with data transformation completed after it is loaded to the target through a data catalog. Businesses can speed up and smooth out their data onboarding process by investing in data integration solutions that use automated Change Data Capture (CDC). This enables data from all different sources to be replicated and streamed in near real-time to one or more destination(s) of choice that will always be kept up to date with the freshest data as and when changes occur at the source.

CDC can shift the approach to ELT (extract, load, transform), an alternative to the outdated ETL. ELT decouples the Transformation from the Extracts (data from the source) and Loads (into the target systems) where the data transformation occurs further downstream. Unlike traditional ETL, ELT lends itself well to automation, reducing the time-consuming and labor-intensive tasks of manual programing.

Address the non-technical barriers

Data onboarding inefficiencies are not limited to technical problems alone. Imagine if a sales team couldn’t access the marketing and manufacturing data needed to build a better pitch and secure additional retail buys ahead of a product release. Would they be able to meet their quarterly goals? Would fewer products be delivered to retailers – and subsequently sold – as a result of this barrier?

While there may be compliance issues involved in sharing too much information, businesses must find a happy medium that protects data while enabling more employees to leverage the right data more heavily in their roles. Through an enterprise-wide data catalog, online and offline data sources can be brought together and made available to employees in a single source of truth while still providing strong governance using role-based access controls. This can enable innovation by allowing more accurate and trusted data to be shared more widely, without the fear that sensitive information will end up in the hands of the wrong employee or be misused.

Data onboarding done right

Data onboarding continues to challenge many enterprises, preventing them from taking full advantage of their data. Thankfully organizations have ready-made technology solutions to provide ongoing, governed access to more data to those who need it in their daily work. Modernizing data onboarding will enable enterprises to deliver greater value and bottom-line impact than they ever thought possible.

Adam Mayer

About Adam Mayer

Adam Mayer is Senior Technical Product Marketing Manager at Qlik. He works within the Global Product Marketing team at Qlik, covering the entire Qlik product portfolio. He is responsible delivering the company’s Internet of Things (IoT) and GDPR go-to-market strategy. With a strong technical background in computing, underpinned by an incisive engineering perspective, Adam is an avid follower of new technology and holds a deep fascination of all things IoT, particularly on the data analytics side and finding new ways to make it as translatable, visual, and understandable to as many people as possible. He has over 20 years of B2B customer facing experience within the IT and automotive sectors, having previously held positions at Canon, Sony, Tevo, Fujitsu and Toshiba. 

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