It is estimated that by 2025, the digital universe (the amount of data created and copied annually) will grow to 163 zettabytes (ZB), or one trillion gigabytes.1 How will your organization manage this data tsunami to your advantage?
An antidote to the big data problem is the enterprise data hub (EDH). A data management solution, EDH provides storage, processing, and analytics applications that support both emerging and legacy use cases. The needs of new open source technologies, machine learning, artificial intelligence, and cloud-based architectures are calling for a versatile EDH that promises flexibility, faster data access, and lower costs than traditional data stores.
Yet organizations that have built large-scale EDHs without considering their users’ consumption needs will fail to reap these benefits. In this paper, we envision the journey toward a “consumption-driven,” smart EDH and outline the success factors and pitfalls.
Why do EDH projects fail?
It is estimated that 85% of big data projects fail due to problems presented by legacy technologies and pre-existing corporate biases.2 Yet many organizations only maintain a technical focus on landing data into an EDH; the end result is a high-cost platform that provides little business value in return, making it hard to justify the program altogether. Here, we name the five pitfalls to watch for when deploying an EDH.
Lack of business objective
The scope of an analytics project should not be limited to the unique objectives arising from select teams. Companies that do not have robust strategy around analytics beyond a few use cases will struggle to derive value from these projects.
Lack of integration across legacy systems
In many organizations, legacy systems have multiplied due to mergers and de-mergers, increasing data integration challenges.
Lack of data assurance
An EDH is usually missing one or more of the key elements of data assurance including data metrics, data quality, data reconciliation, data cleansing, and data cataloguing and lineage.
Lack of responsiveness to evolving industry trends and business needs
Bound by legacy systems, established organizations often struggle to respond to changing business needs. In contrast, fintech companies can, often, quickly and cost-effectively keep pace with evolving business needs.
Lack of agility
Established organizations are hampered by a lack of agility owing to multiple stakeholders, long processes for gathering requirements, rigid business processes, and a dearth of input from business teams until an output is delivered. This lack of agility is another nail in the coffin for data projects.
Critical success factors for an EDH
Organizations aspiring to make business decisions based on reliable data must create a smart EDH. What does a successful EDH deployment look like?
Consistent insight at a lower total cost of ownership
Insight gleaned from data should be consistent and repeatable. Any incremental “data items” required for analysis should be cost-effective while data is democratized and available to every user, anytime and anywhere.
Data innovation through data consumption patterns
Although data consumption primarily focuses on a unified and enriched view, it often leads to new data discoveries, which foster growth and innovation.
Revenue growth
Consumers now do most of their banking through web and mobile apps. The data held in an EDH can help business users surface insights about these omni-channel customers, including their experiences and preferences, creating marketing opportunities and revenue growth.
Improved efficiencies
A smart, consumption-driven EDH makes processes more efficient by providing timely and accurate data.
