(Last Updated On: October 20, 2023)

Modernising the data ecosystem is the foundation of a successful digital transformation strategy. Real-world Big Data application success requires an understanding of the multiple nature of the digital transformation journey, which is centred on the Big Data ecosystem’s plan, process, tooling, architecture, and culture. 

Agility Research explains the main data ecosystem modernization difficulties faced by enterprises like data analyst consulting firm at various modernization journey maturity stages.

Data Ecosystem Modernization Challenges

Data quality: Given the different data types, complex application needs, and data volumes, enterprises face multiple challenges in maintaining accurate and consistent data quality.

Data inconsistency: There is a high probability that the enterprise’s data will be inconsistent. To overcome this challenge and achieve greater business value, the enterprise will have to host its source of data independently and reconcile all the data in real time.

To correct your data set, it is essential to have proper data governance and management protocols in place. Different data types and sources of data pose a challenge to enterprises in terms of integrating the data into a single source.

Data explosion: The volumes of unstructured information and communication networks are growing exponentially. Enterprises cannot possibly handle all the information they need.

Uncontrollable Data Proliferation: There are many uncontrolled data sources and applications in the market today. Other organisations will likely contaminate the enterprise’s data.

Wealth management: The global wealth management and financial services industry operate as a highly complex ecosystem in which various analytics and predictive models are used. The models need to be continuously updated to achieve the best results.

Data management: Enterprises are facing many data management difficulties from both sides. Firstly, enterprises cannot control the data effectively and have a hard time sharing data across different applications and sources. Secondly, given that data quality is low, enterprises face a degree of data inconsistency.

Data privacy risks: Due to the open-source nature of information technology, enterprises face significant concerns about their privileged user domain, IP address, personal data protection rights, and civil liberties issues.

Solving the Cultural and Expertise Challenge: One of the biggest challenges that enterprises face is related to the skills and culture of their employees. This is primarily since a business user’s skill set in Big Data analytics differs greatly from an IT employee’s skill set. There needs to be a business person with a strong understanding of Big Data technology and capable of creating new value through their Big Data analytics initiative. 

Key Recommendations:

Actively test and adopt emerging technology: Enterprises should determine the best approach for each modernization project and test different technologies without committing to any particular technology. This allows the enterprise to quickly identify which technology will provide them with the greatest value.

Prioritise service providers: While it is important to leverage emerging technology, enterprises should also develop their skills internally and look for service providers who can provide additional support.

Understand the right talent: Having the right talent capable of developing a full-stack skill set is critical to properly executing your digital transformation strategy. 

Determine an enterprise architecture: Modernising an enterprise’s data system requires an enterprise data architecture that includes all required organisational elements. This includes technical, process, and data elements that enable effective data flow, governance, and knowledge sharing across the enterprise.

Understand the importance of data governance: Enterprises face significant challenges that lead to poor decision-making without proper data governance. 

Final Words!

All in all, an enterprise’s data ecosystem provides various benefits to the organization. It helps them collect and analyse data about customers’ preferences, interests, and behaviours that ultimately help enterprises to derive better insights about their customers and thereby create relevant marketing campaigns. 

However, modernising the enterprise’s data ecosystem is challenging for enterprises. It requires a considerable amount of investment and time. Therefore, before moving forward with this transformation, enterprises should identify key challenges that are unique to their organisation.