Overcome The 4 Common Barriers for Actionable Data Insights

Identify roadlocks and the respective solutions in maximizing your data potentials

In today’s digitalized world, companies are adopting advanced data analytics to enhance their decision-making, accountability, productivity, make better predictions and monitor performance, to the extent that they can improve their competitive advantages. Harnessing the full potential of data analytics, however, does come with challenges. The key issues faced by organizations are executing their big data strategy in both analytics and deep systems or infrastructures, all while dealing with the complexity of modern data. If organizations lack the awareness of these challenges and the expertise to deal with them, they could miss opportunities for prospective business growth.

1. Limited big data management capabilities

The total amount of data processed globally increased rapidly to 64.2 zettabytes in 2020 and is projected to grow to 180 zettabytes in the next five years (Statista, 2020). Handling such enormous volumes of data is a significant challenge as it requires exponential resources and investments in both professional and strong infrastructures to store, manipulate, analyze and manage, without trading off on speed, thus enabling the delivery of real-time insight.

The solution for this is to employ data analytics platforms that can process several different types of data, while effectively overcoming data storage issues, providing data insights that help enterprises make more informed decisions.

2. Data Privacy and Security Issues

Cybersecurity issues keep evolving as data is growing at exponential rates and has proved itself a valuable asset for organizations. Without a proper data privacy and security solution in place, data breaches and regulatory violation can impose hefty charges and leak strategically confidential information, endangering the stance of the business in the market. Therefore, data privacy and security remains a priority among business leaders since they have invested in the right security protocols to sufficiently protect their data repositories.  In 2019, spending in the cybersecurity industry reached around 40.8 billion U.S. dollars, with forecasts suggesting that the market will eclipse 60 billion U.S. dollars by 2021 as the best-case scenario (Statista, 2020).

Measures to invest in data security services include identity and access management, data encryption, data segregation, etc. Overall, it is essential to keep data security as the top requirement for data analytic solutions, with a secure by design approach. Organizations should choose IT security service providers whose expertise can cope with ever-changing cybersecurity threats.

3. Gaps in data governance impacting data quality

Compromising on data quality can result in inaccurate analytics, which will negatively affect the planning and executing of strategies, increase customer dissatisfaction and result in costly overheads. ​Poor data quality is directly impacted by issues related to data governance procedures, such as human error, collection or modelling errors and even malicious behaviours. Having an automated data governance system in place, as well as a data validation procedure that is tailored to each stage of ETL (Extract, Transform and Load) process, can improve the quality of incoming data across multiple levels (syntactic, semantic, grammatical, business, etc.).

4. Lack of a robust data discovery and classification procedure

An organization that uses multiple data sources, CRM systems, web applications, databases, files, etc. – can incorrectly interweave data sets and be unaware of causal relationships between data points, when lacking a proper data discovery and classification mechanism to identify these inconsistencies. In such a scenario, integrating and standardizing data in a large storage repository such as Data Lake, Data Warehouse, Data integration software, etc. becomes a pre-requisite for effective and accurate analysis. With the correct integration strategy, application-specific and intra-departmental data silos will be mitigated, boosting productivity and resulting in better analytics results.

Overcome challenges to unlock your data potential

Adnovum’s data experts can analyze, propose and design data analytics solutions that cater specifically to your business objectives. Leveraging Data Analytics Platform as a tool and Machine Learning-based solutions allows us to optimize your current data analytics process and empower your business advantages as a result. Contact our experts to learn more.