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The last year has been a technology milestone for businesses in varied forms. The inexorable digital world has fast-forwarded the need to leverage data for faster decision-making thanks to remote work. Moving workloads to the cloud and technology investments as a competitive advantage and performance optimizer has become a business imperative. The digital transformation we’ve seen play out over the last few years has hastened the next five years’ advancement into one.


Digital transformation and big data companies with an increased focus on data engineering deliver business value by unlocking data velocity at speed. In this context, the past year has been a transformational tipping point focused on infrastructure, data, and operational productivity.


All looks good with endless opportunities for how organizations build services and deploy infrastructure. But enterprises face many challenges in moving data initiatives from boardroom discussions to implementations that work. They also need to meet performance, security, scalability, and governance requirements that operate accordingly. In addition, implementation costs must be kept under check as they can quickly spiral out of control.  


Let’s delve into the top 8 challenges faced by data enterprises today –


Taking a broader look, here are ten big challenges that enterprises should be aware of and some pointers on addressing them:

  1. Managing and storing large volumes of data

Data always comes in different forms and formats, typically involving large volumes of data sets housed in disparate systems and platforms. The key challenge for any enterprise is consolidating highly voluminous data sets by extracting the data from various CRM and ERP systems and various other sources into a unified and manageable data architecture. Once done successfully, it makes the job of a data architect easier to narrow down on insights.

  1. Managing costs under control 

Another common big data challenge is keeping operational costs from getting out of control. Using pre-existing data consumption metrics, which many enterprises often use to estimate their new big data infrastructure costs, could be a big mistake.

Companies underestimate the demand for computing that arises from richer data sets. The cloud, in particular, solves this challenge by making it easier for big data platforms to churn out richer and more granular data. However, this capability can skyrocket the costs since cloud systems elastically scale up to meet the growing user demand.

  1. Scaling big data systems efficiently 

Enterprises spend a lot of money storing and managing big data if they don’t have a clear picture of how they want to use it. Old information in the repository needs regular curation, especially when the data predating the COVID-19 pandemic might be outdated or irrelevant and no longer accurate in today’s market.

It’s easier to start from one data model and expand to others, but while doing this, you quickly realize the model doesn’t fit your new data points. A generic data lake with the pertinent data structure can make it more manageable to reuse data efficiently and cost-effectively.

  1. Governing big data environments

It becomes harder to address big data governance issues as big data applications grow across more systems. Moreover, this challenge only compounds as the new cloud architecture captures and stores all data in an unaggregated form. Putting more time in identifying and managing data governance issues from the beginning will make it easier to cater to a wide range of new use cases in a more extended period.

  1. Ensuring broader use cases are understood and supported

Enterprises often tend to overthink the technology but ignore the context of the data itself and its uses for the business. Usually, the maximum effort goes into managing big data storage architectures and security frameworks, but onboarding users and use cases get side-lined, resulting in misalignment and mismanagement.

  1. Modern data stacks are often underbuilt for operations

Though the current approach of analytics-led initiatives offers a strong foundation, the shift towards more complex use cases is still nascent. Enterprises are beginning to see how infrastructure investment can help accelerate supply chain, demand forecasting, capacity planning, preventive maintenance, user engagement, and more. These operational needs usually fall under cracks if not planned from the start.

  1. Very less machine learning is used to boost database performance

Leveraging machine learning can significantly impact resource utilization and query cost, especially in a multi-tenant environment. Machine learning can typically happen at query optimization, workload forecasting, cost modeling, and data partitioning. Most companies use some subset of technologies but doing machine learning at scale is among the most challenging data problems today. There is too little-to-no machine learning for improving database performance.

  1. Finding and fixing data quality and lineage

The algorithms and AI applications built on big data can render bad results when data quality issues creep into big data systems. How do architects and analysts know when to feel confident about a specific dataset? These concerns can become more intense and more challenging to audit when data analytics teams attempt to pull different data types. Duplicate entries are most common when data flows from various sources, jeopardizing the accuracy of business insights generated by data analytics.

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