Data overload: The real conundrum for an enterprise

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Written by: Neelesh Kripalani, Chief Technology Officer, Clover Infotech

Organizations have embraced the era of big data with open arms. The problem is that many organizations have started feeling inundated with data. As organizations seek to keep pace with rapidly growing environments, it continues to get more critical for enterprise leaders to employ data-driven decision making. They are unable to make any productive sense of it all as well as they can’t separate valuable information from noise.

Many enterprise decision makers are struggling to keep up with the volume and variety of data in their organizations—and the challenges only get tougher. While data is integral to every key business outcome, more isn’t necessarily a good thing.

Challenges of Data Overload

Ever-growing Data

Technology environments continue to grow more dynamically. Environments now tend to feature a diverse mix of technologies, clouds, containers, microservices, big data and business intelligence platforms. Increasing layers and types of security technologies continue to be implemented as well.

Future Potential

Data has a lifespan. At some point in time, it becomes irrelevant, obsolete, or outdated. But often it is held onto anyway in the mistaken belief that some-day it might be useful. Collecting and storing data costs money – it requires storage, electricity to power it. Moreover, if the information is sensitive such as customer’s personally identifiable information, then its security and data compliance is an added responsibility.

Massive Data Lakes

Siloed organizational structures, and legacy technologies can’t keep pace with the data quantities, speed, and scale that define businesses today. In recent years, organizations have tried to address data overload by implementing centralized data lakes. But without an underlying data strategy, organizations have found themselves scrubbing through a massive data lake with disconnected information that hardly provided any valuable insights.

Data Paradox

According to a study named ‘Data Paradox’ by Forrester and Dell Technologies, businesses are struggling to reconcile several conflicting data realities (data paradoxes) such as:

Businesses believe that they are data-driven, yet many are not treating data as capital and do not prioritize its use across the organization.
Businesses are gathering data faster than they can analyze and use, yet they constantly need more data than their current capabilities can provide.
Businesses recognize that an as-a-service model would enable them to be agile, scale and reduce complexity, but only a minority have made the transition.
The way enterprise leaders are approaching data-driven decision-making is fundamentally changing. CIOs must look for new ways to store, analyze, and report on data, employing approaches that are augmented by AI and machine learning.

Steps to Curb Data Overload

If organizations want to avoid drowning in data while thirsting for insights, they must develop a smart data strategy that focuses on the few things they really need. Rather than worrying about ‘big data’, organizations must embrace ‘smart data’ — which means, they must define the questions they need answered, and then collect and analyze the really relevant data which will accelerate decision making while ensuring cost-effective storage.

The volume and velocity of data that is analyzed continues to grow. Business leaders must accelerate their ability to sift through massive amounts of data and gain the actionable insights they need to optimize performance and investments. Investing in siloed solutions will only lead to drowning in data, while lacking real insights into business outcomes.

Decision-making processes must undergo fundamental transformation, growing increasingly event-driven and automated. Teams must embrace AI-driven intelligence platforms that provide breakthroughs in the way decision-making is accelerated and automated within an organization.

With the help of infrastructure modernization, organizations can meet data where it lives, at the edge. It brings the infrastructure and applications closer to where data needs to be captured, analyzed, and acted on. By optimizing data pipelines, data can flow freely and securely with the help of AI and ML augmentation.

Conclusion

Over the next three to five years, organizations will have to commit to digital transformation on a massive scale, including fundamental cultural and operational transformations. Data will be a vital tool to reach these business objectives and smart-data approach will enable organizations to achieve better business outcomes and help them to be ready for the zettabyte era.

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