Businesses aim to deliver quality products and services efficiently and quickly depending on the customer requirements. Currently, speed and efficiency rely on the valuable insights derived from Big Data. However, a few challenges exist in capturing the information and analyzing it.

The primary blockage is the unorganized data. A lack of proper organization will not yield any meaningful insights.

Major pain points of Big Data infrastructure

Specific major problems lie hidden in data management. Hence, the necessity exists to resolve them initially before going ahead to collect the right information, assess it, and utilize it to make various business decisions.

Below discussed are a few pain points concerning big data management and the ways to resolve them:

1. Preventing business goals to  drive data and cloud strategy

Businesses have to define their business goals first clearly. The workflow should follow this: The business goals drive the data strategy, which drives the cloud strategy. Big Data, however, does not allow this to happen.

2. Not quick enough to meet customer needs

Data talks about customers, including their purchase patterns, buying behavior, thoughts and reactions. Businesses have to be quick enough to retrieve data and respond quickly.

Organizations can minimize the time to market, find unique paths to attract customers, and interact with them through agile data operations. However, many organizations fail to do so quickly and usefully.

3. Unsuccessful in optimizing the digital world

Organizations have to collect vast volumes of data, manage it, secure it and enforce laws impacting it. Mobile devices, smart watches, cars, and IoT devices are all interconnected. Consequently, the types of data and the sources businesses are receiving are also changing.

Knowing the data location and who has used it, altered it, or interacted with it involves complex procedures. Data collection, management, and governance of data surprises and confuses many organizations and hinders utilizing this data to the maximum.

4. Lacking flexible infrastructure to give space to big data management

Big Data speaks of the three V’s namely the volume, velocity, and variety. However, certain limitations and a lack of clarity exist in understanding the infrastructure necessary to manage them. So, the current need demands using a flexible and agile infrastructure to satisfy the current business needs and upgrade them to meet the future essentials.

Even though many enterprises seek solutions in the cloud, pain points still exist. Cost predictions, diminishing unused resources, and preventing customer lock-in are some challenges most organizations avoid.

The data management architecture requires a new approach to adopt. If the analytics cannot reach out to all the data sources,  all customer requirements are unmet. Despite the available data, much of it remains unused, which is of no use.

Failure to extract data insights in real-time does not give you the present information but the past data, which cannot make future predictions accurately. Thus, it results in resource wastage and costs if such a  pain points persist.

A new architectural approach to data management

An enterprise data cloud addresses the pain points by easily giving agility, flexibility, and alignment with the business strategy. An enterprise data cloud provides a single platform to see your data sets, manage security and data governance consistently and manage your cloud spend.

Enterprise data enables extracting meaningful insights out of data. Digital transformation should know how people use the data, where it is stored, and how it is accessed. Due to this, most organizations tend to ignore or prevent using their transformation strategies completely.

Following are the ways to address big data infrastructure pain points

1. Slow storage media: One best way to upgrade your data infrastructure SSDs(solid-state disks), which run comparatively faster. Otherwise, you can even use in-memory data processing, which is relatively faster than conventional storage.
2. Lack of scalability: A solution exists in deploying heavy data workloads in the cloud by which you can grow the infrastructure size virtually while you need it. As an alternative, you can keep your workload on-premise. However, keep the cloud infrastructure ready to manage the spillover when they arise until you create a new on-premise infrastructure to handle it permanently.

3. Weak internet connectivity: Have more bandwidth to solve the issue. But, this costs you more. Another better approach would be to reduce the amount of data transfer across the network.