Big Data Management Challenges And Tips To Overcome

EM Admin
Sep 19, 2018
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Is it really hard to store, secure and translate data into a value?

Simply ask, do you need to swallow iron pills for conserving your business data? Is it possible that the datasets could pile up automatically & securely?

Possible, Yes!

But, you should preferably brainstorm in depth. What plan can maximize benefits-you should premeditate. It’s one of the best ways to sail across the big data challenges. Also, you will see a dozen of business solutions and opportunities emerging through a pool of complexities. Before diving into dreams, you should debate with the possibility of failure, the roadblocks and how to leap across them.

Let’s have a glance over five current challenges in the big data management that can cause a crack into.

Five Challenges Along With Viable Solutions for Big Data Management:

1. Backup:

The digital world takes this term as a process of duplicating data. The digital space is expanding by leaps and bounds. So, it is happening progressively in the outsourcing data entry. Yet, you can’t get off the lingering challenges of unavailability of a vital data set. Perhaps, your data entry executive skipped by mistake. Or, a digital flaw missed saving hundreds of new & important human resource records or, invoices or, any vital digital record. A study of the Ponemon Institute researched that 64 percent of data loss is an outcome of human error.

You can’t copy and paste millions of data entries manually almost every day to replicate them. However, this practice ensures their hype-availability. But, if you deal with zillions of data every day, you need a game-plan to come out of tiresome copy & paste services and combating the loss of vital data.

Viable Solutions to Overcome Data Loss:

Automate backup of the intended data sets.
Hire a third-party or a hi-tech virtual assistant, like SaaS, for data security, automation, availability, data integrity, confidentiality and the privacy of data.

2. Data Aggregation: This phrase is a past of data mining process where you search through, go through data cleansing, collect and summarize millions of datasets. Working in the digital space is a boon.You get an easy access to the real time ETL (Extract, Transform and Load) operations. But, user’s interference can kill its loading speed. Besides, synchronization in table format, scalability and alert automation emerge a bottle-neck. All these issues showcase its flip side that can prove a curse for the business domain.

Viable Solutions to Overcome Data Aggregation Challenges:

Use external cache to cater the data that a user intended to get during ETL operation.
You can try the direct trickled feed/ “Trickle and Flip” approach to synchronize the data from multiple sources in a table.
Carefully set the timer or schedule in the application for on-time automated alert.

3. Unified Visibility: Different view of a dataset in multiple operations can downsize the data standardization. Thereby, it adversely impacts the business decisions. Various organizations outsource ERP (Enterprise Resource Planning) supply chains more often. Manufacturing, distribution and procurement constitute to form the supply chain. If the data come from multiple channels, it’s obvious that their data structure would be diverse. Consequently, this variation results in paralyzing a company’s operation.

Viable Solutions to Overcome Visibility Challenges:

Create graphical layout of the manufacturing data.
Use connected graph network model to visualize POS data, inventory data and order data.
Integrate smart alerts powered by machine learning.
Manually, collaborate with all networks of the data sources.

4. Data Orchestration: It’s the pulling of the real time data where the user is feeding it from. You can feed data and also manage feeds from irrespective size and n-number of smart devices, like a mobile phone, iPad or a laptop.

Mostly, this data management element is a choice of brands, like eCommerce website Amazon or Flipkart. Their feeds could break down your repport if occurs any miscommunication through the product feeds. Since it’s based on the real-time data, it helps in getting the real insight of the customers. But, a wrong product price can decline the profit margin and conversion rate.

Viable Solutions to Overcome Data Orchestration Challenges:

Integrate with upgraded data suppliants, such as application/software that you use for analysis and customer engagement.
Maintain meta-data
Use APIs management about data activities.

5. Automation: Data are the lifeblood of the most organizations presently. It’s the best way to classify a huge data at creation. One can easily define how much space the data requires to occupy over the time. But, when it comes to data processing, data indexing, de-duplication, data normalization or data verification of millions of data, it lets you sweat out. Your core competencies suffer. Also, your valuable capital exhausts.

Viable Solutions to Overcome Data Automation Challenges: