Source: analyticsinsight.net
Understanding the daily challenges, countered in the Big Data Domain.
In today’s connected world, users and devices together create over 2.5 quintillion bytes of data daily! And access to the right data and analytics tools can greatly enhance decision-making. This is where machine learning helps in by processing thousands of data points in real-time without the intervention of any human intervention to generate actionable intelligence. So, without any doubt, big data is an emerging disruptive technology in today’s IT domain. However, like any other new technologies, big data also has its own set of challenges, especially from the noise about its potential and capabilities. According to the NewVantage Partners Big Data Executive Survey 2017, 95 percent of the Fortune 1000 business leaders surveyed said that these firms had undertaken a big data project in the last five years. However, less than half (48.4 percent) said that their big data initiatives had achieved measurable results. So, let us dive into some of the common challenges faced in this sector.
Synchronization across Disparate Data Sources
A typical big data processing includes extraction, transformation, and load approach to data integration. Here a greater part of the data is brought to a staging area and synchronized as the data sets are processed in preparation for loading into the target system. However, as the number of origination points expands and the speed at which data is produced and delivered increases. This creates a huge challenge to integrate and sync the data touchpoints, especially when they are far isolated and diverse. Though some vendors are currently offering various ETL and data integration tools designed to make the process easier, many enterprises say that they have not solved the data integration problem yet.
Handling a Large Volume of Data
The data explosion is real. Today, data is exceeding the amount that can be stored and computed, as well as retrieved. While introducing new processing and storing capacities may not be an issue, managing is. This is because companies have to look into possibilities of scaling up in a manner that is less complex, and also ensures that the system’s performance doesn’t decline and upscaling lies within budget. One of the best solutions for this is opting for creating hybrid relational databases combined with NoSQL databases.
Privacy and Security
This is one of the rising concerns among many industry experts. This challenge includes sensitive, conceptual, technical as well as legal significance. Big data technologies do evolve, but their security features are still neglected since it’s hoped that security will be granted on the application level. Also, as operations grow, several businesses cannot maintain regular checks due to the simultaneous generation of large amounts of data. Moreover, when it comes to collecting data, privacy laws differ from one politico-geographic area to another. Also, they can be applied differently depending on data type and quantity. But in any case, a single data breach can leave companies and their clients open to identity theft, liability, and loss of competitive information, so security is a big challenge that needs to be taken seriously. To counter this, leaders must plan for big data security right during the planning stage of solution architecture.
Big Data Handling Costs
The management of big data, right from the adoption stage to product launch, requires huge expenditure. Along with that, there are additional costs in developing, setting up, configuring, and maintaining new software even though the frameworks needed are open source. In the case of the cloud-based platform, too, organizations need to spend a hefty sum when it comes to hiring new staff (developers and administrators), cloud services, development, and also meet costs associated with the development, setup as well as maintenance of the needed frameworks. This is why planning as per business necessities and strategizing to allow smooth addition of extra spending must be prioritized. Another solution is instituting data lakes-these can provide cheap storage opportunities for the data one don’t need to analyze at the moment.
Recruiting and retaining big data talent
There isn’t any doubt about a large shortage of genuinely skilled and experienced individuals in big data. Although we have data scientists, data miners, data analysts, or big data specialists graduating every year; most of them either find themselves deviating away from their chosen career or end up giving insights that fail to solve the issue under consideration. And a significant share of those remaining in the pool is clueless when assigned to extract meaningful and valuable data. So to resolve this situation, a majority of organizations are turning to automated analysis solutions that utilize machine learning, AI, and automation to extract meaning from data by involving minimal manual coding.
These are some of the major bottlenecks in the path of Big data adoption in business culture. There are several minors yet everyday challenges faced in this discipline too. These include, data governance, organizational resistance, outdated or inadequate data models, poor data quality, and amplified biases.