Companies unleashing Big Data Analytics highlight the positive aspects and the galore future possibilities. Big data analytics is helping businesses to analyze unstructured, semi-structured as well as structured data to gain valuable business insights. Leveraging the Big Data Analytics, businesses are making faster and smarter decisions. It is driving the development of innovative products and services for improving customer experience. It is being used in numerous ways to provide smarter learning experience for children, improving the quality of healthcare, facilitating better travelling and hospitality services, carrying out sentiment analysis and much more. Big Data Analytics can be immensely powerful but quite creepy at the same time. It possesses great power but the need of the hour is to utilize it carefully and ethically. Unethical usage may lead to new moral problems. Here, comes the darker side of the force.
With data volume increasing with each passing day, the problem lies with how to extract value from the voluminous amount of data. There is a need to address the quality aspect of unstructured and semi-structured data. It is necessary to employ scalable, robust, and other high-end technology tools. It also takes some really good, proficient Data scientists to ensure data quality and deal with Garbage-in problem.
Data Scrapers for Data Analysis extract data from client’s website including keywords, titles, and content categories. Companies are benefitting from Data scraping by keywords driving traffic to a website, content categories attracting user engagement, titles ranking high in SEO and many more such use cases via web crawling. Data Insights through data analysis are being collected, aggregated, and correlated to gain monetary benefits. The dark side of big data however includes open misuse of data by companies. This misuse of data is being carried out in different forms including using it for undisclosed purpose or sharing it with third parties for some revenue generation. Companies are openly misusing data by using collected data for profit and other professional motives.
In today’s world, it is impossible to transact without being tracked on a daily basis. Most people are aware of the fact that their data is being collected, analyzed and being used. Receiving a purchase suggestion, special offer or discount coupon occasionally is alright. However, the line is crossed when a company follows you everywhere tracking your every online transaction. Dark side of the force in HR comes when recruiters hunt for the employable prospects’ personal or financial data using data analytics. Stalking a prospect online is not the kind of experience companies wish to create for their stakeholders. Companies need to understand the thin line between stalking and offering the right suggestions or solutions. Moreover, the companies need to take up the responsibility to offer a satisfying customer experience without stalking them. Apart from the companies going rogue with big data, big data itself causes havoc for companies and they might find it hard to tame it. This is where, Data Crawling service providers such as PromptCloud, come to your rescue. The common issues companies might face are:
Big data contributes to the pile of unstructured data silos that make data integration a challenging job. Enterprise big data is coming from different directions from transactional and BI databases, mainframes and ERP systems, and from customers and suppliers. Moreover, these sources have their own data model worsening the situation. To enhance the complexity of the situation, enterprise data makes data integration difficult as it is distributed as well as decentralized. Extracting value from enterprise data is a tedious and challenging job.
Unstructured data is huge and growing in voluminous amount with data being generated from different sources every second. Businesses want to extract meaningful data from unstructured data sets and solve different business problems. Depending upon what business decisions companies need to take, companies need to cater the issues of data integration, data quality and unstructured text analytics. After catering to these issues, companies will be able to effectively utilize the Big Data analytics, text mining, sentiment analysis, contextual analysis, and others as well.
Sentiment analysis of Big Data generated by customers through their reviews, text messages, comments, and social networking updates, is being considered as an important aspect in understanding customers’ behavior or feedback. It can be valuable sometimes but false praise and fake reviews are being generated to gain benefits often resulting in skewed results of sentiment analyses. It is one of the dark aspects of Social Media Marketing.
We are entering a perfect storm around big data and predictive analytics. There is a growing violent flow of unstructured data from different sources on a daily basis. The cost to acquire, store and process unstructured and semi-structured data has dropped and will continue to drop. New generation of trained practitioners and cutting edge technology tools possess the ability to extract insights from diverse sets as never before.
Organizations are facing varied challenges as there is no specific set of guidelines to follow. Few written standards and guidelines exist. In the era of ever changing technological capabilities, those guidelines are going to be outdated quickly. An innovative framework considering privacy issues and minimizing negative impact is required to deal with the dark side of the analytics.
Several Data Providers for Data analytics are offering Big Data solutions to unleash Big Data benefits and drive actionable insights from big data. These Data Providers need to understand the thin boundary between ethical and unethical data usage. The most important aspect regarding the dark side of Big Data can be understood keeping in mind the customer’s perspective.
Data Analytics companies need to have a clear outlook to avoid creepy behaviour while using Big Data Analytics. Machine learning, computer algorithms, and Big Data analytics cannot judge between creepy and non-creepy behaviour. It is the service provider that needs to take up the responsibility to ensure data usage remains moral and ethical. The first step towards ensuring ethical big data practice is to ensure customer satisfaction without stalking or harming them.
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