Big Data is expanding faster than ever. Therefore, all technologies, views, and applications that Big Data offers are changing quickly. Big Data could either lead to a bright future or devastation. Despite this extreme, Big Data is vital for businesses and represents an important part of consumer finance as it carries the immense potential to backup consumer financial health correctly.
Both consumers and providers can take advantage of Big Data benefits as businesses develop a tailored approach towards each consumer while keeping up the efficiency that leads to the provider’s growth.
Big Data allows you to scale
Big Data has given growing companies the means to outshine their competitors. Both newcomers and established competitors are likely to empower their businesses by leveraging data-driven solutions that help them compete well. Data wranglers in the medical field have started analyzing the results of pharmaceuticals that had global recommendation while finding risks and benefits that did not come to view during essentially limited clinical trials.
Other Big Data evangelists are using data integrated into products, from soft toys to manufacturing products, to better understand the actual usage these products are being given globally. The developed insights from the data are used to improve the quality and develop well-designed products.
Big Data will open up new opportunities and completely different categories of companies, for instance, the ones who gather and evaluate industry-specific data. Currently, most of these are companies that delve in digital environments where data about suppliers and buyers, services and products and client priorities can be captured and evaluated. Ambitious corporates have started to develop their analytics capabilities in order to make the most of Big Data.
Along with the sheer scale of Big Data, the data frequency nature in real time is also playing a key role in the digital transformation. For instance, nowcasting is the prediction of metrics (like customer reliability) on certain product instantly, something that was only performed retrospectively is now used at a larger scale, building the power of prediction. Likewise, data frequency enhances real-time experimentation with theories that were difficult to carry previously.
What are the Big Data challenges businesses face?
Hiring the right talent
You will require professionals with Big Data skills in order to develop, control, and operate those applications that discover insights. This has increased the demand for Big Data experts and most businesses offer handsome salaries for a data scientist profile.
According to Payscale, data engineers earn an average of $63,000 a year; an earning that could go up to $140,000. Data scientists can earn up to $130,000 a year, and intelligence analysts earn up to $100,000 a year.
Due to a lack of potential skills, companies have tried different approaches. Initially, they try to increase their budgets while focusing on hiring and retention.
Then they attempt to develop the required skills by providing training and encouraging their employees to take on certifications like a Big Data architect certification to help them improve their skills. In the recent times, however, organizations are looking at technology to bridge the gap between supply and demand. They have started purchasing analytics solutions with machine learning capabilities.
Big Data accompanies legal challenges
Big Data poses legal risks that are data specific, like IP ownership, licensing issues, or security questions about control over Big Data sets.
An enterprise should consciously read all license terms on which a big data set is offered and conclude warranties and security with the data’s owner. With the evolution of the data analysis process, every business should ensure a certain degree of control over the intellectual property rights on the results that the data generates.
One key area of risk for businesses is competition law. Issues on market share may arise due to the control of huge sets of big data or other anti-trust concerns from EU regulators.
Mining the relevant data
With the high data influx, the volume and variety of data are likely to skyrocket as well. Most businesses are still stuck at a stage where they need to make a decision on which data is relevant. Data science revolves around the data you need along, which is usually different from what you have. A common mistake that most organizations do is failing to mine the data that often affects decision making.
A consumer-based industry like the e-commerce industry will face a big loss if decisions are based on insights developed from the wrong data.
There should be a change in the phrasing of the commonly used question: “Are you collecting data at all?” which should instead be “Are you collecting the right data?”