Big Data can be a double edged sword- expansive information about your customers, processes or products can help you grow and improve, but too much can bury you in cost and confusion. Here's four Big Data challenges companies need to be aware of.
Every second of every minute of every hour of every day, data is constantly being produced, some of it having greater significance than its counterpart. The term “Big Data” has been used a lot in the recent years and it refers to the enormous amounts of data that is either preexisting or being produced. Big data does not discriminate when it comes down to the form of data it contains, whether it be unstructured, semi-structured or structured. However, when it comes to organizations, they have a predilection for structured informative data. The trouble arises when companies are unable to differentiate between useful data and useless data chunks, since data does not come holding labels of what kind it is.
Big data, in terms of how companies define it, can be classified as the data that holds significant, valid information about their customers, data regarding their competitors, online data, offline data, data regarding the perception about the company in the minds of the general public and so forth. On the surface, big data analysis for the companies is utilization of available technological platforms to generate solutions, track, organize, house, manage and make sense of the information being provided to them. But this process is easier said than done. Companies cannot keep up with the enormous amount of data being generated and therefore face many challenges in turning it into actionable insights.
Since companies depend on this information to increase their business and gain certain advantages in the market, such as exploiting a competitive edge, furthering market share, creating loyalty among consumers, or establishing an ideal product or service, it's imperative that they be able to extract value from their big data troves.
Below are four challenges companies faces when trying to gain value from big data.
When it comes to quality, businesses tend not to compromise. After all, quality assurance is what sells their product out in the market. However, product quality or quality associated with a physical entity is not the challenge faced by businesses while analyzing data. Inferior quality of goods can always be improved by switching to products that can help in producing goods that have better quality and composition. The process becomes somewhat difficult when an immaterial thing is being questioned, data of a lower-grade cannot be transformed into superior data. Low-grade data can be incomplete chunks of data being stored in the company’s database, data that is outdated or containing redundant records of a single file. Such data works against the business entity rather than working in their favor. What’s worse than having no data is having meaningless data.
There is such a thing as “too much information”, however in many circumstances too much is better. When doing a statistical research, the larger the target population the more data you can collect and the better your analysis will be. Similarly in terms of “big data”, the data under surveillance is huge and holds too much information. Too much information can be great, but the focus of any big data program is that the data gathered can ultimately be utilized by decision makers to support business strategies.
Businesses implement different methodologies to keep themselves updated with the consumer's need. In other words, follow every tactic possible to gain information. Marketing teams collect data on behalf of the company by asking for feedback from the customers. Sales help in collecting data regarding customers involved in the purchasing process. Customer Relationship Management (CRM) allows recording calls for quality purposes along with gaining significant insight about the interactions between the company and its consumers. On web-based interactions, companies monitor the content being reviewed, downloaded or just being visited and use this information in their data analysis. Social media archiving, a newly surfaced phenomenon, is generating even more information by recording companies’ interaction with their consumers via social media platforms or general interaction between the consumers.
As observed, data holds a lot of significance for businesses but the challenge being faced is that data sources are unlimited and data being generated is nearly infinite. And, while the available sources of information can seemingly go on forever, company's information technology resources are limited and can only process so much at a time. As a result, useful data is lost in the process of analyzing limited sets of data.
Garbage in, garbage out. This idiom accurately captures the intensity of such situations. Businesses invest their time and asset for the evaluation of data. If the results are worthless and contain no significant intel on the consumer market, product or the consumer, than the time dedicated to the analysis is nothing more than a liability to the business. Useless data is bound to produce useless results. The dire situation can also lead to non-profitable investments and decisions. The data is sometimes declared meaningless after actions have been taken to implement it. Failed products are an example of ‘failing to use big data for the betterment of the business’, as companies’ manufacture products based on ‘low-grade data’ and the products take cash away from the company instead of generating it. This is a classical scenario of confusing what the consumer really wants with what the company thinks they would want.
In many circumstances, the time devoted to analyzing big data turns out to be profitable for the company. However, if this is not the case, than the time dedicated to such tasks have all been for naught. Hence, some companies simply shun the practice of collecting and analyzing big data.
Another challenge that surfaces is that if data is ever-changing, how can significant patterns be created? Tracking trends and offering products and services according to them is extremely difficult for businesses. They invest time in research, marketing, advertising, placing and pricing of the product, just to find out that the trends have changed and their products are no longer being demanded by the public. Product recall and losses are caused by such circumstances, therefore companies reject the concept of data analysis being absolute and conclusive. The ironic end result of such a situation is that even after the vast amount of analyzed data, decisions are still made on the company’s personal preferences, while the data is used as a general blueprint rather than an absolute result.
Certainly, these are just a few of the challenges companies face while attempting to extract value from their big data programs. Generally speaking, though, the benefits which can be had far exceed the investment required to establish a successful data and analytics program within your organization.