Challenges of Managing Big Data Opportunities

Although many people might be new to the concept of big data, the world of business is not. Recent figures show that the big data analytics market will peak at $103 billion by 2023, given that 97.2% of organizations are already investing in big data, alongside artificial intelligence (AI). What’s more, giant data-driven companies, such as Netflix reportedly save up to $1 billion per year on customer retention, thanks to big data analytics.

However, as profitable and insightful as big data seems, it doesn’t come without drawbacks. This article highlights everything that you need to know about this trend, including the challenges of big data and how to overcome them as an organization. Keep reading to learn more.

What is Big Data?

Simply put, big data refers to voluminous amounts of data that increase exponentially with time, hence difficult or nigh impossible to process with traditional methods. For this reason, the benefits of big data are unrivalled when it comes to generating real-time business insights for marketing campaigns, machine learning based on big dataset, predictive modeling, or any function that requires a better understanding of dynamic consumer behaviors.

The 5 Vs of Big Data

Although big data mimics various characteristics, there are 5 prevalent traits, dubbed the 5 Vs, that make this concept stand out from standard data sets. It is some of these traits, such as volume and velocity that create issues in big data. That said, let’s explore each trait in detail:

Volume

The concept itself is primarily known as big data, thanks to the massive amounts of data volume involved. It is the volume of data that classifies a particular set of information as “big data” or not. Online businesses started dealing with big data when the number of internet users surpassed the 1-billion mark in 2005. To put it into better perspective, experts project that the amount of created and replicated data on the internet will likely grow beyond 180 zettabytes over the next five years.

Velocity

Velocity translates to the high speed at which big data is collected from various sources. For some organizations, focusing on velocity gives them a greater competitive edge in terms of real-time analytics to understand and meet the prevailing demand. Typically, big data should be available at the right time to help organizations draw the right business insights from it. Take a time-bound event and a food restaurant as an example. Consumer data with regard to the event will only be useful during the function. After that, the data might not be that important, unless for promoting upcoming event sales.

Variety

The variety trait depicts the heterogeneous nature of big data sources, which can be structured, semi-structured, or unstructured altogether. Regardless of the type of data, their sources can emanate from either within the enterprises (in-house systems and devices), or external collection points, such as IoT devices and social networks. The data source can have varying layers that offer different values to the underlying organization. As noted, variety can be segmented into:

• Structured Data: This data is organized in predefined length, volume, as well as format.
• Semi-structured Data: This data is semi-organized and doesn’t conform fully to the predefined formal data format. A great example of this type of data includes information on work logs.
• Unstructured Data: This is unorganized data, probably collected for the first time. Examples include images, texts, and videos.

Veracity

Veracity can loosely be translated to quality. The organization has collected voluminous data from multiple sources at high speeds, but is it accurate enough to draw insights from? Veracity creates both big data opportunities and challenges in many ways. For instance, inasmuch as big data is beneficial, too much of it can create confusion. At the same time, less amount of data means businesses can’t draw full insights from it. Big data veracity can be credited to several disparate data types and sources associated with the whole concept.

Value

All the above four Vs boil down to the ultimate V of big data, which stays on top of the concept’s pyramid—value. Businesses can spend considerable resources at the above stages, but the ultimate goal is to draw value, by leveraging insights to offer customers what they need, at the right time. That said, businesses should convert big data into something that adds value to their operations, whether it’s insights, patterns, or trends.

Prevalent Big Data Challenges and How to Solve Them

Challenges of big data engineering and analytics tend to center around how businesses can establish and extract value from the same. Once that is defined, big data issues can be converted into opportunities that businesses can explore for growth and greater customer satisfaction. Here is an overview of the challenges of utilizing big data in the public sector and how to overcome them.

Insufficient Awareness, Understanding, and Education

Change is often scary, but inevitable and beneficial along the way of its implementation. A good number of organizations cannot benefit from the opportunities and challenges presented by big data, simply because they don’t understand how the concept works and applies in business scenarios. For instance, when employees don’t understand data storage and how to use databases, retrieving big data and drawing insights from the same will be nigh impossible.

Solution

Organizations should embrace big data conferences and seminars and make it the initiative for everyone on their teams to participate. Most importantly, big data training should be inculcated in all levels of the company, from the bottom to the top, especially in departments that regularly deal with data, such as marketing, product innovation, and sales.

Big Data Challenges in Healthcare

The benefits of big data cannot be overemphasized in the healthcare industry. Thanks to real-time analytics from big data, medical providers can offer optimum healthcare, expand the in-depth of their research, as well as manage chronic conditions, such as cancer easily. However, these functions are typically plagued by various challenges with big data, such as aggregation and data cleaning, given that the medical industry relies on accuracy.

Solution

Healthcare centers and service providers alike should devise better methods of aggregating and cleaning patient records from multiple sources such as session notes, wearables, and medical history databases. For cleaning, service providers should turn to both manual and automated processes that follow logic rules to enhance quality consistency. They can also leverage medical imaging technologies for better aggregation and storage.

Hiring and Retaining Workers with Big Data Skills

Leveraging big data analytics on an enterprise scale requires various professionals, such as data engineers, data scientists, as well as data analysts. However, finding, hiring, and retaining these professionals can be challenging due to the growing talent shortage in specialist IT roles. At the same time, the readily available professionals may demand steep compensation, especially if they are going to work on long-term projects.

Solution

Businesses are opting for new recruitment models, such as outstaffing and dedicated teams to hire big data professionals, without spending significant time and resources. Alternately, some organizations are also resorting to custom AI-powered big data analytics tools to automate some IT roles that are hard to fill due to acute talent shortages.

Dealing with Data Integration and Preparation Complexities

Businesses collect mind-boggling amounts of data every day, which extend beyond 2.5 quintillion bytes. This data is collected from all online and offline sources that you can think about, including ERP applications, email systems, customer and employee logs, presentations, and even business reports. Combining and preparing data from these sources for big data applications can be pretty daunting for many businesses.

Solution

These challenges in big data can be addressed by employing various data integration and preparation tools, such as:

• Centerprise Data Integrator
• IBM InfoSphere
• Microsoft SQL QlikView
• ArcESB
• Informatica PowerCenter
• Symphony Solutions

Storage and Data Security

Among the top big data risks and challenges that businesses have to deal with, daily include storage and security. The amount of information that organizations store in databases and data centers is growing exponentially, making them challenging to handle. At the same time, businesses that leverage big data insights are growing, which translates to rapidly increasing unstructured data sources. A data storage solution that is challenging to handle also implicates various cybersecurity threats.

Solution

Businesses can turn to modern data handling techniques to significantly reduce the size of big data before storage. These techniques include compression for reducing the number of bits in a data set, deduplication to eliminate duplicates from a knowledge set, or even tiering for data storage on multiple tiers. After that, an organization can leverage real-time data analytics to reveal cybersecurity risks and mitigate them before they manifest. Alternatively, businesses can expand their cybersecurity teams to enhance the safety of their big data.

Case Studies of Big Data Challenges and Opportunities

Businesses are already using big data to optimize their operations and speed up the time to market for their innovative products, especially in the healthcare industry. Here are some use cases of how Symphony Solutions helps businesses overcome the challenges of big data:

Use Case 1: Improving Accuracy in Big Data

Our client Goat Interactive uses Google Tag Manager for tracking data and conversations associated with its third-party affiliate partners. However, the growing amount of data in the African sports industry called for an upgrade in the client’s existing solutions for web data and analytics. Another challenge was data loss or data inaccuracy, thanks to the growing number of multiple affiliate parties that complicated tagging in the over 20 GTM containers.

The experts at Symphony Solutions solved these challenges by adopting GTM server-side implementations and successfully migrating the entire data within three months. This was followed by GTM container configuration and front-end development to enable server-side tagging implementation, which increased the client’s dimension for measuring performance without compromising user experience.

Use Case 2: Data Segregation and Storage in a Big Data Environment

A global pharmaceutical and biotech process research reaches out to us, seeking to replace its outmoded practices tied to email information transfers, as well as network sharing of files stored in multiple independent systems. Our experts started the job by creating an agreed-on Managed Product Development engagement model, before designing a cloud-native solution that:

• Organized information to make it easily retrievable
• Enhances the migration of files via a web-based solution
• Facilitates the designing of the best product prototype

Sum Up: Big Data is Valuable, Not Challenging

The current business landscape is highly digitized, from the consumer to the top levels of management in organizations. This means newer data sources will keep emerging, creating more big data opportunities and challenges. Leverage this guide to know how to overcome the challenges in big data by conducting staff training, hiring the right people, implementing cybersecurity risks, and aggregating your information for easier retrieval and analytics. Contact us today to get insider insights into big data engineering services and associated applications, such as data lakes and data warehouses.

FAQ on Managing Big Data