Challenges of Big Data Analysis

Advances in technology provide businesses and organizations with new functionality, the ability to streamline processes, and greater efficiency. Tech solutions also generate massive amounts of data. This big data holds the key to intelligent decision-making, more accurate forecasting, and even smart systems that can learn and take action on their own. To unlock those benefits, however, businesses first need to overcome the challenges associated with establishing a big data analysis process.

Data Silos

Traditional business models give certain departments access to specific types of data. For example, financial data to accounting, operational data to production managers, and customer data to marketers. Unfortunately, siloing data in this manner can create redundant work, limited productivity, and inability to innovate. 
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Eliminate data silos so organizations can use information from all sources in big data analysis takes integrating systems. For operations using legacy equipment, it can be a real challenge to find ways to connect it to a network, especially if it uses a proprietary system. Beyond technology, a company may need to make organizational changes and encourage new perspectives among employees, who may be used to managing and protecting specific data sets, rather than sharing them. 

Using Both Structured & Unstructured Data

Data from one source can be valuable, but when considered with other types of data at the same time, it can reveal deeper insights. Data from IT systems, for example, may fuel smarter decision-making when it’s analyzed with customer feedback, video, or environmental conditions from Internet of Things (IoT) sensors. 
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Not all types of data, however, are generated in the same way or in the same form. Data must be organized and formatted, and data sets must be cleaned and prepared to use them in big data analysis. 

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Data Storage & Access Speed

​Increasing volumes of data require increasing capacity to store and manage it — and, practically, a strategy that keeps mission-critical data accessible while controlling costs. Organizations can turn to data lakes or data warehouses to store data in its original format. It’s necessary, however, to manage data properly to ensure data quality and prevent data loss. 

​Some solutions are designed to produce descriptive analytics, which tell what has happened. But data analyzed and used in the moment can have much greater value. Predictive analysis can provide the most likely outcome, and prescriptive analysis can recommend next steps, even automate some processes. These capabilities have significant value in a wide range of applications, including healthcare where seconds can make life-or-death differences. 

Shortage of Skilled Professionals

There is a talent shortage in IT, in general, but there are even fewer available professionals with data analytics skills. Indeed reported in January 2019, that the demand for data scientists increased by 29 percent year over year — and it has increased 344 percent since 2013. Heading into 2019, there were about 151,000 unfilled data scientist jobs. 

A high demand for data scientists puts pressure on organizations to find qualified professionals — and keep them — with an attractive salary and benefits package. 

Multiple Technology Options

​Having a knowledgeable and skilled data analyst is also critical for choosing and implementing the right technology. There is a broad range of big data analysis solutions, including Data Analytics as a Service (DAaaS) options. DAaaS can be a more cost-effective solution, since it eliminates upfront infrastructure costs and gives organizations capabilities they may not otherwise be able to afford. DAaaS also gives teams access to the provider’s resources and expertise.  

Security & Privacy

Storing and analyzing data may create risks for cyberattack and data breach. Securing data and protecting privacy is particularly critical in highly regulated industries that store and use sensitive data. Healthcare organizations must comply with HIPAA, for example, which requires compliance with Security Rule that includes standards for protecting data stored or transferred digitally. 

Data Analysis For Data Analysis’ Sake

One of the biggest challenges related to big data analysis is losing sight of initial goals — or not setting specific goals in the first place. Maximizing the value your organization receives from big data analysis starts with a well-defined plan for the actionable insights you need to run your operation and the best way to achieve them. 
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Allow your plan (and not new technology you may feel inclined to use to keep up with competitors) to drive decisions about which data to collect, the analytics solutions to use, and the team you build to manage your project. 

What Are You Missing?

For many organizations, big data analysis is the key to operating more efficiently and competitively. Insights from big data can help minimize product contamination by analyzing temperature or other environmental data or correct issues with production that can result in product defects. Data-based insights can also enable just-in-time manufacturing using data from suppliers, workflows, and customer demand. Predictive analytics fueled by data from equipment monitoring facilitates proactive maintenance and minimizes downtime.

Is your operation getting the most from the data at your disposal to operate more effectively, profitably, and competitively? 

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About the Author

Carevoyance contributor Bernadette Wilson of B Wilson Marketing Communications is an experienced journalist, writer, editor, and B2B marketer, specializing in content for technology companies.