Top 7 Big Data Challenges

Learn more about Big Data.

The volume of information collected by organizations continues to grow by leaps and bounds. According to IDC estimates, the overall quantity of stored data doubles about every two years. By 2025, the world is on track to create an astonishing 463 exabytes of data daily.

The focus of Big Data initiatives is using these massive information stores to extract hidden insights and patterns that can inform business decisions of all kinds. The potential rewards are great, and yet organizations face major challenges in guiding their Big Data strategies to success. Let's go through the top 7 challenges facing big data, and how to solve them.



1. Managing the accelerated growth of data volumes

Data quantities continue to expand, much of it in unstructured data formats such as audio, video, social media, photos, and smart-device inputs. These can be difficult to search and analyze, requiring sophisticated technologies like AI and machine learning.

For storage and management, companies are making increasing use of NoSQL databases such as MongoDB and MongoDB Atlas, its database-as-a-service (DbaaS) version, which runs on any of the three most popular cloud services, and can be moved among them with no changes required. MongoDB is a preferred Big Data option thanks to its ability to easily handle a wide variety of data formats, support for real-time analysis, high-speed data ingestion, low-latency performance, flexible data model, easy horizontal scale-out, and powerful query language. Other helpful technologies are Spark, business intelligence (BI) applications, and the Hadoop distributed computing system for batch analytics.



2. Uncovering insights rapidly

Organizations don’t collect and store Big Data for its own sake; they analyze it to unearth intelligence to drive better decision making. Big Data initiatives are often undertaken to:

  • Boost operational efficiencies that can lower expenses
  • Reduce time to market for innovative product features
  • Identify promising new market segments
  • Guide the development of new products and services
  • Create a culture of evidence-based decision making

Achieving these goals depends on ingesting as much data as possible and uncovering insights quickly. Toward this end, companies invest in real-time analytics tools that let them respond to marketplace developments faster than their competitors. It’s also a best practice to tap into a great variety of sources. For example, a sports apparel company should not only scour its own historical data for customer buying patterns, but look to social media like Instagram – and even to competitors’ eCommerce sites – to identify and respond to the very latest trends.



3. Integrating data from dissimilar sources

Big data comes in diverse forms, flavors, and formats, and originates in many different places. Here are just a few examples:

  • Website logs
  • Call centers
  • Enterprise apps
  • Social media streams
  • Email systems
  • Webinars

Ingesting all this data into a single repository, and then transforming it into a unified format for analysis tools, is a complex and continuing challenge. Any company that is serious about mining the potential of Big Data needs to make correspondingly serious investments in extract, transform, load (ETL) technology, and data integration tools.



4. Finding and keeping the best Big Data talent

The applications for Big Data are practically limitless if organizations can find enough people with the skills to implement them. Not many people are actually trained in Big Data, and businesses face a major shortage of experienced and certified data scientists and data analysts.

To remedy the problem, many companies are increasing hiring budgets and jump-starting recruitment and retention. Others are ramping up training to develop and promote talent from within. Some are also tapping into the global pool of seasoned and skilled Big Data consultants and specialists. And more than a few enterprises are investing in analytic software with sufficient built-in AI and machine learning that business generalists can use the tools on their own.



5. Big Data security

Security challenges are as diverse as the sources of data coming into your Big Data store.

Information is collected from a wide range of inputs, some of which should not be assumed safe and in compliance with organizational standards. Aggregating data sources not originally intended to be combined can endanger privacy and security.

With their large quantities of valuable confidential information, Big Data environments are especially attractive for hackers and cybercriminals. This is why it’s important to build in security at an early stage of architecture planning. Comprehensive protection is almost impossible to “bolt-on” later.

Big Data security is a comprehensive and continuous requirement that must be baked into the daily routine of business. General Big Data security best practices include:

  • Create access and authentication policies. Make sure only authorized users can reach into your data stores
  • Monitor who’s scrutinizing your data. And utilize threat intelligence to thwart unauthorized access
  • Safeguard your data. Protect both your raw data and analytics results. Use encryption to prevent the leakage of sensitive records.
  • Protect communications. Safeguard data in transit to keep it private and uncorrupted
  • Vet your cloud and technology providers. Learn what security mechanisms your Big Data hosting vendors provide. Make sure they conduct periodic security audits



6. Organizational resistance

To seize the opportunities Big Data offers, companies need to rethink processes, workflows, and even the way problems are approached. This kind of change is notoriously challenging for large organizations. Failed attempts to build a data-driven culture are more often attributable to organizational impediments than technology hurdles. Typical obstacles are insufficient company alignment with Big Data goals, and lack of middle management adoption and understanding.

The Big Data paradigm needs to be embraced by senior management and evangelized down to lower organizational levels. Companies must invest in strong leaders, such as chief data officers, who understand the promise of Big Data and will drive initiatives forward. IT departments should support these efforts by presenting company-wide training and workshops.



7. Data governance

Governance is about validating data: making sure records reconcile and that they are usable, accurate, and secure. Governance is very much related to data integration: enterprises often obtain information from different systems and find that the records don’t agree. Sales figures from a company’s CRM system may be different than those recorded on their eCommerce platform. Similarly, a hospital may have differing addresses for a patient in different systems.

The challenges of data governance are complex and require a blend of policies and technology. Organizations typically form an internal group tasked with writing governance policies and procedures. They also invest in data management tools with sophisticated capabilities for data cleansing, integration, quality assurance, and integrity management.




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