Challenges & Best Practices in Data Analytics

Datamation content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.

Companies are swimming in a sea of data that takes many forms, including both quantitative and qualitative assets that, when analyzed, can provide valuable insights. But the volume of data and the rapid pace of innovation in the data analytics field can lead to challenges collecting, storing, and using that data. Businesses of all sizes struggle to keep vast amounts of data accurate and actionable and accessible to everyone who needs it while keeping customer data private and secure.

Following industry-standard best practices can help set organizations on the path to success with their own data, as can knowing about the trends driving the future of data analytics.

Data Analytics Challenges

Getting usable, high quality data analytics takes investment and work—and there are significant challenges along the way. Here are some of the most common obstacles and pain points organizations encounter:

• Not asking the right questions. The first step in getting actionable insights is to know what you are trying to discover. The temptation is to take the easy route and use data only to measure performance, but that is not forward-looking.
• Data silos. Data often resides in a variety of locations and is overseen by different stakeholders. Lack of coordination and siloed data make standardization more difficult and are impediments to getting an integrated, holistic view of your performance.
• Accuracy and quality. Collecting data from a lot of sources increases the risk that some of the data is lower quality or incomplete, which can run the gamut from missing fields to inaccurate information. The challenge for companies is in determining which data are good and cleaning the various inputs so that everything is standardized and usable.
• Security and privacy. The more data companies collect, the greater the likelihood it contains sensitive customer data that needs to be protected. Cybersecurity policies and practices that protect customer data, data encryption, and strong data governance policies are paramount, and in some cases—General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and HIPAA, for example—legally mandated.

Best Practices in Data Analytics

There are many ways for brands to better use data analytics to improve business. To overcome the challenges, companies can look to the best practices being used by their peers.

Cloud Computing

More companies are turning to the cloud to help manage their data analytics, either as the sole location for data storage and processing or as part of a hybrid data architecture. Cloud solutions can help reduce the physical and personnel demands of on-premises solutions. Cloud-based big data platforms offer enterprise-level security, large amounts of storage, data backups, high-speed processing, and scalability that will allow your data analytics to grow along with your business.

Learn more about the cost of cloud computing with our comparison and pricing guide.

Data Quality Solutions

Data quality solutions help businesses wrangle their data and make sure that the data they use for analytics is clean, standardized, and usable. Most of the data quality solutions on the market leverage the power of artificial intelligence and machine learning (AI/ML) in the data cleaning and transformation process, providing augmented analysis and improving quality.

Data Governance

Strong data governance also helps data analytics programs. Data governance is the development of policies and procedures that specify how your business works with data, including data gathering, storage, taxonomy, availability, security, and preservation.

Data governance also helps with regulatory compliance, ensuring that your internal policies and procedures comply with external privacy regulations. It improves the accessibility of data within your organization by reducing or eliminating data silos and making sure that all data is being cataloged and described using a universal set of terms.

Learn about the top cloud compliance tools.

Is Data Analytics Really Working for Companies?

Data analytics are at the core of many businesses’ efforts to optimize their operations, increase customer engagement, reduce churn and improve their products. Predictive analytics has given companies the tools to make decisions based on likely outcomes, while social listening and sentiment analysis help them keep their finger on the pulse of the market.

Here are just a few highly public examples of how companies are using data analytics:

• Spotify uses data to create personalized playlists on users’ home screens.
• Netflix recommends content based on users’ previous viewing habits.
• Marketing campaigns target customers based on past purchases.

There are even more ways that data analytics helps companies behind the scenes. Manufacturers and retailers use analytics to help them manage their inventory, aligning supply with demand and helping avoid clogging up valuable warehouse space with excess products. Logistics and delivery companies use data analytics such as traffic patterns and weather forecasts to plan more efficient routes for their drivers.

Businesses are collecting more data than ever from websites, apps, loyalty programs, mobile devices, smart appliances, email, surveys, and countless other sources that data scientists and data analysts leverage to produce actionable insights using a variety of data analysis methods.

The Future of Data Analytics

Data analytics is evolving quickly with the growth of AI and machine learning. Data analytics tools are simplifying the process and making the results more accessible to a larger group of users. Here’s a look at some of the emerging trends that will drive data analytics in the near future.

Increased Focus on Integration and Sharing

The focus of data analytics is shifting from the rise of big data and the tools needed to store and process the vast amounts of information being generated to optimizing these tools to reduce redundant data and increase speed, and on making data accessible to all users.

Data Fabric and Data Mesh

Data fabric is a metadata-driven data management design that unifies disparate data sources and makes them accessible to individuals across the organization—the goal is flexible, reusable, and augmented data. Data mesh is an architectural framework that brings together decentralized data for sharing and governance—like data fabric, it improves access to data across a business, facilitates scalability, and improves security through defined governance policies.

While similar, they are different yet complementary ways to manage the large amounts of data being used for enterprise analytics.

Edge Computing

Edge computing refers to the networks and devices closer to the end user—point of sale systems, mobile devices, or sensors, for example—that can process data locally and connect to the cloud. These networks and devices can use online data and algorithms to improve customer service and augment worker effectiveness.

Edge computing increases the speed at which data can be processed by doing it close to the source, rather than sending it back to the cloud and returning it, and can facilitate faster and real-time analytics.

Bottom Line

The world of data analytics continues to grow and change rapidly. Best practices that ensure data is high quality, managed safely and securely and accessible to everyone across your business is critical to making the most of this valuable business asset.

Read Best Data Analysis Methods to see more ways enterprise users can take advantage of the data they gather.