Common Challenges in Data Analytics

Data analytics has become an integral part of decision making for businesses and organizations across industries. With the rise of big data, companies are now able to gather and analyze vast amounts of information to acquire valuable insights and drive informed decisions. However, as data analytics continues to evolve, teams face multiple challenges that can impede their ability to uncover these insights and turn them into actionable strategies. The road to actionable insights is fraught with obstacles, from ensuring data quality and overcoming technical hurdles to fostering effective collaboration among team members. 

At its core, data analytics is the examination of large sets of data to find patterns and trends that can inform decision making. This involves using various tools, techniques, and methodologies to extract meaningful insights from the data. However, this process is far from straightforward and comes with its own unique set of challenges.

This blog covers some of the most common data analytics challenges facing teams today and equips you with practical strategies to conquer them. 

Managing Large Volumes of Data 

Managing large volumes of data is a major challenge in data analytics. As technology continues to advance, businesses generate and collect more and more data from various sources like social media, customer interactions, sales transactions, and website traffic. However, managing these vast amounts of data manually has become nearly impossible for most teams.

One key reason teams struggle with managing large volumes of data is reliance on manual processes. This inevitably leads to errors and consumes a significant amount of time and resources. With thousands or even millions of rows of data to be analyzed, it becomes crucial to have an automated process in place for collecting and managing all of it.

Automated data collection tools allow businesses to efficiently gather large volumes of structured and unstructured information from different sources. These tools simplify the process by automatically extracting relevant information from multiple databases and other sources without any manual input. This ensures that all necessary information is collected accurately and on time. Savant’s automated workflows can collect data from numerous sources without human intervention, guaranteeing high-quality, up-to-date data to power your analytics needs.

Automated data management systems further help teams organize and store their massive datasets in a centralized location. This simplifies the process of accessing and analyzing the information when needed, saving valuable time that would otherwise be spent searching through various files or databases.

The exponential growth of data volumes can quickly overwhelm teams that aren’t equipped with the appropriate tools or strategies to handle it effectively. The more extensive the datasets become, the harder it gets for humans to analyze them accurately without making mistakes or missing critical insights..

Automated data collection and management systems can help businesses streamline and simplify their processes, saving time, resources, and improving overall accuracy in their analyses. 

Ensuring Data Quality 

The quality of data is a crucial factor in determining decision-making accuracy and reliability. High-quality data is imperative for successful analytics. 

Problem 1: Incomplete Data 

Incomplete data refers to missing or partial information that does not provide a complete understanding of a situation or problem at hand. For instance, if sales data from different regions is incomplete, it would be challenging to identify trends or patterns accurately. This could lead to ineffective marketing strategies and incorrect forecasting, resulting in lost revenue opportunities and wasted spend.

Problem 2: Inaccurate Data 

Inaccurate data refers to incorrect information that does not reflect reality accurately. It can include spelling errors, outdated information, duplicate entries or mismatched values between different datasets. For example, if inventory levels are inaccurately recorded in a retail store’s database, it could result in overstocking or stock shortages leading to financial losses and poor customer experiences.

To overcome the challenges posed by incomplete and inaccurate data, organizations must focus on implementing robust processes for validating their datasets regularly:

Data Validation – Conduct thorough checks on incoming data to identify any anomalies such as missing values or inconsistent entries.

Data Cleansing – Once anomalies are identified through validation techniques, rectify them through automated tools or manual data entry.

Data Governance – Establish a framework that consistently ensures data accuracy, completeness, consistency, and integrity. Define policies and procedures for effectively managing data throughout its lifecycle. 

Also Read: How To Get Started With Data Orchestration Tools and Processes

Data Integration From Multiple Sources 

Data integration is the process of combining data from disparate sources into a single, unified view. This can quickly become a challenge when different formats, structures, and definitions across sources come into play. 

Organizations often use different applications and databases to store their data, resulting in silos of information that cannot communicate with each other easily. As a result, when trying to integrate these systems, issues such as incompatible formats or missing metadata often arise, leading to errors in the integrated dataset. Disparate systems may also have varying levels of security protocols or access controls, which further complicate the process of accessing and merging data from them. 

To overcome these challenges, organizations can leverage Extract-Transform-Load (ETL) processes and tools specifically designed to manage large volumes of data from different sources.

The first step in this approach is extracting data from various sources using ETL tools like Informatica or Talend, which help retrieve structured or unstructured data efficiently while maintaining its integrity. This is followed by the transformation stage, where the extracted data goes through processes like cleansing (removing duplicates or irrelevant records), harmonizing (standardizing field names), and enrichment (adding new attributes), so that it conforms to a common format.

After transforming the datasets across all systems into uniform structures, the next step is to load all this data into a consolidated storage system like a data warehouse or data lake. As each dataset is obtained and transformed, new rows of information are created or updated in the storage system, making it ready for immediate analysis.

Integrating data from numerous sources can be complicated, but with proper planning and implementation of ETL, the entire process can be automated to quickly and easily create unified datasets that provide valuable insights to drive effective decision making. 

Million Dollar Baby’s (MDB) success story highlights the impact of Savant’s automation capabilities. Savant’s no-code platform for data integration helped MDB automate critical tasks such as inventory updates, marketing analytics, and supply chain alerts. This not only saved over 500 work hours per month and reduced data infrastructure costs by 40%, but also accelerated data migration by 50%. With such efficiency, MDB was able to effectively manage complex supply chain and inventory challenges without requiring a large IT team. 

You, too, can save time, cut costs, and boost productivity. Discover the impact of Savant’s analytics automation today. Explore how we can assist you.

Real-Time Data Accessibility 

Real-time data is crucial for organizations to make timely and accurate decisions and gain a competitive edge. However, accessing real-time data is a challenge for many teams. 

One of the main issues with traditional data storage methods is that they are designed to handle structured, batched data rather than high-speed, real-time streams. This means that while historical data may be readily accessible, accessing up-to-the-minute or even hourly data can be a cumbersome process. Another problem with traditional databases is that they often have fixed schemas, making it difficult to handle constantly changing or unstructured data sources such as social media feeds or IoT devices. This leads to delays in updating databases and limited ability to support real-time analytics.

Also, as more organizations adopt cloud computing and distributed systems for their operations, there is an increasing need for scalable solutions like Savant that can handle large volumes of heterogeneous datasets flowing to and from various sources in real time.

Real-time stream processing frameworks allow organizations to analyze incoming streams of big data on the fly before storing them in databases for subsequent analysis. Similarly, investing in robust pipeline architecture enables organizations to efficiently process, store, and deliver real-time data to end users in a continuous and automated manner so that data stays fresh and accurate for analysis at all times. 

Data Visualization 

Data visualization plays an essential role in effectively communicating insights to stakeholders. Infographics are 30x more likely to be read than plain text, and humans processes images around 60,000 times faster than text. Creating visually appealing and informative dashboards and charts involves many intricacies. One of the common challenges in this aspect is selecting appropriate visualization formats for their data. With a vast range of options such as bar charts, line graphs, scatter plots, etc., it becomes crucial to choose the right one that conveys the message accurately and effectively. Presenting large volumes of complex data while maintaining clarity is another hurdle that requires teams to find ways to simplify information without losing its essence or oversimplifying it to the point where it becomes misleading.

To overcome these complexities and create effective visualizations that tell insightful stories with data, teams can leverage specialized tools like Tableau, Google Data Studio, Infogram, and Power BI, which are specifically designed for data storytelling. These tools also offer customization options like color palettes, themes, and icons, enabling teams to maintain consistency with their branding while also making their visuals more engaging.

One of the biggest advantages of using specialized tools is their ability to handle large volumes of complex datasets efficiently. They automatically crunch numbers and present them in visually appealing ways, allowing teams to focus on extracting insights rather than struggling with data manipulation. These tools also have collaboration features that allow teams to work together in real time and share their visualizations easily, promoting better communication and decision making within the team.

Scaling Analytics Systems 

The exponential growth of data collected by organizations presents a significant challenge for data analytics teams. The rise of IoT devices, social media platforms, and other big data sources has made data analysis more complex than ever before. Traditional on-premises systems struggle to efficiently process and store all this data, leading to slower processing times, increased costs for hardware upgrades, and hindered insights. As data volumes grow, traditional systems can quickly become overwhelmed, causing slower processing times and increased costs for hardware upgrades or additional servers.

Many organizations are turning towards scalable cloud-based frameworks for their analytics needs to address the difficulties posed by expanding data volumes. These frameworks provide a flexible solution that can easily adapt to varying business needs without significant upfront investments in hardware or infrastructure. Savant offers a cloud-based framework for analytics automation, enabling users to access, analyze, and deliver insights from modern data sources in a scalable, flexible, and accessible manner.

Cloud-based frameworks offer virtually unlimited storage capacity, allowing organizations to store vast amounts of raw or processed data without worrying about exceeding limits or slowing down operations due to insufficient resources. 

In addition to scalability benefits, cloud-based frameworks also eliminate the upfront costs associated with traditional on-premises systems, such as purchasing expensive hardware equipment or maintaining an IT team to manage it. This allows organizations of all sizes to utilize the power of big data analytics without breaking the bank. 

Such frameworks also support advanced analytics and processing abilities such as machine learning and artificial intelligence, empowering teams to gain valuable insights from large datasets in a fraction of the time it would take with traditional systems. Embracing scalable cloud-based frameworks is crucial for organizations seeking better ways to manage and utilize their rapidly growing data volumes.

Overcoming the Roadblocks to Better Analytics

It’s important to remember that these data analytics challenges are not insurmountable. The first step to overcoming them is acknowledging them and understanding how they can impact your organization’s analytics efforts. By recognizing the problem areas, teams can then focus on finding solutions tailored to their specific needs.

Staying current with technological developments and investing in effective solutions is essential. As advances such as artificial intelligence and machine-learning-based tools become increasingly accessible, organizations must adapt to keep up with the pace of change.

Make insightful decisions with Savant. Save time and money while improving accuracy and efficiency. Simplify your processes with Savant’s no-/low-code analytics automation platform. Book a demo now!

FAQs

Q. How can companies centralize analytics across multiple business units?

A. Centralized analytics platforms aggregate data across departments, enforce standardized metrics, and allow custom team-level access, creating a unified view while supporting both organization-wide and department-specific insights.

Q. How can organizations quickly adapt analytics workflows to new business requirements?

A. Flexible workflow builders, modular templates, and drag-and-drop UI enable rapid creation or refinement of analytics pipelines, allowing businesses to pivot and deploy new solutions quickly in response to evolving needs.

Q. How can organizations reduce IT dependency with no-code analytics tools?

A. No-code platforms put powerful analytics capabilities directly into the hands of business users, letting them create, adjust, and deploy analyses independently, minimizing the workload on IT and accelerating insight delivery.

Q. How do cloud-based analytics tools manage data privacy?

A. Cloud analytics tools feature robust security measures such as encryption, compliance with global privacy regulations (GDPR, HIPAA), audit trails, and fine-grained access controls to protect sensitive data and ensure privacy standards are met.