Today, data is in the DNA of digital enterprises. The global big data market is set to reach $103 billion by 2027. Every single click, view, and visit, along with every small detail from the real world, is expected to be captured to create an extensive digital trail that becomes a goldmine of information for mature data organizations.
However, organizations unaware of data management challenges open their business to grave risks. According to Gartner research, the average financial impact of poor data quality on organizations is more than $12.9 million annually.
The stakes become quite high for companies handling consumer data. Data privacy is becoming imperative, and customers are more vigilant about where their data is being used and how it is managed. Now more than ever, it is crucial for companies to think about what challenges they face.
There is a high cost to pay for low-quality data management. In this blog, we'll look at the top 6 big data challenges organizations face persistently and provide practical solutions for overcoming them not just in 2023 but over the next few years.
Top 6 data management challenges businesses will face in 2023
Data management is a broad term encompassing everything from data governance and quality to data warehousing and mining. While the challenges associated with managing data are constantly evolving, some issues continue to plague organizations of all sizes. Creating a future-proof data management strategy is a complex task. Following are the six data management challenges organizations must address:
Handling Voluminous Data
Today, with data becoming fuel for businesses, organizations vigorously gather data from many streams. This includes structured data (e.g., databases), unstructured data (e.g., social media posts), and semi-structured data (e.g., JSON). This data is stored in different formats, such as text, images, audio, and video. It needs to be managed effectively to be useful for businesses.
However, the sheer volume of data and the variety generated daily can be overwhelming for businesses, making it challenging to identify the most crucial information. To make matters worse, this data is often spread across different departments and systems, making it even harder to get a holistic view of the business.
Dealing with Big Data Cost
A Forrester survey found that 82% of data management decision-makers find controlling and forecasting data costs difficult, so the challenge of investing in big data looms heavily. The cost of big data can be divided into two main categories: the cost of acquiring and storing the data, and processing and analyzing it.
Additionally, the volume of internal and external data sources, different security and privacy concerns that apply to them, and keeping the overall costs in check – all make it cumbersome for a company to deal with big data expenses. Organizations need help to keep pace with data growth and its management.
Costs can quickly grow as an organization realizes it needs more data storage space and tools to analyze the data.Data is never 100% consistent, so the larger the dataset, the more likely it is to be prone to errors. That only adds to the company's cost if there’s potential damage.Another factor is maintenance. It is important to ensure that the data architecture is sound and that technologies used to handle data are not outdated. Maintenance is also an expensive task that organizations must consider.Even with a shift to cloud infrastructure, which most businesses opt for, cloud expenditure needs to be monitored very closely.Lastly, hiring one of the big-name consultant companies may be out of your budget, and contracting with unproven consultants might lead to expensive mistakes. So, finding the right fit data management consultants is important.
Bridging the Big Data Talent Gap
The ideal big data expert understands the bigger picture. While there are big data professionals who might be good at any one discipline, the data community needs a workforce that combines expertise across data strategy, roadmap, architecture, engineering, data reporting and analytics, and much more.
Grappling with Data Silos and Poor Data Quality
Data management is critical in making business decisions, managing data and business operations, customer support, and many other functions. But data is not used to its full potential in many organizations simply because it resides in a myriad of silos across teams and departments. It is often stored in multiple sources, making it harder to identify which data is the latest/most accurate or which can be used from all the existing copies.
Data quality concerns can also arise due to inaccurate or incomplete input or errors in the data. Poor data quality may introduce inaccuracies and biases into the decision-making processes that can jeopardize the success of an organization.
Synchronizing Data Gathered from Distinct SourcesPeople are inevitably tasked with aggregating, normalizing, and analyzing data to extract insights. This can be tedious as data may come in different formats, at different rates, and from various locations, like inside the organization from multiple departments or sites, third-party vendors or service providers, or both.
Often different teams, each with their own goals and KPIs, work with the data simultaneously. This commonly leads to misalignment in strategic decisions, mismatched data, partial explanations, and more questions. That’s why consolidating all data, keeping it updated across all the various sources, and synchronizing it becomes challenging - and it is crucial.
Real-Time Data Challenges
Real-time reporting is key to organizational growth. For example, the booking portals in hospitality must sync with a hotel’s real-time booking update.
A few more examples:
Telecom companies use real-time data to track usage and quality of connection to deliver good customer service.
In Commercial Real Estate, the developers need to be aware of changes in their key demographic, macroeconomic trends, real-time listing updates of demand and supply, etc. - which is possible through real-time data analysis.
The aviation industry benefits largely from data analytics.
Ticket booking analysis helps the industry target customers with personalized offers while optimizing the price in real-time using predictive analysis techniques. This allows airlines to gain more bookings in the given timeframe.
Airlines use AI to define destinations and adjust prices for specific markets, find efficient distribution channels, and manage seats to keep the airline competitive and customer-friendly.
Airlines also save costs using real-time baggage tracking and built-in ML algorithms. Real-time tracking avoids losing, damaging, or delaying bags, considerably reducing damage costs traditionally paid to customers.
The built-in AI systems collect and analyze flight data about route distance, altitudes, weather, aircraft type, and weight to estimate the optimal fuel needed for a flight and save on fuel costs.
These real-time bits of information quickly adjust factors such as cost and availability. However, this data is generated at high volumes and speeds, making it difficult to process using traditional methods. The obstacles with real-time data are:
Storage - how to store the voluminous data generated every microsecond
Data quality - for data to be optimally used, correct data needs to be entered
Uncertainty - how to glean the appropriate actionable insights with the huge amount of collected data
Complexities of data integration
And understanding the relationship and dependencies between all the gathered data
Best Practices for Data Management in 2023 and Beyond
As businesses increasingly rely on data to make decisions, managing that data effectively becomes more critical. Here are some best practices to look out for in 2023 and beyond.
Handling Voluminous Data
Data Lakes: A data lake is a central location where all kinds of data may be kept in their original form. This makes it easy to process and analyze the data using various tools.Data Warehouses: Data warehouses are centralized repositories of information used for reporting and analysis. They often store large volumes of data that would otherwise be difficult to manage.
Data Marts: Data marts are smaller, more specialized data warehouses. They typically contain only information relevant to a specific business unit or department.
NoSQL Databases: Numerous unstructured data sets may be handled by NoSQL databases. They provide high performance and scalability while being easy to use.
Data Discovery Tools: Data discovery tools are used to find insights from large amounts of data. Data preparation, modeling, visual analysis, and advanced statistical analysis are the key functions of data discovery software like Tableau, Power BI, Looker, etc.
Big Data Platforms: Organizations should keep an eye on advanced storage/compute solutions based on GPUs. New GPUs for 2023 will see the ability to securely partition tenants, which has previously been a dealbreaker for some data applications.
Advanced Analytics Techniques: Use advanced analytics techniques such as predictive modeling and machine learning to identify the most critical information automatically.
Dealing with Big Data Cost
Organizations must invest in new technologies and architectures to store, process, and manage data cost-effectively and address this challenge. Here are some promising solutions:
Open Source Technologies: Open-source tools offer many of the same features as commercial toolsets and are highly customizable and applicable for embedded solutions or for incorporating into commercial data solution offerings. RAPIDS (https://rapids.ai/) is a suite of open source software libraries that allows you to execute end-to-end data science and analytics pipelines entirely on GPUs.
Cloud Computing: Cloud computing provides a scalable and economical solution for processing and storing big data. It offers pay-as-you-go pricing models to help organizations save on upfront capital costs. Additionally, cloud providers offer managed services that can take care of the operational complexities involved in managing big data environments.
NoSQL Databases: NoSQL databases are purpose-built for storing and processing large amounts of unstructured data. They use a variety of data models (e.g., key-value pairs, columnar, document) that are more flexible than the traditional relational model. As a result, they can scale horizontally to support extensive data management.
Compression Techniques: Data compression can help reduce the size of big data sets, lowering storage and processing costs. Many compression algorithms are available, so selecting the one best suited for the intended data is essential. For example, Snappy is commonly used for database compression, while gzip enables HTTP compression. LZ4 works for general-purpose analysis, and Zstd is designed for real-time compression.
Eliminate Unnecessary Data: One of the simplest ways to reduce big data costs is to stop collecting and storing unnecessary information in the first place. Organizations must establish clear criteria for what constitutes "useful" data and purge everything else regularly. You can use data cleansing tools like OpenRefine, IBM InfoSphere QualityStage, and TIBCO Clarity.
Bridging the Big Data Talent Gap
Many organizations find it challenging to build their team internally. It requires heavily investing in training and development programs to help employees gain the skills to work with big data or steep monetary commitments in talent sourcing.
There are several ways to find talented individuals who may still need to gain experience with big data.
Hire data-savvy employees: Look for employees who have shown an aptitude for working with large amounts of data or have successfully implemented new technologies within organizations.
Train existing employees: Provide training and development opportunities for employees who want to learn how to work with big data.
Partner with educational institutions: Reach out to institutions that offer big data and analytics programs and ask them to recommend students or recent graduates who may be a good fit for the organization.
Alternatively, Enterprise Data Management Solutions Consultancies can bridge the talent gap. Organizations should seriously consider hiring consultants or contractors with expertise in big data management. These consultancies solve enterprises’ most complex data management challenges every day and provide them with best practices and data-driven insights through automation and machine learning.
Grappling with Data Silos and Poor Data Quality
Organizations can address data silos and poor data quality through organizational changes and technical solutions. Here are a few solutions that can be a part of your organization’s data management best practices.
Implement a Data Governance Framework: This will help organizations to establish clear rules and processes for managing data. It will also ensure that everyone in the organization follows the same guidelines.
Implement Centralized Storage and Access: Another way to break down data silos is to implement centralized storage and access for all data. This way, everyone has the same version of the data and can easily access it when needed.Invest in Data Quality Tools: These tools can help organizations clean up organizational data and improve its quality by identifying, understanding, and correcting flaws in data to ensure effective governance across operational business processes and decision-making. For example, Cloudingo finds and eliminates duplicate records in the Salesforce database. Other similar tools are IBM InfoSphere QualityStage, SAS Data Management, and TIBCO Clarity.
Use Data Visualization Tools: Data visualization tools like Tableau, Dundas BI, and Google Charts make complex data sets more understandable and accessible to everyone. They allow you to input a dataset and graphically alter it. Most tools come with pre-built templates for creating simple visualizations. This can help break down barriers between different groups who may need help understanding each other's data.Onboard Data Management Firms/Consultancies: Data management consultancies can help organizations make sense of all the data they collect. They can also help organizations find ways to improve data quality. Their services include:Developing data management strategiesDesigning and implementing data management systemsProviding support and training on using these systemsIn addition, data management firms can also provide consulting services for data governance, quality, and security. Organizations can benefit from the expertise and experience of these professionals to improve their data management practices.
Synchronizing Data Gathered from Distinct Sources
The best approach depends on the application's specific needs. Here are a few:
Data Integration Platforms: A data integration platform is a tool that helps to connect different data sources and allows for easy data synchronization. This option is ideal if organizations have a lot of disparate data sources that need to be connected. A few examples are - Snaplogic, Dell Boomi, and Pentaho Data Integration.
Data Synchronization Software: Data synchronization software is designed to help two or more databases stay consistent with each other. This option is best if organizations have a few databases that need to be in sync. For example, the tool - rsync synchronizes files between two systems, while a cloud-based service like Dropbox or Google Drive automatically syncs your files across your devices.
Manually Sync Data: If organizations only have a few data sets that must be synchronized, they can do it manually. This option requires more upfront work but can be less expensive than a platform or software solution.
Data Mesh: Data mesh enables end users to quickly access crucial data without transferring it to a data lake, warehouse, or professional team. It focuses on decentralization and spreading data ownership across teams and reduces bottlenecks.
Real-Time Data Challenges
A few solutions to managing real-time data management:Business Intelligence Reporting Software: Organizations can analyze raw data without delay by merging real-time reporting with business intelligence software. BI reporting software manages real-time data challenges by collecting data from various resources and providing it in an easy-to-read format like Dashboards.Dashboards: Pixel-perfect dashboards that are rich and dynamic can be offered to thousands of users. They generate customized reports for online, print, or mobile platforms. Dashboards can combine data and various KPIs and deliver at-a-glance summaries. Users can view the state of business, generate insight into the historical and real-time context, and act faster.
Data Integration: Data integration software can build a data mart or warehouse to extract, transform, and load (ETL) data from different sources for reporting and analysis purposes. With data virtualization technology, disparate relational or non-relational data sources can be combined and easily accessible to anyone in the organization. A few examples are Azure Data Factory, Informatica, and Qubole.
Employ caching: Cache reduces the need to access the underlying slower and more durable storage layer to expedite data retrieval and improves performance in real-time data processing systems. When employed correctly, caching can reduce latency and improve throughput.
Use a message queue: A message queue is a software component that stores messages until an application or system can process them. A message queue can help decouple applications and systems, making it easier to scale them independently. Examples of queues are Kafka, Heron, real-time streaming, Amazon SQS, and RabbitMQ.
An organization's data management strategy should ensure that data is accurate and secure. A robust data management system makes data more visible, organized, and structured. A modern data management system is critical if businesses are to cope with the growing amount of data. This is possible with a holistic and optimized data ecosystem managed by experts.
Read our customer success stories to learn how Waterloo Data is pioneering big data management solutions. You can also reach out to us at info@waterloodata.com.
