Data teams face different challenges while storing and analyzing intricate datasets in data warehouses. Find out how you can overcome these challenges by using eCommerce analytics offered by digital commerce managed services tools.
Data and analytics play an important role in running an eCommerce business successfully. The analytics tools enable businesses to track and analyze business performance through reports and dashboards, giving meaningful insights. However, when dealing with extensive datasets, businesses face challenges related to storage and analysis. Data warehouses store and manage data to ensure that analytics tools can easily process it to extract meaningful conclusions by analyzing query results. In this blog, we’ll talk about the common challenges in data warehousing and how you can overcome them using managed analytics.
In the context of eCommerce, data warehousing refers to collecting, storing, and organizing data sets from multiple datasets, enabling businesses to get insights and facilitate data-driven decision-making in eCommerce.
Let’s have a quick look at the benefits of data warehousing in eCommerce.
Errors and improper updates lead to inaccurate data which impacts data quality. As businesses are increasingly implementing digital commerce solutions, they may face the problem of unintentional data silos. This makes data integration difficult throughout the system.
Inconsistencies may lead to inaccurate data in data warehouses. This further compromises the reliability of insights and reports generated from inappropriate data.
Slow performance of data warehouses results in sluggish query speeds which makes it difficult for the users to make quick decisions. Advanced solutions for data warehousing in eCommerce optimize processes and enhance performance.
eCommerce data analytics includes analyzing large data sets to understand market trends, and customer preferences, and obtain meaningful insights to find out what’s working and what’s not in your eCommerce business.
Understanding eCommerce sales analysis allows business owners to make strategic decisions, bringing high productivity and profitability. It helps them to understand the patterns in customer behavior analytics in eCommerce, and their preferences to personalize marketing strategies that resonate with target demographics. Further, it makes it easy to adjust pricing depending on factors such as competition and demand.
By using data analytics, businesses can send customized product recommendations to shoppers to increase customer engagement and sales. Also, it predicts future trends to tailor the marketing strategies to yield high ROI.
Now let’s discuss the biggest challenges that eCommerce marketers face when dealing with data.
Metrics such as page views or social media followers often look fascinating, however, do not correlate with customer engagement or conversions. Businesses should focus on high-priority metrics (cart abandonment, customer lifetime value, and conversion rates) to analyze the growth of their eCommerce business.
eCommerce solution providers must update their data sources in real-time to ensure the accuracy and integrity of data.
Data silos result in a fragmented view of business performance. Therefore, it is essential to integrate data sets to give a comprehensive view of the business, facilitating prompt decision-making considering all aspects of business.
It is easy to misinterpret data when the context is lacking. For example, a quick increase in website visitors may initially appear positive, but it could be due to a controversial ad or the virality of bad reviews floating around the Internet. Thus, the analytics team must understand and cross-reference data to ensure accuracy.
Now let’s find out some of the top tools managed analytics tech stack include:
Amazon Athena, Azure Synapse Analytics, Amazon Redshift, Azure Data Lake Storage, SAP, Amazon S3, MongoDB, MySQL, Azure Integration Services, Hadoop, Google Big Query, and Microsoft SQL Server
Microsoft SQL Server, AWS Glue, Python, Apache Airflow, Talend, and Azure Data Factory
Tableau, Microsoft SQL Server, Power BI, Metabase, MicroStrategy, Excel, Qlik Sense, and Redash
Kafka, Amazon Kinesis, and Microsoft Azure
Microsoft SQL Server, Apache Spark, and Databricks
Oracle Enterprise Resource Planning Cloud, Microsoft Dynamics 365, and SAP S/4 HANA
Azure, AWS, and Google Cloud Platform
Managed Analytics are indispensable for eCommerce analytics solution companies. They transform raw data into practical insights ready for implementation. With the assistance of analytics experts, you can obtain the most out of existing business data, and make precise decisions with the help of data-driven tactics.
The key steps of managed analytics include:
Collects data from diverse sources such as databases, files, or APIs.
Stores collected data in a centralized system such as a data warehouse to ensure accuracy and consistency.
Uses advanced analytics tools and algorithms to examine patterns and trends and generate insights to make informed decisions.
Presents insights in understandable formats including visuals, dashboards, and automated reports, making it easy to comprehend information and take necessary action.
Monitors the performance of the organization, optimizes processes, and enhances efficiency through regular improvements.
Data governance sets the guidelines and standards to handle eCommerce data to ensure its reliability, accuracy, security, and consistency. It safeguards eCommerce data such as customer information, transaction records, and confidential information to maintain privacy.
With Big Data, you can easily manage large amounts of data generated in eCommerce including customer interactions, transactions, and much more. It also tracks patterns and trends to offer predictive analysis.
A robust data architecture organizes and structures eCommerce data, optimizing data flow. It also makes it easy to integrate data from different sources and access it.
Data lakes serve as central repositories to store structured and unstructured eCommerce data, making it easy to store, process, and analyze all types of eCommerce data. Analysts extract actionable insights to facilitate a holistic approach to eCommerce analytics.
Data warehouses store and organize eCommerce data, thereby ensuring easy accessibility for advanced analytics. By preventing data silos, they facilitate easy data management and enable easy trend analysis and performance evaluation.
eCommerce analytics solutions offer impactful observations beyond the numerical data. They give actionable insights to refine strategies, target the desired audience, and increase sales. By implementing managed analytics services and solutions, businesses can not only optimize sales and marketing results but also secure a dominant place in the market.
Originally Published on Medium
Data Engineering
AI & Machine Learning
By Use Cases
Business Intelligence & Tableau
