What challenges do you face when using OLAP tools for real-time data analytics?

OLAP tools are designed to help you perform complex data analysis and generate insightful reports from multidimensional data sources. However, when you need to use OLAP tools for real-time data analytics, you may face some challenges that affect the performance, accuracy, and usability of your analysis. In this article, we will discuss some of the common challenges that you may encounter when using OLAP tools for real-time data analytics and how to overcome them.

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1 Data freshness

One of the main challenges of using OLAP tools for real-time data analytics is ensuring that the data you are analyzing is fresh and up-to-date. OLAP tools rely on pre-aggregated data cubes that are built from the underlying data sources, such as relational databases, data warehouses, or data lakes. However, these data sources may change frequently and rapidly, especially in scenarios such as online transactions, streaming data, or sensor data. Therefore, you need to ensure that the data cubes are refreshed regularly and consistently to reflect the latest changes in the data sources. This may require using incremental or differential updates, scheduling frequent refreshes, or using triggers or events to initiate refreshes.

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Zain Haider ❄

Data Engineer @Dubai Islamic Bank | xWestern Union | Tableau | PowerBI | BI | SQL | Developer, Snowflake Whisperer
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With OLAP I can say I often struggle to handle the high volume and velocity of real-time data streams, and maintaining the multidimensional data structures can be resource-intensive, impacting performance. For example, If a bank uses an OLAP system to analyze real-time fluctuations in foreign exchange rates. If the system isn't capable of near-instantaneous updates, traders and automated trading systems may rely on outdated information, which could lead to suboptimal trading decisions and financial losses.

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2 Data quality

Another challenge of using OLAP tools for real-time data analytics is ensuring that the data you are analyzing is of high quality and reliability. OLAP tools depend on the accuracy, completeness, and consistency of the data sources and the data cubes. However, real-time data may be prone to errors, inconsistencies, or anomalies, such as missing values, duplicates, outliers, or incorrect formats. Therefore, you need to apply data quality checks and validations, such as data cleansing, data profiling, data integration, or data reconciliation, before loading the data into the data cubes or before performing the analysis. This may require using data quality tools, rules, or processes, or using data governance frameworks or policies.

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Sheila Omwirhiren

Finance Analyst | Financial Concepts, Analytical Skills
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In my experience, data quality was one of the biggest issues with OLAP tools for real-time. Much of my time resource was dedicated to preparing data - that is looking for the inevitable errors, omissions, and duplications.

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3 Data complexity

A third challenge of using OLAP tools for real-time data analytics is dealing with the complexity and variety of the data you are analyzing. OLAP tools enable you to perform multidimensional analysis, such as slicing, dicing, drilling, or pivoting, on the data cubes. However, real-time data may come from diverse and heterogeneous sources, such as structured, semi-structured, or unstructured data, or from different formats, such as text, images, audio, or video. Therefore, you need to ensure that the data cubes are designed and structured properly to accommodate the different types and formats of data and to support the analysis needs. This may require using data modeling techniques, such as star schema, snowflake schema, or fact constellation, or using data transformation tools, such as ETL (extract, transform, load) or ELT (extract, load, transform).

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4 Data security

A fourth challenge of using OLAP tools for real-time data analytics is ensuring that the data you are analyzing is secure and compliant. OLAP tools allow you to access and share the data cubes with other users or applications, such as dashboards, reports, or visualizations. However, real-time data may contain sensitive or confidential information, such as personal data, financial data, or health data, that need to be protected from unauthorized or malicious access, use, or disclosure. Therefore, you need to ensure that the data cubes are encrypted, authenticated, and authorized, and that the data sources and the OLAP tools follow the relevant data security and privacy standards, regulations, or policies, such as GDPR (General Data Protection Regulation), HIPAA (Health Insurance Portability and Accountability Act), or PCI DSS (Payment Card Industry Data Security Standard).

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Ranjan Mohapatra

Data Architect || Data Engineer || Data Modeler || Data Warehouse || Business Intelligence || Power BI ||Azure
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Data security becomes a big challenge when reading from OLAP . It’s usually difficult to apply Row level and object level security on the same table . Using tabular models it’s possible to some extent but the data model should be designed in such a way that you don’t have to apply RLS & OLS on the same table . Hiding or showing the sensitive information on report should be driven by the role of an user for the organisation. Usually its difficult to solve it when data comes in real time into a OLAP.

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5 Data scalability

A fifth challenge of using OLAP tools for real-time data analytics is ensuring that the data you are analyzing is scalable and performant. OLAP tools enable you to perform fast and efficient analysis on the data cubes, which are optimized for querying and reporting. However, real-time data may be large and voluminous, generating high amounts of data traffic, load, and demand on the data sources and the OLAP tools. Therefore, you need to ensure that the data cubes are scalable and performant, and that the data sources and the OLAP tools can handle the high volume, velocity, and variety of data. This may require using data compression techniques, such as bitmap indexing, run-length encoding, or dictionary encoding, or using data partitioning techniques, such as horizontal partitioning, vertical partitioning, or hybrid partitioning, or using cloud-based or distributed OLAP tools, such as AWS Redshift, Google BigQuery, or Apache Kylin.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What challenges do you face when using OLAP tools for real-time data analytics?

1

2

3

4

5

6

1 Data freshness

2 Data quality

3 Data complexity

4 Data security

5 Data scalability

6 Here’s what else to consider

Data Analytics

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What challenges do you face when using OLAP tools for real-time data analytics?

1

2

3

4

5

6

1 Data freshness

2 Data quality

3 Data complexity

4 Data security

5 Data scalability

6 Here’s what else to consider

Data Analytics

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