Introduction

In the ever-evolving landscape of technology and information, “big data” emerged as one of the most prominent buzzwords of 2012.

With its rise, a multitude of new technologies and discussions were sparked. However, a significant challenge faced by implementers was the lack of consensus on what exactly constitutes big data.

While everyone agrees that big data revolves around volume, the understanding of its true essence varies among experts and practitioners.

Unlock the keys to empowering decision-makers by delving into insights from How to Empower Decision Makers, highlighting the need for clarity and strategic guidance in data-driven decision processes.

Is Big Data still relevant in 2023?

Big data is still relevant, and it is driving changes in how organizations process, store, and analyze data

The benefits of big data are spurring even more innovation, and enterprises that make advanced use of it are realizing tangible business benefits

Reasons why big data is still relevant:

• Thanks to huge increases in computing power, new ways of processing data, and widespread migration to the cloud, we can do more with big data in 2023 than ever before
• Big data is proving its value to organizations of all types and sizes in a wide range of industries
• Big data is used to analyze insights, which can lead to better decisions and strategic business moves

Social Intelligence and Value

One of the highly anticipated areas where big data was expected to deliver significant value to business analytics is social intelligence. By mining social media platforms, companies can gain insights into the sentiments and opinions of their clients and prospects about their products, brand, and overall company image. This approach eliminates the need for assumptions drawn from focus groups and surveys, allowing for more accurate planning and swift response to emerging trends.

Top Big Data use cases in 2023

Of course, social intelligence is just one of many opportunities for big data analytics. Top use cases for big data analytics in 2023 include:

1. 360-degree view of customers
2. Data warehouse offload
3. Optimized supply chain and logistics management
4. Robust security intelligence
5. Fraud prevention
6. Price optimization
7. Equipment maintenance
8. Improved customer acquisition and retention
9. Personalized product recommendations
10. Better business intelligence
11. Medical Research
12. Proactive issue handling
13. Personalized offers
14. Reduce customer churn
15. Predictive maintenance
16. Real-time inventory management
17. Predictive analytics for healthcare
18. Predictive maintenance for manufacturing
19. Predictive maintenance for energy
20. Predictive maintenance for transportation
21. Predictive maintenance for construction
22. Predictive maintenance for agriculture

These use cases are not exhaustive, and there are many other types of big data solutions currently in use today

The Focus on Volume

The prevailing focus among commentators and technology solutions was primarily directed towards addressing the immense volume associated with big data.

Traditional relational databases were initially designed for easy data search and reporting. However, those who have dealt with large datasets know the painstakingly long hours, or even days, it can take to obtain results from queries.

To tackle this, technologies like the open-source Apache Hadoop and Spark frameworks were developed. These advancements leveraged distributed architectures and in-memory data management to support handling large data volumes efficiently. Subsequently, cloud-based solutions like Amazon EMR, Snowflake, Databricks or Google Big Query have become the go-to platforms for big data analytics.

The Challenges of Variety and Velocity

Nevertheless, the genuine challenge in dealing with big data lies not in its volume, but rather in its structure (Variety), or rather the lack thereof, and its Velocity (or speed of change).

Big data manifests itself in a vast array of formats, ranging from machine-generated feeds and telecommunications call records to unstructured web sources and business communications.

The velocity of big data analytics also continues to increase. Data analytics will focus increasingly on data freshness – with the ultimate goal of real-time, automated decision-making. Streaming data pipelines, based on technologies like Apache Kafka, are becoming mainstream and replacing traditional ETL approaches to data integration.

Reasons why Big Data might be losing its hype

• Big data is a buzzword that has been highly overused by the budding Tech-Marketers, and this overuse devalues it.
• The biggest reason that investments in big data fail to pay off is that most companies don’t do a good job with the information they already have.

Extracting Relevant Information

The real challenge emerges when valuable and relevant information becomes buried amidst an overwhelming volume of clutter. Extracting relevant content from unstructured text fields and connecting it across multiple user profiles and applications becomes crucial. Applying filters to reduce unnecessary volumes is a common-sense solution since investing in infrastructure to store irrelevant data holds no value.

Ensuring Data Quality in Big Data

Therefore, the primary requirement is to ensure that big data is fit for purpose, and this is where data quality plays a pivotal role.

It is crucial to address data quality issues, even beyond the scope of free-format text data found on the internet.

Business correspondence, such as emails, letters, and facsimiles, often contains valuable and time-critical information or instructions. However, due to the sheer volume of such communications, these critical aspects can easily be overlooked, leading to additional administrative costs or even potential legal liability if not responded to promptly.

Bridging the Gap with Technology

Applications like the Precisely Data Integrity suite bridge the gap between traditional business analytics and big data analytics. By leveraging these technologies, companies can derive value from existing data sets today, and simplify connection to modern big data platforms in the cloud.

The Importance of Data Quality

In the age of big data, the quantity of data is rapidly surpassing the quality of data. To filter out useless and irrelevant information and focus on insights, it becomes essential to apply data quality tools. As data volumes continue to grow exponentially, the role of data governance becomes increasingly critical to prevent infrastructure costs from spiralling out of control.

Explore the debate on whether bigger data equates to better data with insights from Is Bigger Data Better Data?, examining the nuances of data volume and quality.

Conclusion

Whether big data is solely about volume or encompasses a combination of volume, variety and velocity, data quality will remain a crucial factor in deriving meaningful value. When planning for big data initiatives, it is imperative not to overlook the significance of data quality.

By optimizing data quality, businesses can unlock the full potential of their enterprise information assets and stay ahead in the data-driven landscape.

Learn How to Use Data Quality to Improve Business Processes. Explore the transformative impact of reliable data on operational efficiency.