Insights Blog The Journey from Big Data to Smart Data: How Quality Engineering Can Help


The Journey from Big Data to Smart Data: How Quality Engineering Can Help

With quality engineering embedded into your business process, you can focus more on the data observability and make sure that the data in that data lake is high on the quality quotient. 

Since Big Data was first put into practice, many businesses have spent years developing and stowing big data but have been unable to unlock its full value. This data-driven information flow continues to increase exponentially, so businesses need to find better ways to manage, store and utilize it efficiently. 

Today, Smart Data is the new transformational tool that allows organizations to optimize business decisions efficiently. 

What is Smart Data?

Smart Data is described as the filtered, cleansed and contextualized form of Big Data. So, in other words we can say that Smart Data is Quality Data, or data that is fit for its intended use.  

Smart data enables companies to analyze, dissect and transform data into actionable insights. It helps transform vast amounts of information into knowledge, in turn feeding all kinds of valuable data for businesses to operate smoothly. 

Big Data to Smart Data: Role of Quality Engineering

Businesses have gradually realized that they cannot remain fixated on the idea of Big Data. Instead, they need an upgrade from Big Data to Smart Data to stay ahead of the curve in the highly competitive market. 

Most organizations that wish to convert massive amounts of unstructured data into Smart Data focus only on the data analytics part of it. However, your business also needs to realize that converting Big Data to Smart Data is a multiphase journey that starts with data quality and enables smart data analytics. 

When embarking on the journey from Big Data to Smart Data, businesses follow the age-old approach of collecting everything and storing it in a Data Warehouse or Data Lake. They overlook the fact that if data landing in the Data Lake is not of quality in the first place, the subsequent steps can get more complicated. 

Using low-quality data collected over the years not only messes up your analytics but also turns this historical data in the data lakes turn into Data Swamps. So, your first goal should be to keep bad data from getting into the analytics and skewing the results. 

Ideally, you can achieve it by ensuring that only quality data lands into your data lakes, and from there only quality flows downstream. However, this is usually not the case because there are scenarios where businesses have already had poor quality existing data in their data lakes that requires cleansing. 

Quality Engineering (QE) plays an integral role in ensuring that quality of data that goes into the analytics engine is in excellent condition. So, with QE embedded into your business process, you can focus more on the data observability and make sure that the data in that data lake is high on the quality quotient. 

Now that we know the importance of Quality Engineering (QE) in this journey let us analyze how it plays a significant role in facilitating businesses arrive at Smart Data. 

Real-time data analysis  

In this fast-paced world, customers’ attention spans are getting shorter with every passing day. So, companies need to up their game to make the most of whatever limited time they have. In this regard, getting real-time insights into the collected data can be crucial for your businesses. 

Smart Data is the key to helping organizations with real-time data mining and visualization. However, just like every other process, implementing Smart data in an organization requires a rigorous quality checking process. As a result, businesses need to employ Quality Engineering frameworks right when the data arrives in the data lakes. 

Quality Engineering processes also use AI and ML to assess the Smart Data structures. Using AI and ML in QE delivers fast and accurate results, thereby helping companies with real-time data analysis and visualization. 

A high degree of customization 

As companies grow, so is the diverse nature of their customers. Moreover, businesses have realized that it is impossible to follow a one-size-fits-all approach for their products and services. As a result, the focus on customization has gained a lot of momentum in the business circles. 

Upgrading from Big Data to Smart Data helps companies achieve this customization. In addition, companies can also leverage AI and ML to contextualize data based on specific business transactions and arrange it to meet a specific purpose.  

During this whole process, large chunks of data are involved. These data sets, both Data-in-flight and Data-in-rest, follow a pattern. When these patterns shift, whether due to a valid business shift or anomaly shift, companies need to be aware of it. Being unaware of such changes may mess up your business’s analytics. 

This is where Quality engineering comes in. It can provide greater data observability and help monitor and control data quality, making your organization DataOps ready. 

Accelerated digital transformation 

Owing to the rapid digital revolution, every industry had to transform and adapt itself to the latest digital technology. Initially, Big Data was solely driving this digital revolution across sectors, but this is no longer the case today.  

Big Data alone cannot help your organization achieve digital transformation at the scale and quality you require. In fact, you need something more advanced and efficient. This is where Smart Data comes in. 

Smart Data has all the characteristics to help businesses in this newfangled digital transformation. However, arriving at Smart Data from Big Data is a complex and multistage journey. So, without precision and efficiency, your business cannot undertake this journey.  

In this regard, the blend of Quality Engineering with Smart Data plays a crucial role. It helps organizations improve the accuracy and proficiency in the process. 

Key takeaways 

This journey from Big Data to Smart Data helps businesses extract maximum benefits out of Big Data. However, businesses cannot undertake this journey without quality engineers who are skilled in AI and ML and are crucial to ensuring a streamlined Quality Engineering approach. 

Organizations that embark on journey can no longer rely on the traditional QA methods as it can be detrimental for businesses in the long run. They must move to quality smart data facilitated by QE frameworks. 

Not every organization possess the required proficiency in QE frameworks. But they need to outsource their services for such QE knowledge and expertise and therefore turn to Qualitest. 

Qualitest, an AI-driven Quality Engineering company, is an industry leader in this domain, which is also a trusted choice for companies looking to outsource their QA process. For a better idea about our services, consult with an expert today. 

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