Big data analytics has reached a new peak with technology innovations, where every business wants to utilize it to get new high-value insights from customer data. Big data analytics, using big data tools like Hadoop, analyze structured, semi-structured, and unstructured data to improve customer experience. Also, there is a rise in demand for real-time analytics. Some examples include real-time market data analysis, tackling financial crimes like credit card fraud or anti-money laundering, monitoring of asset and preventative maintenance, and improving click-through in on-line advertising. Interestingly all these data are operational and transactional data fed to big data systems and as big data streaming data. Let see what transactional data in the next section is.

What is transactional data?

As the term suggests, transactional data means data that is related to the transactions of the organization. For example, when a product is purchased or sold, the same is captured simultaneously. It is the transactional data for that product. In transactional data, the primary data is master data, referred to in different transactions — for example, customer, product, or supplier data. Generally, master data does not change or created with every transaction. Thus for a particular customer, although multiple transaction records are created for different purchases, the customer information does not change.

Why is transactional data vital for business?

Dumping transactional data is meaningless unless we pay the required attention to it. Also, it is imperative to maintain a competitive edge. For this, capturing, streaming, and storing transactional data in big data analytics is essential to ensure peak transaction volumes, peak data arrival rates, and peak ingestion rates. Big data analytics systems need to be integrated back into core operational transaction processing systems to get prescriptive insights. This helps in continuous optimization and maximizing operational effectiveness.

So, no doubt, transactional data is mission critical to big data analytics to success.

Who uses transactional data for big data analytics?

In an organization, transactional data is used by two major teams –

- IT operational teams

- Business managers and data analysts team

IT operations use real-time transaction monitoring and transaction data streaming products to quickly find and fix performance issues, leading to significant service disruptions. This way, they can manage their transaction networks in a timely, cost-effective way.

Business managers and data analysts use real-time customer transaction data to gain a customer-centric view of how their products or services are being used. They can use transaction data to obtain visual insights to serve existing customers better, acquire new ones, and enhance profitability through improved service offerings.

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Why Is Big data analytics significant here?

In this context, we can break the big data strategy into two major parts:

- Big data analytical processing

- Big data operational transaction and non-transactional event  processing

Big data analytics depend on transactional data

Though big data fed into platforms like Hadoop can be unstructured or semi-structured, organizations often load transactional data, structured and master data. So, this multi-structured data provides a perfect context for data analysis in the big data analytics environment. For example, clickstream data often needs both customer and product master data along with customer transaction data. Since understanding customer online behavior has become a strategic way nowadays, clickstream data has become paramount. Thus, in big data analytics, the combination of all four data shows a new path of analysis. These data include – customer data, product data, non-transactional clickstream data, and transaction data.

How Big data impact transactional data?

With the pervasive use of IoT and social media, operational non-transactional data are generating at a scale. This data includes sensor data, machine-generated log data, gaming data, a news feed, user clicks, reviews, etc. Big data analytics is used in this case, and to capture the data NoSql database is mostly used as the data remain in a self-described format like JSON, XML, BSON, etc. Another reason to select NoSQL is - it supports schemaless data, performs automatic partitioning, high availability, cluster scalability, etc. In this format, NoSQL doesn't support ACID properties, instead of supports BASE (Basically Available, Soft state, Eventual consistency), which always guarantees data availability but eventual consistency.

Since we are discussing transactional data, how this non-transactional data relate to it? As presently, most of the transaction happens online, there is a relentless increase in online transactional data. Moreover, with the pervasive use of mobile devices, mobile commerce is in the rage. This is giving transactional data volumes to new levels. However, if we compare it with clickstream data in weblogs, this increase is small compared to clickstream data. Where the transactional level is reaching unprecedented volume, it is interesting to know that edge devices like web and mobile edge devices embed traditional DBMSs to catch data on edge for analysis. Thus data from billions of connected devices can be harnessed and used in a meaningful way in big data analytics. So, capturing and using this non-transactional data to analyze transactional data is, no doubt, is paramount to the success of everyday business-related big data analytics.

Exploiting transactional data for enriched customer experience with big data analytics

Real-time analytics with relational and NoSQL transactional data-

During the business operations, companies lack insight into the area of operations due to the below reasons:

- Data is not being collected to monitor operational activity

- Applications of analytical queries on live transaction data are not possible

- No integrated insights and recommendations into operational business processes

Today, business requirements urge that all of the conditions mentioned above must be fulfilled to improve operational effectiveness. If you don't have information on what is happening to your business, then improvement is not possible.

To solve this, organizations are initiating to:

- Embedding sensors, which are known as smart products to instrument business operations and grids. Examples are GPS sensors in smart drilling on oil wells, smartphones, consumption sensors

- Monitoring live transaction activity

- Monitoring live market activity

- Monitoring events during business process execution

This also extends data requirements to capture, transform, integrate, and analyze:

- An increasingly high volume of transactional data (which is increasingly high volume)

- Click streaming data from sensors, smart devices, and markets

- Event data occurring during operational business process execution

- Web feeds e.g., news, weather, etc.

- Rich media streaming data e.g., video streams

Companies can exploit richer transaction data for competitive advantage by capturing and analyzing this kind of data. Consequently, it keeps business operations optimized. When companies run operational, analytical queries in OLTP systems, it allows staff working in operations to prioritize to take preventative maintenance. This protects from an unplanned outage.

Conclusion

Ultimately, transactional big data analytics help provide better answers to complicated data patterns and business-related strategy questions. Today, business executives want to leverage more sophisticated analytics in their business processes and industry-specific analysis to aid executive decision-making.