Driving business intelligence with data and analytical architectures

This is the age of analytics. In this age of analytics, big data holds the center stage. This is not only due to the characteristics of big data but also due to the large number of projects that are associated with it. There is hardly any domain or application that is not dependent upon Big Data Analytics in one way or the other. In this way, big data and deep insights are intricately connected with each other. That said, big data falls under the wider domain of data science. As data science is a relatively new subject, it has attained a lot of maturity in a short span of time. The popularity of data science has cleared the road for its formal incorporation into the education curriculum. The best data science course in India is offered by institutions that are extremely enthusiastic about entrepreneurship culture.

The state of Analytics

The state of analytics in the present time is a great opportunity for businesses to invest in data-driven products and ventures. In order to optimize various kinds of business operations, it is important to get an analytical overview of pricing as well as the profitability of products. It is also important to identify the pulse of the market and various prospective domains of investment. This is not possible without getting an effective insight into various types of business risks and analyzing the instances of fraud. The state of Analytics also enables us to predict new business opportunities so that the customer base can be increased and customer prospects may be improved. Analytics is also important as it enables us to comply with the given set of rules and regulations from time to time.

Driving business intelligence with data

The horizons of business intelligence are constantly expanding with the applications of data analytics. Business intelligence needs raw material in the form of long reports, informative dashboards as well as annual revenue details. In addition to this, the information related to the historical data of the customers as well as their transaction history can be harnessed for generating reports. This is where analytics comes to the aid of business intelligence. By providing hindsight about customer activity based on historical records, the process of customer recommendation as well as personalized recommendation can be quantitatively realized.

Over a period of time, data science has significantly contributed to the development of business intelligence. Needless to mention, data science has gradually evolved to suit the requirements of business analytics. In the past, the analytical approach that we carried out for business intelligence was explanatory in nature. This approach gradually evolved from an explanatory one to an exploratory one. In the exploratory approach, the art of predictive analytics and data mining was utilized to get deep business insights. Similarly, the analytics of large data sets related to business applications enabled us to create alerts as per customer requirements. Different types of dashboards and ad hoc reporting enabled us to get an overview of customer track records.

Analytical architecture

When we look at the analytical architecture, we find four main data processing units. The data source that comprises data warehouses, as well as data lakes, acts as a repository of both structured and unstructured data sets. The second unit of the analytical architecture comprises departmental warehouses that are utilized by analysts for deriving effective insights. These insights may be in the form of either dashboards or quantitative reports. When data is processed in the form of an organized report, it is organized in a third compartment and becomes available for public access. Finally, the data which is freely available is used by various organizations as per their requirement. To summarise a typical data analytical architectural setup, we consider a flow diagram. In this flow diagram, data flow from a centralized data warehouse to the local system of departmental warehouses where analysis is done. After this, data is gradually channelized for business intelligence and reporting purposes. At the end of the flow diagram, we provide the data for downstream analytics and get it ready for visualization purposes.

Concluding remarks

Data has evolved both in form and volume over the span of the last few decades. From terabytes to petabytes and petabytes to exabytes, the volume and velocity of data have simply skyrocketed. As such, our analytics capabilities have responded to the corresponding and simultaneous changes. However, given the rate at which the data cloud is expanding, the arc of analytics will need to be reviewed, redrawn, revised, and revisited.

Saajan Sharma likes to read and write actively on upcoming HR trends and how HR is reshaping the business landscape. He likes to help businesses stay informed and up to date with established and emerging technologies like Payroll Software, SAP, etc.