An Introduction into Data Analytics
It is all too easy to collect humongous quantities of data from diverse sources. However, it is not so easy to know what to do with it! Here is where data analytics comes in – data analytics helps us understand how to manage the data effectively and use it to derive important information, helping businesses optimize performance.
A Briefing about Data Analytics
Data analytics refers to an automated, scientific methodology of analyzing data. Here, the collated data is used as the raw material. The methodology for analyzing it comprises of using algorithms (a specific rule or set of rules for resolving problems) and mechanical processes. The outcome of the analysis is specific information, which could relate to useful metrics, existing market trends, etc. Sometimes, the analyst tries to find meaningful correlations between one set of data and another. Data analytics aids in making informed decisions, enhancing productivity, rectifying errors in operating, etc. It also helps an organization to verify the usefulness or decide to discard existing models/theories. Steps Involved in Data Analysis The foremost job of the business enterprise is to work out the diverse categories for placing data – Structured and Unstructured. Once the data is collected from these different sources, unstructured is converted into structured format. How are the two different? While unstructured data comprises of information that carries no value, structured data comprises of highly formatted and organized data that is easily searchable in databases or files. For instance, data that is collected from various social channels is unstructured and conveys no meaning until it is structured as per various measurable parameters such as type of content, topic and so on. The four types of Data Analytics
- Diagnostic – The analyst strives to find out the ‘why’ of outcomes. For instance, the latest advertising campaign, despite being highly creative, could have failed to make much of an impact on the audience. The expert studies various types of data inputs and engages in a little bit of hypothesizing, to comprehend the reasons for this occurrence.
- Descriptive – Here, the analyst provides a detailed report of ‘what’ has occurred over certain duration (a month, three months, etc). For example, the sales graph may have improved in the current month.
- Predictive – As the name suggests, this type of analysis strives to figure out future trends. To illustrate, the sales graph might not have exhibited expected behavior during the previous summer, because it was so hot. Therefore, there is speculation about whether this summer will be similarly hot too, and affect sales.
- Prescriptive – It refers to an organization’s intention to take a specific course of action. Taking the example outlined above, if this year’s weather conditions imitated the previous weather, the organization could change the timings of sales, or create an additional evening shift.
Uses of Data Analytics
Data analytics is a valuable tool for most of the industries. Banking Banking sector is extremely data-centric with massive amount of structured and unstructured data residing in systems. To stay relevant, they have an increasing pressure to understand the changing customer needs and preferences. To uncover insights into these behaviors, descriptive as well as predictive analytics
can be leveraged so that banks can understand how to target, acquire and retain customers. For an instance, when a customer is looking to buy a vehicle, banks can offer him promotional deals around loan for ease of buying. This application can be further extended in the future basis the needs of the customer. Some of the key areas where banks and financial enterprises are increasingly utilizing the power of data analytics include customer segmentation, fraud detection, customer data management, customer spending patterns and so on. Insurance Integration of next-gen technology and life saving solutions has led to creation of proliferation of data, presenting opportunities to use data analytics in leveraging information about potential risks, customers, markets and natural disasters. Infusing data analytics into an insurer’s DNA gives him actionable insights based on the customer data. for instance, if one of the customer’s has any sort of gap in the insurance coverage, data analytics system will give an alert highlighting the opportunity to bring in the missing element. The actionable insights also help in figuring out insurance fraud before it happens. It also helps insurers in pricing policies and premiums. Payments Another area that has greatly improved with data analytics is Payments. Usage of data helps to understand customer behavior and offers personalized payment methods. It helps in forecasting sales and other transactions to reach targets. For instance, an ecommerce firm can look at the performance of a specific product and design its business in a way that customers continue to buy their products. Trends can be predicted looking at the data which is collected through payment transactions. To conclude, data analytics is a process that involves inspecting the collected data, cleansing it and transforming it into something that informs conclusion and supports decision making. To know more about how SLK can help you with Intelligent Data Analytics so that you always stay ahead of the curve, check out our Data Insights and Analytics offering.
Authored by Admin