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POST TIME: 23 May, 2019 11:30:51 PM
Data analytics, an effective way for better business management
Analysis is focused on understanding the past: what happened. Analytics focuses on why it happened
Masihul Huq Chowdhury

Data analytics, an effective way for 
better business management

Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Analysis refers to breaking a whole into its separate components for individual examination.

 Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. Data are collected and analysed to answer questions, test hypotheses or disprove theories. Analysis is focused on understanding the past; what happened. Analytics focuses on why it happened and what will happen next. Data analytics is a multidisciplinary field. There is extensive use of computer skills, mathematics and statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data. Probably the largest sector to use predictive analytics, retail is always looking to improve its sales position and forge better relations with customers. One of the most ubiquitous examples is Amazon’s recommendations. When you make a purchase, it puts up a list of other similar items that other buyers purchased.

Much of this is in the pre-sale area – with things like sales forecasting and market analysis, customer segmentation, revisions to business models, aligning IT to business units, managing inventory to account for seasonality, and finding best retail locations. But it also acts post-sale, acting to reduce returns, get the customer to come back and extend warranty sales.

The data are necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analysis or customers (who will use the finished product of the analysis). The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of people). Specific variables regarding a population (e.g., age and income) may be specified and obtained. Data may be numerical or categorical (i.e., a text label for numbers). Data are collected from a variety of sources. The requirements may be communicated by analysts to custodians of the data, such as information technology personnel within an organization. The data may also be collected from sensors in the environment, such as traffic cameras, satellites, recording devices, etc. It may also be obtained through interviews, downloads from online sources, or reading documentation. Data initially obtained must be processed or organized for analysis. For instance, these may involve placing data into rows and columns in a table format (i.e., structured data) for further analysis, such as within a spreadsheet or statistical software. Once processed and organized, the data may be incomplete, contain duplicates, or contain errors. The need for data cleaning will arise from problems in the way that data are entered and stored.

Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data,[6] deduplication, and column segmentation. Such data problems can also be identified through a variety of analytical techniques. For example, with financial information, the totals for particular variables may be compared against separately published numbers believed to be reliable.

Unusual amounts above or below pre-determined thresholds may also be reviewed. There are several types of data cleaning that depend on the type of data such as phone numbers, email addresses, employers etc. Quantitative data methods for outlier detection can be used to get rid of likely incorrectly entered data. Textual data spell checkers can be used to lessen the amount of mistyped words, but it is harder to tell if the words themselves are correct.

One bank customer may have both a personal account and a business account. The same customer may be looking to refinance a mortgage and also open up a business line of credit. This one individual may also be exploring how to pay for a child’s college education as well as preparing for their own retirement. In far too many financial institutions, the customer data gathered about these different activities lives in silos. Separated data and siloed customer information means the bank holds multiple views of a customer, and not one of them is coherent or paints a complete picture. With the right underlying big data architecture, banks, credit unions, and other financial institutions can form a single view of their customers.When data silos are eradicated, a more complete picture forms, and every single interaction the customer has with the organization is indicated. The purpose of this view is to reduce friction. Your customer should have a seamless experience, no matter where or when an interaction takes place. When employees access customer information, they should see the entire history of that customer relationship. This conveys a sense of customer care that resonates.

This is how customer loyalty and retention is fostered. Banks and other finance sector businesses are taking cues from the retail sector when it comes to refining the customer experience.

Consider just a few of the retail innovations sparked by digitization and big data: offers based on geolocation data, tap-and-go payments, and product suggestions based on purchase history. Retailers recognized the inherent value of data early on. They have applied that knowledge to personalize customer offers and to predict future steps in their customers’ buying journey. Consumers expect the retail outlets they patronize to work this way, and many now want a similar level of service from their banks. This infographic highlights that shift. Survey data shows that 45 percent of bank customers want their bank to show them deals and discounts, 40 percent want more personalized service, and 63 percent will share information about themselves so they can receive information about products and services that are relevant to them. Banks and their customers appreciate the digitization of financial transactions, while also recognizing that it opens up new opportunities for fraud. Fortunately, the finance sector has information at its fingertips that plays a huge role in fighting financial crimes: customer transaction data.

Banks know a lot about how, where, and when customers access their accounts. They know which devices they use. They know the shops they frequent. They can tell how quickly they input account passwords. Empowered by big data architecture, these data points create a “signature” unique to the individual customer. That signature makes anomalies easier to spot. Through the use of big data analytics, companies can dramatically improve their financial fraud prevention. These capabilities offer multifold benefits: Customers are happier their assets stay protected, banks are happier to stop crime, and shareholders are happier supporting brands that don’t appear in headlines about data breaches.Banks, credit unions, and other finance sector businesses hold a treasure trove of consumer data. Investing in big data and analytics capabilities can help make the most of every customer interaction by using that knowledge to better serve their customer base.Retail data analytics deal with identifying potential customers based on their past purchases, finding the most appropriate way to handle them via targeted marketing strategies and then deciding what the next offering should be.The main functions of Data Analytics are: Price Optimization, Less Expensive Business Development, Future Performance Prediction, Demand Prediction, Select Better Return on Investment (ROI) Opportunities, Trend Forecast and Customer Identification.

It also involves the potential risks associated with big data when it comes to the privacy and the security of the data. The Big Data tools used for analysis and storage utilizes the data disparate sources. This eventually leads to a high risk of exposure of the data, making it vulnerable. Thus, the rise of voluminous amount of data increases privacy and security concerns.

To overcome these Big Data challenges in the companies and large organisations, a corporate training program in Big Data should be organized by the business owners and managers.

The writer, a banker by profession, has worked both in local and overseas market with various foreign and local banks in different positions