Data Analytics in Business
Written by : Vimi ( Business Analyst, ATBC)
Data …data… data…This has been the buzzword around. The face of our planet has been transformed by “data”. We have a long history of recording data. During the early ages, it was the tally marks on walls of the caves, stones etc.. But as we evolved, the amount and diversity of data collected has also evolved and expanded. The most remarkable growth in data collection developed in 19th Century when population data was collected for Census reports. With the emergence of Computers in mid 20th Century, data collection and storage saw a steep growth. Further innovation and internet in 1990’s led to historically unprecedented amount of data collection, complexity, and analysis. Businesses of all scales rely on data to some extent. Data analytics is the way to unlock the rich variety of data that the enterprises generate.
Data is everywhere, but to sort and get what is relevant and useful to one’s business requires a skill. And here comes Data analytics to our rescue..
What is data analytics?
The term data analytics refers to the process of collecting and assessing datasets (sets of information).Various techniques are used to extract data and analyze patterns. These patterns and trends can be used as pointers in gaining valuable insights.
Data goes through a lifecycle of being generated, collected, processed, stored, managed, analyzed, visualized and interpreted. The resulting information is imperative to future predictions.
Few factors to be considered for data collection are:
· What ? Intended goal from data
· Who ? For whom is the data related.
· When ? Data collection timeframe
· How ? Most suitable data collection methods
Few methods of data Collection are :
– Surveys: Physical or digital questionnaires
– Transactional Tracking: Tracking the data of purchase of customers enables decisions on target audience.
– Interviews and Focus Groups: Through interviews and focus groups, feedback about any product can be gathered.
– Observation: observations enables one to see firsthand how users interact with your product or site.
– Online Tracking Online forms are beneficial for gathering qualitative data about users
– Forms They’re relatively inexpensive and simple to set up. This data can be used to contact people who may be interested in the product
–Social Media Monitoring This is the easiest way to track audience’s interests and motivation.
Data extraction:
For businesses, the data may be historical data or new information for a specific initiative. Based on mode of collection, data is classified as:
First party data – Data collected by company about its own customers.
Second party data – Data Company obtains from a known organization
Third party data – Aggregated data a company buys from Marketplace
Data analytics has different facets to it and few types of data analytics are:
Predictive analysis :This mode of analysis focuses on past data for forecasting future events or outcomes. Past analysis data is assessed and reported to provide predictions for upcoming options. Often in most organizations, sales forecast is made based on previous year’s sales data. Analysis of past data give foresight to right decisions for future. Risk assessment, Customer satisfaction, Qualified leads etc. are areas where Predictive analytics can be applied.
Descriptive analysis focusses on reports or events from past data. The data indicated based on analysis of specific subject from past data is depicted. Sales leads, revenue and key performance indicators are other vital business components that commonly uses descriptive analysis.
Diagnostic analysis answers why a particular scenario occurred. While descriptive analysis results in a data for a specific subject, Diagnostic analysis digs into reason for data produced by descriptive analysis. Business at a particular demographic may witness high growth rate. Diagnostic analysis gives implications on what attributed to this growth. The analysis also leads to potentials marketing efforts that lead to this growth.
Prescriptive analysis combines the data from predictive, descriptive, and diagnostic analysis and provides a data that can be used to plan or implement strategies for business. This is the pointer data for business in deciding the future course of business.
Data Scientists and Analysts use data analytics in research and business to boost performance and improve their bottom line. Data analysis helps organization understand their customers, evaluate their ad campaigns, personalize content, create content strategies and develop products.
Data Analytics for Business
Basically, analytics is about making good business decisions. Just giving reports with numbers doesn’t help. We must provide information in a way that best suits our decision-makers.
— Director of HR analytics for an entertainment company
Data analytics are in high demand across all industries. Organizations can gain a competitive edge from data analysis insights. Data analysis has become more accessible than ever before. This indispensable data can be used to maximize profits and customize user or customer experience to increase efficiency and loyalty.
Implementing data analytics for business
First step towards implementing data analytics is what data to collect and how it can be collected. Data can be derived from social media, GPS, other transaction information and various other sources.
Next step is to evaluate the accuracy of data and its relevance to business goals and strategy.
Finally it has to be transformed into information that can be easily understood. The data analyzed is then used as a pointer to improve the business. Organizations may use it to tighten its security, drive traffic to its website, refine customer service or build revenue directly.
Benefits of using Data analytics for your business
1. Better decision making
Quick rate and speed of information gathering empowers organizations to make quicker and more efficient decisions and minimum financial losses.
2. Excellent data access
The data stored is easily managed using analytics. The authorized user has full access to the information stored.
3. Improved performance
Analytics helps organizations to anticipate their capacity to meet client’s request. This has also helped them to achieve customer requirements and meet their commitment
4. Effective revenue
Analytics help generate higher revenues
5. Personalize the customer experience
Customer data is collected from many channels eg. retail, social media e-commerce
6. Streamline Operations
Operational efficiency is streamlined through data analytics
7. Mitigate risk and handle setbacks
Risks are inevitable part of Business. They include customer or employee theft, employee safety, legal liability etc.
8. Enhance Security
All organizations face data security threat.Organisations use data analytics to diagnose the causes of past data breaches by processing and visualizing relevant data.
