Data Analytics – Finding that hidden gem

Several months ago I wrote a newsletter on big data that explains various types and methods of analytics used.  The term causes confusion for enterprise companies, especially as they consider business intelligence (BI) products from vendors.  This month we will discuss data analytics a subset of big data in more detail.

Business intelligence has traditionally been about reporting historical trends and current business performance.  Business intelligence applications provide easy access to businesses allowing them to quickly gain business insights.  The definition of analytics is a quantitative technique and process that is used to enhance productivity.  Data is extracted and categorized to identify and analyze behavioral data patterns. Techniques vary according to organizational requirements.

Both data analytics and business intelligence use proactive and reactive approaches.  Here is a breakdown of each.

Business Intelligence

• Reactive – Reactive intelligence is used for historical analysis and trends, and making decisions at the senior management level. Reactive intelligence can clearly indicate shortfalls in production of goods, poor sales targets, and missed deadlines.
• Proactive – Proactive intelligence’s main object is using a proactive approach to identify potential problems that have a detrimental impact on business so management can address the issues before they occur.

Data Analytics

• Reactive – Reactive data is data usually around complaints about company operations. Sources of reactive data include technical support calls records, product returns, repair service, and customer service calls.  Most businesses make it a point to track this data and make it available for process improvement.  This data can also be used to find out what aspects are in the  nature of the product or service that causes a problem.  For example, if a business received repeated calls to its support center that an issue is not being resolved in a timely manner, then it might begin the process of tracking down the cause.
• Proactive – Proactive data is data collected before the customer experiences their first, or subsequent, encounter with the business product or service. Sources of proactive data are interviews with potential customers, focus groups, surveys, market research, and benchmarking.  A business frequently uses  reactive data to point the way to where proactive data collection will do the most good.

 Data Mining – Data mining is the process of analyzing hidden patterns of data according to different perspectives for categorization into useful information.  This information is collected and assembled in common areas for efficient analysis.

Data can be broken down into two categories transactional and non-operational.  Transactional data deals with day-to-day operations like sales, inventory, and costs.  Non-operational data is normally used for sales forecasting.  Organizations with a strong consumer focus deal with data mining techniques providing clear pictures of products sold, price, competition, and customer demographics.

For example, the retail giant Wal-Mart transmits all its relevant information to a data warehouse.  This data can be easily be accessed by suppliers enabling them to identify customer buying patterns on shopping habits, most shopped days, most sought for products, and other data utilizing mining techniques.

Data analytics is all about automating insights into a dataset and supposes the usage of queries and data aggregation procedures.  Data analytics uses data mining techniques and tools to discover hidden patterns in the dataset.

In closing, businesses are using these technologies to gain more insight and patterns by using this data as a research tool to generate more income and by becoming more aware on what their consumers about what they expect from a product the company sells.