With increase in the number of parameters and channels to identify sales trends & consumer behaviour, it has given business analysts a whole new paradigm to understand the granularity of a business but on the flip side it has made their life tougher.
Today, customers touch points are densely & diversely populated across mobile, desktop, television, email, social media, point of sale etc. We can understand how a single channel is performing and affecting the business. However, it gets complex to understand the results when a single touch point gets influenced by crossovers from other touch points.
A perfect example is the retail business industry - This requires the business to understand seasonality, consumer psychology & purchasing patterns, offer effectiveness, sales concepts, real time stock replenishment etc. All this has created a necessary requirement for a system to analyze the contribuition of each touch point in the sales process.
What Is BIG DATA?
Big data is a term to describe the humongous amount of unstructured & semi-structured data that a company creates (sometimes as a by-product) which may take too much time & cost to load into a relational database for analysis. Reportedly, this big data accounts for almost 80% of an organization data.
According to IDC, it is imperative that big organization should focus on ever increasing volume, variety and velocity of information that forms BIG DATA.
VOLUME: In past, data volume has created big storage issues, but with technological advancements cost of storage has decreased immensely, however it has opened a new can of worms such as relevant data identification and then interpreting meaningful information from that data set.
VARIETY: Various formats of data makes it more complex to compile the database with relevant hierarchical data storage system. It is estimated that 76% of an organization’s data is not numerical in nature, however, they can’t be ignored while analysis & decision making.
VELOCITY: Analyzing data that is being generated at high speeds & then processing that data in real time has become a challenge to most of big organizations.
BIG DATA ANALYTICS
By employing data scientists and econometricians to analyze big data, companies can make better business decisions by interpreting huge volume of data in tandem with conventional business intelligence (BI) programs.
Big data analytics can be done with software tools commonly used as a part of advanced analytics disciplines like predictive analysis & data mining. But, unstructured data sources used for big data analytics may not fit for traditional data warehouses. As a result, tools like NoSQL database, Hadoop, MapReduce etc. have come into existence as a new class of analytics software. These tools support the processing of large datasets across clustered system in an open source software framework.
Advantages of Big Data Analytics:
- Analyze millions of SKUs to determine optimal prices that maximize profit and clear inventory.
- Recalculate entire risk portfolios in minutes and understand future possibilities to mitigate risk.
- Mine customer data for insights that drive new strategies for customer acquisition, retention, campaign optimization and next best offers.
- Quickly identify customers who matter the most at a particular juncture, for example:
- Generate retail coupons at the point of sale based on the customer's current and past purchases, ensuring a higher redemption rate.
- Send tailored recommendations to mobile devices at just the right time, while customers are in the right location to take advantage of offers.
- Analyze data from social media to detect new market trends and changes in demand.
The use of big data will become a key basis for competition & growth of an organization. It will create a new a level of productivity growth & consumer surplus and will also help in painting a better picture of customer interaction with the enterprise over time. From the standpoint of competitiveness & potential capture of value, all big companies need to take big data seriously in the near future.