Companies today are generating much more data than they can currently store and analyse; they're being overwhelmed with data. Many generate petabytes of information they aren't making the best use of. And not all of the data are alike. Some of it has value, and some not so much. The problem with data has been the difficulty in analysing what companies currently have and the time and expense involved in using conventional systems to process data.
There has been a buzz around "Big Data" as a way to resolve these issues and to help businesses get better information quickly, so they can make better business decisions.
Business intelligence (BI) and analytics have been around for some time. So how does Big Data differ from BI and analytics?
Big Data isn't a precise term; rather, it's a characterisation of the never-ending accumulation of all kinds of data, most of it unstructured. It's a type of exploratory BI that hasn't been used much before. It requires a different mindset: one that begins with exploration, whose results create hypotheses that are tested before moving on to validation and consolidation. Basically, Big Data is used to glean intelligence from data and translate it into a more in-depth competitive advantage through a better analytical process.
These methods may be used to answer questions such as: "What indicators might there be that predate a surge in Web traffic?" or "What fabrics and colours are gaining popularity among influencers, and what sources might be able to provide the materials to us?" or "What's the value of an influencer on Web traffic through his or her social network?"
Here are some other examples of insights that may be gleaned from analysis of Big Data information flows:
Customer loyalty scoring for sales strategy targeting: Fusing multi-channel customer interaction data and account data to compute a propensity score for a customer to take up a new product. This score can be used to drive sales strategy.
Graphing customer and entity interactions: Using customer and transactional data (payment journal) to develop a view of the interactions and information flows between customers and other entities.
Analysis of customer behaviours in response to market events: Using customer interaction data (channel logs) and external market information to analyse the impact on margins and profits, and channel activity, in response to market events such as interest rate rises.
Know your customer: Developing a single-customer view that blends a business, financial and risk view of the customer. This could be further enhanced in future points of contact (POC) with life event information.
Identify cross-selling opportunities: Develop a single view of a customer across all channels for opportunities for up- or cross-selling of products.
Channel analysis by customer: Using customer interaction data (channel logs) develop a customer profile per channel with a view to learning the cost of each channel.
There are three key differences from the old analytical approaches: the "three Vs" that make Big Data special: Volume, Velocity and Variety:
Volume: This is the era of data explosion. Some 90% of the data in the world today have been created in the last two years alone and the trend continues. Large amounts of data give businesses the potential for discovery by analysing larger samples. Traditional relational database management systems (RDBMS) and old analytical methods have scalability issues; a large volume of data cannot be processed within a reasonable time.
Velocity: Data now move faster than ever, and real-time information _ e.g., Amazon's live recommendations _ makes it possible for companies to be much more agile than their competitors.
Variety: Data come from various sources and the results are mostly unstructured, e.g., messages, updates, images, sensors, etc. As well, many sources are fairly new, such as Facebook and Twitter. Businesses can gain more insights with the ability to compare and analyse data of different types, but the structured databases that store most corporate information are not suited to storing and processing Big Data.
Strengthening human expertise with Big Data technologies, companies gain significant competitive advantages: problems are avoided sooner; opportunities are identified earlier, and mass customisation can be performed on a larger scale.
Gaining Big Data insights provides businesses with big opportunities in the market _ for example, reducing customer churn by analysing call centre and help desk data; building a good corporate reputation by monitoring data from social networks and news sites; as well as facilitating real-time forecasting by analysing weather forecasts, travel reservations, automotive traffic and retail point-of-sale data.
Big Data approaches can become a key value creator for businesses, letting them tap into a wild and woolly world of information previously out of reach. These new data management and storage technologies can also provide economies of scale in more traditional data analysis.
It's important to note that Big Data analysis doesn't replace other systems. Rather, it supplements the BI systems, data warehouses, and database systems essential to financial reporting, sales management, production management, and compliance systems. The difference is that these information systems deal with the knowns that must meet high standards for rigour, accuracy, and compliance _ whereas the emerging Big Data analytics tools help you deal with the unknowns that could affect business strategy or its execution.
Vilaiporn Taweelappontong is a partner and Jane Saetent is an associate consultant in Consulting Services for PwC Thailand. For more information, contact leadingtheway@th.pwc.com