The challenge of capturing the data lies in the speed with which the data moves, the range of data types, and the complexity of managing and processing the data in more meaningful ways that can lead to better decision-making and trend discoveries.
Data sets currently available within your organization that remain unexploited are a lost opportunity.
Social Media
2. The correlation with social media is far greater than you think.
Social media contains a wealth of information, which if correlated with CRM data or other in-house data sets, can give you good insights into behavioral trends, customer sentiment and consumer risk.
For example, by bringing social media and CRM data together and running analytics on it, a company can use the insights to develop community-style interaction, collaboration, and camaraderie to its marketing, sales, and customer services operations.
The correlation between your in-house data and the social media data is really where the magic lies. The synthesis of those views creates a much more valid picture -- it helps to sort the noise from the key signals you are looking for.
Big data is helping a financial services company in Canada understand social relationships and create targeted campaigns to retain its customers better. MicroStrategy recently rolled out a cloud-based gateway to Facebook, that allows its clients (such as a major Telco) to enrich client and prospect data with Facebook’s Social Graph and increase its chances to cross-sell/upsell friends-and-family phone plans.
While the B2C (Business to Consumer) big data analytics applications typically bring data from Yelp, Facebook, Twitter, OpenTable and Topix; the B2B (Business to Business) big data analytics applications typically bring data from LinkedIn, GlassDoor and Business rating sites. Many organizations also bring data from internal social media sites that are typically powered by technologies such as Yammer, Jive, and Chatter.
3. You don’t have to start with big data.
Not all advanced analytics are about big data. Many business analytics needs can be met by taking data from your multiple transactional systems such as CRM, Financials and supply chain management, bringing them together into a data warehouse or a data mart, and then running analytics against them. In fact, deploying traditional business analytics solutions is the first step you need to take if you are starting your journey with analytics beyond simple dashboards and reports.
Secondly, it is important to ensure that the master data across various transactional systems is consistent, before you decide to bring big data into the equation. Otherwise the data quality issues would make it difficult to identify patterns from large data sets. Master Data Management is a good place to start to ensure your master data is consistent across systems. Just moving your current dashboards and BI reports from historical snapshots to focusing on predictive analytics can be a huge step in increasing the value of your data to your organization.
