“PwC predicts the use of big data eventually may transform the commercial insurance business model, as insurers find ways to use these new streams of information to radically alter business processes”
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important, it’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves. But big data is also the reality of doing business. It’s the proliferation of structured and unstructured data that floods your organization on a daily basis, and if managed well, can deliver powerful insights.
Imagine being able to analyze data to determine the root cause of failures, or detect fraudulent claims before they affect revenue. Implementing the right solutions to make the most of your big data – from data management to analytics – can be key to your business success.
Definition of Big Data
While the term “big data” is relatively new, the act of gathering and storing large amounts of information for eventual analysis is ages old. The concept gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three Vs:
- Volume. Organizations collect data from a variety of sources, including policy transactions, social media and information from sensor or machine-to-machine data. In the past, storing such large volumes of data would’ve been a problem – but new technologies (such as Hadoop) have eased the burden.
- Velocity. Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.
- Variety. Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial and accounting transactions.
Scyllogis however consider two additional dimensions when it comes to big data:
- Variability. In addition to the increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. Is something trending in social media? Daily, seasonal and event-triggered peak data loads can be challenging to manage. Even more so with unstructured data.
- Complexity. Today’s data comes from multiple sources, which makes it difficult to link, match, cleanse and transform data across systems. However, it’s necessary to connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out of control.
The amount of data being created and stored on a global level is almost inconceivable, and it just keeps growing. That means there’s even more potential to glean key insights from business information – yet only a small percentage of data is actually analyzed. What does that mean for insurers and brokers? How can they make better use of the raw information that flows into their organizations every day?
Why Is Big Data Important?
The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:
- Determining root causes of failures, issues and defects in near-real time.
- Recalculating entire risk portfolios in minutes.
- Detecting fraudulent claims before it affects your organization.
More precise insights based on all available data – not just a subset. Fast answers to your most difficult questions, and more confident decision making. Our insurance analytics solutions enables you to take full advantage of big data that include:
- Telematics. Visually explore billions of records and journey points. Weed out unimportant variables. And quickly develop, test and use the best modeling techniques to identify risk factors and create new pricing models.
- Social media analysis. Listen to and monitor conversations about your company on social media and online channels. Add context to those conversations. And engage with customers on a more personal level than ever before.
- Catastrophe modelling. Perform in-depth analyses in real time to assess the scope of a catastrophe, then make critical decisions that could significantly affect your company’s long-term financial stability.
- Risk analysis. Reduce the impact of risks on your business. Our advanced analytics let you anticipate enterprise risks and initiate risk control measures to minimize losses, before it’s too late.
Big data. Bigger advantage.
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