Organizations are drowning in their Big Data and are unsure of how to even begin to find the value in it. While many have set up “data lakes” as an initial step, they still struggle with understanding what data they have. The fact is, these large warehouses and repositories that store endless streams of high-volume, diverse data that flow into organizations every day may provide a good way to collect Big Data, but they do nothing to help organizations drive real business benefits.
A usually law-abiding citizen gets into a minor car accident and decides that it’s time to cash in. He exaggerates his injuries and, with the help of some unscrupulous medical providers, he places a bodily injury claim. While this may seem harmless to some, this type of “soft fraud” happens all the time. It is a contributor to the total cost of insurance fraud (non-health insurance), which is estimated to be more than $40 billion per year. This costs the average U.S. family between $400 and $700 per year in the form of increased premiums. The bottom line is that fraud, soft or not, impacts all of us.
To better attract and retain customers, leading insurance companies must establish strategies that focus on improving their customers’ experience and optimizing their underwriting and pricing practices. This is easier in theory than reality as a recent study from Bain & Company concludes - insurance carriers are usually only good at one strategy, not both. However, I believe that insurance companies can accomplish these two objectives by systematically accessing and analyzing their full universe of data, including free form text and scanned documents, to gain new insights into their customers, business and market environment.
by Len Dubois, Sr. Vice President, Marketing & Sales Support, Trillium Software
(Part 2: Empowering the right people with the tools they need to get the job done)
In the last blog post of this series, Jon Asprey, VP of Strategic Consulting at Trillium Software, details how cross-department collaboration to reach an understanding of data requirements, interdependencies, data quality issues and gaps is essential to developing a structured data governance process.
(Part 1: Establishing the required organizational structure)
Creating a culture of data awareness can help firms comply with complex regulations and enhance business performance. As financial services firms continue to struggle with the effective management of data quality, ingraining the right culture into the organization is critical. For firms to successfully implement structured data governance and control, it is imperative that all stakeholder groups involved in data management be considered. Stakeholders from across the organization need to look beyond their departmental silos and work together to understand the data requirements of each group, interdependencies based on how data is being used and what data quality issues or gaps exist. This understanding is the basis for developing a culture of data awareness within and across an organization.
After London, Birmingham is the UK’s second largest city. With a population of around one million it grew rapidly during the Industrial Revolution to become a powerhouse of British industry. Much of the heavy industry has long since vanished but it remains a vibrant and diverse place with a rich cultural heritage. For instance, many of Britain’s best known rock bands – Black Sabbath, the Moody Blues, Duran Duran & the ELO – all started life in Birmingham. And between them they created some lasting records – for instance Nights in White Satin, Paranoid, Mr. Blue Sky, and Say a Prayer.
I recently attended the 2014 America's Claims Event in Washington DC. It was evident from the presentations I attended and my conversations with attendees that maximizing the value of data is front-and-center on the minds of claims professionals. I was very interested in the fact that these discussions about data, especially those focused on analyzing unstructured, free-form text data to uncover the most actionable information, were much more prominent and frequent than in the past few years at this event.
Letting genies out of bottles can be a risky business. In Aladdin's case the genie helped him to achieve his most coveted wishes. Other genies can however be more malevolent, turning wishes into nightmares. And so it is with releasing the data genie. Opening up your data to the wider world can similarly fulfil wishes, or lead to censure & misery.
One of our data industry's current hot topics is Open Data, a subject which I've discussed in previous blogs. Open Data is a self-explanatory concept, unlike many in our profession. It's data released by organisations which others are free to use, usually at no or very minimal cost. As you might expect, national governments and their institutions are at the forefront of this, given the public demand for transparency in public affairs. The UK government, for example, has made over 17,000 data sets available via its web sites. 64 countries globally are doing the same, including the US where one estimate claims there are over one million accessible data sets, provided by federal and state governments, academia and other public spirited bodies. These data sets include a potential goldmine, covering such diverse topics as meteorology, environmental impacts, demographics, health, education, transport and so on.
After months of debate and speculation, the Foreign Account Tax Compliance Act (FATCA) finally became a reality on the 1st July.
In case you have not heard of it, FATCA is a US tax avoidance measure which came about after the financial crisis at the end of the last decade. It requires Foreign Financial Institutions (FFI's) to identify U.S. citizen's accounts, report on their income and assets and, in some circumstances, withhold on payments to account holders.
The data team in a professional, premier, or major college sports organization is an awfully lonely group. Other departments, especially the more glamorous ones such as marketing or player personnel, are treated to large budgets and plenty of attention. These groups receive executive buy-in and are seen as strategic to the organization. And while the team spends money and time satisfying the latest "entitled" player or updating the already excessive facilities, the data professionals (like in many organizations) struggle to win funding and approval to build out their critical data assurance and data management projects.