Tag Archives: java

Are Our Systems Modern Yet? Sort Of

As insurers continue to replace core systems in record numbers, at some point one has to ask whether modern systems are in fact the norm rather than the exception. In short, the answer is “sort of.”

On the one hand, the percentage of total systems that carriers have in place that have been replaced by modern systems is quite low. Though the idea early on was to consolidate carriers’ myriad legacy systems onto a single modern platform, in reality most carriers either replaced systems on a one-for-one basis or simply put in a new system for a new line of business but never migrated additional lines onto that system.

So while implementing a modern system has become a popular path for insurers to take, the overwhelming majority of business processed today is still on legacy solutions. In addition, some of the “modern” systems that were put in place as recently as seven or eight years ago are quickly becoming modern legacy systems themselves. Systems that were built using C++, early iterations of Java or other technologies that didn’t scale well, didn’t offer an N-tier architecture or didn’t lend themselves to easy upgrades or customer/agent-focused user experience won’t likely last the 30 years that their predecessors did. Some are already being considered for replacement, and so the cycle begins anew.

Are modern systems the new norm? For those shopping for a new system, absolutely. But if you step into most carriers’ home offices, you’ll more likely find a legacy system or a mix of old and new. And while the pace of replacement continues to climb, there’s still a long way to go before we can say-without qualification-that modern systems are indeed the new norm!

The Science (and Art) of Data, Part 1

Most insurers are inundated with data and have difficulty figuring out what to do with all of it. The key is not just having more data, more number-crunching analysts and more theoretical models, but instead identifying the right data. The best way to do this is via business-savvy analysts who can ask the right strategic questions and develop smart models that combine insights from raw data, behavioral science and unstructured data (from the web, emails, call center recordings, video footage, social media sites, economic reports and so on). In essence, business intelligence needs to transcend data, structure and process and be not just a precise science but also a well-integrated art.

The practitioners of this art are an emerging (and rare) breed: data scientists. A data scientist has extensive and well-integrated insights into human behavior, finance, economics, technology and, of course, sophisticated analytics. As if finding this combination of skills wasn’t difficult enough, a data scientist also needs to have strong communication skills. First and foremost, he must ask the right questions of people and about things to extract the insights that provide leads for where to dig, and then present the resulting insights in a manner that makes sense to a variety of key business audiences. Accordingly, if an organization can find a good data scientist, then it can gain insights that positively shape its strategy and tactics – and gain them more quickly than less-well-prepared competitors.

What it takes to be an effective data scientist

The following table highlights the five key competencies and related skills of a qualified data scientist.

Competencies

Key Skills

Business Impact

1. Business or Domain Expertise

   Deep understanding of:

  • Industry domain, including macro-economic effects and cycles, and key drivers;
  • All aspects of the business (marketing, sales, distribution, operations, pricing, products, finance, risk, etc.).
  • Help determine which questions need answering to make the most appropriate decisions;
  • Effectively articulate insights to help business leadership answer relevant questions in a timely manner.

2. Statistics

  • Expertise in statistical techniques (e.g., regression analysis, cluster analysis and optimization) and the tools and languages used to run the analysis (e.g., SAS or R);
  • Identification and application of relevant statistical techniques for addressing different problems;
  • Mathematical and strategic interpretation of results.
  • Generate insights in such a way that the businesses can clearly understand the quantifiable value;
  • Enable the business to make clear trade-offs between and among choices, with a reasonable view into the most likely outcomes of each.

3. Programming

  • Background in computer science and comfortable in programming in a variety of languages, including Java, Python, C++ or C#;
  • Ability to determine the appropriate software packages or modules to run, and how easily they can be modified.
  • Build a forward-looking perspective on trends, using constantly evolving new computational techniques to solve increasingly complex business problems (e.g., machine learning, natural language processing, graph/social network analysis, neural nets, and simulation modelling);
  • Ability to discern what can be built, bought or obtained free from open source and determine business implications of each.

4. Database Technology Expertise

  Thorough understanding of:

  • External and internal data sources;
  • Data gathering, storing and retrieval methods (Extract-Transform-Load);
  • Accessing data from external sources (through screen scraping and data transfer protocols);
  • Manipulating large big data stores (like Hadoop, Hive, Mahoot and a wide range of emerging big data technologies).
  • Combine the disparate data sources to generate very unique market, industry and customer insights;
  • Understand emerging latent customer needs and provide inputs for high-impact offerings and services;
  • Develop insightful, meaningful connections with customers based on a deep understanding of their needs and wants.

5. Visualization and Communications Expertise

Comfort with visual art and design to:

  • Turn statistical and computational analysis into user-friendly graphs, charts and animation;
  • Create insightful data visualizations (e.g., motion charts, word maps) that highlight trends that may otherwise go unnoticed;
  • Use visual media to deliver key message (e.g., reports, screens – from mobile screens to laptop/desktop screens to HD large visualization walls, interactive programs and, perhaps soon, augmented reality glasses).
  • Enable those who aren’t professional data analysts to effectively interpret data;
  • Engage with senior management by speaking their language and translating data-driven insights into decisions and actions;
  • Develop powerful, convincing messages for key stakeholders that positively influence their course of action.

While it may seem unrealistic to find a single individual with all the skills we've listed, there are some data scientists who do, in fact, fit the profile. They may not be equally skilled in all areas but often have the ability to round out their skills over time. They typically tend to be in high-tech sectors, where they have had the opportunities to develop these abilities as a matter of necessity.

However, because of the increasing demand for data scientists and their scarcity, insurers (and companies in other industries) should consider if they want to build, rent or buy them. Although buying or renting capabilities can be viable options – and do offer the promise of immediate benefits – we believe that building a data science function is the best long-term approach. Moreover, and as we will address in our next post, in light of the shortage of data scientists, a viable approach is creating a data science office of individuals who collectively possess the core competencies of the ideal data scientist.