{"id":26793,"date":"2019-04-24T15:30:30","date_gmt":"2019-04-24T20:30:30","guid":{"rendered":"https:\/\/centricconsulting.com\/?p=26793"},"modified":"2022-08-26T14:18:12","modified_gmt":"2022-08-26T18:18:12","slug":"enriched-and-raw-data-in-insurance-cant-they-just-get-along","status":"publish","type":"post","link":"https:\/\/centricconsulting.com\/blog\/enriched-and-raw-data-in-insurance-cant-they-just-get-along\/","title":{"rendered":"Enriched and\u00a0Raw Data in Insurance: Can\u2019t They Just Get Along?"},"content":{"rendered":"

Raw data in insurance\u00a0<\/span>needs\u00a0<\/span>process<\/span>ing<\/span>,<\/span>\u00a0clean<\/span>ing<\/span>, and\u00a0<\/span>polish<\/span>ing<\/span>.<\/span>\u00a0<\/span>Only then can it be packaged.<\/span>\u00a0This is what we call data\u00a0<\/span>enrichment.<\/span><\/h2>\n
\n

Part of a blog series.<\/a><\/em><\/p>\n

Effectively mining the <\/span>data<\/span>\u00a0you already have\u00a0<\/span>can be<\/span>\u00a0a low<\/span>–<\/span>cost\u00a0<\/span>effort<\/span>\u00a0with a high<\/span>–<\/span>value return.<\/span>\u00a0<\/span>We\u2019ve all<\/span>\u00a0heard of the Titanic disaster of 1912.<\/span>\u00a0<\/span>Many of us know about \u201cUnsinkable\u201d Molly Brown. She\u00a0<\/span>performed<\/span>\u00a0actions that saved lives on that cold April morning, and untold thousands since.<\/span>\u00a0<\/span><\/p>\n

However,<\/span>\u00a0we\u00a0<\/span>don\u2019t know<\/span>\u00a0about her husband, James Joseph Brown.<\/span>\u00a0<\/span>He worked for the IBEX Mining Company.<\/span>\u00a0<\/span>In 1893<\/span>,<\/span>\u00a0the Sherman Silver Purchase Act caused a free fall in silver prices.<\/span>\u00a0<\/span>The company needed a new strategy.<\/span>\u00a0<\/span>Enter J.J. Brown\u2019s ingenuity.<\/span>\u00a0<\/span>He developed a new method to hold back loose sand.<\/span>\u00a0<\/span>The company was able to dig past the silver they<\/span>\u00a0found in the \u201cLittle Jonny Mine\u201d to find an enormous vein of gold.<\/span>\u00a0<\/span>J.J. Brown found a way to better mine the land\u00a0<\/span>he\u00a0<\/span>already owned.<\/span>\u00a0<\/span><\/p>\n

Today, the insurance<\/a> industry has mountains of data. Too many insurance companies lack the process to mine this mountain of data to its full potential.<\/strong> They have found a way to get the more accessible silver, but the gold buried under a mountain of loose sand remains elusive.<\/p>\n

Just like in precious metal mining, data<\/a> mining cannot effectively use the \u201cRaw\u201d ore.<\/span>\u00a0<\/span>The raw ore must be processed.<\/span>\u00a0<\/span>Impurities removed, and the valuable parts are<\/span>\u00a0then<\/span>\u00a0polished<\/span>,<\/span>\u00a0<\/span>packaged,<\/span>\u00a0and put on the market.<\/span>\u00a0<\/span>Data mining is the same. <\/span>Raw data\u00a0needs\u00a0processing,\u00a0cleaning, and\u00a0polishing\u00a0\u2013 conforming\u00a0into\u00a0recognizable and inter-relatable\u00a0structures.\u00a0Only then can it packaged in a report and put on the \u201cmarket.\u201d\u00a0We know this process\u00a0as enrichment.<\/p>\n

What is Enriched Data?<\/h2>\n

Many insurance companies have more than one source system.<\/span>\u00a0<\/span>Over the years, they<\/span>\u00a0acquired other companies that use a different system for policy, claims, or billing.<\/span>\u00a0<\/span>Many systems, that have run their company for a decade or longer, do not keep up with the insurance company\u2019s pace of change.<\/span>\u00a0<\/span><\/p>\n

New products, new processes, and poor performance under an\u00a0<\/span>ever-increasing<\/span>\u00a0load<\/span>\u00a0<\/span>force<\/span>d<\/span>\u00a0companies to\u00a0<\/span>migrate to new platforms.<\/span>\u00a0<\/span>Even within the same systems, errors\u00a0<\/span>are<\/span>\u00a0identified and fixed, but the existing dat<\/span>a<\/span>\u00a0still reflect<\/span>s<\/span>\u00a0some of these errors.<\/span>\u00a0<\/span>The data is not the same across all systems<\/span>,<\/span>\u00a0and within each system<\/span>,<\/span>\u00a0there remain<\/span>\u00a0data anomalies.<\/span>\u00a0<\/span><\/p>\n

How can you have a\u00a0comprehensive\u00a0report if the underlying data is in different\u00a0structures with disparate codes and questionable quality?\u00a0You must enrich the data.\u00a0<\/strong><\/p>\n

The Value of Enrichment<\/h2>\n

There are many ways to discuss value.<\/span>\u00a0<\/span>Let\u2019s talk about the cost of not\u00a0<\/span>correctl<\/span>y enriching the data.<\/span>\u00a0<\/span>An insurance company\u00a0<\/span>looked<\/span>\u00a0into agency performance.<\/span>\u00a0<\/span>They set up criteria to measure which agencies\u00a0<\/span>brought<\/span>\u00a0in the most profitable business and which agencies\u00a0<\/span>cost<\/span>\u00a0the company money.<\/span>\u00a0<\/span>They identified several agencies\u00a0<\/span>writing very few P&C policies<\/span>,<\/span>\u00a0and in general<\/span>,<\/span>\u00a0the loss ratios were very high.<\/span>\u00a0<\/span><\/p>\n

They decided to put these companies on a development path.<\/span>\u00a0<\/span>There were some rather harsh adjustments recommended.<\/span>\u00a0<\/span>Seems like a good use of data, right?<\/span>\u00a0<\/span>Not so fast.<\/span>\u00a0<\/span>Let\u2019s close this loop.<\/span>\u00a0<\/span>T<\/span>he company\u00a0<\/span>failed to<\/span>\u00a0realize\u00a0<\/span>their data warehouse did not include its L&A book of business.<\/span>\u00a0<\/span>Several of these\u00a0<\/span>\u201cpoorly\u201d performing agencies were not\u00a0<\/span>actively\u00a0<\/span>marketing P&C insurance.<\/span>\u00a0<\/span>They\u00a0<\/span>only sold<\/span>\u00a0P&C insurance to existing L&A clients as a service to keep the more lucrative L&A business.<\/span>\u00a0<\/span>One<\/span>\u00a0of these \u201cpoorly\u201d performing agencies was the largest, most profitable L&A agency the company had.<\/span>\u00a0<\/span><\/p>\n

The lack of proper enrichment led this company to conclusions that were incorrect and put a very valuable relationship at risk.\u00a0<\/strong><\/p>\n

The Cost of Enrichment<\/h2>\n

Enriched data is good.<\/span>\u00a0<\/span>Enriching data is hard.<\/span>\u00a0<\/span>In this blog series<\/span><\/a>,<\/span>\u00a0we have discussed some of the risks and challenges.<\/span>\u00a0<\/span>Data governance drives the enrichment process.<\/span>\u00a0<\/span>Data governance is <\/span>a\u00a0<\/span>load for the business side and<\/span> the technology side.<\/span>\u00a0<\/span>For every source system,\u00a0<\/span>every object,\u00a0<\/span>every attribute, and\u00a0<\/span>every measure there are\u00a0<\/span>multiple<\/span>\u00a0steps and\u00a0<\/span>many<\/span>\u00a0people working together to ensure accuracy and\u00a0<\/span>comprehension.<\/span>\u00a0<\/span>This process is not cheap.<\/span>\u00a0<\/span>Nor is it fast.<\/span>\u00a0<\/span><\/p>\n

To ensure proper review and thought, data cannot\u00a0become\u00a0enriched overnight.\u00a0<\/strong>Companies without a careful process might find their properly enriched data co-mingled with improperly enriched data that cause a complete loss of trust in the entire data set.<\/p>\n

The Future is Hard to Predict<\/h2>\n

From where will tomorrow’s challenges emerge?<\/span>\u00a0<\/span>Unfortunately, there is no crystal ball.<\/span>\u00a0<\/span>A down<\/span>turn in the economy may turn your primary three<\/span>–<\/span>year thrust from expanding into new ventures toward lowering costs\u00a0<\/span>or<\/span>\u00a0cash flow belt-tightening.<\/span>\u00a0<\/span>While both are great goals, only one\u00a0<\/span>is<\/span>\u00a0the\u00a0<\/span>primary driver<\/span>.<\/span>\u00a0<\/span><\/p>\n

Because the future is hard to predict, data governance –\u00a0that drives data enrichment and in turn\u00a0drives formal reporting – must remain flexible.\u00a0<\/strong>Also, the value propositions for finding new ways to glean information from raw data facts is not a straight road.\u00a0A<\/span>n<\/span>\u00a0insurance executive was talking the other day about how the idea that\u00a0<\/span>\u201c<\/span>credit scores could predict claim<\/span>\u00a0costs\u201d\u00a0<\/span>was not universally accepted when first introduce<\/span>d<\/span>.<\/span>\u00a0<\/span>Many insurance executives<\/span>,<\/span>\u00a0at th<\/span>at<\/span>\u00a0time<\/span>,<\/span>\u00a0did not understand how the credit score\u00a0<\/span>relates<\/span>\u00a0to if an incident would happen, or how severe it would be.<\/span>\u00a0<\/span><\/p>\n

But now we know that credit score is a very\u00a0<\/span>useful\u00a0<\/span>predictor<\/span>.<\/span>\u00a0<\/span>How many other ideas didn\u2019t pan out?<\/span>\u00a0<\/span>How many\u00a0<\/span>were other<\/span>\u00a0ideas\u00a0<\/span>not pursued because\u00a0<\/span>there was no supporting data<\/span>?<\/span>\u00a0<\/span>The future is unpredictable and\u00a0<\/span>undoubted<\/span>ly full of unexpected twists and turns.<\/span>\u00a0<\/span><\/p>\n

Find Balance with a Structured Data Lake<\/h2>\n

Let\u2019s embrace the coming uncertainty.<\/span>\u00a0<\/span>Let\u2019s prepare for the inevitable change.<\/span>\u00a0<\/span>There is a simple and easy solution.<\/span>\u00a0<\/span>Let\u2019s collect all the data we can co-locate<\/span>.<\/span>\u00a0<\/span>We know\u00a0<\/span>collection<\/span>\u00a0<\/span>as\u00a0<\/span>a data lake<\/a>.<\/span>\u00a0<\/span>Much like a city\u00a0<\/span>builds<\/span>\u00a0a reservoir to hold water for consumption\u00a0<\/span>by its citizens at a future time,\u00a0<\/span>we can build a reservoir to hold data.<\/span>\u00a0<\/span><\/p>\n

Some of this data we\u00a0<\/span>immediately pipe into our data warehouse and onto operational reports and analytics dashboards.<\/span>\u00a0<\/span>The rest of the data\u00a0<\/span>stays<\/span>\u00a0in the reservoir until we find a proper use.<\/span>\u00a0<\/span>Some analyst<\/span>,<\/span>\u00a0some<\/span>day\u00a0<\/span>is going to dev<\/span>elop<\/span>\u00a0a big, new idea.<\/span>\u00a0<\/span>That\u00a0<\/span>future\u00a0<\/span>analyst<\/span>\u00a0need<\/span>s<\/span>\u00a0data to drive conclusions.<\/span>\u00a0I<\/span>f we haven\u2019t collected the data along the way, that analyst\u00a0<\/span>has to\u00a0<\/span>spend weeks, if not months\u00a0<\/span>collecting the data.<\/span>\u00a0<\/span>M<\/span>uch of the data\u00a0<\/span>is likely<\/span>\u00a0lost<\/span>\u00a0over time<\/span>.<\/span>\u00a0<\/span><\/p>\n

So,\u00a0now that analyst\u00a0requires\u00a0years to collect enough history to test her hypothesis.\u00a0With a full reservoir of historical data collected in our Data Lake, that analyst can write her algorithms, test her hypothesis, and if correct, bring the next big idea forward in a small fraction of the time.<\/strong><\/p>\n

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Just-in-Time Promotion<\/h2>\n

An analyst\u00a0<\/span>find<\/span>s<\/span>\u00a0the next big idea because we\u00a0<\/span>previously thought<\/span>\u00a0to build a Data Lake.<\/span>\u00a0<\/span>Th<\/span>is new idea\u00a0<\/span>receives validity<\/span>\u00a0using a combination of cleansed<\/span>,<\/span>\u00a0enriched data and some new raw data that nobody thought was of much value.<\/span>\u00a0<\/span><\/p>\n

Let\u2019s close this loop.<\/span>\u00a0<\/span>Let\u2019s get these new data feeds, objects or attributes over to ou<\/span>r<\/span> data governance team.<\/span>\u00a0<\/span>Let\u2019s figure out how much clean<\/span>ing<\/span>\u00a0and polishing\u00a0<\/span>we need to do\u00a0<\/span>to\u00a0<\/span>promote<\/span> this to our data warehouse and get this new idea democratized to everyone in our enterprise. <\/span>Let\u2019s\u00a0allow\u00a0necessity\u00a0to\u00a0help us figure out the right elements to\u00a0on which to\u00a0spend our precious data governance<\/a> and enrichment budget.\u00a0<\/strong><\/p>\n

J.J. Brown used hay to help hold back the loose sand and found one\u00a0of\u00a0the largest gold strikes in Colorado history.\u00a0Let\u2019s use a co-located comprehensive Data-Lake strategy to prepare us to find our next\u00a0big data treasure.<\/p>\n","protected":false},"excerpt":{"rendered":"

Raw data in insurance\u00a0needs\u00a0processing,\u00a0cleaning, and\u00a0polishing.\u00a0Only then can it packaged.\u00a0This is what we call data\u00a0enrichment.\u00a0<\/p>\n","protected":false},"author":230,"featured_media":32269,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_oasis_is_in_workflow":0,"_oasis_original":0,"_oasis_task_priority":"","_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","footnotes":""},"categories":[1],"tags":[18616,3759],"coauthors":[15529],"class_list":["post-26793","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-data-and-analytics","tag-insurance","resource-categories-blogs","orbitmedia_post_topic-data-analytics","orbitmedia_post_industry-insurance"],"acf":[],"publishpress_future_action":{"enabled":false,"date":"2025-03-27 02:40:54","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category"},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/centricconsulting.com\/wp-json\/wp\/v2\/posts\/26793"}],"collection":[{"href":"https:\/\/centricconsulting.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/centricconsulting.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/centricconsulting.com\/wp-json\/wp\/v2\/users\/230"}],"replies":[{"embeddable":true,"href":"https:\/\/centricconsulting.com\/wp-json\/wp\/v2\/comments?post=26793"}],"version-history":[{"count":0,"href":"https:\/\/centricconsulting.com\/wp-json\/wp\/v2\/posts\/26793\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/centricconsulting.com\/wp-json\/wp\/v2\/media\/32269"}],"wp:attachment":[{"href":"https:\/\/centricconsulting.com\/wp-json\/wp\/v2\/media?parent=26793"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/centricconsulting.com\/wp-json\/wp\/v2\/categories?post=26793"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/centricconsulting.com\/wp-json\/wp\/v2\/tags?post=26793"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/centricconsulting.com\/wp-json\/wp\/v2\/coauthors?post=26793"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}