Data in a Changing World

The Insurance industry traditionally uses data to inform decision-making and manage growth and profitability across marketing, underwriting, pricing and policy servicing processes. However, like most established financial institutions, insurance companies have many data repositories and different teams managing analytics functions. Traditionally, they also struggle to share this information or communicate with one another, with many organisations having their own processes for capturing data. These factors combine to cause poor quality and inconsistent data, creating barriers toward seamless integration.

The Insurance industry recognises the importance of maintaining a competitive edge, with many companies looking to adopt a ‘single platform’ approach using Cloud Services from AWS, Azure or Google in the short to medium term. Such a platform needs to be flexible to support different skill sets, react to changing market conditions and able to integrate alternative sources of data. Fundamental to this is the quality of data across different data sources, ensuring it is trusted, of a high degree of integrity, and complete for business decisioning purposes.

Challenges

Customer insights are isolated to silos and scattered across lines of business, functional areas and even channels. As a result, much of the work that surrounds the handling of data becomes manual and time consuming, with no common keys or even set definitions of key terms, i.e., ‘customer’. It is estimated that as much as 70% of a highly qualified analyst’s time is spent locating and fixing the data.

The challenge for Insurance companies is being able to recognize the same customer across product lines and/or at different stages of the policy lifecycle. Direct and agency channels may compete for the same customer or attract a high-risk prospect that was turned down previously by underwriting. Since the claims department data is not available to pricing and marketing to inform their decisions, the result is often extra expenditures and a larger than necessary marketing budget that could easily be streamlined should these inefficiencies be addressed. It also causes poor customer experiences, which harm the brand.

There is, however, a significant demand for customer-centric solutions which allow insurance companies to link different pieces of data about a customer. These solutions use Data Quality tools to match, merge and link records, creating a holistic view across product lines and throughout the policy lifecycle.

Customer-centric solutions help insurance companies realise important business goals, including more accurate targeting, longer retention, and better profitability.

Opportunity

Generating valuable insights from expanding data sets is becoming significantly harder. On top of this, leveraging the right technology, people and process to analyse data remains a key challenge for Executives. Prepping the data is often where the real heavy lifting is done and using Data Quality automation and a Self-Service approach can really benefit a company in terms of significantly reducing costs and accelerating decision making.

While the Insurance industry faces a plethora of challenges with data and analytics, it’s imperative that executives recognize that the quality of the data is fundamental to capitalising on market opportunities. By overcoming these barriers, the industry will be better prepared to embark on the next frontier of Data and Analytics (D&A).

About Datactics

Datactics helps Insurance companies drive valuable Data Insights, supports Operational Data needs and process, including Data Governance and Compliance & Regulation by removing roadblocks common in data management. We specialise in class-leading, self-service data quality and fuzzy matching software solutions, designed to empower business users who know the data to visualise and fix the data.

To have further conversations about the drivers and benefits of a Self-Service Data Quality platform in Insurance, book a quick call with Kieran Seaward.     

And for more from Datactics, find us on Linkedin, Twitter, or Facebook.

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The drivers and benefits of a holistic, self-service data quality platform | Part 2

To enable the evolution towards actionable insight from data, D&A platforms and processes must evolve too. At the core of this evolution is the establishment of ‘self-service’ data quality – whereby data owners and SMEs have ready access to robust tools and processes, to measure and maintain data quality themselves, in accordance with data governance
policies. From a business perspective such a self-service data quality platform must be:

❖ Powerful enough to enable business users and SMEs to perform complex data operations
without highly skilled technical assistance from IT
❖ Transparent, accountable and consistent enough to comply with firm wide data governance
policies
❖ Agile enough to quickly onboard new data sets and changing data quality demands of end
consumers such as AI and Machine learning algorithms
❖ Flexible and open so it integrates easily with existing data infrastructure investment without
requiring changes to architecture or strategy
❖ Advanced to make pragmatic use of AI and machine learning to minimize manual
intervention

This goes way beyond the scope of most stand-alone data prep tools and ‘home grown’ solutions that are often used as a tactical one-off measure for a particular data problem. Furthermore, for the self-service data quality platform to truly enable actionable data across the enterprise, it will need to provide some key technical functionality built-in:

• Transparent & Continuous Data Quality Measurement
Not only should it be easy for business users and SMEs to implement large numbers of data domain specific data quality rules, but also those rules should be simple to audit, and easily explainable, so that ‘DQ breaks’ can be easily explored and the root cause of the break established.

In addition to data around the actual breaks, a DQ platform should be able to produce DQ dashboards enabling drill-down from high level statistics down to actual failing data points and publish high level statistics into data governance systems.

• Powerful Data Matching – Entity Resolution for Single View and Data Enrichment
Finding hidden value in data or complying with regulation very often involves joining together several disparate data sets. For example, enhancing a Legal Entity Master Database with an LEI, screening customer accounts against sanctions and PEP lists for KYC, creating a single view of client from multiple data silos for GDPR or FSCS compliance. This goes further than simple deduplication of records or SQL joins – most data sets are messy and don’t have unique identifiers and so fuzzy matching of numerous string fields must be implemented to join one data set with another. Furthermore, efficient clustering algorithms are required to sniff out similar records from other disparate data sets in order to provide a single consolidated view across all silos.

• Integrated Data Remediation Incorporating Machine Learning 
It’s not enough just to flag up broken data, you also need a process and technology for fixing the breaks. Data quality platforms should have this built in so that after data quality measurement, broken data can be quarantined, data owners alerted and breaks automatically assigned to the relevant SMEs for remediation Interestingly, the manual remediation process lends itself very well to machine learning. The process of manually remediating data captures domain specific knowledge about the data – information that can be readily used by machine learning algorithms to streamline the resolution of similar breaks in the future and thus greatly reduce the overall time and effort spent on manual remediation. 

“The process of manually remediating data captures domain specific knowledge about the data – information that can be readily used by machine learning algorithms to streamline the resolution of similar breaks in the future”   

• Data Access Controls Across Teams and Datasets 
Almost any medium to large sized organization will have various forms of sensitive data, and policies for sharing that data within the organization e.g. ‘Chinese walls’ between one department and another. In order to enable integration across teams and disparate silos of data, granular access controls are required – especially inside the data remediation technology where sensitive data may be displayed to users. Data access permissions should be set automatically where possible (e.g. inheriting Active Directory attributes) and enforced when displaying data, for example by row- and field-level access control, and using data masking or obfuscation where appropriate. 

• Audit Trails, Assigning and Tracking Performance 
  Providing business users with tools to fix data could cause additional headaches when it  comes to being able to understand who did what, when, why and whether or not it was the right thing to do. It stands to reason, therefore, that any remediation tool should have builtin capability to do just that with the associated performance of data break remediation 
  measured, tracked and managed. 

• AI Ready 
  There’s no doubt that one of the biggest drivers of data quality is AI. AI data scientists can spend up to 80% of their time just preparing input data for machine learning algorithms, which is a huge waste of their expertise. A self-service data quality platform can address many of the data quality issues by providing ready access to tools and processes that can ensure a base level of quality and identify anomalies in data that may skew machine learning models. Furthermore the same self-service data quality tools can assist data scientists to generate metadata that can be used to inform machine learning models – such ‘Feature Engineering’ can be of real value when the data set is largely textual as it can generate numerical indicators which are more readily consumed by ML algorithms. 

“AI data scientists can spend up to 80% of their time just preparing input data for machine learning algorithms, which is a huge waste of their expertise”

To have further conversations about the drivers and benefits of a Self-Service Data Quality platform, please book a quick call with Kieran Seaward.     

And for more from Datactics, find us on Linkedin, Twitter, or Facebook.

The post Key Features a Self-Service DQ Platform Should Have appeared first on Datactics.

The drivers and benefits of a holistic, self-service data quality platform | Part 1

Change

There has been increasing demand for higher and higher data quality in recent years – highly regulated sectors, such as banking have had a tsunami of financial regulations such as BCBS239, MiFID, FATCA, and many more stipulating or implying exacting standards for data and data processes. Meanwhile, there is a growing trend for more and more firms to become more Data and Analytics (D&A) driven, taking inspiration from Google & Facebook, to monetize their data assets.

This increased focus on D&A has been accelerated by easier and lower-cost access to artificial intelligence (AI), machine learning (ML), and business intelligence (BI) visualization technologies. However, in the now-waning hype of these technologies comes the pragmatic realization that unless there is a foundation of good quality reliable data, insights derived from AI and analytics may not be actionable. With AI and ML becoming more of a commodity, and a level playing field, the differentiator is in the data and the quality of the data.

“Unless there is a foundation of good quality reliable data, insights derived from AI and analytics may not be actionable”

Problems 

As the urgency for regulatory compliance or competitive advantage escalates, so too does the urgency for high data quality. A significant obstacle to quickly achieve high data quality is the variety of disciplines required to measure data quality, enrich data and fix data. By its nature, digital data, especially big data can require significant technical skills to manipulate and for this reason, was once the sole responsibility of IT functions within an organization. However, maintaining data also requires significant domain knowledge about the content of the data, and this domain knowledge resides with the subject matter experts (SMEs) who use the data, rather than a central IT function. Furthermore, each data set will have its own SMEs with special domain knowledge required to maintain the data, and a rapidly growing and changing number of data sets. If a central IT department is to maintain the quality of data correctly it must therefore liaise with an increasingly large number of data owners and SMEs in order to correctly implement DQ controls and remediation required. These demands create a huge drain on IT resources and a slow-moving backlog of data quality change requests within IT that simply can’t keep up. 

Given the explosion in data volumes, this model clearly won’t scale and so there is now a growing trend to move data quality operations away from central IT and back into the hands of data owners. While this move can greatly accelerate data quality and data onboarding processes, it can be difficult and expensive for data owners and SMEs to meet the technical challenges of maintaining and onboarding data. Furthermore, unless there is common governance around data quality across all data domains there stands the risk of a ‘wild west’ scenario, where every department manages data quality differently with different processes and technology. 

Opportunity

The application of data governance policies and the creation of an accountable Chief Data Officer (CDO) goes a long way to mitigate against the ‘wild west’ scenario. Data quality standards such as the Enterprise Data Management Council’s (EDMC) Data Capability Assessment Model (DCAM)1 provide opportunities to establish consistency in data quality measurement across the board.

The drive to capitalize on data assets for competitive advantage has had the result that the CDO function is quickly moving from an operational cost centre towards a product-centric profit centre. A recent publication by Gartner (30th July 2019) 2 describes three generations of CDO: “CDO 1.0” focused on data management; “CDO 2.0” embraced analytics; “CDO 3.0” assisted digital transformation, and Gartner now predicts a fourth, “CDO 4.0” focused on monetizing data-oriented products. Gartner’s research suggests that to enable this evolution, companies should strive to develop data and analytics platforms that scale across the entire company and this implies data quality platforms that scale too. 

To have further conversations about the drivers and benefits of a Self-Service Data Quality platform, book a quick call with Kieran Seaward.     

And for more from Datactics, find us on Linkedin, Twitter, or Facebook.

The post The Changing Landscape of Data Quality appeared first on Datactics.