Data is the key to growth &mdash; it is vital for businesses to understand how they can harness and apply external data, because it provides an <a href="https://www.capgemini.com/gb-en/research/data-sharing-masters/">unbeatable competitive edge</a>.
<h1>What is external data?&nbsp;</h1>
External data comes from sources that are outside of an enterprise. It includes customer surveys, social media data, and third-party market research.
There are several ways that external data can drive business growth:
<ol>
<li style="font-weight: 400;" aria-level="1">Gaining unbiased insights into customers and target markets. </li>
<li style="font-weight: 400;" aria-level="1">Identifying new business opportunities. </li>
<li style="font-weight: 400;" aria-level="1">Tracking and measuring a company&rsquo;s progress over time. </li>
<li style="font-weight: 400;" aria-level="1">Benchmarking and measuring performance against other companies. </li>
<li style="font-weight: 400;" aria-level="1">Allocating resources more efficiently and enabling better decisions.</li>
</ol>
<h1>Why is external data necessary?&nbsp;</h1>
External data sources can provide essential information for businesses.
Insurance companies apply insights from external data to <a href="https://demyst.com/blog/looking-ahead-high-expectations-low-margins-for-error?utm_marketing_source=BPST&amp;gaAd=BPST">meet customer expectations</a>. External data can also be used to <a href="https://demyst.com/blog/assessing-driver-risk-without-using-credit-scores?utm_marketing_source=BPST&amp;gaAd=BPST">price auto insurance</a> more accurately.
In financial services, external data is part of ID verification processes for <a href="https://demyst.com/blog/digital-footprints-for-id-verification?utm_marketing_source=BPST&amp;gaAd=BPST">identifying and preventing fraudulent transactions</a>. Marketplace companies use external data to <a href="https://demyst.com/blog/improving-trust-on-managed-platforms?utm_marketing_source=BPST&amp;gaAd=BPST">maintain trust</a> and improve their onboarding workflows for new vendors. And <a href="https://demyst.com/blog/how-lenders-and-insurers-can-use-geolocation-data?utm_marketing_source=BPST&amp;gaAd=BPST">geolocation data</a> can provide a comprehensive view of business clients.
In other industries, external data is used to <a href="https://demyst.com/blog/penalties-mount-for-marketers-using-toxic-data?utm_marketing_source=BPST&amp;gaAd=BPST">improve marketing efforts</a> and <a href="https://demyst.com/blog/supply-chain-risk-management?utm_marketing_source=BPST&amp;gaAd=BPST">manage risks in the supply chain</a>.
<h1>How is external data accessed?</h1>
A data integration platform &mdash; also described as an orchestration layer &mdash; allows enterprises to connect with different data sources, combining that information in a central repository.&nbsp;
Data integration platforms need to make important decisions regarding data coverage, consistency, and compliance:
<ul>
<li style="font-weight: 400;" aria-level="1">Coverage: Some information may only be available from specific data providers. Other data providers may have more complete coverage in markets that are less relevant. </li>
<li style="font-weight: 400;" aria-level="1">Consistency: Data providers can present information in unique formats or sort identical properties under different metadata structures. Enterprises trying to compare equivalent data from multiple providers may need to perform additional <a href="https://demyst.com/blog/game-changer-demysts-entity-resolution-tech?utm_marketing_source=BPST&amp;gaAd=BPST">entity resolution</a> work. </li>
<li style="font-weight: 400;" aria-level="1">Compliance: Privacy considerations and the ethics of data collection processes should be carefully examined as part of <a href="https://demyst.com/blog/vendor-due-diligence-for-data-privacy">due diligence</a> efforts when integrating information from new sources.</li>
</ul>
Enterprises that build their own data integration platform must also allocate appropriate resources for the platform&rsquo;s ongoing maintenance and support.
<h1>Does every company need external data capabilities?</h1>
External data provides access to new markets, new customers, and new insights for making effective decisions. However, enterprises need to weigh the benefits of external data against the costs of managing their own data integration platforms.
Data exchanges like the <a href="https://resources.snowflake.com/solution-briefs/improve-data-analytics-with-demystdata-and-snowflake">Snowflake Data Marketplace</a>, the <a href="https://partners.amazonaws.com/partners/001E000001d9PSOIA2/DemystData">AWS Data Exchange</a>, and <a href="https://demyst.com/platform?utm_marketing_source=BPST&amp;gaAd=BPST">the Demyst platform</a> can offer cost-effective access to external data. Organizations can also work with partners who have already developed external data expertise and made the necessary investments in data infrastructure.
Without access to external data, companies are limited in their ability to make decisions, find new opportunities, and keep up with competitors. The relevant question is not whether organizations need external data, but whether they need to build their own data infrastructure and manage relationships with multiple data providers.
By working with Demyst, enterprises can access <a href="https://demyst.com/platform">a single global interface</a> for discovering, building, and deploying the right external data solutions.

External data provides access to new markets, new customers, and new insights for making effective decisions.

The What, Why, and How of External Data

When external data is paired with machine learning, the results can change the world. The potential of such partnerships was recognized by Fast Company&rsquo;s <a href="https://www.fastcompany.com/90742276/world-changing-ideas-awards-2022-ai-and-data-finalists-and-honorable-mentions" rel="follow">World Changing Ideas 2022</a> list, which honored Demyst and TruEra for their work using AI and data to empower positive change.
TruEra and Demyst worked together to develop a solution to mitigate bias in AI-based credit decisioning models, such as those used by financial services companies, demonstrating how external data can be a key part of driving <a href="https://marketing.truera.com/external-data-and-ai-quality-management-white-paper" rel="follow">new and effective machine learning use cases</a>. This will improve applications that are based on machine learning models, which can be held back by model performance or bias issues, particularly in regulated industries.
Scott Albin, Chief Strategy Officer at Demyst, said, &ldquo;We are proud to be recognized by Fast Company. It&rsquo;s a clear demonstration that machine learning use cases can be both expanded and dramatically improved by the use of high quality, curated, and compliant external data.&rdquo;
Collaboration between TruEra and Demyst previously showed how third-party data and AI quality management solutions can work together to improve the accuracy and fairness of credit decisioning models. Their project won the <a href="https://finance.yahoo.com/news/truera-demyst-win-codifying-responsible-130000175.html" rel="follow">Global Veritas Challenge 2021 at the Singapore Fintech Festival</a>, the world&rsquo;s largest fintech event.
Demyst already accelerates the deployment of external data solutions for the world&rsquo;s leading banks, insurers, and fintechs. The work with TruEra has developed a clear and straightforward process to show how software can improve model performance while <a href="https://youtu.be/60IJwbfjivs" rel="follow">achieving the goals of responsible, fair AI</a>&nbsp;
Fast Company&rsquo;s list honors projects that harness the power of data to empower change. The collaboration with TruEra is a compelling example of external data unlocking innovation to drive business growth. And this growth can be accelerated by <a href="https://www.demyst.com/platform?utm_marketing_source=BPST&amp;gaAd=FIXIT" rel="follow">the Demyst platform</a>, which eliminates the friction associated with external data procurement, testing, and deployment.

External data can be a key part of driving new and effective machine learning use cases.

Fast Company Recognizes How External Data Can Change the World

Paypal&rsquo;s recent discovery of <a href="https://www.finextra.com/newsarticle/39630/paypal-reveals-45-million-accounts-were-illegitimate-shares-plummet">4.5 million illegitimate accounts on its platform</a> should be a warning heeded by all: even the largest, most sophisticated payment processors can fall victim to bad actors.&nbsp;
Heightened customer expectations and persistent criminal attacks have made the situation near untenable. If legitimate transactions are denied, unhappy customers may take their business elsewhere. However, lenient approval processes leave companies vulnerable to <a href="https://demyst.com/blog/synthetic-identities-genuine-fraud/">synthetic fraud</a> and <a href="https://www.experian.com/blogs/insights/2020/12/solving-fraud-problem-first-party-fraud/">first-party fraud</a>.&nbsp;
These tensions have placed an increased emphasis on performing ID verification quickly and effectively. The process needs to be rapid enough to satisfy customer demands while remaining rigorous enough to protect against fraud.&nbsp;
An effective ID verification process considers 5 data points:&nbsp;
<ul>
<li style="font-weight: 400;" aria-level="1">A customer&rsquo;s name is their primary identifier, but it&rsquo;s also the easiest to steal. The question is whether the name can be matched with other known facts to confirm a valid identity.&nbsp; </li>
<li style="font-weight: 400;" aria-level="1">Although a customer&rsquo;s physical address is an important data point, it can change frequently. More than 10% of all US address records <a href="https://demyst.com/blog/how-to-verify-a-us-persons-address/">need to be updated each year</a>.&nbsp; </li>
<li aria-level="1">The phone number associated with a transaction is another data point that can change often &mdash; in the United States, approximately 100,000 mobile phone numbers are <a href="https://medium.com/codex/two-thirds-of-mobile-phone-numbers-are-reused-7fbf03e1b88f">reassigned each day</a>.&nbsp; </li>
<li aria-level="1">The email address of a customer provides additional details for determining whether a customer request is legitimate. It can be checked to see whether it was established recently and whether it matches known email addresses. </li>
<li style="font-weight: 400;" aria-level="1">An IP address also offers important clues about the validity of a transaction. If the IP address is from a region that is nowhere near a customer&rsquo;s known address, then the likelihood of fraud is much higher.&nbsp;</li>
</ul>
These details need to be verified against sources of truth that are accurate and up-to-date, accommodating the customer&rsquo;s desire for convenience while protecting the business against fraud.
Milena Babayev, Sr. Product Marketing Manager at Ekata, a Mastercard company, says &ldquo;Everyone wants to better leverage data insights &mdash; and your own, internal data, may not always be enough to predict fraud in real-time. The right identity verification solution is able to help you answer the right questions, such as when was the email first seen in digital interaction? Or is the IP address risky? Or are there any anomalies in usage of the identity elements? This is where Ekata&rsquo;s digital identity verification comes in &mdash; helping businesses reduce friction and improve fraud risk analysis &mdash; resulting in a better customer experience.&rdquo;
Companies can also strengthen their defenses by consulting multiple data providers for improved accuracy. Demyst&rsquo;s <a href="https://demyst.com/app/api-catalog/53">recipe for assessing fraud risk</a> applies sophisticated <a href="https://demyst.com/blog/demysts-entity-resolution-multi-source-recipes/">entity resolution technology</a> to conduct thorough examinations of individual details, including IP addresses.
The Demyst platform also benefits from Ekata&rsquo;s insights in other workflows, including <a href="https://demyst.com/app/api-catalog/64">a recipe for bad lead filtering</a>, which can improve marketing effectiveness and make outreach efforts more efficient. 
<a href="https://demyst.com/platform">Create an account and explore hundreds of operationalized external data sources ready for testing and deployment.</a>

Even sophisticated payment processors can fall victim to bad actors. An effective ID verification process considers 5 data points.

5 Checks for Flagging Fraud Risk

Even the largest, most sophisticated payment processors can fall victim to bad actors.

Market forces are changing the way that businesses collect, process, and apply external data. Mark Hookey, CEO of Demyst, has seen a major shift driven by data's widespread democratization.
&ldquo;There has never been a more interesting time to be in the data space,&rdquo; Hookey said. He shared his perspective as part of the Demyst webinar series for <a href="https://demyst.com/webinars/video?url=ZOHrC5NTp67NwEwqXNju&amp;gaAd=BPST">2022 data predictions</a>.
The automated cleaning and collection of data has lowered barriers and made it more accessible to a larger audience. &ldquo;There's a certain tipping point of the technology that is helping people claim data, which in turn will significantly boost the value people can extract from external data for their customers,&rdquo; Hookey explained.
Overall, this is good news that will lead to better business outcomes. Enterprises no longer need special relationships, access, or privileges to create a rich data ecosystem.&nbsp;
However, these changes have also shifted the dynamics that govern markets for external data. Massively increased access has meant that data providers are facing greater competition and lower profit margins for individual data products. Overall, Hookey expects this to increase revenues for data providers, because companies will be using more data.&nbsp;&nbsp;
&ldquo;Look to Netflix and the way that changed the dynamics for the movie studios,&rdquo; Hookey said. &ldquo;Obviously, in the short term, it caused a lot of flux and some real challenges.&rdquo; Over a longer period of time, consumers were given more options for streaming video, and <a href="https://www.forbes.com/sites/bradadgate/2021/06/17/nielsen-streaming-video-audience-share-is-higher-than-broadcast-tv/">they now spend more time watching it</a>.
&ldquo;It's the same in the world of data,&rdquo; Hookey said. &ldquo;People are consuming a lot more than they used to, and that's good for everyone.&rdquo;
Incumbents that have dominated external data markets can no longer rely on captive audiences and high switching costs; consumers are now using data exchanges like the <a href="https://www.snowflake.com/data-marketplace/">Snowflake Data Marketplace</a>, the <a href="https://aws.amazon.com/data-exchange/">AWS Data Exchange</a>, and <a href="https://demyst.com/platform?utm_marketing_source=BPST&amp;gaAd=BPST" rel="follow">the Demyst platform</a>, where they can leverage the unique strengths of multiple datasets.&nbsp;
Clients have become more sophisticated, <a href="https://demyst.com/blog/game-changer-demysts-entity-resolution-tech/">combining data from multiple providers</a> for clearer business insights. One example is the way that financial institutions are managing fraud risk.&nbsp;
&ldquo;People used to just talk about fraud,&rdquo; Hookey said. &ldquo;And now they talk about getting into the weeds of all of the specific, different types of fraud.&rdquo;&nbsp;
When data products specialize in fighting specific types of fraud &mdash; like <a href="https://demyst.com/blog/synthetic-identities-genuine-fraud/">synthetic fraud</a> or <a href="https://www.experian.com/blogs/insights/2020/12/solving-fraud-problem-first-party-fraud/">first-party fraud</a> &mdash; they can be <a href="https://fintechbusinessweekly.substack.com/p/fighting-fraud-with-sentilinks-naftali">less effective at fighting other types of fraud</a>. The democratization of data means that financial institutions can apply multiple products simultaneously, delivering the best results for their specific needs.&nbsp;
External data&rsquo;s tipping point will deliver a larger selection and lower costs for consumers, while data providers will see greater volumes of transactions and enough competition to keep them from getting complacent.&nbsp;
According to Hookey, &ldquo;It depends on the data source, and it depends on the quality, and that means it's a marketplace where the value goes more to the people that have high quality products.&rdquo;
View <a href="https://demyst.com/webinars/video?url=ZOHrC5NTp67NwEwqXNju&amp;gaAd=BPST">the full recording of this presentation</a> on Demyst&rsquo;s website.

External data’s democratization means more sophisticated clients and more developed marketplaces.

External Data’s Tipping Point

<a href="https://demyst.com/catalog/demyst/opencorporates/opencorporates-companies-search">OpenCorporates Companies and Officer Search</a> provides transparent company data for risk management processes, which include third-party risk management and verification for Know Your Customer (KYC) onboarding and ongoing monitoring.
Simone Spencer, Vice President of Marketing at Demyst, talked with Shanti Salas, Vice President for North America at OpenCorporates, to discuss how transparent data can justify risk-based decisions with clear, current information.
Their conversation follows.&nbsp;
Simone Spencer: Can you start by giving us an explanation of transparent company data?&nbsp;
Shanti Salas: Transparent company data has three key qualities.
<ul>
<li style="font-weight: 400;" aria-level="1">Traceability&nbsp;</li>
</ul>
First, it is collected only from official public sources, and is traceable back to these sources &ndash; providing a clear line of sight on where it came from and when it was collected.
<ul>
<li style="font-weight: 400;" aria-level="1">Single standardised schema</li>
</ul>
Second, it is unified in one place according to a single, standardised schema. Users of company data shouldn&rsquo;t have to grapple with hundreds of schemas with different quirks from individual company registers around the world. Nor should they need to deal with different vendors to acquire each dataset. It is much easier to rely on a single source to do the heavy lifting to provide an easily understandable snapshot of the world&rsquo;s companies as data.
<ul>
<li style="font-weight: 400;" aria-level="1">Fresh reflection of official company registers</li>
</ul>
Finally, it should be up-to-date and a faithful reflection of the official company registers. We strive to bring down as much as possible the time between a company being incorporated, then appearing in the register, and finally appearing in our database.
Professionals in organisations of all kinds use company data for a vast range of activities, but why should they care that it&rsquo;s transparent? Ultimately, transparency gives them defensibility. If they know where and when their data came from, they can justify and stand behind risk-based decisions.&nbsp;
Decisions are more defensible &ndash; and almost always better &ndash; when based on clearly traceable and up-to-date information from official sources.
Spencer: And how does OpenCorporates gather transparent data?&nbsp;
Salas: We only collect company data from official sources to ensure transparency and quality. Our dataset has a clearly defined model that uses the official record of legal entities to identify companies. This sets us apart from many legacy business information providers who use inconsistent models. They often identify companies by vague proxies like their name or their address.
We are increasingly gathering our data by automated means. This lets our users receive updates to company information as soon as possible after the changes take effect in the register. By contrast, legacy providers often rely on data that is manually keyed in, or buy and resell data from each other rather than official sources.&nbsp;
Spencer: Automation does sound like a way to increase accuracy and efficiency. How can transparent company data be used for risk management purposes?&nbsp;
Salas: Transparent company data is critical to any company&rsquo;s risk management processes. It supports: verification for Know Your Customer (KYC) onboarding and ongoing monitoring, supply chain and third party risk management, investigations and master data management.
Many clients tell me they use our data as a foundational layer &ndash; a golden source &ndash; on top of which other data sources are then layered.
Spencer: Is freshness important for a foundational layer of data?
Salas: The universe of companies is in constant flux. Thousands of incorporations and dissolutions take place every day, as well as changes to companies&rsquo; directors and key characteristics. It&rsquo;s only ramping up as companies are increasingly being created in an automated fashion.
This means the third parties you need to monitor are changing too. To make the most informed risk management decisions, one needs data that accurately reflects the official company register as it stands today &ndash; not months ago when a legacy vendor might have acquired its data. Without fresh data, risk professionals might unwittingly risk-rate a customer lower risk than is really warranted, or miss a red flag in an investigation, exposing them to greater risk.
Spencer: How have expectations changed for company data in a post-pandemic world?
Salas: Fraud has been a particularly salient topic in the last two years. Astronomical figures are said to have been fraudulently claimed in Covid-19 business relief funds. This has brought with it a realisation that company data is pivotal in tackling fraud. For example, high-profile investigations by the <a href="https://blog.opencorporates.com/2020/10/29/covid-relief-funds-how-opencorporates-data-helped-expose-exploitation-of-the-us-paycheck-protection-program/">Miami Herald</a> and <a href="https://www.thetimes.co.uk/article/fraud-fears-over-firms-furlough-millions-cpqbxxvnv">The Times</a> exposed alleged misuse of this funding in the US and the UK, respectively.&nbsp;
I&rsquo;m proud to say that both investigations were powered by OpenCorporates data. In fact, this is part of a general trend: more organisations are seeing the value of having a foundational set of company data to underpin how they generate insights for all kinds of use cases.
What does that mean for transparent company data? Well, people now (rightly) expect it to be available in a way that can be easily used to solve real-world problems, not siloed in hundreds of individual company registers.
Spencer: The investigations powered by OpenCorporates data did a lot to expose the misuse of business relief funds. What do you expect the future landscape for transparent data to look like? Any trends to keep an eye out for in 2022 and beyond?&nbsp;
Salas: The continued rise of transparent company data will be the clearest trend in 2022. More and more organisations are coming to us looking to use our traceable, fresh, well-modelled data to underpin their risk management activities.
Another positive trend is that countries around the world continue to open access to their company data. There was good progress in Illinois recently, where a bill was passed to make its previously restrictive Secretary of State data open to the public.&nbsp;
There is also greater focus on company data being released in a structured and usable way. Illinois&rsquo; Secretary of State will provide it in a machine-readable format, and we hope the EU&rsquo;s Open Data Directive will also prioritise ease of use. Transparent company data is good for business, and good for the whole world.
Spencer: Thank you for your time today.&nbsp;OpenCorporates data is available through the Demyst platform and can be used to increase the effectiveness of <a href="https://demyst.com/app/api-catalog/46">firmographics research</a> and to <a href="https://demyst.com/catalog/demyst/opencorporates/opencorporates-companies-search">understand the universe of companies for KYB and other risk management purposes</a>.&nbsp;

Simone Spencer, Vice President of Marketing at Demyst, talked with Shanti Salas, Vice President for North America at OpenCorporates, to disc

OpenCorporates Spotlight:

Simone Spencer, Vice President of Marketing at Demyst, talked with Shanti Salas, Vice President for North America at OpenCorporates, to discuss how transparent data can justify risk-based decisions with clear, current information.

OpenCorporates Spotlight: Data Transparency Deep Dive

Companies that want to become <a href="https://www.capgemini.com/research/data-sharing-masters/">data-sharing masters</a> must manage that data carefully. When performing due diligence on a new data provider, it&rsquo;s not enough to review representations and warranties while hoping for the best &mdash; Chief Data Officers (CDOs) must ask detailed questions to understand how external data sources operate.&nbsp;
There are five key areas to consider when reviewing data providers.
<h1>Sourcing Practices&nbsp;</h1>
 Find out whether a provider has automated processes in place for collecting data, and understand their policies and practices for automation. &nbsp;
The provider may be using government records, publicly available sources, third-party records, or information collected from individuals, which each have their own implications for managing consent and implementing privacy policies.&nbsp;
Even publicly available data can provide sensitive personal information that may be subject to regulatory oversight. Ask whether providers regularly receive <a href="https://iapp.org/news/a/why-some-dsr-services-create-compliance-concerns/">data subject requests</a> and whether and how they are prepared to respond to them.&nbsp;
<h1>Lawful Purpose</h1>
 Consent processes may be influenced by the data provider&rsquo;s <a href="https://iapp.org/resources/article/the-eu-general-data-protection-regulation/#A6">lawful reasons for processing personal data</a>. Whenever a provider claims that its collection of personal data is supported by a lawful reason, then that claim should be reviewed.&nbsp;
<h1>Consent Policies</h1>
 If a provider&rsquo;s lawful basis for processing personal data is based on consent, Ask questions to examine that process:&nbsp;
<ul>
<li style="font-weight: 400;" aria-level="1">Is the consent language clear &mdash; does the provider explain what they are collecting and sharing and why? </li>
<li style="font-weight: 400;" aria-level="1">Has the provider clearly described an individual&rsquo;s legal rights under relevant jurisdictions? </li>
<li style="font-weight: 400;" aria-level="1">Do individuals actively provide their consent, or is consent assumed by default? Do individuals need to take additional steps to prevent their data from being collected? </li>
<li style="font-weight: 400;" aria-level="1">Can an individual withdraw consent, and how does the provider respond to that request?</li>
</ul>
The actual mechanisms for acquiring consent also matter. Individuals should give consent freely and affirmatively, after being informed of their rights, as opposed to assuming that they have given consent based on their participation in a survey or registration for a service.
When data providers are reviewed for inclusion in the Demyst platform, the main considerations are whether consent was obtained, whether that consent was informed and freely given, and what process was used to obtain it.&nbsp;
<h1>Reviewing and Renewing Relationships</h1>
 Data privacy regulations are continuing to be updated, and due diligence needs to be an ongoing process that responds to these regulatory changes. And data providers should be able to explain how they are adapting their business processes to comply with changing regulations.
Any company onboarding new data sources must not only inspect the provider&rsquo;s current data collection practices, they must also review those practices at regular intervals in the future.&nbsp;
<h1>Data Supply Chains</h1>
 When a data provider&nbsp;aggregates data from other sources, or relies on other organizations to collect its data, then those collection practices should be considered.
It may not be possible to trace collection practices all the way back to the source of the data, but asking a provider about its own due diligence processes can reveal unexpected risks in the data supply chain.&nbsp;
<h1>This Is No Substitute for Legal Advice</h1>
 The due diligence process will be different for each data provider; data privacy regulations involve too many variables for a simple checklist. Extra care is necessary when dealing with topics like sensitive personal information, criminal information, and information pertaining to children.&nbsp;
Ultimately, companies onboarding new data providers will need to make their own risk assessments after receiving legal advice from qualified attorneys. And they will need to update those risk assessments as their business relationships continue.&nbsp;
This is <a href="https://demyst.com/external-data-predictions-report-2022?gaAd=BPST">what the future will look like for external data</a> &mdash; any organization that wants to benefit from greater insights data must either make substantial investments in data capabilities or work with a partner who already has those capabilities.&nbsp;
<a href="https://demyst.com/app/data-catalog/connectors">The Demyst platform</a> works with data providers that meet stringent qualifications while reducing the friction associated with external data procurement, testing, and deployment. Certified providers in the Demyst data ecosystem are asked more than 150 questions for a detailed understanding of their privacy policies.&nbsp;
Browse the catalog to learn more about <a href="https://demyst.com/app/data-catalog/connectors">the data sources available through Demyst</a>.

Data leaders must commit to an ongoing inspection of every link in their data supply chain.

Vendor Due Diligence for Data Privacy

The insurance industry remains in a state of transformation &mdash; it&rsquo;s possible that recovering from the global pandemic was more disruptive to business processes than the pandemic itself.&nbsp;
Customers continue to raise their expectations for insurers and digital experiences, which makes it important to stay competitive by improving product offerings. At the same time, these customers have changed their behavior in ways that make expectations more challenging to meet.
The good news is that insurers have never had more powerful tools available for improving client service and reducing their own operational burdens. Vast data sets can offer<a href="https://demyst.com/blog/assessing-driver-risk-without-using-credit-scores/"> alternatives to credit-based insurance scoring models</a> and provide<a href="https://demyst.com/app/api-catalog/57"> customer 360 profiles</a> with greater speed and flexibility than ever before.
One such tool is the <a href="https://demyst.com/app/catalog/2358">BusinessCore</a> database from AnalyticsIQ. Demyst interviewed Curtis Marshall, SVP of Business Data at AnalyticsIQ, to discuss the expectations of insurance customers and ways that insurers can meet them.
What follows is a transcript of the interview.&nbsp;
Demyst: Please give us an overview of your role as at AnalyticsIQ
Curtis Marshall: At AnalyticsIQ, I am the SVP of Business Data where I lead the strategy for our B2B data, BusinessCore, our B2B2C linkage solution, and insurance partnerships. BusinessCore is a one-of-a-kind database that provides insight into the hearts and minds of the individuals making decisions for their organizations. Our team has also developed a strong, proprietary B2B2C linkage that links an individual&rsquo;s consumer profile at home to their professional profile at work, giving organizations a true 360-degree view of an individual &ndash; regardless of whether they are at home or at work. 
What data is available from AnalyticsIQ?
AnalyticsIQ offers comprehensive consumer and B2B data known as PeopleCore and BusinessCore respectively.
PeopleCore, our proprietary consumer database, provides thousands of attributes ranging from foundational demographics and lifestyle interests to purchase predictors and psychological motivations on over 251 million individuals and 122 million households. Our data provides marketers and data scientists with a deep understanding into who people are, what people do, and - most importantly &ndash; why they make the decisions they make.
BusinessCore offers traditional firmographic data on over 23 million businesses and attributes on nearly 102 million professionals that you can&rsquo;t get anywhere else such as employee decision making or influence status or marketing content preferences. This facilitates personalization in B2B marketing typically reserved for B2C brands.
What are some of the data points that insurers can use to gain insight into their customers&rsquo; needs?
Often in insurance, one of the best opportunities to acquire new customers or additional business from existing clients is after a life event like having children, buying a home or car, starting a business, or getting married. But without an invitation to your client&rsquo;s wedding, it can be difficult to get the timing right without the right data. Having data that can provide insight into the individual experience in such areas is a must.
However, with so much competition, simply reaching out at the right time may not be enough. Insurers need to know their customers on a deeper level. Are they value seekers looking for a deal or are they conscious shoppers that want the best coverage? Are they a business decision maker who is more likely to answer your email rather than your LinkedIn invite? Today, insurance companies can better connect with their customers and clients by understanding their lifestyle and decision drivers. 
Have you been seeing noteworthy changes in the way insurers approach customer acquisition?&nbsp; Although insurance may be thought of as a traditional industry, many insurers are allocating more dollars than ever to the digital ecosystem for custom acquisition &ndash; from advertising on social media or advanced TV to the customer experience and ability to acquire policies and quotes in real-time online. We&rsquo;ve even seen the emergence of platform-based, insuretech solutions offering streamlined click, quote, bind processes for individuals and businesses, so coverage is nearly immediate. Insurance companies must lean into these changes to not only fend off competition but also reap the rewards presented by emerging opportunities.
How have customer expectations for insurers changed in the past few years?
Customers today expect a simple, streamlined experience that allows them to get an immediate, bindable quote personalized to their life. Consumers and businesses that are shopping for insurance want coverage based on their situation sooner than later. Insurers must have the technologies and processes &ndash; along with data to back them up &ndash; in place to make it as easy as possible for a prospect to become a customer.
Which technologies can insurers apply to improve the customer experience?
In order to answer the call of the simplified digital experience that consumers and businesses expect today, insurers must have comprehensive, individual-level data and the ability to extract actionable observations. By boosting what they know about applicants or understanding their best policy holders, insurers can personalize recommendations and expediate the underwriting process while not giving up insights into liability.
This type of data can also enhance an insurer&rsquo;s understanding of their current in-force policies to identify other lines of business and other opportunities they would have not otherwise known existed.
What capabilities will determine success for insurers in the year ahead?
Going into 2022 and beyond, the most successful insurers will be those that embrace innovation and become data-driven beyond their first-party data in order to gain that 360-degree picture of their policy holders and prospects. With a B2B2C view and predictive insights, insurers can uncover new opportunities, all while minimizing risk by ensuring they are writing the right policies and more.
Curtis, thank you for your time.&nbsp;
For an expanded look at the changes coming to external data in the year ahead, download <a href="https://demyst.com/external-data-predictions-report-2022?gaAd=BPST">Demyst's External Data Predictions Report for 2022</a>.

Curtis Marshall of AnalyticsIQ discusses how insurers can meet rising customer expectations.

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