Category Archives: Data Quality
This article was originally posted in Supply and Demand Chain Executive.
Supplier data management is costing organizations millions of dollars each year. According to AMR Research/Gartner, supplier management organizations have increased their employee headcount and system resources by 35%, and are spending up to $1,000 per supplier annually, to manage their supplier information across the enterprise.
Why is clean, consistent and connected supplier information important for effective supplier management?
In larger organizations, supplier information is typically fragmented across departmental, line of business and/or regional applications. In most cases, this means it’s inaccurate, inconsistent, incomplete and disconnected across the different siloed systems. Adding, changing or correcting information in one system doesn’t automatically update it in other systems. As a result, supply chain, sourcing and buying teams struggle to get access to a single view of the supplier so they can understand the total supplier relationship across the business. As a result, it’s difficult to achieve their goals, such as:
- Quickly and accurately assessing supplier risk management and compliance
- Accelerating supplier onboarding to get products to market quickly
- Improving supplier collaboration or supplier relationship management
- Quickly and accurately evaluating supplier spend management
- Monitoring supplier performance management
To effectively manage global supply chain, sourcing and procurement activities, organizations must be able to quickly and easily answer questions about their suppliers’ or vendors’ companies, contacts, raw materials or products and performance.
Quite often, organizations have separate procurement teams in different regions and they have their own regional applications. As a result, the same supplier may exist several times in the company’s supplier management system, but captured with different company names. The buying teams don’t have a single trusted view of their global supplier information. So, the different teams would purchase one product from the same supplier – each of them with different pricing and payment terms – without even knowing about it. They don’t have a clear understanding of the total relationship with that vendor and cannot benefit from negotiated corporate discounts.
If the data that’s fueling operational and analytical systems isn’t clean, consistent and connected, they cannot quickly and easily access the information they need to answer these questions and manage their supply chain, sourcing or procurement operations efficiently or effectively. And this results in ineffectiveness, missed procurement opportunities and high administration costs.
For example, a fashion retailer needs to ensure that all compliance documents of its global vendors are current and complete. If the documents are incomplete or expired, the retailer could face serious problems and might risk penalties, safety issues and costs related to its supplier incompliance.
A Business Value Assessment recently conducted by Informatica among companies across all major industries, business models and sizes, revealed that by leveraging trusted data quality, annual supplier spend could be reduced by an average of $6M. Breaking this down by industry, the possible annual reduction in supplier spending, thanks to improved supplier information and data quality, could be $9.02M for Consumer Packaged Goods (CPG), $4.2M for retail and $2.76M for industrial manufacturing companies.
To reduce costs related to poor supplier information quality, these are the five best practices that will help your company achieve a more effective supplier relationship management:
- Make it strategic
Get senior management buy-in and stakeholder support. Make the business case and get the time, money and resources you’ll need.
- Leave your data where it is
Effective supplier relationship management lets you leave your data where it naturally lives: in the apps and data stores your business users depend on. You just need to identify these places, so you can access the data and share clean, consistent and connected supplier data back.
- Apply Data Quality at the application level
It’s far better to clean your data at the source, before trying to combine it with other data.
Apply standards and practices at the application level, to ensure you’re working with the most accurate and complete data, and your mastering job will be much, much easier and deliver far better results.
- Use specialized Master Data Management and Data Integration platforms
It takes specialized technology that’s optimized for collecting, reconciling, managing, and linking diverse data sets (as well as resolving duplicates and managing hierarchies) to achieve total supplier information management. Your program is going to have to relate to the supplier domain, as well as to other, equally important data types. Attempting this with homemade integration tools or point-to-point integrations is a major mistake. This wheel has already been invented.
- Share clean data with key supplier apps on an ongoing basis
It’s no good having clean, consistent and connected supplier data if you can’t deploy it to the point of use: to the applications and analytics teams and tools that can turn it into insight (and money)—including your enterprise data warehouse, where spend analytics happen.
These best practices may seem basic or obvious, but failing to apply them is the main reason global supplier data programs stumble. They will help fuel operational and analytical applications with clean, consistent and connected supplier information for a more accurate view of suppliers’ performance, compliance and risk. As a result, supply chain, sourcing and buying teams will be empowered to cut costs by negotiating more favorable pricing and payment terms and streamlining their processes.
I live in a small town in Maine. Between my town and the surrounding three towns, there are seven Main Streets and three Annis Roads or Lanes (and don’t get me started on the number of Moose Trails). If your insurance company wants to market to or communicate with someone in my town or one of the surrounding towns, how can you ensure that the address that you are sending material to is correct? What is the cost if material is sent to an incorrect or outdated address? What is the cost to your insurance company if a provider sends the bill out to the wrong ?
How much is poor address quality costing your business? It doesn’t just impact marketing where inaccurate address data translates into missed opportunity – it also means significant waste in materials, labor, time and postage . Bills may be delivered late or returned with sender unknown, meaning additional handling times, possible repackaging, additional postage costs (Address Correction Penalties) and the risk of customer service issues. When mail or packages don’t arrive, pressure on your customer support team can increase and your company’s reputation can be negatively impacted. Bills and payments may arrive late or not at all directly impacting your cash flow. The cost of bad address data causes inefficiencies and raises costs across your entire organization.
The best method for handling address correction is through a validation and correction process:
When trying to standardize member or provider information one of the first places to look is address data. If you can determine that John Q Smith that lives at 134 Main St in Northport, Maine 04843 is the same John Q Smith that lives at 134 Maine Street in Lincolnville, Maine 04849, you have provided a link between two members that are probably considered distinct in your systems. Once you can validate that there is no 134 Main St in Northport according to the postal service, and then can validate that 04849 is a valid zip code for Lincolnville – you can then standardize your address format to something along the lines of: 134 MAIN ST LINCOLNVILLE,ME 04849. Now you have a consistent layout for all of your addresses that follows postal service standards. Each member now has a consistent address which is going to make the next step of creating a golden record for each member that much simpler.
Think about your current method of managing addresses. Likely, there are several different systems that capture addresses with different standards for what data is allowed into each field – and quite possibly these independent applications are not checking or validating against country postal standards. By improving the quality of address data, you are one step closer to creating high quality data that can provide the up-to-the minute accurate reporting your organization needs to succeed.
The Oil and Gas (O&G) industry is an important backbone of every economy. It is also an industry that has weathered the storm of constantly changing economic trends, regulations and technological innovations. O&G companies by nature have complex and intensive data processes. For a profitable functioning under changing trends, policies and guidelines, O&G’s need to manage these data processes really well.
The industry is subject to pricing volatility based on microeconomic pattern of supply and demand affected by geopolitical developments, economic meltdown and public scrutiny. The competition from other sources such as cheap natural gas and low margins are adding fuel to the burning fire making it hard for O&G’s to have a sustainable and predictable outcome.
A recent PWC survey of oil and gas CEOs similarly concluded that “energy CEOs can’t control market factors such as the world’s economic health or global oil supply, but can change how they respond to market conditions, such as getting the most out of technology investments, more effective use of partnerships and diversity strategies.” The survey also revealed that nearly 80% of respondents agreed that digital technologies are creating value for their companies when it comes to data analysis and operational efficiency.
O&G firms run three distinct business operations; upstream exploration & production (E&P’s), midstream (storage & transportation) and downstream (refining & distribution). All of these operations need a few core data domains being standardized for every major business process. However, a key challenge faced by O&G companies is that this critical core information is often spread across multiple disparate systems making it hard to take timely decisions. To ensure effective operations and to grow their asset base, it is vital for these companies to capture and manage critical data related to these domains.
E&P’s core data domains include wellhead/materials (asset), geo-spatial location data and engineer/technicians (associate). Midstream includes trading partners and distribution and downstream includes commercial and residential customers. Classic distribution use cases emerge around shipping locations, large-scale clients like airlines and other logistics providers buying millions of gallons of fuel and industrial lube products down to gas station customers. The industry also relies heavily on reference data and chart of accounts for financial cost and revenue roll-ups.
The main E&P asset, the well, goes through its life cycle and changes characteristics (location, ID, name, physical characterization, depth, crew, ownership, etc.), which are all master data aspects to consider for this baseline entity. If we master this data and create a consistent representation across the organization, it can then be linked to transaction and interaction data so that O&G’s can drive their investment decisions, split cost and revenue through reporting and real-time processes around
- Crew allocation
- Royalty payments
- Safety and environmental inspections
- Maintenance and overall production planning
E&P firms need a solution that allows them to:
- Have a flexible multidomain platform that permits easier management of different entities under one solution
- Create a single, cross-enterprise instance of a wellhead master
- Capture and master the relationships between the well, equipment, associates, land and location
- Govern end-to-end management of assets, facilities, equipment and sites throughout their life cycle
The upstream O&G industry is uniquely positioned to take advantage of vast amount of data from its operations. Thousands of sensors at the well head, millions of parts in the supply chain, global capital projects and many highly trained staff create a data-rich environment. A well implemented MDM brings a strong foundation for this data driven industry providing clean, consistent and connected information about the core master data so these companies can cut the material cost, IT maintenance and increase margins.
To know more on how you can achieve upstream operational excellence with Informatica Master Data Management, check out this recorded webinar with @OilAndGasData
When’s the last time you visited your local branch bank and spoke to a human being? How about talking to your banker over the phone? Can’t remember? Well you’re not alone and don’t worry, it’s not a bad thing. The days of operating physical branches with expensive workers to greet and service customers are being replaced with more modern and customer friendly mobile banking applications that allow consumers to deposit checks from the phone, apply for a mortgage and sign closing documents electronically, to eliminating the need to go to an ATM and get physical cash by using mobile payment solutions like Apple Pay. In fact, a new report titled ‘Bricks + Clicks: Building the Digital Branch,’ from Jeanne Capachin and Jim Marous takes an in-depth look at how banks and credit unions are changing their branch and customer channel strategies to meet the demand of today’s digital banking customer.
Why am I talking about this? These market trends are dominating the CEO and CIO agenda in today’s banking industry. I just returned from the 2015 IDC Asian Financial Congress event in Singapore where the digital journey for the next generation bank was a major agenda item. According the IDC Financial Insights, global banks will invest $31.5B USD in core banking modernization to enable these services, improve operational efficiency, and position these banks to better compete on technology and convenience across markets. Core banking modernization initiatives are complex, costly, and fraught with risks. Let’s take a closer look. (more…)
Last week was Informatica’s first ever Data Mania event, held at the Contemporary Jewish Museum in San Francisco. We had an A-list lineup of speakers from leading cloud and data companies, such as Salesforce, Amazon Web Services (AWS), Tableau, Dun & Bradstreet, Marketo, AppDynamics, Birst, Adobe, and Qlik. The event and speakers covered a range of topics all related to data, including Big Data processing in the cloud, data-driven customer success, and cloud analytics.
While these companies are giants today in the world of cloud and have created their own unique ecosystems, we also wanted to take a peek at and hear from the leaders of tomorrow. Before startups can become market leaders in their own realm, they face the challenge of ramping up a stellar roster of customers so that they can get to subsequent rounds of venture funding. But what gets in their way are the numerous data integration challenges of onboarding customer data onto their software platform. When these challenges remain unaddressed, R&D resources are spent on professional services instead of building value-differentiating IP. Bugs also continue to mount, and technical debt increases.
Enter the Informatica Cloud Connector SDK. Built entirely in Java and able to browse through any cloud application’s API, the Cloud Connector SDK parses the metadata behind each data object and presents it in the context of what a business user should see. We had four startups build a native connector to their application in less than two weeks: BigML, Databricks, FollowAnalytics, and ThoughtSpot. Let’s take a look at each one of them.
With predictive analytics becoming a growing imperative, machine-learning algorithms that can have a higher probability of prediction are also becoming increasingly important. BigML provides an intuitive yet powerful machine-learning platform for actionable and consumable predictive analytics. Watch their demo on how they used Informatica Cloud’s Connector SDK to help them better predict customer churn.
Can’t play the video? Click here, http://youtu.be/lop7m9IH2aw
Databricks was founded out of the UC Berkeley AMPLab by the creators of Apache Spark. Databricks Cloud is a hosted end-to-end data platform powered by Spark. It enables organizations to unlock the value of their data, seamlessly transitioning from data ingest through exploration and production. Watch their demo that showcases how the Informatica Cloud connector for Databricks Cloud was used to analyze lead contact rates in Salesforce, and also performing machine learning on a dataset built using either Scala or Python.
Can’t play the video? Click here, http://youtu.be/607ugvhzVnY
With mobile usage growing by leaps and bounds, the area of customer engagement on a mobile app has become a fertile area for marketers. Marketers are charged with acquiring new customers, increasing customer loyalty and driving new revenue streams. But without the technological infrastructure to back them up, their efforts are in vain. FollowAnalytics is a mobile analytics and marketing automation platform for the enterprise that helps companies better understand audience engagement on their mobile apps. Watch this demo where FollowAnalytics first builds a completely native connector to its mobile analytics platform using the Informatica Cloud Connector SDK and then connects it to Microsoft Dynamics CRM Online using Informatica Cloud’s prebuilt connector for it. Then, see FollowAnalytics go one step further by performing even deeper analytics on their engagement data using Informatica Cloud’s prebuilt connector for Salesforce Wave Analytics Cloud.
Can’t play the video? Click here, http://youtu.be/E568vxZ2LAg
Analytics has taken center stage this year due to the rise in cloud applications, but most of the existing BI tools out there still stick to the old way of doing BI. ThoughtSpot brings a consumer-like simplicity to the world of BI by allowing users to search for the information they’re looking for just as if they were using a search engine like Google. Watch this demo where ThoughtSpot uses Informatica Cloud’s vast library of over 100 native connectors to move data into the ThoughtSpot appliance.
Can’t play the video? Click here, http://youtu.be/6gJD6hRD9h4
On March 25th, Josh Lee, Global Director for Insurance Marketing at Informatica and Cindy Maike, General Manager, Insurance at Hortonworks, will be joining the Insurance Journal in a webinar on “How to Become an Analytics Ready Insurer”.
Register for the Webinar on March 25th at 10am Pacific/ 1pm Eastern
Josh and Cindy exchange perspectives on what “analytics ready” really means for insurers, and today we are sharing some of our views (join the webinar to learn more). Josh and Cindy offer perspectives on the five questions posed here. Please join Insurance Journal, Informatica and Hortonworks on March 25th for more on this exciting topic.
See the Hortonworks site for a second posting of this blog and more details on exciting innovations in Big Data.
- What makes a big data environment attractive to an insurer?
CM: Many insurance companies are using new types of data to create innovative products that better meet their customers’ risk needs. For example, we are seeing insurance for “shared vehicles” and new products for prevention services. Much of this innovation is made possible by the rapid growth in sensor and machine data, which the industry incorporates into predictive analytics for risk assessment and claims management.
Customers who buy personal lines of insurance also expect the same type of personalized service and offers they receive from retailers and telecommunication companies. They expect carriers to have a single view of their business that permeates customer experience, claims handling, pricing and product development. Big data in Hadoop makes that single view possible.
JL: Let’s face it, insurance is all about analytics. Better analytics leads to better pricing, reduced risk and better customer service. But here’s the issue. Existing data sources are costly in storing vast amounts of data and inflexible to adapt to changing needs of innovative analytics. Imagine kicking off a simulation or modeling routine one evening only to return in the morning and find it incomplete or lacking data that requires a special request of IT.
This is where big data environments are helping insurers. Larger, more flexible data sets allowing longer series of analytics to be run, generating better results. And imagine doing all that at a fraction of the cost and time of traditional data structures. Oh, and heaven forbid you ask a mainframe to do any of this.
- So we hear a lot about Big Data being great for unstructured data. What about traditional data types that have been used in insurance forever?
CM: Traditional data types are very important to the industry – it drives our regulatory reporting and much of the performance management reporting. This data will continue to play a very important role in the insurance industry and for companies.
However, big data can now enrich that traditional data with new data sources for new insights. In areas such as customer service and product personalization, it can make the difference between cross-selling the right products to meet customer needs and losing the business. For commercial and group carriers, the new data provides the ability to better analyze risk needs, price accordingly and enable superior service in a highly competitive market.
JL: Traditional data will always be around. I doubt that I will outlive a mainframe installation at an insurer; which makes me a little sad. And for many rote tasks like financial reporting, a sales report, or a commission statement, those are sufficient. However, the business of insurance is changing in leaps and bounds. Innovators in data science are interested in correlating those traditional sources to other creative data to find new products, or areas to reduce risk. There is just a lot of data that is either ignored or locked in obscure systems that needs to be brought into the light. This data could be structured or unstructured, it doesn’t matter, and Big Data can assist there.
- How does this fit into an overall data management function?
JL: At the end of the day, a Hadoop cluster is another source of data for an insurer. More flexible, more cost effective and higher speed; but yet another data source for an insurer. So that’s one more on top of relational, cubes, content repositories, mainframes and whatever else insurers have latched onto over the years. So if it wasn’t completely obvious before, it should be now. Data needs to be managed. As data moves around the organization for consumption, it is shaped, cleaned, copied and we hope there is governance in place. And the Big Data installation is not exempt from any of these routines. In fact, one could argue that it is more critical to leverage good data management practices with Big Data not only to optimize the environment but also to eventually replace traditional data structures that just aren’t working.
CM: Insurance companies are blending new and old data and looking for the best ways to leverage “all data”. We are witnessing the development of a new generation of advanced analytical applications to take advantage of the volume, velocity, and variety in big data. We can also enhance current predictive models, enriching them with the unstructured information in claim and underwriting notes or diaries along with other external data.
There will be challenges. Insurance companies will still need to make important decisions on how to incorporate the new data into existing data governance and data management processes. The Chief Data or Chief Analytics officer will need to drive this business change in close partnership with IT.
- Tell me a little bit about how Informatica and Hortonworks are working together on this?
JL: For years Informatica has been helping our clients to realize the value in their data and analytics. And while enjoying great success in partnership with our clients, unlocking the full value of data requires new structures, new storage and something that doesn’t break the bank for our clients. So Informatica and Hortonworks are on a continuing journey to show that value in analytics comes with strong relationships between the Hadoop distribution and innovative market leading data management technology. As the relationship between Informatica and Hortonworks deepens, expect to see even more vertically relevant solutions and documented ROI for the Informatica/Hortonworks solution stack.
CM: Informatica and Hortonworks optimize the entire big data supply chain on Hadoop, turning data into actionable information to drive business value. By incorporating data management services into the data lake, companies can store and process massive amounts of data across a wide variety of channels including social media, clickstream data, server logs, customer transactions and interactions, videos, and sensor data from equipment in the field.
Matching data from internal sources (e.g. very granular data about customers) with external data (e.g. weather data or driving patterns in specific geographic areas) can unlock new revenue streams.
See this video for a discussion on unlocking those new revenue streams. Sanjay Krishnamurthi, Informatica CTO, and Shaun Connolly, Hortonworks VP of Corporate Strategy, share their perspectives.
- Do you have any additional comments on the future of data in this brave new world?
CM: My perspective is that, over time, we will drop the reference to “big” or ”small” data and get back to referring simply to “Data”. The term big data has been useful to describe the growing awareness on how the new data types can help insurance companies grow.
We can no longer use “traditional” methods to gain insights from data. Insurers need a modern data architecture to store, process and analyze data—transforming it into insight.
We will see an increase in new market entrants in the insurance industry, and existing insurance companies will improve their products and services based upon the insights they have gained from their data, regardless of whether that was “big” or “small” data.
JL: I’m sure that even now there is someone locked in their mother’s basement playing video games and trying to come up with the next data storage wave. So we have that to look forward to, and I’m sure it will be cool. But, if we are honest with ourselves, we’ll admit that we really don’t know what to do with half the data that we have. So while data storage structures are critical, the future holds even greater promise for new models, better analytical tools and applications that can make sense of all of this and point insurers in new directions. The trend that won’t change anytime soon is the ongoing need for good quality data, data ready at a moment’s notice, safe and secure and governed in a way that insurers can trust what those cool analytics show them.
Please join us for an interactive discussion on March 25th at 10am Pacific Time/ 1pm Eastern Time.
Register for the Webinar on March 25th at 10am Pacific/ 1pm Eastern
Let’s face it, building a Data Governance program is no overnight task. As one CDO puts it: ”data governance is a marathon, not a sprint”. Why? Because data governance is a complex business function that encompasses technology, people and process, all of which have to work together effectively to ensure the success of the initiative. Because of the scope of the program, Data Governance often calls for participants from different business units within an organization, and it can be disruptive at first.
Why bother then? Given that data governance is complex, disruptive, and could potentially introduce additional cost to a company? Well, the drivers for data governance can vary for different organizations. Let’s take a close look at some of the motivations behind data governance program.
For companies in heavily regulated industries, establishing a formal data governance program is a mandate. When a company is not compliant, consequences can be severe. Penalties could include hefty fines, brand damage, loss in revenue, and even potential jail time for the person who is held accountable for being noncompliance. In order to meet the on-going regulatory requirements, adhere to data security policies and standards, companies need to rely on clean, connected and trusted data to enable transparency, auditability in their reporting to meet mandatory requirements and answer critical questions from auditors. Without a dedicated data governance program in place, the compliance initiative could become an on-going nightmare for companies in the regulated industry.
A data governance program can also be established to support customer centricity initiative. To make effective cross-sells and ups-sells to your customers and grow your business, you need clear visibility into customer purchasing behaviors across multiple shopping channels and touch points. Customer’s shopping behaviors and their attributes are captured by the data, therefore, to gain thorough understanding of your customers and boost your sales, a holistic Data Governance program is essential.
Other reasons for companies to start a data governance program include improving efficiency and reducing operational cost, supporting better analytics and driving more innovations. As long as it’s a business critical area and data is at the core of the process, and the business case is loud and sound, then there is a compelling reason for launching a data governance program.
Now that we have identified the drivers for data governance, how do we start? This rather loaded question really gets into the details of the implementation. A few critical elements come to consideration including: identifying and establishing various task forces such as steering committee, data governance team and business sponsors; identifying roles and responsibilities for the stakeholders involved in the program; defining metrics for tracking the results. And soon you will find that on top of everything, communications, communications and more communications is probably the most important tactic of all for driving the initial success of the program.
A rule of thumb? Start small, take one-step at a time and focus on producing something tangible.
Sounds easy, right? Well, let’s hear what the real-world practitioners have to say. Join us at this Informatica webinar to hear Michael Wodzinski, Director of Information Architecture, Lisa Bemis, Director of Master Data, Fabian Torres, Director of Project Management from Houghton Mifflin Harcourt, global leader in publishing, as well as David Lyle, VP of product strategy from Informatica to discuss how to implement a successful data governance practice that brings business impact to an enterprise organization.
If you are currently kicking the tires on setting up data governance practice in your organization, I’d like to invite you to visit a member-only website dedicated to Data Governance: http://governyourdata.com/. This site currently has over 1,000 members and is designed to foster open communications on everything data governance. There you will find conversations on best practices, methodologies, frame works, tools and metrics. I would also encourage you to take a data governance maturity assessment to see where you currently stand on the data governance maturity curve, and compare the result against industry benchmark. More than 200 members have taken the assessment to gain better understanding of their current data governance program, so why not give it a shot?
Data Governance is a journey, likely a never-ending one. We wish you best of the luck on this effort and a joyful ride! We love to hear your stories.
At long last, the anxiously awaited rules from the FAA have brought some clarity to the world of commercial drone use. Up until now, commercial drone use has been prohibited. The new rules, of course, won’t sit well with Amazon who would like to drop merchandise on your porch at all hours. But the rules do work really well for insurers who would like to use drones to service their policyholders. So now drones, and soon to be fleets of unmanned cars will be driving the roadways in any numbers of capacities. It seems to me to be an ambulance chaser’s dream come true. I mean who wouldn’t want some seven or eight figure payday from Google for getting rear-ended?
What about “Great Data”? What does that mean in the context of unmanned vehicles, both aerial and terrestrial? Let’s talk about two aspects. First, the business benefits of great data using unmanned drones.
An insurance adjuster or catastrophe responder can leverage an aerial drone to survey large areas from a central location. They will pin point the locations needing attention for further investigation. This is a common scenario that many insurers talk about when the topic of aerial drone use comes up. Second to that is the ability to survey damage in hard to reach locations like roofs or difficult terrain (like farmland). But this is where great data comes into play. Surveying, service and use of unmanned vehicles demands that your data can answer some of the following questions for your staff operating in this new world:
Where am I?
Quality data and geocoded locations as part of that data is critical. In order to locate key risk locations, your data must be able to coordinate with the lat/long of the location recorded by your unmanned vehicles and the location of your operator. Ensure clean data through robust data quality practices.
Where are my policyholders?
Knowing the location of your policyholders not only relies on good data quality, but on knowing who they are and what risks you are there to help service. This requires a total customer relationship solution where you have a full view of not only locations, but risks, coverages and entities making up each policyholder.
What am I looking at?
Archived, current and work in process imaging is a key place where a Big Data environment can assist over time. By comparing saved images with new and processing claims, claims fraud and additional opportunities for service can be detected quickly by the drone operator.
Now that we’ve answered the business value questions and leveraged this new technology to better service policyholders and speed claims, let’s turn to how great data can be used to protect the insurer and drone operator from liability claims. This is important. The FAA has stopped short of requiring commercial drone operators to carry special liability insurance, leaving that instead to the drone operators to orchestrate with their insurer. And now we’re back to great data. As everyone knows, accidents happen. Technology, especially robotic mobile technology is not infallible. Something will crash somewhere, hopefully not causing injury or death, but sadly that too will likely happen. And there is nothing that will keep the ambulance chasers at bay more than robust great data. Any insurer offering liability cover for a drone operator should require that some of the following questions be answered by the commercial enterprise. And the interesting fact is that this information should be readily available if the business questions above have been answered.
- Where was my drone?
- What was it doing?
- Was it functioning properly?
Properly using the same data management technology as in the previous questions will provide valuable data to be used as evidence in the case of liability against a drone operator. Insurers would be wise to ask these questions of their liability policyholders who are using unmanned technology as a way to gauge liability exposure in this brave new world. The key to the assessment of risk being robust data management and great data feeding the insurer’s unmanned policyholder service workers.
Time will tell all the great and imaginative things that will take place with this new technology. One thing is for certain. Great data management is required in all aspects from amazing customer service to risk mitigation in operations. Happy flying to everyone!!
In our house when we paint a room, my husband does the big rolling of the walls or ceiling, I do the cut-in work. I am good at prepping the room, taping all the trim and deliberately painting the corners. However, I am thrifty and constantly concerned that we won’t have enough paint to finish a room. My husband isn’t afraid to use enough paint and is extremely efficient at painting a wall in a single even coat. As a result, I don’t do the big rolling and he doesn’t do the cutting in. It took us awhile to figure this out, and a few rooms had to be repainted while we were figuring it out. Now we know what we are good at, and what we need help with.
Payers roles are changing. Payers were previously focused on risk assessment, setting and collecting premiums, analyzing claims and making payments – all while optimizing revenues. Payers are pretty good at selling to employers, figuring out the cost/benefit ratio from an employers perspective and ensuring a good, profitable product. With the advent of the Affordable Healthcare Act along with a much more transient insured population, payers now must focus more on the individual insured and be able to communicate with the individuals in a more nimble manner than in the past.
Individual members will shop for insurance based on consumer feedback and price. They are interested in ease of enrollment and the ability to submit and substantiate claims quickly and intuitively. Payers are discovering that they need to help manage population health at a individual member level. And population health management requires less of a business-data analytics approach and more social media and gaming-style logic to understand patients. In this way, payers can help develop interventions to sustain behavioral changes for better health.
When designing such analytics, payers should consider the following key design steps:
- Extend data warehouses to an analytics appliance
- Invest in a big data platform to absorb patients’ social data
- Build predictive analytics for patient behavior
- Bridge collaborative and behavioral analytics with claims to build revenue and profitability
Due to payers’ mature predictive analytics competencies, they will have a much easier time in the next generation of population behavior compared to their provider counterparts. As clinical content is often unstructured compared to the claims data, payers need to pay extra attention to context and semantics when deciphering clinical content submitted by providers. Payers can use help from vendors that can help them understand unstructured data, individual members. They can then use that data to create fantastic predictive analytic solutions.