Category Archives: Data Quality
What do all marketers have in common? Marketing guru Seth Godin famously said that all marketers are storytellers. Stories, not features and benefits, sell.
Anyone who buys a slightly more expensive brand of laundry detergent because it’s “better” proves this. Godin wrote that if someone buys shoes because he or she wants to be associated with a brand that is “cool,” that brand successfully told its story to the right market.
A story has heroes we identify with. It has a conflict, which the heroes try to overcome. A good story’s DNA is an ordinary person in unusual circumstances. When is the last time you had an unusual result from your marketing campaigns? Perhaps a pay-per-click ad does poorly in your A/B testing. Or, there’s a high bounce rate from your latest email campaign.
Many marketers aren’t data scientists. But savvy marketers know they have to deal with big data, since it has become a hot topic central to many businesses. Marketers simply want to do their jobs better — and big data should be seen as an opportunity, not a hindrance.
When you have big data that could unlock great insight into your business, look beyond complexity and start with your strength as a marketer: Storytelling.
To get you started, I took the needs of marketers and applied them to these “who, what, why and how” principles from a recent article in the Harvard Business Review by the author of Big Data at Work, Tom Davenport:
Who is your hero? He or she is likely your prospective or existing customer.
What problem did the hero have? This is the action of the story. Here’s a real-life example from the Harvard Business Review article: Your hero visits your website, and adds items to the shopping cart. However, when you look at your analytics dashboard, you notice he or she never finishes the transaction.
Why do you care about the hero’s problem? Identifying with the hero is important for a story’s audience. It creates tension, and gives you and other stakeholders the incentive you need to dig into your data for a resolution.
How do you resolve the problem? Now you see what big data can do — it solves marketing problems and gives you better results. In the abandoned shopping cart example, the company found that people in Ireland were not checking out. The resolution came from the discovery that the check-out process asked for a postal code. Some areas of Ireland have no postal codes, so visitors would give up.
Remember it’s possible that the data itself is the problem. If you have bad contact data, you can’t reach your customers. Find the source of your bad data, and then you can return to your marketing efforts with confidence.
While big data may sound complicated or messy, if you have a storytelling path like this to take, you can find the motivation you need to uncover the powerful information required to better engage with your audience.
Engaging your audience starts with having accurate, validated information about your audience. Marketers can use data to fuel their campaigns and make better decisions on strategy and planning. Learn more about data quality management in this white paper.
Time to Celebrate! Informatica is Once Again Positioned as a Leader in Gartner’s Magic Quadrant for Data Quality Tools!
It’s holiday season once again at Informatica and this one feels particularly special because we just received an early present from Gartner: Informatica has just been positioned as a leader in Gartner’s Magic Quadrant for Data Quality Tools report for 2014! Click here to download the full report.
And as it turns out, this is a gift that keeps on giving. For eight years in a row, Informatica has been ranked as a leader in Gartner’s Magic Quadrant for Data Quality Tools. In fact, for the past two years running, Informatica has been positioned highest and best for ability to execute and completeness of vision, the two dimensions Gartner measures in their report. These results once again validate our operational excellence as well as our prescience with our data quality products offerings. Yes folks, some days it’s hard to be humble.
Consistency and leadership are becoming hallmarks for Informatica in these and other analyst reports, and it’s hardly an accident. Those milestones are the result of our deep understanding of the market, continued innovation in product design, seamless execution on sales and marketing, and relentless dedication to customer success. Our customer loyalty has never been stronger with those essential elements in place. However, while celebrating our achievements, we are equally excited about the success our customers have achieved using our data quality products.
Managing and producing quality data is indispensable in today’s data-centric world. Gaining access to clean, trusted information should be one of a company’s most important tasks, and has previously been shown to be directly linked to growth and continued innovation.
We are truly living in a digital world – a world revolving around the Internet, gadgets and apps – all of which generate data, and lots of it. Should your organization take advantage of its increasing masses of data? You bet. But remember: only clean, trusted data has real value. Informatica’s mission is to help you excel by turning your data into valuable information assets that you can put to good use.
To see for yourself what the industry leading data quality tool can do, click here.
And from all of our team at Informatica, Happy holidays to you and yours.
The other day I ran across an article on CMO.com from a few months ago entitled “Total Customer Value Trumps Simple Loyalty in Digital World”. It’s a great article, so I encourage you to go take a look, but the basic premise is that loyalty does not necessarily equal value in today’s complicated consumer environment.
Customers can be loyal for a variety of reasons as the author Samuel Greengard points out. One of which may be that they are stuck with a certain product or service because they believe there is no better alternative available. I know I can relate to this after a recent series of less-than-pleasant experiences with my bank. I’d like to change banks, but frankly they’re all about the same and it just isn’t worth the hassle. Therefore, I’m loyal to my unnamed bank, but definitely not an advocate.
The proverbial big fish in today’s digital world, according to the author, are customers who truly identify with the brand and who will buy the company’s products eagerly, even when viable alternatives exist. These are the customers who sing the brand’s praises to their friends and family online and in person. These are the customers who write reviews on Amazon and give your product 5 stars. These are the customers who will pay markedly more just because it sports your logo. And these are the customers whose voices hold weight with their peers because they are knowledgeable and passionate about the product. I’m sure we all have a brand or two that we’re truly passionate about.
Total Customer Value in the Pool
My 13 year old son is a competitive swimmer and will only use Speedo goggles – ever – hands down – no matter what. He wears Speedo t-shirts to show his support. He talks about how great his goggles are and encourages his teammates to try on his personal pair to show them how much better they are. He is a leader on his team, so when newbies come in and see him wearing these goggles and singing their praises, and finishing first, his advocacy holds weight. I’m sure we have owned well over 30 pair of Speedo goggles over the past 4 years at $20 a pop – and add in the T-Shirts and of course swimsuits – we probably have a historical value of over $1000 and a potential lifetime value of tens of thousands (ridiculous I know!). But if you add in the influence he’s had over others, his value is tremendously more – at least 5X.
This is why data is king!
I couldn’t agree more that total customer value, or even total partner or total supplier value, is absolutely the right approach, and is a much better indicator of value. But in this digital world of incredible data volumes and disparate data sources & systems, how can you really know what a customer’s value is?
The marketing applications you probably already use are great – there are so many great automation, web analytics, and CRM systems around. But what fuels these applications? Your data.
Most marketers think that data is the stuff that applications generate or consume. As if all data is pretty much the same. In truth, data is a raw ingredient. Data-driven marketers don’t just manage their marketing applications, they actively manage their data as a strategic asset.
How are you using data to analyze and identify your influential customers? Can you tell that a customer bought their fourth product from your website, and then promptly tweeted about the great deal they got on it? Even more interesting, can you tell that that five of their friends followed the link, 1 bought the same item, 1 looked at it but ended up buying a similar item, and 1 put it in their cart but didn’t buy it because it was cheaper on another website? And more importantly, how can you keep this person engaged so they continue their brand preference – so somebody else with a similar brand and product doesn’t swoop in and do it first? And the ultimate question… how can you scale this so that you’re doing this automatically within your marketing processes, with confidence, every time?
All marketers need to understand their data – what exists in your information ecosystem , whether it be internally or externally. Can you even get to the systems that hold the richest data? Do you leverage your internal customer support/call center records? Is your billing /financial system utilized as a key location for customer data? And the elephant in the room… can you incorporate the invaluable social media data that is ripe for marketers to leverage as an automated component of their marketing campaigns?
This is why marketers need to care about data integration…
Even if you do have access to all of the rich customer data that exists within and outside of your firewalls, how can you make sense of it? How can you pull it together to truly understand your customers… what they really buy, who they associate with, and who they influence. If you don’t, then you’re leaving dollars, and more importantly, potential advocacy and true customer value, on the table.
This is why marketers need to care about achieving a total view of their customers and prospects…
And none of this matters if the data you are leveraging is plain incorrect or incomplete. How often have you seen some analysis on an important topic, had that gut feeling that something must be wrong, and questioned the data that was used to pull the report? The obvious data quality errors are really only the tip of the iceberg. Most of the data quality issues that marketers face are either not glaringly obvious enough to catch and correct on the spot, or are baked into an automated process that nobody has the opportunity to catch. Making decisions based upon flawed data inevitably leads to poor decisions.
This is why marketers need to care about data quality.
So, as the article points out, don’t just look at loyalty, look at total customer value. But realize, that this is easier said than done without a focusing in on your data and ensuring you have all of the right data, at the right place, in the right format, right away.
Now… Brand advocates, step up! Share with us your favorite story. What brands do you love? Why? What makes you so loyal?
Data warehousing systems remain the de facto standard for high performance reporting and business intelligence, and there is no sign that will change soon. But Hadoop now offers an opportunity to lower costs by transferring infrequently used data and data preparation workloads off of the data warehouse and process entirely new sources of data coming from the explosion of industrial and personal devices. This is motivating interest in new concepts like the “data lake” as adjunct environments to traditional data warehousing systems.
Now, let’s be real. Between the evolutionary opportunity of preparing data more cost effectively and the revolutionary opportunity of analyzing new sources of data, the latter just sounds cooler. This revolutionary opportunity is what has spurred the growth of new roles like data scientists and new tools for self-service visualization. In the revolutionary world of pervasive analytics, data scientists have the ability to use Hadoop as a low cost and transient sandbox for data. Data scientists can perform exploratory data analysis by quickly dumping data from a variety of sources into a schema-on-read platform and by iterating dumps as new data comes in. SQL-on-Hadoop technologies like Cloudera Impala, Hortonworks Stinger, Apache Drill, and Pivotal HAWQ enable agile and iterative SQL-like queries on datasets, while new analysis tools like Tableau enable self-service visualization. We are merely in the early phases of the revolutionary opportunity of big data.
But while the revolutionary opportunity is exciting, there’s an equally compelling opportunity for enterprises to modernize their existing data environment. Enterprises cannot rely on an iterative dump methodology for managing operational data pipelines. Unmanaged “data swamps” are simply unpractical for business operations. For an operational data pipeline, the Hadoop environment must be a clean, consistent, and compliant system of record for serving analytical systems. Loading enterprise data into Hadoop instead of a relational data warehouse does not eliminate the need to prepare it.
Now I have a secret to share with you: nearly every enterprise adopting Hadoop today to modernize their data environment has processes, standards, tools, and people dedicated to data profiling, data cleansing, data refinement, data enrichment, and data validation. In the world of enterprise big data, schemas and metadata still matter.
I’ll share some examples with you. I attended a customer panel at Strata + Hadoop World in October. One of the participants was the analytics program lead at a large software company whose team was responsible for data preparation. He described how they ingest data from heterogeneous data sources by mandating a standardized schema for everything that lands in the Hadoop data lake. Once the data lands, his team profiles, cleans, refines, enriches, and validates the data so that business analysts have access to high quality information. Another data executive described how inbound data teams are required to convert data into Avro before storing the data in the data lake. (Avro is an emerging data format alongside other new formats like ORC, Parquet, and JSON). One data engineer from one of the largest consumer internet companies in the world described the schema review committee that had been set up to govern changes to their data schemas. The final participant was an enterprise architect from one of the world’s largest telecom providers who described how their data schema was critical for maintaining compliance with privacy requirements since data had to be masked before it could be made available to analysts.
Let me be clear – these companies are not just bringing in CRM and ERP data into Hadoop. These organizations are ingesting patient sensor data, log files, event data, clickstream data, and in every case, data preparation was the first task at hand.
I recently talked to a large financial services customer who proposed a unique architecture for their Hadoop deployment. They wanted to empower line of business users to be creative in discovering revolutionary opportunities while also evolving their existing data environment. They decided to allow line of businesses to set up sandbox data lakes on local Hadoop clusters for use by small teams of data scientists. Then, once a subset of data was profiled, cleansed, refined, enriched, and validated, it would be loaded into a larger Hadoop cluster functioning as an enterprise information lake. Unlike the sandbox data lakes, the enterprise information lake was clean, consistent, and compliant. Data stewards of the enterprise information lake could govern metadata and ensure data lineage tracking from source systems to sandbox to enterprise information lakes to destination systems. Enterprise information lakes balance the quality of a data warehouse with the cost-effective scalability of Hadoop.
Building enterprise information lakes out of data lakes is simple and fast with tools that can port data pipeline mappings from traditional architectures to Hadoop. With visual development interfaces and native execution on Hadoop, enterprises can accelerate their adoption of Hadoop for operational data pipelines.
No one described the opportunity of enterprise information lakes better at Strata + Hadoop World than a data executive from a large healthcare provider who said, “While big data is exciting, equally exciting is complete data…we are data rich and information poor today.” Schemas and metadata still matter more than ever, and with the help of leading data integration and preparation tools like Informatica, enterprises have a path to unleashing information riches. To learn more, check out this Big Data Workbook
The New York Times reports that beginning in this month and into January, for the first time, the Girl Scouts of America will be able to sell Thin Mints and other favorites online through invite-only websites. The websites will be accompanied by a mobile app, giving customers new digital options.
As the Girl Scouts update from a door-to-door approach to include a newly introduced digital program, it’s just one more sign of where marketing trends are heading.
From digital cookies to digital marketing technology:
If 2014 is the year of the digital cookie, then 2015 will be the year of marketing technology. Here’s just a few of the strongest indicators:
- A study found that 67% of marketing departments plan to increase spending on technology over the next two years, according to the Harvard Business Review.
- Gartner predicts that by 2017, CMOs will outspend CIOs on IT-related expenses.
- Also by 2017, one-third of the total marketing budget will be dedicated to digital marketing, according to survey results from Teradata.
- A new LinkedIn/Salesforce survey found that 56% of marketers see their relationships with the CIO as very important or critical.
- Social media is a mainstream channel for marketers, making technology for measuring and managing this channel of paramount importance. This is not just true of B2C companies. Of high level executive B2B buyers, 75% used social media to make purchasing decisions, according to a 2014 survey by market research firm IDC.
From social to analytics to email marketing, much of what marketers see in technology offerings is often labeled as “cloud-based.” While cloud technology has many features and benefits, what are we really saying when we talk about the cloud?
What the cloud means… to marketers.
Beginning around 2012, multitudes of businesses in many industries began adapting “the cloud” as a feature or a benefit to their products or services. Whether or not the business truly was cloud-based was not as clear, which led to the term “cloudwashing.” We hear the so much about cloud, it is easy for us to overlook what it really means and what the benefits really are.
The cloud is more than a buzzword – and in particular, marketers need to know what it truly means to them.
For marketers, “the cloud” has many benefits. A service that is cloud-based gives you amazing flexibility and choices over the way you use a product or service:
- A cloud-enabled product or service can be integrated into your existing systems. For marketers, this can range from integration into websites, marketing automation systems, CRMs, point-of-sale platforms, and any other business application.
- You don’t have to learn a new system, the way you might when adapting a new application, software, or other enterprise system. You won’t have to set aside a lot of time and effort for new training for you or your staff.
- Due to the flexibility that lets you integrate anywhere, you can deploy a cloud-based product or service across all of your organization’s applications or processes, increasing efficiencies and ensuring that all of your employees have access to the same technology tools at the same time.
- There’s no need to worry about ongoing system updates, as those happen automatically behind the scenes.
In 2015, marketers should embrace the convenience of cloud-based services, as they help put the focus on benefits instead of spending time managing the technology.
Are you using data quality in the cloud?
If you are planning to move data out of an on-premise application or software to a cloud-based service, you can take advantage of this ideal time to ensure these data quality best practices are in place.
Verify and cleanse your data first, before it is moved to the cloud. Since it’s likely that your move to the cloud will make this data available across your organization — within marketing, sales, customer service, and other departments — applying data quality best practices first will increase operational efficiency and bring down costs from invalid or unusable data.
There may be more to add to this list, depending on the nature of your own business. Make sure that:
- Postal addresses are valid, accurate, current and complete
- Email addresses are valid
- Telephone numbers are valid, accurate, and current
- Increase the effectiveness of future data analysis by making sure all data fields are consistent and every individual data element is clearly defined
- Fill in missing data
- Remove duplicate contact and customer records
Once you have cleansed and verified your existing data and move it to the cloud, use a real-time verification and cleansing solution at the point of entry or point of collection in real-time to ensure good data quality across your organization on an ongoing basis.
The biggest roadblock to effective marketing technology is: Bad data.
Budgeting for marketing technology is going to become a bigger and bigger piece of the pie (or cookie, if you prefer) for B2C and B2B organizations alike. The first step all marketers need to take to make sure those investments fully pay off and don’t go wasted is great customer data.
Marketing technology is fueled by data. A recent Harvard Business Review article listed some of the most important marketing technologies. They included tools for analytics, conversion, email, search engine marketing, remarketing, mobile, and marketing automation.
What do they all have in common? These tools all drive customer communication, engagement, and relationships, all of which require valid and actionable customer data to work at all.
You can’t plan your marketing strategy off of data that tells you the wrong things about who your customers are, how they prefer to be contacted, and what messages work the best. Make data quality a major part of your 2015 marketing technology planning to get the most from your investment.
Marketing technology is going to be big in 2015 — where do you start?
With all of this in mind, how can marketers prepare for their technology needs in 2015? Get started with this free virtual conference from MarketingProfs that is totally focused on marketing technology.
This great event includes a keynote from Teradata’s CMO, Lisa Arthur, on “Using Data to Build Strong Marketing Strategies.” Register here for the December 12 Marketing Technology Virtual Conference from MarketingProfs.
Even if you can’t make it live that day at the virtual conference, it’s still smart to sign up so you receive on-demand recordings from the sessions when the event ends. Register now!
According to the article, in Hamilton County Ohio, it’s not unusual to see kids from the same neighborhoods coming to the hospital for asthma attacks. Thus, researchers wanted to know if it was fact or mistaken perception that an unusually high number of children in the same neighborhood were experiencing asthma attacks. The next step was to review existing data to determine the extent of the issues, and perhaps how to solve the problem altogether.
“The researchers studied 4,355 children between the ages of 1 and 16 who visited the emergency department or were hospitalized for asthma at Cincinnati Children’s between January 2009 and December 2012. They tracked those kids for 12 months to see if they returned to the ED or were readmitted for asthma.”
Not only were the researchers able to determine a sound correlation between the two data sets, but they were able to advance the research to predict which kids were at high-risk based upon where they live. Thus, some of the cause and the effects have been determined.
This came about when researchers began thinking out of the box, when it comes to dealing with traditional and non-traditional medical data. They integrated housing and census data, in this case, with that of the data from the diagnosis and treatment of the patients. These are data sets unlikely to find their way to each other, but together they have a meaning that is much more valuable than if they just stayed in their respective silos.
“Non-traditional medical data integration has begun to take place in some medical collaborative environments already. The New York-Presbyterian Regional Health Collaborative created a medical village, which ‘goes beyond the established patient-centered medical home mode.’ It not only connects an academic medical center with a large ambulatory network, medical homes, and other providers with each other, but community resources such as school-based clinics and specialty-care centers (the ones that are a part of NYP’s network).”
The fact of the matter is that data is the key to understanding what the heck is going on when cells of sick people begin to emerge. While researchers and doctors can treat the individual patients there is not a good understanding of the larger issues that may be at play. In this case, poor air quality in poor neighborhoods. Thus, they understand what problem needs to be corrected.
The universal sharing of data is really the larger solution here, but one that won’t be approached without a common understanding of the value, and funding. As we pass laws around the administration of health care, as well as how data is to be handled, perhaps it’s time we look at what the data actually means. This requires a massive deployment of data integration technology, and the fundamental push to share data with a central data repository, as well as with health care providers.
Peak season is here, and delivery companies have plans in place to successfully deliver every last package:
- The USPS added delivery on Sunday and Christmas Day this year, after last year’s double-digit rise in package volumes during peak season.
- In December alone, the United Parcel Service (UPS) forecasts it will deliver 585 million packages, an increase of 11% over last year.
- UPS is also investing $175 million for its peak season preparedness plan, and will add 95,000 season workers (which is nearly 10 times the number of Federal Emergency Management Agency, or FEMA, employees in 2013).
- According to the National Retail Federation, 44% of consumers will do their shopping on the web, which translates to a lot of deliveries.
For retailers, that means a lot of addresses in your company’s database. This will lead to a copious amount of deliveries.
The big rise in deliveries this year got me thinking: What would the holidays be like if there were no such thing as a postal address?
It’s safe to say, the holidays would be a lot less cheery. With our current reliance on mapping applications, it would be tough to get from home to the new toy store and back. Sadly, a lot of holiday greeting cards would get stamped “return to sender.” And without mapping applications or GPS, it would take a little more effort to get to grandmother’s house this year.
I think the only person who would be successfully delivering any gifts this year would be Santa (since he has his own rooftop-to-rooftop accuracy built in with his magical sleigh.)
Of course, one of the biggest places impacted would be the retail industry. The peak season at the end of the year is the time for retail businesses to make or break their reputations.
With the increased volume of deliverability, what mistakes might occur if all address data suddenly disappeared?
In a season without address data quality, your reputation and company could suffer in a number of ways:
- Faulty addresses mean a weak customer database
- Erroneous shipping means you’ll paying for delivery, returns, and re-delivery
- Loss of customers and hurt reputations during peak sales time
A truly data-centric company treats address data as the foundation for customer information, and this would be more challenging to do without quality address verification.
In a peak season without address verification, I imagine companies would have to turn to alternative means to estimate locations and distances such as the geocoding process from Google Maps, which would leave them at a few disadvantages as delivery trucks navigate the icy roads during wintertime.
Informatica’s Address Validation offers these benefits that Google Maps’ geocoding does not, including:
- Address validation and corrections
- Availability as an on-premise solution for customer security and privacy
- User-friendly experience as a leader in lookup and cleansing for 20+ years
- Exact geocoding for properties (not estimates or approximations)
- Partnership with the Universal Postal Union and all five existing global postal certifications
Approximate locations and uncertified data won’t cut it when customers expect on-time delivery, every time. Along with these benefits that make it invaluable for customer shipping and other postal mail uses, Informatica’s Address Validation sets the standard in 240 countries and territories.
Luckily, we do not live in a world without address quality. It is possible to ensure every last package and parcel makes it to its destination on time, while making it to grandmother’s house on time, sending greeting cards to our whole list, and bringing home lovingly selected gifts from the store to wrap and tuck under the tree.
How do you measure how your company is rating with its customer address quality? You can get started with this ebook from Informatica, “Three Ways to Measure Address Quality.”
Or in other words: Did the agency model kill data quality? When you watch the TV series “Homeland”, you quickly realize the interdependence between field operatives and the command center. This is a classic agency model. One arm gathers, filters and synthesizes information and prepares a plan but the guys on the ground use this intel to guide their sometimes ad hoc next moves.
The last few months I worked a lot – and I mean A LOT – with a variety of mid-sized life insurers (<$1B annual revenue) around fixing their legacy-inherited data quality problems. Their IT departments, functioning like Operations Command Centers (intel acquisition, filter and synthesize), were inundated with requests to fix up and serve a coherent, true, enriched central view of a participant (the target) and all his policies and related entities from and to all relevant business lines (planning) to achieve their respective missions (service, retain, upsell, mitigate risk): employee benefits, broker/dealer, retirement services, etc.
The captive and often independent agents (execution); however, often run with little useful data into an operation (sales cycle) as the Ops Center is short on timely and complete information. Imagine Carrie directing her strike team to just wing it based on their experience and dated intel from a raid a few months ago without real-time drone video feeds. Would she be saying, “Guys, it’s always been your neck, you’ll figure it out.” I think not.
This becomes apparent when talking to the actuaries, claims operations, marketing, sales, agency operations, audit, finance, strategic planning, underwriting and customer service, common denominators appeared quickly:
- Every insurer saw the need to become customer instead of policy centric. That’s the good news.
- Every insurer knew their data was majorly sub-standard in terms of quality and richness.
- Every insurer agreed that they are not using existing data capture tools (web portals for agents and policy holders, CRM applications, policy and claims mgmt systems) to their true potential.
- New books-of-business were generally managed as separate entities from a commercial as well as IT systems perspective, even if they were not net-new products, like trust products. Cross-selling as such, even if desired, becomes a major infrastructure hurdle.
- As in every industry, the knee-jerk reaction was to throw the IT folks at data quality problems and making it a back-office function. Pyrrhic victory.
- Upsell scenarios, if at all strategically targeted, are squarely in the hands of the independent agents. The insurer will, at most, support customer insights around lapse avoidance or 401k roll-off indicators for retiring or terminated plan participants. This may be derived from a plan sponsor (employer) census file, which may have incorrect address information.
- Prospect and participant e-mail addresses are either not captured (process enforcement or system capability) or not validated (domain, e-mail verification), so the vast majority of customer correspondence, like scenarios, statements, privacy notices and policy changes, travels via snail mail (and this typically per policy). Overall, only 15-50% of contacts have an “unverified” e-mail address today and of these less than a third subscribed to exclusive electronic statement delivery.
- Postal address information is still not 99% correct, complete or current, resulting in high returned mail ($120,000-$750,000 every quarter), priority mail upgrades, statement reprints, manual change capture and shredding cost as well as the occasional USPS fine.
- Data quality, as unbelievable as it sounds, took a back-burner to implementing a new customer data warehouse, a new claims system, a new risk data mart, etc. They all just get filled with the same old, bad data as business users were – and I quote –“used to the quality problem already”.
- Premium underpricing runs at 2-4% annually, foregoing millions in additional revenue, due to lack of a full risk profile.
- Customer cost –of-acquisition (CAC) is unknown or incorrect as there is no direct, realistic tracking of agency campaign/education dollars spent against new policies written.
- Agency historic production and projections are unclear as a dynamic enforcement of hierarchies is not available, resulting in orphaned policies generating excess tax burdens. Often this is the case when agents move to states where they are not licensed, they passed or retired.
What does a cutting-edge insurer look like instead? Ask Carrie Mathison and Saul Bernstein. They already have a risk and customer EDW as well as a modern (cloud based?) CRM and claims mgmt system. They have considered, as part of their original implementation or upgrade, capabilities required to fix the initial seed data into their analytics platforms. Now, they are looking into pushing them back into operational systems like CRM and avoiding bad source system entries from the get-go.
They are also beyond just using data to avoid throwing more bodies in every department at “flavor-of-the-month” clean-up projects, e.g. yet another state unclaimed property matching exercise, total annual premium revenue written in state X for tax review purposes by the state tax authority.
So what causes this drastic segmentation of leading versus laggard life insurers? In my humble opinion, it is the lack of a strategic refocusing of what the insurer can do for an agent by touching the prospects and customers directly. Direct interaction (even limited) improves branding, shortens the sales cycle and rates based on improved insights through better data quality.
Agents (and insurers) need to understand that the wealth of data (demographic, interactions, transactions) corporate possesses already via native and inherited (via M&A) can be a powerful competitive differentiator. Imagine if they start tapping into external sources beyond the standard credit bureaus and consumer databases; dare I say social media?
Competing based on immediate instead of long term needs (in insurance: life time earnings potential replacement), price (fees) and commission cannot be the sole answer.
Information workers deal with information, or in other words, data. They use that data to do their jobs. They make decisions in business with that data. They impact the lives of their clients.
Many years ago, I was part of a formative working group researching information worker productivity. The idea was to create an index like Labor Productivity indexes. It was to be aimed at information worker productivity. By this I mean the analysts, accountants, actuaries, underwriters and statisticians. These are business information workers. How productive are they? How do you measure their output? How do you calculate an economic cost of more or less productive employees? How do you quantify the “soft” costs of passing work on to information workers? The effort stalled in academia, but I learned a few key things. These points underline the nature of an information worker and impacts to their productivity.
- Information workers need data…and lots of it
- Information workers use applications to view and manipulate data to get the job done
- Degradation, latency or poor ease of use in any of items 1 and 2 have a direct impact on productivity
- Items 1 and 2 have a direct correlation to training cost, output and (wait for it) employee health and retention
It’s time to make a super bold statement. It’s time to maximize your investment in DATA. And past time to de-emphasize investments in applications! Stated another way, applications come and go, but data lives forever.
My five-year old son is addicted to his iPad. He’s had one since he was one-year old. At about the age of three he had pretty much left off playing Angry Birds. He started reading Wikipedia. He started downloading apps from the App Store. He wanted to learn about string theory, astrophysics and plate tectonics. Now, he scares me a little with his knowledge. I call him my little Sheldon Cooper. The apps that he uses for research are so cool. The way that they present the data, the speed and depth are amazing. As soon as he’s mastered one, he’s on to the next one. It won’t be long before he’s going to want to program his own apps. When that day comes, I’ll do whatever it takes to make him successful.
And he’s not alone. The world of the “selfie-generation” is one of rapid speed. It is one of application proliferation and flat out application “coolness”. High school students are learning iOS programming. They are using cloud infrastructure to play games and run experiments. Anyone under the age of 27 has been raised in a mélange of amazing data-fueled computing and mobility.
This is your new workforce. And on their first day of their new career at an insurance company or large bank, they are handed an aging recycled workstation. An old operating system follows and mainframe terminal sessions. Then comes rich-client and web apps circa 2002. And lastly (heaven forbid) a Blackberry. Now do you wonder if that employee will feel empowered and productive? I’ll tell you now, they won’t. All that passion they have for viewing and interacting with information will disappear. It will not be enabled in their new work day. An outright information worker revolution would not surprise me.
And that is exactly why I say that it’s time to focus on data and not on applications. Because data lives on as applications come and go. I am going to coin a new phrase. I call this the Empowered Selfie Formula. The Empowered Selfie Formula is a way in which the focus on data liberates information workers. They become free to be more productive in today’s technology ecosystem.
Enable a BYO* Culture
Many organizations have been experimenting with Bring Your Own Device (BYOD) programs. Corporate stipends that allow employees to buy the computing hardware of their choice. But let’s take that one step further. How about a Bring Your Own Application program? How about a Bring Your Own Codebase program? The idea is not so far-fetched. There are so many great applications for working with information. Today’s generation is learning about coding applications at a rapid pace. They are keen to implement their own processes and tools to “get the job done”. It’s time to embrace that change. Allow your information workers to be productive with their chosen devices and applications.
Empower Social Sharing
Your information workers are now empowered with their own flavors of device and application productivity. Let them share it. The ability to share success, great insights and great apps is engrained into the mindset of today’s technology users. Companies like Tableau have become successful based on the democratization of business intelligence. Through enabling social sharing, users can celebrate their successes and cool apps with colleagues. This raises the overall levels of productivity as a grassroots movement. Communities of best practices begin to emerge creating innovation where not previously seen.
As an organization it is important to measure success. Find ways to capture key metrics in productivity of this new world of data-fueled information work. Each information worker will typically be able to track trends in their output. When they show improvement, celebrate that success.
Invest in “Cool”
With a new BYO* culture, make the investments in cool new things. Allow users to spend a few dollars here and there for training online or in-person. There they can learn new things will make them more productive. It will also help with employee retention. With small investment larger ROI can be realized in employee health and productivity.
Foster Healthy Competition
Throughout history, civilizations that fostered healthy competition have innovated faster. The enterprise can foster healthy competition on metrics. Other competition can be focused on new ways to look at information, valuable insights, and homegrown applications. It isn’t about a “best one wins” competition. It is a continuing round of innovation winners with lessons learned and continued growth. These can also be centered on the social sharing and community aspects. In the end it leads to a more productive team of information workers.
Revitalize Your Veterans
Naturally those information workers who are a little “longer in the tooth” may feel threatened. But this doesn’t need to be the case. Find ways to integrate them into the new culture. Do this through peer training, knowledge transfer, and the data items listed below. In the best of cases, they too will crave this new era of innovation. They will bring a lot of value to the ecosystem.
There is a catch. In order to realize success in the formula above, you need to overinvest in data and data infrastructure. Perhaps that means doing things with data that only received lip service in the past. It is imperative to create a competency or center of excellence for all things data. Trusting your data centers of excellence activates your Empowered Selfie Formula.
You are going to have users using and building new apps and processing data and information in new and developing ways. This means you need to trust your data. Your data governance becomes more important. Everything from metadata, data definition, standards, policies and glossaries need to be developed. In this way the data that is being looked at can be trusted. Chief Data Officers should put into place a data governance competency center. All data feeding and coming from new applications is inspected regularly for adherence to corporate standards. Remember, it’s not about the application. It’s about what feeds any application and what data is generated.
Very much a part of data governance is the quality of data in the organization. Also adhering to corporate standards. These standards should dictate cleanliness, completeness, fuzzy logic and standardization. Nothing frustrates an information worker more than building the coolest app that does nothing due to poor quality data.
Data needs to be in the right place at the right time. Any enterprise data takes a journey from many places and to many places. Movement of data that is governed and has met quality standards needs to happen quickly. We are in a world of fast computing and massive storage. There is no excuse for not having data readily available for a multitude of uses.
And finally, make sure to secure your data. Regardless of the application consuming your information, there may be people that shouldn’t see the data. Access control, data masking and network security needs to be in place. Each application from Microsoft Excel to Informatica Springbok to Tableau to an iOS developed application will only interact with the information it should see.
The changing role of an IT group will follow close behind. IT will essentially become the data-fueled enablers using the principles above. IT will provide the infrastructure necessary to enable the Empowered Selfie Formula. IT will no longer be in the application business, aside from a few core corporation applications as a necessary evil.
Achieving a competency in the items above, you no longer need to worry about the success of the Empowered Selfie Formula. What you will have is a truly data-fueled enterprise. There will be a new class of information workers enabled by a data-fueled competency. Informatica is thrilled to be an integral part of the realization that data can play in your journey. We are energized to see the pervasive use of data by increasing numbers of information workers. The are creating new and better ways to do business. Come and join a data-fueled world with Informatica.
This is a guest author post by Philip Howard, Research Director, Bloor Research.
I recently posted a blog about an interview style webcast I was doing with Informatica on the uses and costs associated with data integration tools.
I’m not sure that the poet John Donne was right when he said that it was strange, let alone fatal. Somewhat surprisingly, I have had a significant amount of feedback following this webinar. I say “surprisingly” because the truth is that I very rarely get direct feedback. Most of it, I assume, goes to the vendor. So, when a number of people commented to me that the research we conducted was both unique and valuable, it was a bit of a thrill. (Yes, I know, I’m easily pleased).
There were a number of questions that arose as a result of our discussions. Probably the most interesting was whether moving data into Hadoop (or some other NoSQL database) should be treated as a separate use case. We certainly didn’t include it as such in our original research. In hindsight, I’m not sure that the answer I gave at the time was fully correct. I acknowledged that you certainly need some different functionality to integrate with a Hadoop environment and that some vendors have more comprehensive capabilities than others when it comes to Hadoop and the same also applies (but with different suppliers, when it comes to integrating with, say, MongoDB or Cassandra or graph databases). However, as I pointed out in my previous blog, functionality is ephemeral. And, just because a particular capability isn’t supported today, doesn’t mean it won’t be supported tomorrow. So that doesn’t really affect use cases.
However, where I was inadequate in my reply was that I only referenced Hadoop as a platform for data warehousing, stating that moving data into Hadoop was not essentially different from moving it into Oracle Exadata or Teradata or HP Vertica. And that’s true. What I forgot was the use of Hadoop as an archiving platform. As it happens we didn’t have an archiving use case in our survey either. Why not? Because archiving is essentially a form of data migration – you have some information lifecycle management and access and security issues that are relevant to archiving once it is in place but that is after the fact: the process of discovering and moving the data is exactly the same as with data migration. So: my bad.
Aside from that little caveat, I quite enjoyed the whole event. Somebody or other (there’s always one!) didn’t quite get how quantifying the number of end points in a data integration scenario was a surrogate measure for complexity (something we took into account) and so I had to explain that. Of course, it’s not perfect as a metric but it’s the only alternative to ask eye of the beholder type questions which aren’t very satisfactory.
Anyway, if you want to listen to the whole thing you can find it HERE: