Category Archives: Governance, Risk and Compliance

Top 5 Data Themes in Emerging Markets

Top 5 Data Themes in Emerging Markets

Top 5 Data Themes in Emerging Markets

Recently, my US-based job led me to a South African hotel room, where I watched Germany play Brazil in the World Cup. The global nature of the event was familiar to me. My work covers countries like Malaysia, Thailand, Singapore, South Africa and Costa Rica. And as I pondered the stunning score (Germany won, 7 to 1), my mind was drawn to emerging markets. What defines an emerging market? In particular, what are the data-related themes common to emerging markets? Because I work with global clients in the banking, oil and gas, telecommunications, and retail industries, I have learned a great deal about this. As a result, I wanted to share my top 5 observations about data in Emerging Markets.

1) Communication Infrastructure Matters

Many of the emerging markets, particularly in Africa, jumped from one or two generations of telco infrastructure directly into 3G and fiber within a decade. However, this truth only applies to large, cosmopolitan areas. International diversification of fiber connectivity is only starting to take shape. (For example, in Southern Africa, BRICS terrestrial fiber is coming online soon.) What does this mean for data management? First, global connectivity influences domestic last mile fiber deployment to households and businesses. This, in turn, will create additional adoption of new devices. This adoption will create critical mass for higher productivity services, such as eCommerce. As web based transactions take off, better data management practices will follow. Secondly, European and South American data centers become viable legal and performance options for African organizations. This could be a game changer for software vendors dealing in cloud services for BI, CRM, HCM, BPM and ETL.

2) Competition in Telecommunication Matters

If you compare basic wireless and broadband bundle prices between the US, the UK and South Africa, for example, the lack of true competition makes further coverage upgrades, like 4G and higher broadband bandwidths, easy to digest for operators. These upgrades make telecommuting, constant social media engagement possible. Keeping prices low, like in the UK, is the flipside achieving the same result. The worst case is high prices and low bandwidth from the last mile to global nodes. This also creates low infrastructure investment and thus, fewer consumers online for fewer hours. This is often the case in geographically vast countries (Africa, Latin America) with vast rural areas. Here, data management is an afterthought for the most part. Data is intentionally kept in application silos as these are the value creators. Hand coding is pervasive to string data together to make small moves to enhance the view of a product, location, consumer or supplier.

3) A Nation’s Judicial System Matters

If you do business in nations with a long, often British judicial tradition, chances are investment will happen. If you have such a history but it is undermined by a parallel history of graft from the highest to the lowest levels because of the importance of tribal traditions, only natural resources will save your economy. Why does it matter if one of my regional markets is “linked up” but shipping logistics are burdened by this excess cost and delay? The impact on data management is a lack of use cases supporting an enterprise-wide strategy across all territories. Why invest if profits are unpredictable or too meager? This is why small Zambia or Botswana are ahead of the largest African economy, Nigeria.

4) Expertise Location Matters

Anybody can have the most advanced vision on a data-driven, event-based architecture supporting the fanciest data movement and persistence standards. Without the skill to make the case to the business it is a lost cause unless your local culture still has IT in charge of specifying requirements, running the evaluation, selecting and implementing a new technology. It is also done for if there are no leaders who have experienced how other leading firms in the same or different sector went about it (un)successfully. Lastly, if you don’t pay for skill, your project failure risk just tripled. Duh!

5) Denial is Universal

No matter if you are an Asian oil company, a regional North American bank, a Central American National Bank or an African retail conglomerate. If finance or IT invested in any technologies prior and they saw a lack of adoption, for whatever reason, they will deny data management challenges despite other departments complaining. Moreover, if system integrators or internal client staff (mis)understand data management as fixing processes (which it is not) instead of supporting transactional integrity (which it is), clients are on the wrong track. Here, data management undeservedly becomes a philosophical battleground.

This is definitely not a complete list or super-thorough analysis but I think it covers the most crucial observations from my engagements. I would love to hear about your findings in emerging markets.

Stay tuned for part 2 of this series where I will talk about the denial and embrace of corporate data challenges as it pertains to an organization’s location.

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Posted in Governance, Risk and Compliance, Public Sector, Retail, Telecommunications, Utilities & Energy | Tagged | Leave a comment

The Ones Not Screwing Up with Compliance Will Win

A few weeks ago, a regional US bank asked me to perform some compliance and use case analysis around fixing their data management situation.  This bank prides itself on customer service and SMB focus, while using large-bank product offerings.  However, they were about a decade behind the rest of most banks in modernizing their IT infrastructure to stay operationally on top of things.

compliance

Bank Efficiency Ratio per AUM (Assets under Management), bankregdata.com

This included technologies like ESB, BPM, CRM, etc.  They also were a sub-optimal user of EDW and analytics capabilities. Having said all this; there was a commitment to change things up, which is always a needed first step to any recovery program.

THE STAKEHOLDERS

As I conducted my interviews across various departments (list below) it became very apparent that they were not suffering from data poverty (see prior post) but from lack of accessibility and use of data.

  • Compliance
  • Vendor Management & Risk
  • Commercial and Consumer Depository products
  • Credit Risk
  • HR & Compensation
  • Retail
  • Private Banking
  • Finance
  • Customer Solutions

FRESH BREEZE

This lack of use occurred across the board.  The natural reaction was to throw more bodies and more Band-Aid marts at the problem.  Users also started to operate under the assumption that it will never get better.  They just resigned themselves to mediocrity.  When some new players came into the organization from various systemically critical banks, they shook things up.

Here is a list of use cases they want to tackle:

  • The proposition of real-time offers based on customer events as simple as investment banking products for unusually high inflow of cash into a deposit account.
  • The use of all mortgage application information to understand debt/equity ratio to make relevant offers.
  • The capture of true product and customer profitability across all lines of commercial and consumer products including trust, treasury management, deposits, private banking, loans, etc.
  • The agile evaluation, creation, testing and deployment of new terms on existing and products under development by shortening the product development life cycle.
  • The reduction of wealth management advisors’ time to research clients and prospects.
  • The reduction of unclaimed use tax, insurance premiums and leases being paid on consumables, real estate and requisitions due to the incorrect status and location of the equipment.  This originated from assets no longer owned, scrapped or moved to different department, etc.
  • The more efficient reconciliation between transactional systems and finance, which often uses multiple party IDs per contract change in accounts receivable, while the operating division uses one based on a contract and its addendums.  An example would be vendor payment consolidation, to create a true supplier-spend; and thus, taking advantage of volume discounts.
  • The proactive creation of central compliance footprint (AML, 314, Suspicious Activity, CTR, etc.) allowing for quicker turnaround and fewer audit instances from MRAs (matter requiring attention).

MONEY TO BE MADE – PEOPLE TO SEE

Adding these up came to about $31 to $49 million annually in cost savings, new revenue or increased productivity for this bank with $24 billion total assets.

So now that we know there is money to be made by fixing the data of this organization, how can we realistically roll this out in an organization with many competing IT needs?

The best way to go about this is to attach any kind of data management project to a larger, business-oriented project, like CRM or EDW.  Rather than wait for these to go live without good seed data, why not feed them with better data as a key work stream within their respective project plans?

To summarize my findings I want to quote three people I interviewed.  A lady, who recently had to struggle through an OCC audit told me she believes that the banks, which can remain compliant at the lowest cost will ultimately win the end game.  Here she meant particularly tier 2 and 3 size organizations.  A gentleman from commercial banking left this statement with me, “Knowing what I know now, I would not bank with us”.  The lady from earlier also said, “We engage in spreadsheet Kung Fu”, to bring data together.

Given all this, what would you suggest?  Have you worked with an organization like this? Did you encounter any similar or different use cases in financial services institutions?

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Posted in Banking & Capital Markets, Data Quality, Financial Services, Governance, Risk and Compliance | Tagged , , , | Leave a comment

And Now, Time for Real Disruptive Innovation

Disruptive Innovation

Disruptive Innovation

Lately, there’s been a raging debate over Clayton Christensen’s definition of “disruptive innovation,” and whether this is the key to ultimate success in markets. Clayton, author of the ground-breaking book, The Innovator’s Dilemma, says industry-leading firms tend to pursue high-margin revenue-producing business, leaving lower end, less profitable parts of their markets to new, upstart players. For established leaders, there’s often not enough profit in selling to under-served or unserved markets. However, what happens is the upstarts end up gradually moving up the food chain with their new business models, eating gradually into leaders’ positions and either chasing them upstream, or out of business.

The interesting thing is that many of the upstarts do not even intend to take on the market leader in the segment. Christensen cites the classic example of Digital Equipment Corporation in the 1980s, which was unable to make the transition from large, expensive enterprise systems to smaller, PC-based equipment. The PC upstarts in this case did not take on Digital directly – rather they addressed unmet needs in another part of the market.

Christensen wrote and published The Innovator’s Dilemma more than 17 years ago, but his message keeps reverberating across the business world. Lately, Jill Lapore questioned some of thinking that has evolved around disruptive innovation in a recent New Yorker article. “Disruptive innovation is a theory about why businesses fail. It’s not more than that. It doesn’t explain change. It’s not a law of nature,” she writes. Christensen responded with a rebuttal to Lapore’s thesis, noting that “disruption doesn’t happen overnight,” and that “[Disruptive innovation] is not a theory about survivability.”

There is something Lapore points out that both she and Christensen can agree on: “disruption” is being oversold and misinterpreted on a wide scale these days. Every new product that rolls out is now branded as “disruptive.” As stated above, the true essence of disruption is creating new markets where the leaders would not tread.

Data itself can potentially be a source of disruption, as data analytics and information emerge as strategic business assets. While the ability to provide data analysis at real-time speeds, or make new insights possible isn’t disruption in the Christensen sense, we are seeing the rise of new business models built around data and information that could bring new leaders to the forefront. Data analytics can either play a role in supporting this movement, or data itself may be the new product or service disrupting existing markets.

We’ve already been seeing this disruption taking place within the publishing industry, for example – companies or sites providing real-time or near real-time services such as financial updates, weather forecasts and classified advertising have displaced traditional newspapers and other media as information sources.

Employing data analytics as a tool for insights never before available within an industry sector also may be part of disruptive innovation. Tesla Motors, for example, is disruptive to the automotive industry because it manufactures entirely electric cars. But the formula to its success is its employment of massive amounts of data from its array of vehicle in-devices to assure quality and efficiency.

Likewise, data-driven disruption may be occurring in places that may have been difficult to innovate. For example, it’s long been speculated that some of the digital giants, particularly Google, are poised to enter the long-staid insurance industry. If this were to happen, Google would not enter as a typical insurance company with a new web-based spin. Rather, the company would be employing new techniques of data gathering, insight and analysis to offer an entirely new model to consumers – one based on data. As Christopher Hernaes recently related in TechCrunch, Google’s ability to collect and mine data on homes, business and autos give it a unique value proposition n the industry’s value chain.

We’re in an era in which Christensen’s mode of disruptive innovation has become a way of life. Increasingly, it appears that enterprises that are adept and recognizing and acting upon the strategic potential of data may be joining the ranks of the disruptors.

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Posted in Business/IT Collaboration, Data Transformation, Governance, Risk and Compliance | Leave a comment

Takeaways from the Gartner Security and Risk Management Summit (2014)

Last week I had the opportunity to attend the Gartner Security and Risk Management Summit. At this event, Gartner analysts and security industry experts meet to discuss the latest trends, advances, best practices and research in the space. At the event, I had the privilege of connecting with customers, peers and partners. I was also excited to learn about changes that are shaping the data security landscape.

Here are some of the things I learned at the event:

  • Security continues to be a top CIO priority in 2014. Security is well-aligned with other trends such as big data, IoT, mobile, cloud, and collaboration. According to Gartner, the top CIO priority area is BI/analytics. Given our growing appetite for all things data and our increasing ability to mine data to increase top-line growth, this top billing makes perfect sense. The challenge is to protect the data assets that drive value for the company and ensure appropriate privacy controls.
  • Mobile and data security are the top focus for 2014 spending in North America according to Gartner’s pre-conference survey. Cloud rounds out the list when considering worldwide spending results.
  • Rise of the DRO (Digital Risk Officer). Fortunately, those same market trends are leading to an evolution of the CISO role to a Digital Security Officer and, longer term, a Digital Risk Officer. The DRO role will include determination of the risks and security of digital connectivity. Digital/Information Security risk is increasingly being reported as a business impact to the board.
  • Information management and information security are blending. Gartner assumes that 40% of global enterprises will have aligned governance of the two programs by 2017. This is not surprising given the overlap of common objectives such as inventories, classification, usage policies, and accountability/protection.
  • Security methodology is moving from a reactive approach to compliance-driven and proactive (risk-based) methodologies. There is simply too much data and too many events for analysts to monitor. Organizations need to understand their assets and their criticality. Big data analytics and context-aware security is then needed to reduce the noise and false positive rates to a manageable level. According to Gartner analyst Avivah Litan, ”By 2018, of all breaches that are detected within an enterprise, 70% will be found because they used context-aware security, up from 10% today.”

I want to close by sharing the identified Top Digital Security Trends for 2014

  • Software-defined security
  • Big data security analytics
  • Intelligent/Context-aware security controls
  • Application isolation
  • Endpoint threat detection and response
  • Website protection
  • Adaptive access
  • Securing the Internet of Things
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Posted in Big Data, CIO, Data Governance, Data Privacy, Data Security, Governance, Risk and Compliance | Tagged , , , , , , , , | Leave a comment

Informatica Named a Leader in Gartner MQ for Data Archiving

Structured Data Archiving and Application Retirement

Gartner MQ for Structured Data Archiving and Application Retirement

For the first time, Gartner has released a Magic Quadrant for Structured Data Archiving and Application Retirement . This MQ describes an approach for how customers can proactively manage data growth. We are happy to report that Gartner has positioned Informatica as a leader in the space. This is based on our ‘completeness of vision’ and our ‘ability to execute.’

This magic quadrant focuses on what Gartner calls Structured Data Archiving. Data Archiving is used to index, migrate, preserve and protect application data in secondary databases or flat files. These are typically located on lower-cost storage, for policy-based retention.  Data Archiving makes data available in context of the originating business process or application. This is especially useful in the event of litigation or of an audit.

The Magic Quadrant calls out two use cases. These use cases are “live archiving of production applications” and “application retirement of legacy systems.”  Informatica refers to both use cases, together, as “Enterprise Data Archiving.” We consider this to be a foundational component of a comprehensive Information Lifecycle Management strategy.

The application landscape is constantly evolving. For this reason, data archiving is a strategic component of a data growth management strategy. Application owners need a plan to manage data as applications are upgraded, replaced, consolidated, moved to the cloud and/or retired. 

When you don’t have a plan in production, data accumulates in the business application. When this happens, performance bothers the business. In addition, data bloat bothers IT operations. When you don’t have a plan for legacy systems, applications accumulate in the data center. As a result, increasing budgets bother the CFO.

A data growth management plan must include the following:

  • How to cycle through applications and retire them
  • How to smartly store the application data
  • How to ultimately dispose data while staying compliant

Structured data archiving and application retirement technologies help automate and streamline these tasks.

Informatica Data Archive delivers unparalleled connectivity, scalability and a broad range of innovative options (i.e. Smart Partitioning, Live Archiving, and retiring aging and legacy data to the Informatica Data Vault), and comprehensive retention management and data reporting and visualization.  We believe our strengths in this space are the key ingredients for deploying a successful enterprise data archive.

For more information, read the Gartner Magic Quadrant for Structured Data Archiving and Application Retirement.

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Posted in Application ILM, Application Retirement, Data Archiving, Database Archiving, Governance, Risk and Compliance | Tagged , , , , , , | Leave a comment

Regulatory Compliance is an Opportunity, not a Cost!

Regulatory Compliance Regardless of the industry, new regulatory compliance requirements are more often than not treated like the introduction of a new tax.  A few may be supportive, some will see the benefits, but most will focus on the negatives – the cost, the effort, the intrusion into private matters.  There will more than likely be a lot of grumbling.

Across many industries there is currently a lot of grumbling, as new regulation seems to be springing up all over the place.  Pharmaceutical companies have to deal with IDMP in Europe and UDI in the USA.  This is hot on the heels of the US Sunshine Act, which is being followed in Europe by Aggregate Spend requirements.  Consumer Goods companies in Europe are looking at the consequences of beefed up 1169 requirements.  Financial Institutes are mulling over compliance to BCBS-239.  Behind the grumbling most organisations across all verticals appear to have a similar approach to regulatory compliance.  The pattern seems to go like this:

  1. Delay (The requirements may change)
  2. Scramble (They want it when?  Why didn’t we get more time?)
  3. Code to Spec (Provide exactly what they want, and only what they want)

No wonder these requirements are seen as purely a cost and an annoyance.  But it doesn’t have to be that way, and in fact, it should not.  Just like I have seen a pattern in response to compliance, I see a pattern in the requirements themselves:

  1. The regulators want data
  2. Their requirements will change
  3. When they do change, regulators will be wanting even more data!

Now read the last 3 bullet points again, but use ‘executives’ or ‘management’ or ‘the business people’ instead of ‘regulators’.  The pattern still holds true.  The irony is that execs will quickly sign off on budget to meet regulatory requirements, but find it hard to see the value in “infrastructure” projects.  Projects that will deliver this same data to their internal teams.

This is where the opportunity comes in.  pwc’s 2013 State of Compliance Report[i] shows that over 42% of central compliance budgets are in excess of $1m.  A significant figure.  Efforts outside of the compliance team imply a higher actual cost.  Large budgets are not surprising in multi-national companies, who often have to satisfy multiple regulators in a number of countries.  As an alternate to multiple over-lapping compliance projects, what if this significant budget was repurposed to create a flexible data management platform?  This approach could deliver compliance, but provide even more value internally.

Almost all internal teams are currently clamouring for additional data to drive ther newest application.  Pharma and CG sales & marketing teams would love ready access to detailed product information.  So would consumer and patient support staff, as well as down-stream partners.  Trading desks and client managers within Financial Institutes should really have real-time access to their risk profiles guiding daily decision making.  These data needs will not be going away.  Why should regulators be prioritised over the people who drive your bottom line and who are guardians of your brand?

A flexible data management platform will serve everyone equally. Foundational tools for a flexible data management platform exist today including Data Quality,  MDM, PIM and VIBE, Informatica’s Virtual Data Machine.  Each of them play a significant role in easing of regulatory compliance, and as a bonus they deliver measureable business value in their own right.  Implemented correctly, you will get enhanced data agility & visibility across the entire organisation as part of your compliance efforts.  Sounds like ‘Buy one Get One Free’, or BOGOF in retail terms.

Unlike taxes, BOGOF opportunities are normally embraced with open arms.  Regulatory compliance should receive a similar welcome – an opportunity to build the foundations for universal delivery of data which is safe, clean and connected.  A 2011 study by The Economist found that effective regulatory compliance benefits businesses across a wide range of performance metrics[ii].

Is it time to get your free performance boost?


[i] Deeper Insight for Greater Strategic Value, pwc 2013
[ii] Compliance and competitiveness, The Economist 2011
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Posted in Financial Services, Governance, Risk and Compliance, Healthcare | Tagged , , , | Leave a comment

Talk Amongst Yourselves: Why Twitter Needs #DataChat

Listeng to #DataChatWithin Government organizations, technologists are up against a wall of sound. In one ear, they hear consumers cry for faster, better service.

In the other, they hear administrative talk of smaller budgets and scarcer resources.

As stringent requirements for both transparency and accountability grow, this paradox of pressure increases.

Sometimes, the best way to cope is to TALK to somebody.

What if you could ask other data technologists candid questions like:

  • Do you think government regulation helps or hurts the sharing of data?
  • Do you think government regulators balance the privacy needs of the public with commercial needs?
  • What are the implications of big data government regulation, especially for users?
  • How can businesses expedite the government adoption of the cloud?
  • How can businesses aid in the government overcoming the security risks associated with the cloud?
  • How should the policy frameworks for handling big data differ between the government and the private sector?

What if you could tell someone who understood? What if they had sweet suggestions, terrific tips, stellar strategies for success? We think you can. We think they will.

That’s why Twitter needs a #DataChat.

Twitter Needs #DataChat

Third Thursdays, 3:00 PM EST

What on earth is a #DataChat?
Good question. It’s a Twitter Chat – A public dialog, at a set time, on a set topic. It’s something like a crowd-sourced discussion. Any Twitter user can participate simply by including the applicable hashtag in each tweet. Our hashtag is #DataChat. We’ll connect on Twitter, on the third Thursday of each month to share struggles, victories and advice about data governance. We’re going to begin this week, Thursday April 17, at 3:00 PM Eastern Time. For our first chat, we are going to discuss topics that relate to data technologies in government organizations.

What don’t you join us? Tell us about it. Mark your calendar. Bring a friend.

Because, sometimes, you just need someone to talk to.

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Posted in Big Data, Data Governance, Governance, Risk and Compliance, Public Sector | Tagged , , , , | Leave a comment

If you Want Business to “Own” the Data, You Need to Build An Architecture For the Business

If you build an IT Architecture, it will be a constant up-hill battle to get business users and executives engaged and take ownership of data governance and data quality. In short you will struggle to maximize the information potential in your enterprise. But if you develop and Enterprise Architecture that starts with a business and operational view, the dynamics change dramatically. To make this point, let’s take a look at a case study from Cisco. (more…)

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Posted in Business Impact / Benefits, Business/IT Collaboration, CIO, Data Governance, Data Integration, Enterprise Data Management, Governance, Risk and Compliance, Integration Competency Centers | Tagged , , , , | Leave a comment

Murphy’s First Law of Bad Data – If You Make A Small Change Without Involving Your Client – You Will Waste Heaps Of Money

I have not used my personal encounter with bad data management for over a year but a couple of weeks ago I was compelled to revive it.  Why you ask? Well, a complete stranger started to receive one of my friend’s text messages – including mine – and it took days for him to detect it and a week later nobody at this North American wireless operator had been able to fix it.  This coincided with a meeting I had with a European telco’s enterprise architecture team.  There was no better way to illustrate to them how a customer reacts and the risk to their operations, when communication breaks down due to just one tiny thing changing – say, his address (or in the SMS case, some random SIM mapping – another type of address).

Imagine the cost of other bad data (thecodeproject.com)

Imagine the cost of other bad data (thecodeproject.com)

In my case, I  moved about 250 miles within the United States a couple of years ago and this seemingly common experience triggered a plethora of communication screw ups across every merchant a residential household engages with frequently, e.g. your bank, your insurer, your wireless carrier, your average retail clothing store, etc.

For more than two full years after my move to a new state, the following things continued to pop up on a monthly basis due to my incorrect customer data:

  • In case of my old satellite TV provider they got to me (correct person) but with a misspelled last name at my correct, new address.
  • My bank put me in a bit of a pickle as they sent “important tax documentation”, which I did not want to open as my new tenants’ names (in the house I just vacated) was on the letter but with my new home’s address.
  • My mortgage lender sends me a refinancing offer to my new address (right person & right address) but with my wife’s as well as my name completely butchered.
  • My wife’s airline, where she enjoys the highest level of frequent flyer status, continually mails her offers duplicating her last name as her first name.
  • A high-end furniture retailer sends two 100-page glossy catalogs probably costing $80 each to our address – one for me, one for her.
  • A national health insurer sends “sensitive health information” (disclosed on envelope) to my new residence’s address but for the prior owner.
  • My legacy operator turns on the wrong premium channels on half my set-top boxes.
  • The same operator sends me a SMS the next day thanking me for switching to electronic billing as part of my move, which I did not sign up for, followed by payment notices (as I did not get my invoice in the mail).  When I called this error out for the next three months by calling their contact center and indicating how much revenue I generate for them across all services, they counter with “sorry, we don’t have access to the wireless account data”, “you will see it change on the next bill cycle” and “you show as paper billing in our system today”.

Ignoring the potential for data privacy law suits, you start wondering how long you have to be a customer and how much money you need to spend with a merchant (and they need to waste) for them to take changes to your data more seriously.  And this are not even merchants to whom I am brand new – these guys have known me and taken my money for years!

One thing I nearly forgot…these mailings all happened at least once a month on average, sometimes twice over 2 years.  If I do some pigeon math here, I would have estimated the postage and production cost alone to run in the hundreds of dollars.

However, the most egregious trespass though belonged to my home owner’s insurance carrier (HOI), who was also my mortgage broker.  They had a double whammy in store for me.  First, I received a cancellation notice from the HOI for my old residence indicating they had cancelled my policy as the last payment was not received and that any claims will be denied as a consequence.  Then, my new residence’s HOI advised they added my old home’s HOI to my account.

After wondering what I could have possibly done to trigger this, I called all four parties (not three as the mortgage firm did not share data with the insurance broker side – surprise, surprise) to find out what had happened.

It turns out that I had to explain and prove to all of them how one party’s data change during my move erroneously exposed me to liability.  It felt like the old days, when seedy telco sales people needed only your name and phone number and associate it with some sort of promotion (back of a raffle card to win a new car), you never took part in, to switch your long distance carrier and present you with a $400 bill the coming month.  Yes, that also happened to me…many years ago.  Here again, the consumer had to do all the legwork when someone (not an automatic process!) switched some entry without any oversight or review triggering hours of wasted effort on their and my side.

We can argue all day long if these screw ups are due to bad processes or bad data, but in all reality, even processes are triggered from some sort of underlying event, which is something as mundane as a database field’s flag being updated when your last purchase puts you in a new marketing segment.

Now imagine you get married and you wife changes her name. With all these company internal (CRM, Billing, ERP),  free public (property tax), commercial (credit bureaus, mailing lists) and social media data sources out there, you would think such everyday changes could get picked up quicker and automatically.  If not automatically, then should there not be some sort of trigger to kick off a “governance” process; something along the lines of “email/call the customer if attribute X has changed” or “please log into your account and update your information – we heard you moved”.  If American Express was able to detect ten years ago that someone purchased $500 worth of product with your credit card at a gas station or some lingerie website, known for fraudulent activity, why not your bank or insurer, who know even more about you? And yes, that happened to me as well.

Tell me about one of your “data-driven” horror scenarios?

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Posted in Banking & Capital Markets, Business Impact / Benefits, Business/IT Collaboration, Complex Event Processing, Customer Acquisition & Retention, Customer Services, Customers, Data Aggregation, Data Governance, Data Privacy, Data Quality, Enterprise Data Management, Financial Services, Governance, Risk and Compliance, Healthcare, Master Data Management, Retail, Telecommunications, Uncategorized, Vertical | Tagged , , , , , , , , , | Leave a comment