Category Archives: Vertical

Death of the Data Scientist: Silver Screen Fiction?

Maybe the word “death” is a bit strong, so let’s say “demise” instead.  Recently I read an article in the Harvard Business Review around how Big Data and Data Scientists will rule the world of the 21st century corporation and how they have to operate for maximum value.  The thing I found rather disturbing was that it takes a PhD – probably a few of them – in a variety of math areas to give executives the necessary insight to make better decisions ranging from what product to develop next to who to sell it to and where.

Who will walk the next long walk.... (source: Wikipedia)

Who will walk the next long walk…. (source: Wikipedia)

Don’t get me wrong – this is mixed news for any enterprise software firm helping businesses locate, acquire, contextually link, understand and distribute high-quality data.  The existence of such a high-value role validates product development but it also limits adoption.  It is also great news that data has finally gathered the attention it deserves.  But I am starting to ask myself why it always takes individuals with a “one-in-a-million” skill set to add value.  What happened to the democratization  of software?  Why is the design starting point for enterprise software not always similar to B2C applications, like an iPhone app, i.e. simpler is better?  Why is it always such a gradual “Cold War” evolution instead of a near-instant French Revolution?

Why do development environments for Big Data not accommodate limited or existing skills but always accommodate the most complex scenarios?  Well, the answer could be that the first customers will be very large, very complex organizations with super complex problems, which they were unable to solve so far.  If analytical apps have become a self-service proposition for business users, data integration should be as well.  So why does access to a lot of fast moving and diverse data require scarce PIG or Cassandra developers to get the data into an analyzable shape and a PhD to query and interpret patterns?

I realize new technologies start with a foundation and as they spread supply will attempt to catch up to create an equilibrium.  However, this is about a problem, which has existed for decades in many industries, such as the oil & gas, telecommunication, public and retail sector. Whenever I talk to architects and business leaders in these industries, they chuckle at “Big Data” and tell me “yes, we got that – and by the way, we have been dealing with this reality for a long time”.  By now I would have expected that the skill (cost) side of turning data into a meaningful insight would have been driven down more significantly.

Informatica has made a tremendous push in this regard with its “Map Once, Deploy Anywhere” paradigm.  I cannot wait to see what’s next – and I just saw something recently that got me very excited.  Why you ask? Because at some point I would like to have at least a business-super user pummel terabytes of transaction and interaction data into an environment (Hadoop cluster, in memory DB…) and massage it so that his self-created dashboard gets him/her where (s)he needs to go.  This should include concepts like; “where is the data I need for this insight?’, “what is missing and how do I get to that piece in the best way?”, “how do I want it to look to share it?” All that is required should be a semi-experienced knowledge of Excel and PowerPoint to get your hands on advanced Big Data analytics.  Don’t you think?  Do you believe that this role will disappear as quickly as it has surfaced?

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Posted in Big Data, Business Impact / Benefits, CIO, Customer Acquisition & Retention, Customer Services, Data Aggregation, Data Integration, Data Integration Platform, Data Quality, Data Warehousing, Enterprise Data Management, Financial Services, Healthcare, Life Sciences, Manufacturing, Master Data Management, Operational Efficiency, Profiling, Scorecarding, Telecommunications, Transportation, Uncategorized, Utilities & Energy, Vertical | Tagged , , , , | 1 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

Understand Customer Intentions To Manage The Experience

I recently had a lengthy conversation with a business executive of a European telco.  His biggest concern was to not only understand the motivations and related characteristics of consumers but to accomplish this insight much faster than before.  Given available resources and current priorities this is something unattainable for many operators.

Unlike a few years ago – remember the time before iPad – his organization today is awash with data points from millions of devices, hundreds of device types and many applications.

What will he do next?

What will he do next?

One way for him to understand consumer motivation; and therefore intentions, is to get a better view of a user’s network and all related interactions and transactions.  This includes his family household, friends and business network (also a type of household).  The purpose of householding is to capture social and commercial relationships in a grouping of individuals (or businesses or both mixed together) in order to identify patterns (context), which can be exploited to better serve a customer a new individual product or bundle upsell, to push relevant apps, audio and video content.

Let’s add another layer of complexity by understanding not only who a subscriber is, who he knows and how often he interacts with these contacts and the services he has access to via one or more devices but also where he physically is at the moment he interacts.  You may also combine this with customer service and (summarized) network performance data to understand who is high-value, high-overhead and/or high in customer experience.  Most importantly, you will also be able to assess who will do what next and why.

Some of you may be thinking “Oh gosh, the next NSA program in the making”.   Well, it may sound like it but the reality is that this data is out there today, available and interpretable if cleaned up, structured and linked and served in real time.  Not only do data quality, ETL, analytical and master data systems provide the data backbone for this reality but process-based systems dealing with the systematic real-time engagement of consumers are the tool to make it actionable.  If you add some sort of privacy rules using database or application-level masking technologies, most of us would feel more comfortable about this proposition.

This may feel like a massive project but as many things in IT life; it depends on how you scope it.  I am a big fan of incremental mastering of increasingly more attributes of certain customer segments, business units, geographies, where lessons learnt can be replicated over and over to scale.  Moreover, I am a big fan of figuring out what you are trying to achieve before even attempting to tackle it.

The beauty behind a “small” data backbone – more about “small data” in a future post – is that if a certain concept does not pan out in terms of effort or result, you have just wasted a small pile of cash instead of the $2 million for a complete throw-away.  For example: if you initially decided that the central lynch pin in your household hub & spoke is the person, who owns the most contracts with you rather than the person who pays the bills every month or who has the largest average monthly bill, moving to an alternative perspective does not impact all services, all departments and all clients.  Nevertheless, the role of each user in the network must be defined over time to achieve context, i.e. who is a contract signee, who is a payer, who is a user, who is an influencer, who is an employer, etc.

Why is this important to a business? It is because without the knowledge of who consumes, who pays for and who influences the purchase/change of a service/product, how can one create the right offers and target them to the right individual.

However, in order to make this initial call about household definition and scope or look at the options available and sensible, you have to look at social and cultural conventions, what you are trying to accomplish commercially and your current data set’s ability to achieve anything without a massive enrichment program.  A couple of years ago, at a Middle Eastern operator, it was very clear that the local patriarchal society dictated that the center of this hub and spoke model was the oldest, non-retired male in the household, as all contracts down to children of cousins would typically run under his name.  The goal was to capture extended family relationships more accurately and completely in order to create and sell new family-type bundles for greater market penetration and maximize usage given new bandwidth capacity.

As a parallel track aside from further rollout to other departments, customer segments and geos, you may also want to start thinking like another European operator I engaged a couple of years ago.  They were trying to outsource some data validation and enrichment to their subscribers, which allowed for a more accurate and timely capture of changes, often life-style changes (moves, marriages, new job).  The operator could then offer new bundles and roaming upsells. As a side effect, it also created a sense of empowerment and engagement in the client base.

I see bits and pieces of some of this being used when I switch on my home communication systems running broadband signal through my X-Box or set-top box into my TV using Netflix and Hulu and gaming.  Moreover, a US cable operator actively promotes a “moving” package to help make sure you do not miss a single minute of entertainment when relocating.

Every time now I switch on my TV, I get content suggested to me.  If telecommunication services would now be a bit more competitive in the US (an odd thing to say in every respect) and prices would come down to European levels, I would actually take advantage of the offer.  And then there is the log-on pop up asking me to subscribe (or throubleshoot) a channel I have already subscribed to.  Wonder who or what automated process switched that flag.

Ultimately, there cannot be a good customer experience without understanding customer intentions.  I would love to hear stories from other practitioners on what they have seen in such respect

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Posted in Business Impact / Benefits, Complex Event Processing, Customer Acquisition & Retention, Customer Services, Customers, Data Integration, Data Quality, Master Data Management, Profiling, Real-Time, Telecommunications, Vertical | Tagged , , , , , , , , , | Leave a comment

Where Is My Broadband Insurance Bundle?

As I continue to counsel insurers about master data, they all agree immediately that it is something they need to get their hands around fast.  If you ask participants in a workshop at any carrier; no matter if life, p&c, health or excess, they all raise their hands when I ask, “Do you have broadband bundle at home for internet, voice and TV as well as wireless voice and data?”, followed by “Would you want your company to be the insurance version of this?”

Buying insurance like broadband

Buying insurance like broadband

Now let me be clear; while communication service providers offer very sophisticated bundles, they are also still grappling with a comprehensive view of a client across all services (data, voice, text, residential, business, international, TV, mobile, etc.) each of their touch points (website, call center, local store).  They are also miles away of including any sort of meaningful network data (jitter, dropped calls, failed call setups, etc.)

Similarly, my insurance investigations typically touch most of the frontline consumer (business and personal) contact points including agencies, marketing (incl. CEM & VOC) and the service center.  On all these we typically see a significant lack of productivity given that policy, billing, payments and claims systems are service line specific, while supporting functions from developing leads and underwriting to claims adjucation often handle more than one type of claim.

This lack of performance is worsened even more by the fact that campaigns have sub-optimal campaign response and conversion rates.  As touchpoint-enabling CRM applications also suffer from a lack of complete or consistent contact preference information, interactions may violate local privacy regulations. In addition, service centers may capture leads only to log them into a black box AS400 policy system to disappear.

Here again we often hear that the fix could just happen by scrubbing data before it goes into the data warehouse.  However, the data typically does not sync back to the source systems so any interaction with a client via chat, phone or face-to-face will not have real time, accurate information to execute a flawless transaction.

On the insurance IT side we also see enormous overhead; from scrubbing every database from source via staging to the analytical reporting environment every month or quarter to one-off clean up projects for the next acquired book-of-business.  For a mid-sized, regional carrier (ca. $6B net premiums written) we find an average of $13.1 million in annual benefits from a central customer hub.  This figure results in a ROI of between 600-900% depending on requirement complexity, distribution model, IT infrastructure and service lines.  This number includes some baseline revenue improvements, productivity gains and cost avoidance as well as reduction.

On the health insurance side, my clients have complained about regional data sources contributing incomplete (often driven by local process & law) and incorrect data (name, address, etc.) to untrusted reports from membership, claims and sales data warehouses.  This makes budgeting of such items like medical advice lines staffed  by nurses, sales compensation planning and even identifying high-risk members (now driven by the Affordable Care Act) a true mission impossible, which makes the life of the pricing teams challenging.

Over in the life insurers category, whole and universal life plans now encounter a situation where high value clients first faced lower than expected yields due to the low interest rate environment on top of front-loaded fees as well as the front loading of the cost of the term component.  Now, as bonds are forecast to decrease in value in the near future, publicly traded carriers will likely be forced to sell bonds before maturity to make good on term life commitments and whole life minimum yield commitments to keep policies in force.

This means that insurers need a full profile of clients as they experience life changes like a move, loss of job, a promotion or birth.   Such changes require the proper mitigation strategy, which can be employed to protect a baseline of coverage in order to maintain or improve the premium.  This can range from splitting term from whole life to using managed investment portfolio yields to temporarily pad premium shortfalls.

Overall, without a true, timely and complete picture of a client and his/her personal and professional relationships over time and what strategies were presented, considered appealing and ultimately put in force, how will margins improve?  Surely, social media data can help here but it should be a second step after mastering what is available in-house already.  What are some of your experiences how carriers have tried to collect and use core customer data?

Disclaimer:
Recommendations and illustrations contained in this post are estimates only and are based entirely upon information provided by the prospective customer  and on our observations.  While we believe our recommendations and estimates to be sound, the degree of success achieved by the prospective customer is dependent upon a variety of factors, many of which are not under Informatica’s control and nothing in this post shall be relied upon as representative of the degree of success that may, in fact, be realized and no warrantee or representation of success, either express or implied, is made.
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Posted in B2B, Big Data, Business Impact / Benefits, Business/IT Collaboration, CIO, Customer Acquisition & Retention, Customer Services, Customers, Data Governance, Data Privacy, Data Quality, Data Warehousing, Enterprise Data Management, Governance, Risk and Compliance, Healthcare, Master Data Management, Vertical | Tagged , , , , , , , , | Leave a comment

Sensational Find – $200 Million Hidden in a Teenager’s Bedroom!

That tag line got your attention – did it not?  Last week I talked about how companies are trying to squeeze more value out of their asset data (e.g. equipment of any kind) and the systems that house it.  I also highlighted the fact that IT departments in many companies with physical asset-heavy business models have tried (and often failed) to create a consistent view of asset data in a new ERP or data warehouse application.  These environments are neither equipped to deal with all life cycle aspects of asset information, nor are they fixing the root of the data problem in the sources, i.e. where the stuff is and what it look like. It is like a teenager whose parents have spent thousands of dollars on buying him the latest garments but he always wears the same three outfits because he cannot find the other ones in the pile he hoardes under her bed.  And now they bought him a smart phone to fix it.  So before you buy him the next black designer shirt, maybe it would be good to find out how many of the same designer shirts he already has, what state they are in and where they are.

Finding the asset in your teenager's mess

Finding the asset in your teenager’s mess

Recently, I had the chance to work on a like problem with a large overseas oil & gas company and a North American utility.  Both are by definition asset heavy, very conservative in their business practices, highly regulated, very much dependent on outside market forces such as the oil price and geographically very dispersed; and thus, by default a classic system integration spaghetti dish.

My challenge was to find out where the biggest opportunities were in terms of harnessing data for financial benefit.

The initial sense in oil & gas was that most of the financial opportunity hidden in asset data was in G&G (geophysical & geological) and the least on the retail side (lubricants and gas for sale at operated gas stations).  On the utility side, the go to area for opportunity appeared to be maintenance operations.  Let’s say that I was about right with these assertions but that there were a lot more skeletons in the closet with diamond rings on their fingers than I anticipated.

After talking extensively with a number of department heads in the oil company; starting with the IT folks running half of the 400 G&G applications, the ERP instances (turns out there were 5, not 1) and the data warehouses (3), I queried the people in charge of lubricant and crude plant operations, hydrocarbon trading, finance (tax, insurance, treasury) as well as supply chain, production management, land management and HSE (health, safety, environmental).

The net-net was that the production management people said that there is no issue as they already cleaned up the ERP instance around customer and asset (well) information. The supply chain folks also indicated that they have used another vendor’s MDM application to clean up their vendor data, which funnily enough was not put back into the procurement system responsible for ordering parts.  The data warehouse/BI team was comfortable that they cleaned up any information for supply chain, production and finance reports before dimension and fact tables were populated for any data marts.

All of this was pretty much a series of denial sessions on your 12-step road to recovery as the IT folks had very little interaction with the business to get any sense of how relevant, correct, timely and useful these actions are for the end consumer of the information.  They also had to run and adjust fixes every month or quarter as source systems changed, new legislation dictated adjustments and new executive guidelines were announced.

While every department tried to run semi-automated and monthly clean up jobs with scripts and some off-the-shelve software to fix their particular situation, the corporate (holding) company and any downstream consumers had no consistency to make sensible decisions on where and how to invest without throwing another legion of bodies (by now over 100 FTEs in total) at the same problem.

So at every stage of the data flow from sources to the ERP to the operational BI and lastly the finance BI environment, people repeated the same tasks: profile, understand, move, aggregate, enrich, format and load.

Despite the departmental clean-up efforts, areas like production operations did not know with certainty (even after their clean up) how many well heads and bores they had, where they were downhole and who changed a characteristic as mundane as the well name last and why (governance, location match).

Marketing (Trading) was surprisingly open about their issues.  They could not process incoming, anchored crude shipments into inventory or assess who the counterparty they sold to was owned by and what payment terms were appropriate given the credit or concentration risk associated (reference data, hierarchy mgmt.).  As a consequence, operating cash accuracy was low despite ongoing improvements in the process and thus, incurred opportunity cost.

Operational assets like rig equipment had excess insurance coverage (location, operational data linkage) and fines paid to local governments for incorrectly filing or not renewing work visas was not returned for up to two years incurring opportunity cost (employee reference data).

A big chunk of savings was locked up in unplanned NPT (non-production time) because inconsistent, incorrect well data triggered incorrect maintenance intervals. Similarly, OEM specific DCS (drill control system) component software was lacking a central reference data store, which did not trigger alerts before components failed. If you add on top a lack of linkage of data served by thousands of sensors via well logs and Pi historians and their ever changing roll-up for operations and finance, the resulting chaos is complete.

One approach we employed around NPT improvements was to take the revenue from production figure from their 10k and combine it with the industry benchmark related to number of NPT days per 100 day of production (typically about 30% across avg depth on & offshore types).  Then you overlay it with a benchmark (if they don’t know) how many of these NPT days were due to bad data, not equipment failure or alike, and just fix a portion of that, you are getting big numbers.

When I sat back and looked at all the potential it came to more than $200 million in savings over 5 years and this before any sensor data from rig equipment, like the myriad of siloed applications running within a drill control system, are integrated and leveraged via a Hadoop cluster to influence operational decisions like drill string configuration or asmyth.

Next time I’ll share some insight into the results of my most recent utility engagement but I would love to hear from you what your experience is in these two or other similar industries.

Disclaimer:
Recommendations contained in this post are estimates only and are based entirely upon information provided by the prospective customer  and on our observations.  While we believe our recommendations and estimates to be sound, the degree of success achieved by the prospective customer is dependent upon a variety of factors, many of which are not under Informatica’s control and nothing in this post shall be relied upon as representative of the degree of success that may, in fact, be realized and no warrantee or representation of success, either express or implied, is made.
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Posted in Application Retirement, B2B, Big Data, Business Impact / Benefits, Business/IT Collaboration, CIO, Data Aggregation, Data Governance, Data Integration, Data Quality, Enterprise Data Management, Governance, Risk and Compliance, Manufacturing, Master Data Management, Mergers and Acquisitions, Operational Efficiency, Uncategorized, Utilities & Energy, Vertical | Tagged , , , , , , , | Leave a comment

Squeezing the Value out of the Old Annoying Orange

I believe that most in the software business believe that it is tough enough to calculate and hence financially justify the purchase or build of an application - especially middleware – to a business leader or even a CIO.  Most of business-centric IT initiatives involve improving processes (order, billing, service) and visualization (scorecarding, trending) for end users to be more efficient in engaging accounts.  Some of these have actually migrated to targeting improvements towards customers rather than their logical placeholders like accounts.  Similar strides have been made in the realm of other party-type (vendor, employee) as well as product data.  They also tackle analyzing larger or smaller data sets and providing a visual set of clues on how to interpret historical or predictive trends on orders, bills, usage, clicks, conversions, etc.

Squeeze that Orange

Squeeze that Orange

If you think this is a tough enough proposition in itself, imagine the challenge of quantifying the financial benefit derived from understanding where your “hardware” is physically located, how it is configured, who maintained it, when and how.  Depending on the business model you may even have to figure out who built it or owns it.  All of this has bottom-line effects on how, who and when expenses are paid and revenues get realized and recognized.  And then there is the added complication that these dimensions of hardware are often fairly dynamic as they can also change ownership and/or physical location and hence, tax treatment, insurance risk, etc.

Such hardware could be a pump, a valve, a compressor, a substation, a cell tower, a truck or components within these assets.  Over time, with new technologies and acquisitions coming about, the systems that plan for, install and maintain these assets become very departmentalized in terms of scope and specialized in terms of function.  The same application that designs an asset for department A or region B, is not the same as the one accounting for its value, which is not the same as the one reading its operational status, which is not the one scheduling maintenance, which is not the same as the one billing for any repairs or replacement.  The same folks who said the Data Warehouse is the “Golden Copy” now say the “new ERP system” is the new central source for everything.  Practitioners know that this is either naiveté or maliciousness. And then there are manual adjustments….

Moreover, to truly take squeeze value out of these assets being installed and upgraded, the massive amounts of data they generate in a myriad of formats and intervals need to be understood, moved, formatted, fixed, interpreted at the right time and stored for future use in a cost-sensitive, easy-to-access and contextual meaningful way.

I wish I could tell you one application does it all but the unsurprising reality is that it takes a concoction of multiple.  None or very few asset life cycle-supporting legacy applications will be retired as they often house data in formats commensurate with the age of the assets they were built for.  It makes little financial sense to shut down these systems in a big bang approach but rather migrate region after region and process after process to the new system.  After all, some of the assets have been in service for 50 or more years and the institutional knowledge tied to them is becoming nearly as old.  Also, it is probably easier to engage in often required manual data fixes (hopefully only outliers) bit-by-bit, especially to accommodate imminent audits.

So what do you do in the meantime until all the relevant data is in a single system to get an enterprise-level way to fix your asset tower of Babel and leverage the data volume rather than treat it like an unwanted step child?  Most companies, which operate in asset, fixed-cost heavy business models do not want to create a disruption but a steady tuning effect (squeezing the data orange), something rather unsexy in this internet day and age.  This is especially true in “older” industries where data is still considered a necessary evil, not an opportunity ready to exploit.  Fact is though; that in order to improve the bottom line, we better get going, even if it is with baby steps.

If you are aware of business models and their difficulties to leverage data, write to me.  If you even know about an annoying, peculiar or esoteric data “domain”, which does not lend itself to be easily leveraged, share your thoughts.  Next time, I will share some examples on how certain industries try to work in this environment, what they envision and how they go about getting there.

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Posted in Application Retirement, Big Data, Business Impact / Benefits, Business/IT Collaboration, CIO, Customer Acquisition & Retention, Customers, Data Governance, Data Quality, Enterprise Data Management, Governance, Risk and Compliance, Healthcare, Life Sciences, Manufacturing, Master Data Management, Mergers and Acquisitions, Operational Efficiency, Product Information Management, Profiling, Telecommunications, Transportation, Utilities & Energy, Vertical | 1 Comment

Bankers, Insurers – How Customer Centric Are You?

The need to be more customer-centric in financial services is more important than ever as banks and insurance companies look for ways to reduce churn as those in the industry know that loyal customers spend more on higher margin products and are likely to refer additional customers. Bankers and insurers who understand this, and get this right, are in a better position to maintain profitable and lasting customer loyalty and reap significant financial rewards. The current market conditions remain significant and will be difficult to overcome without the right information management architecture to help companies be truly customer centric. Here’s why:

  • Customer satisfaction with retail banks has decreased for four consecutive years, with particularly low scores in customer service.[1] Thirty-seven percent of customers who switched primary relationships cited in an industry survey showed poor customer service as the main reasons.
  • The commoditization of traditional banking and insurance products has rapidly increased client attrition and decreased acquisition rates. Industry reports estimate that banks are losing customers at an average rate of 12.5% per year, while average acquisition rates are at 13.5%, making acquisitions nearly a zero-sum game. Further, the cost of acquiring new customers is estimated at five times the rate of retaining existing ones.
  • Switching is easier than ever before. Customer churn is at an all-time high in most European countries. According to an industry survey, 42 percent of German banking customers had been with their main bank for less than a year. As customer acquisition costs running between of €200 to €400, bankers and insurers need to keep their clients at least 5 to 7 years to simply break even.
  • Mergers and acquisitions impact even further the complexity and risks of maintaining customer relationships. According to a recent study, 17 percent of respondents who had gone through a merger or acquisition had switched at least one of their accounts to another institution after their bank was acquired, while an additional 31 percent said they were at least somewhat likely to switch over the next year.[2]

Financial services professionals have long recognized the need to manage customer relationships vs. account relationships by shifting away from a product-centric culture toward a customer-centric model to maintain client loyalty and grow their bottom lines organically. Here are some reasons why:

  • A 5% increase in customer retention can increase profitability by 35% in banking, 50% in brokerage, and 125% in the consumer credit card market.[3]
  • Banks can add more than $1 million to the profitability of their commercial banking business line by simply extending 16 of these large corporate relationships by one year, or by saving two such clients from defecting. In the insurance sector, a one percent increase in customer retention results in $1M in revenue.
  • The average company has between a 60% and 70% probability of success selling more services to a current customer, a 20% to 40% probability of selling to a former customer, and a 5% to 20% probability of making a sale to a prospect.[4]
  • Up to 66% of current users of financial institutions’ social media sites engage in receiving information about financial services, 32% use it to retrieve information about offers or promotions and 30% to conduct customer service related activities.[5]

So what does it take to become more Customer-centric?

Companies who have successful customer centric business models share similar cultures of placing the customer first, people who are willing to go that extra mile, business processes designed with the customer’s needs in mind, product and marketing strategy that is designed to meet a customer’s needs, and technology solutions that helps access and deliver trusted, timely, and comprehensive information and intelligence across the business. These technologies include

Why is data integration important? Customer centricity begins with the ability to access and integrate your data regardless of format, source system, structure, volume, latency, from any location including the cloud and social media sites. The data business needs originates from many different systems across the organization and outside including new Software as a Service solutions and cloud based technologies. Traditional hand coded methods and one off tools and open source data integration tools are not able to scale and perform to effectively and efficiently access, manage, and deliver the right data to the systems and applications in the front lined. A the same time, we live in the Big Data era with increasing transaction volumes, new channel adoption including mobile devices and social media combined generating petabytes of data of which to support a capable and sustainable customer centric business model, requires technology that can handle this complexity, scale with the business, while reducing costs and improving productivity.

Data quality issues must be dealt with proactively and managed by both business and technology stakeholders.  Though technology itself cannot prevent all data quality errors from happening, it is a critical part of your customer information management process to ensure any issues that exist are identified and dealt with in an expeditious manner. Specifically, a Data Quality solution that can help detect data quality errors in any source, allow business users to define data quality rules, support seamless consumption of those rules by developers to execute, dashboards and reports for business stakeholders, and ongoing quality monitoring to deal with time and business sensitive exceptions. Data quality management can only scale and deliver value if an organization believes and manages data as an asset. It also helps to have a data governance framework consisting of processes, policies, standards, and people from business and IT working together in the process.

Lastly, growing your business, improving wallet share, retaining profitable relationships, and lowering the cost of managing customer relationships requires a single, trusted, holistic, and authoritative source of customer information.  Managing customer information has historically been in applications across traditional business silos that lacked any common processes to reconcile duplicate and conflicting information across business systems.  Master Data Management solutions are purposely designed to help breakdown the traditional application and business silos and helps deliver that single view of the truth for all systems to benefit.  Master Data Management allows banks and insurance companies to access, identity unique customer entities, relate accounts to each customer, and extend that relationship view across other customers and employees including relationship bankers, financial advisors, to existing agents and brokers.

The need to attract and retain customers is a continuous journey for the financial industry however that need is greater than ever before. The foundation for successful customer centricity requires technology that can help access and deliver trusted, timely, consistent, and comprehensive customer information and insight across all channels and avoid the mistakes of the past, allow you to stay ahead of your competition, and maximize value for your shareholders.

[1] 2010 UK Retail Banking Satisfaction Study, J.D. Power and Associates, October 2010.

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Posted in Customer Acquisition & Retention, Data Governance, Data Integration, Data Quality, Financial Services, Master Data Management, Vertical | Tagged , , , , , , , | Leave a comment

Meaningful Use, Data Quality and Data Access – Three Sides of the Same Coin

I had the privilege to be invited to testify to the Health I.T. Policy Committee workgroup on the topic of data quality back in November. I’ve been an advocate for the work of the committee for years and am constantly impressed with the considerable insight and genuine passion they bring to their work. The opportunity to testify, however, was my first opportunity to actually participate in the policy-making process and it certainly was both a learning opportunity for me, as well as a chance to share my thoughts on the important topic of data quality. (more…)

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Posted in Data Quality, Healthcare, Vertical | Tagged , , , , , , , , | 3 Comments

MDM for Improved Customer Service

Dennis Moore, senior vice president and general manager, Master Data Management, Informatica, discusses how MDM can help companies improve customer service.

During this video, Dennis answers the following questions:

  • What did you discuss at the 2012 European MDM product advisory council?
  • With the holiday season upon us, how can companies use MDM to best serve their customers?
  • What have other industries learned from Retail? How can they apply what you have shared with us today?

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Posted in Master Data Management, Vertical | Tagged , , | Leave a comment