Tag Archives: Data Management
A mid-sized insurer recently approached our team for help. They wanted to understand how they fell short in making their case to their executives. Specifically, they proposed that fixing their customer data was key to supporting the executive team’s highly aggressive 3-year growth plan. (This plan was 3x today’s revenue). Given this core organizational mission – aside from being a warm and fuzzy place to work supporting its local community – the slam dunk solution to help here is simple. Just reducing the data migration effort around the next acquisition or avoiding the ritual annual, one-off data clean-up project already pays for any tool set enhancing data acquisitions, integration and hygiene. Will it get you to 3x today’s revenue? It probably won’t. What will help are the following:
Hard cost avoidance via software maintenance or consulting elimination is the easy part of the exercise. That is why CFOs love it and focus so much on it. It is easy to grasp and immediate (aka next quarter).
Soft cost reduction, like staff redundancies are a bit harder. Despite them being viable, in my experience very few decision makers want work on a business case to lay off staff. My team had one so far. They look at these savings as freed up capacity, which can be re-deployed more productively. Productivity is also a bit harder to quantify as you typically have to understand how data travels and gets worked on between departments.
However, revenue effects are even harder and esoteric to many people as they include projections. They are often considered “soft” benefits, although they outweigh the other areas by 2-3 times in terms of impact. Ultimately, every organization runs their strategy based on projections (see the insurer in my first paragraph).
The hardest to quantify is risk. Not only is it based on projections – often from a third party (Moody’s, TransUnion, etc.) – but few people understand it. More often, clients don’t even accept you investigating this area if you don’t have an advanced degree in insurance math. Nevertheless, risk can generate extra “soft” cost avoidance (beefing up reserve account balance creating opportunity cost) but also revenue (realizing a risk premium previously ignored). Often risk profiles change due to relationships, which can be links to new “horizontal” information (transactional attributes) or vertical (hierarchical) from parent-child relationships of an entity and the parent’s or children’s transactions.
Given the above, my initial advice to the insurer would be to look at the heartache of their last acquisition, use a benchmark for IT productivity from improved data management capabilities (typically 20-26% – Yankee Group) and there you go. This is just the IT side so consider increasing the upper range by 1.4x (Harvard Business School) as every attribute change (last mobile view date) requires additional meetings on a manager, director and VP level. These people’s time gets increasingly more expensive. You could also use Aberdeen’s benchmark of 13hrs per average master data attribute fix instead.
You can also look at productivity areas, which are typically overly measured. Let’s assume a call center rep spends 20% of the average call time of 12 minutes (depending on the call type – account or bill inquiry, dispute, etc.) understanding
- Who the customer is
- What he bought online and in-store
- If he tried to resolve his issue on the website or store
- How he uses equipment
- What he cares about
- If he prefers call backs, SMS or email confirmations
- His response rate to offers
- His/her value to the company
If he spends these 20% of every call stringing together insights from five applications and twelve screens instead of one frame in seconds, which is the same information in every application he touches, you just freed up 20% worth of his hourly compensation.
Then look at the software, hardware, maintenance and ongoing management of the likely customer record sources (pick the worst and best quality one based on your current understanding), which will end up in a centrally governed instance. Per DAMA, every duplicate record will cost you between $0.45 (party) and $0.85 (product) per transaction (edit touch). At the very least each record will be touched once a year (likely 3-5 times), so multiply your duplicated record count by that and you have your savings from just de-duplication. You can also use Aberdeen’s benchmark of 71 serious errors per 1,000 records, meaning the chance of transactional failure and required effort (% of one or more FTE’s daily workday) to fix is high. If this does not work for you, run a data profile with one of the many tools out there.
If standardization of records (zip codes, billing codes, currency, etc.) is the problem, ask your business partner how many customer contacts (calls, mailing, emails, orders, invoices or account statements) fail outright and/or require validation because of these attributes. Once again, if you apply the productivity gains mentioned earlier, there are you savings. If you look at the number of orders that get delayed in form of payment or revenue recognition and the average order amount by a week or a month, you were just able to quantify how much profit (multiply by operating margin) you would be able to pull into the current financial year from the next one.
The same is true for speeding up the introduction or a new product or a change to it generating profits earlier. Note that looking at the time value of funds realized earlier is too small in most instances especially in the current interest environment.
If emails bounce back or snail mail gets returned (no such address, no such name at this address, no such domain, no such user at this domain), e(mail) verification tools can help reduce the bounces. If every mail piece (forget email due to the miniscule cost) costs $1.25 – and this will vary by type of mailing (catalog, promotion post card, statement letter), incorrect or incomplete records are wasted cost. If you can, use fully loaded print cost incl. 3rd party data prep and returns handling. You will never capture all cost inputs but take a conservative stab.
If it was an offer, reduced bounces should also improve your response rate (also true for email now). Prospect mail response rates are typically around 1.2% (Direct Marketing Association), whereas phone response rates are around 8.2%. If you know that your current response rate is half that (for argument sake) and you send out 100,000 emails of which 1.3% (Silverpop) have customer data issues, then fixing 81-93% of them (our experience) will drop the bounce rate to under 0.3% meaning more emails will arrive/be relevant. This in turn multiplied by a standard conversion rate (MarketingSherpa) of 3% (industry and channel specific) and average order (your data) multiplied by operating margin gets you a benefit value for revenue.
If product data and inventory carrying cost or supplier spend are your issue, find out how many supplier shipments you receive every month, the average cost of a part (or cost range), apply the Aberdeen master data failure rate (71 in 1,000) to use cases around lack of or incorrect supersession or alternate part data, to assess the value of a single shipment’s overspend. You can also just use the ending inventory amount from the 10-k report and apply 3-10% improvement (Aberdeen) in a top-down approach. Alternatively, apply 3.2-4.9% to your annual supplier spend (KPMG).
You could also investigate the expediting or return cost of shipments in a period due to incorrectly aggregated customer forecasts, wrong or incomplete product information or wrong shipment instructions in a product or location profile. Apply Aberdeen’s 5% improvement rate and there you go.
Consider that a North American utility told us that just fixing their 200 Tier1 suppliers’ product information achieved an increase in discounts from $14 to $120 million. They also found that fixing one basic out of sixty attributes in one part category saves them over $200,000 annually.
So what ROI percentages would you find tolerable or justifiable for, say an EDW project, a CRM project, a new claims system, etc.? What would the annual savings or new revenue be that you were comfortable with? What was the craziest improvement you have seen coming to fruition, which nobody expected?
Next time, I will add some more “use cases” to the list and look at some philosophical implications of averages.
As adjunct university faculty, I get to talk to students about how business strategy increasingly depends upon understanding how to leverage information. To make discussion more concrete, I share with students the work of Alvin Toffler. In The Third Wave, Toffler asserts that we live in a world where competition will increasingly take place upon the currency and usability of information.
In a recent interview, Toffler said that “given the acceleration of change; companies, individuals, and governments base many of their daily decisions on obsoledge—knowledge whose shelf life has expired.” He continues by stating that “companies everywhere are trying to put a price on certain forms of intellectual property. But if…knowledge is at the core of the money economy, than we need to understand knowledge much better than we do now. And tiny insights can yield huge outputs”.
Driving better information management in the information age
To me, this drives to three salient conclusions for information age businesses:
- Information needs to drive further down organizations because top decision makers do not have the background to respond at the pace of change.
- Information needs to be available faster which means that we need to reducing the processing time for structure and unstructured information sources.
- Information needs to be available when the organization is ready for it. For multinational enterprises this means “Always On” 24/7 across multiple time zones on any device.
Effective managers today are effective managers of people and information
Effective managers today are effective managers of information. Because processing may take too much time, Toffler’s remarks suggest to me we need to consider human information—the ideas and communications we share every day—within the mix of getting access to the right information when it is needed and where it is needed. Now more than ever is the time for enterprises to ensure their decision makers have the timely information to make better business decisions when they are relevant. This means that unstructured data, a non-trivial majority of business information, needs to be made available to business users and related to existing structured sources of data.
Derick Abell says that “for (management) control to be effective, data must be timely and provided at interval that allows effective intervention”. Today this is a problem for most information businesses. As I see it, information optimization is the basis of powering the enterprise through “Third Wave” business competition. Organizations that have the “right to win” will have as a core capability better-than-class access to current information for decision makers.
Putting in place a winning information management strategy
If you talk to CIOs today, they will tell you that they are currently facing 4 major information age challenges.
- Mobility—Enabling their users to view data anytime, anyplace, and any device
- Information Trust—Making data dependable enough for business decisions as well as governing data across all business systems.
- Competing on Analytics—Getting information to business users fast enough to avoid Toffler’s Obsoledge.
- New and Big Data Sources—Connecting existing data to new value added sources of data.
Some information age
Lots of things, however, get in the way of delivering on the promises of the Information Age. Our current data architecture is siloed, fragile, and built upon layer after layer of spaghetti code integrations. Think about what is involved just to cobble together data on a company’s supply chain. A morass of structured data systems have vendor and transaction records locked up in application databases and data warehouses all over the extended enterprise. So it is not amazing that enterprises struggle to put together current, relevant data to run their businesses upon. Functions like finance depend largely upon manual extracts being massaged and integrated in spreadsheets because of concern over the quality of data being provided by financial systems. Some information age!
How do we connect to new sources of data?
At the same time, many are trying today to extend the information architecture to add social media data, mobile location data, and even machine data. Much of this data is not put together in the same way as data in an application database or data warehouse. However, being able to relate this data to existing data sources can yield significant benefits. Think about the potential benefit of being able to relate social interactions and mobile location data to sales data or to relate machine data to compliance data.
A big problem is many of these new data types potentially have even more data quality gaps than historical structured data systems. Often the signal to noise for this data can be very low for this reason. But this data can be invaluable to business decision making. For this reason, this data needs to be cleaned up and related to older data sources. Finally, it needs to be provided to business users in whatever manner they want to consume it.
How then do we fix the Information Age?
Enabling the kind of Information Age that Toffler imagined requires two things. Enterprises fix their data management and enable the information intelligence needed to drive real business competitive advantage. Fixing data management involves delivering good data that business users can safely make decisions from. It, also, involves ensuring that data once created is protected. CFOs that we have talked to say Target was a watershed event for them—something that they expect will receive more and more auditing attention.
We need at the same time to build the connection between old data sources and new data sources. And this needs to not take as long as in the past to connect data. Delivery needs to happen faster so business problems can be recognized and solved more quickly. Users need to get access to data when and where they need it.
With data management fixed, data intelligence needs to provide business users the ability to make sense out of things they find in the data. Business users need as well to be able to search and find data. They, also, need self-service so they can combine existing and new unstructured data sources to test data interrelationship hypothesis. This means the ability to assemble data and put it together and do it from different sources at different times. Simply put this is about data orchestration without any preconceived process. And lastly, business users need the intelligence to automatically sense and respond to changes as new data is collecting.
Tiny insights can yield huge outputs
Obviously, there is a cost to solving our information age issues, but it is important to remember what Toffler says. “Tiny insights can yield huge outputs”. In other words, the payoff is huge for shaking off the shackles of our early information age business architecture. And those that do this will increasingly have the “right to win” against their competitors as they use information to wring every last drop of value from their business processes.
Solution Brief: The Intelligent Data Platform
A few days ago, I came across a post, 5 C’s of MDM (Case, Content, Connecting, Cleansing, and Controlling), by Peter Krensky, Sr. Research Associate, Aberdeen Group and this response by Alan Duncan with his 5 C’s (Communicate, Co-operate, Collaborate, Cajole and Coerce). I like Alan’s list much better. Even though I work for a product company specializing in information management technology, the secret to successful enterprise information management (EIM) is in tackling the business and organizational issues, not the technology challenges. Fundamentally, data management at the enterprise level is an agreement problem, not a technology problem.
So, here I go with my 5 C’s: (more…)
I recently wrapped up two overseas trips; one to Central America and another to South Africa. As such, I had the opportunity to meet with a national bank and a regional retailer. It prompted me to ask the question: Does location matter in emerging markets?
I wish I could tell you that there was a common theme on how firms in the same sector or country (even city) treat data on a philosophical or operational level but I cannot. It is such a unique experience every time as factors like ownership history, regulatory scrutiny, available/affordable skill set and past as well as current financial success create a unique grey pattern rather than a comfortable black and white separation. This is even more obvious when I mix in recent meetings I had with North American organizations in the same sectors.
Banking in Latin vs North America
While a national bank in Latin America may seem lethargic, unimaginative and unpolished at first, you can feel the excitement when they can conceive, touch and play with the potential of new paradigms, like becoming data-driven. Decades of public ownership did not seem to have stifled their willingness to learn and improve. On the other side, there is a stock market-listed, regional US bank and half the organization appears to believe in meddling along without expert IT knowledge, which reduced adoption and financial success in past projects. Back office leadership also firmly believes in “relationship management” over data-driven “value management”.
To quote a leader in their finance department, “we don’t believe that knowing a few more characteristics about a client creates more profit….the account rep already knows everything about them and what they have and need”. Then he said, “Not sure why the other departments told you there are issues. We have all this information but it may not be rolled out to them yet or they have no license to view it to date.” This reminded me of the “All Quiet on the Western Front” mentality. If it is all good over here, why are most people saying it is not? Granted; one more attribute may not tip the scale to higher profits but a few more and their historical interrelationship typically does.
As an example; think about the correlation of average account balance fluctuations, property sale, bill pay account payee set ups, credit card late charges and call center interactions over the course of a year.
The Latin American bankers just said, “We have no idea what we know and don’t know…but we know that even long standing relationships with corporate clients are lacking upsell execution”. In this case, upsell potential centered on wire transfer SWIFT message transformation to their local standard they report of and back. Understanding the SWIFT message parameters in full creates an opportunity to approach originating entities and cutting out the middleman bank.
Retailing in Africa vs Europe
The African retailer’s IT architects indicated that customer information is centralized and complete and that integration is not an issue as they have done it forever. Also, consumer householding information is not a viable concept due to different regional interpretations, vendor information is brand specific and therefore not centrally managed and event based actions are easily handled in BizTalk. Home delivery and pickup is in its infancy.
The only apparent improvement area is product information enrichment for an omnichannel strategy. This would involve enhancing attribution for merchandise demand planning, inventory and logistics management and marketing. Attributes could include not only full and standardized capture of style, packaging, shipping instructions, logical groupings, WIP vs finished goods identifiers, units of measure, images and lead times but also regional cultural and climate implications.
However, data-driven retailers are increasingly becoming service and logistics companies to improve wallet share, even in emerging markets. Look at the successful Russian eTailer Ozon, which is handling 3rd party merchandise for shipping and cash management via a combination of agency-style mom & pop shops and online capabilities. Having good products at the lowest price alone is not cutting it anymore and it has not for a while. Only luxury chains may be able to avoid this realization for now. Store size and location come at a premium these days. Hypermarkets are ill-equipped to deal with high-profit specialty items. Commercial real estate vacancies on British high streets are at a high (Economist, July 13, 2014) and footfall is at a seven-year low. The Centre for Retail Research predicts that 20% of store locations will close over the next five years.
If specialized, high-end products are the most profitable, I can (test) sell most of them online or at least through fewer, smaller stores saving on carrying cost. If my customers can then pick them up and return them however they want (store, home) and I can reduce returns from normally 30% (per the Economist) to fewer than 10% by educating and servicing them as unbureaucratically as possible, I just won the semifinals. If I can then personalize recommendations based on my customers’ preferences, life style events, relationships, real-time location and reward them in a meaningful way, I just won the cup.
Emerging markets may seem a few years behind but companies like Amazon or Ozon have shown that first movers enjoy tremendous long-term advantages.
So what does this mean for IT? Putting your apps into the cloud (maybe even outside your country) may seem like an easy fix. However, it may not only create performance and legal issues but also unexpected cost to support decent SLA terms. Does your data support transactions for higher profits today to absorb this additional cost of going into the cloud? Focus on transactional applications and their management obfuscates the need for a strong backbone for data management, just like the one you built for your messaging and workflows ten years ago. Then you can tether all the fancy apps to it you want.
Have any emerging markets’ war stories or trends to share? I would love to hear them. Stay tuned for future editions of this series.
Financial services is one of the most data-centric industries in the world. Clean, connected, and secure data is critical to satisfy regulatory requirements, improve customer experience, grow revenue, avoid fines, and ultimately change the world of banking and insurance. Data management improvements have been made and several of the leading companies are empowered by Informatica.
Who are these companies and what are they doing with Informatica?
Fifteen of the top financial services companies will share their stories and success leveraging Informatica for their most critical business needs. These include:
- Capital One
- Bank of New Zealand
- Fannie Mae
- Fidelity Investments
- Morgan Stanley
- Thomson Reuters
- YAPI KREDI BANKASI A.S.
- Navy Federal Credit Union
- Wells Fargo Bank
- Westpac Banking Corporation
- Great American Insurance Group, Property & Casualty Group
- Liberty Mutual
Informatica World 2014 will have over 100 breakout sessions covering a wide range of topics for Line of Business Executives, IT decision makers, Architects, Developers, and Data Administrators. Our great keynote line up includes Informatica executives Sohaib Abbasi (Chief Executive Officer), Ivan Chong (Chief Strategy Officer), Marge Breya (Chief Marketing Officer) and Anil Chakravarthy (Chief Product Officer). Our series of speakers will share Informatica’s vision for this new data-centric world and explain innovations that will propel the concept of a data platform to an entirely new level.
Register today so you don’t miss out.
We look forward to seeing you in May!
“If I had my way, I’d fire the statisticians – all of them – they don’t add value”.
Surely not? Why would you fire the very people who were employed to make sense of the vast volumes of manufacturing data and guide future production? But he was right. The problem was at that time data management was so poor that data was simply not available for the statisticians to analyze.
So, perhaps this title should be re-written to be:
Fire your Data Scientists – They Aren’t Able to Add Value.
Although this statement is a bit extreme, the same situation may still exist. Data scientists frequently share frustrations such as:
- “I’m told our data is 60% accurate, which means I can’t trust any of it.”
- “We achieved our goal of an answer within a week by working 24 hours a day.”
- “Each quarter we manually prepare 300 slides to anticipate all questions the CFO may ask.”
- “Fred manually audits 10% of the invoices. When he is on holiday, we just don’t do the audit.”
This is why I think the original quote is so insightful. Value from data is not automatically delivered by hiring a statistician, analyst or data scientist. Even with the latest data mining technology, one person cannot positively influence a business without the proper data to support them.
Most organizations are unfamiliar with the structure required to deliver value from their data. New storage technologies will be introduced and a variety of analytics tools will be tried and tested. This change is crucial for to success. In order for statisticians to add value to a company, they must have access to high quality data that is easily sourced and integrated. That data must be available through the latest analytics technology. This new ecosystem should provide insights that can play a role in future production. Staff will need to be trained, as this new data will be incorporated into daily decision making.
With a rich 20-year history, Informatica understands data ecosystems. Employees become wasted investments when they do not have access to the trusted data they need in order to deliver their true value.
Who wants to spend their time recreating data sets to find a nugget of value only to discover it can’t be implemented?
Build a analytical ecosystem with a balanced focus on all aspects of data management. This will mean that value delivery is limited only by the imagination of your employees. Rather than questioning the value of an analytics team, you will attract some of the best and the brightest. Then, you will finally be able to deliver on the promised value of your data.
One thing we all have in common in this modern world, is that we have all, at some point in our lives, been on the receiving end of poor customer service.
Don’t get me wrong, a career in customer service is not an easy one, and I’m sure there are many service providers out there who have been wrongly on the receiving end of an angry customer, for reasons out of the businesses hands, that’s another topic in itself. It is hard, however, to ignore that one thing companies often fail on heavily is providing a timely, easy to access and appropriate level of service for their customers. (more…)
Some interesting news hit UK headlines last year that companies could be made to give the public greater access to their personal transaction data in an electronic, portable and machine-readable format. That’s if the midata project has anything to do with it.
Launched in April 2011 midata is part of the UK Government’s consumer empowerment strategy, Better Choices: Better Deals. Essentially, it’s a partnership between government, consumer groups and major businesses. Its aim is to give consumers access to the data that they produce, from the likes of household utilities, and banking, to internet transactions and high street loyalty cards. (more…)
Thomas Davenport, visiting professor at Harvard University and author of the watershed book Competing on Analytics, is once again making waves across the datasphere with his proclamation of data scientist as the “sexiest job of the 21st century.”
To many readers here at the Perspectives site, of course, this is not news, as many data professionals have increasingly been recognizing – and are being recognized – for the increasing power of information in driving new insights and business opportunities. (more…)