Category Archives: Data Quality

CFO Move to Chief Profitability Officer

30% or higher of each company’s businesses are unprofitable

cfoAccording to Jonathan Brynes at the MIT Sloan School, “the most important issue facing most managers …is making more money from their existing businesses without costly new initiatives”. In Brynes’ cross industry research, he found that 30% or higher of each company’s businesses are unprofitable. Brynes claims these business losses are offset by what are “islands of high profitability”. The root cause of this issue is asserted to be the inability of current financial and management control systems to surface profitability problems and opportunities. Why is this the case? Byrnes believes that management budgetary guidance by its very nature assumes the continuation of the status quo. For this reason, the response to management asking for a revenue increase is to increase revenues for businesses that are profitable and unprofitable. Given this, “the areas of embedded unprofitability remain embedded and largely invisible”. At the same time to be completely fair, it should be recognized that it takes significant labor to accurately and completely put together a complete picture on direct and indirect costs.

The CFO needs to become the point person on profitability issues

cfo

Byrnes believes, nevertheless, that CFOs need to become the corporate point person for surfacing profitability issues. They, in fact, should act as the leader of a new and important role, the chief profitability officer. This may seem like an odd suggestion since virtually every CFO if asked would view profitability as a core element of their job. But Byrnes believes that CFOs need to move beyond broad, departmental performance measures and build profitability management processes into their companies’ core management activities. This task requires the CFO to determine two things.

  1. Which product lines, customers, segments, and channels are unprofitable so investments can be reduced or even eliminated?
  2. Which product lines, customers, segments, and channels are the most profitable so management can determine whether to expand investments and supporting operations?

Why didn’t portfolio management solve this problem?

cfoNow as a strategy MBA, Byrnes’ suggestion leave me wondering why the analysis proposed by strategy consultants like Boston Consulting Group didn’t solve this problem a long time ago. After all portfolio analysis has at its core the notion that relative market share and growth rate will determine profitability and which businesses a firm should build share, hold share, harvest share, or divest share—i.e. reduce, eliminate, or expand investment. The truth is getting at these figures, especially profitability, is a time consuming effort.

KPMG finds 91% of CFOs are held back by financial and performance systems

KPMG

As financial and business systems have become more complex, it has become harder and harder to holistically analyze customer and product profitability because the relevant data is spread over a myriad of systems, technologies, and locations. For this reason, 91% of CFO respondents in a recent KPMG survey said that they want to improve the quality of their financial and performance insight from the data they produce. An amazing 51% of these CFOs, also, admitted that the “collection, storage, and retrieval financial and performance data at their company is primarily a manual and/or spreadsheet-based exercise”. Think about it — a majority of these CFOs teams time is spent collecting financial data rather than actively managing corporate profitability.

How do we fix things?

FixWhat is needed is a solution that allows financial teams to proactively produce trustworthy financial data from each and every financial system and then reliably combine and aggregate the data coming from multiple financial systems. Having accomplished this, the solution needs to allow financial organizations to slice and dice net profitability for product lines and customers.

This approach would not only allow financial organizations to cut their financial operational costs but more importantly drive better business profitability by surfacing profitability gaps. At the same time, it would enable financial organizations to assist business units in making more informed customer and product line investment decisions. If a product line or business is narrowly profitable and lacks a broader strategic context or ability to increase profitability by growing market share, it is a candidate for investment reduction or elimination.

Strategic CFOs need to start asking questions of their business counterparts starting with their justification for their investment strategy. Key to doing this involves consolidating reliable profitability data across customers, products, channel partners, suppliers. This would eliminate the time spent searching for and manually reconciling data in different formats across multiple systems. It should deliver ready analysis across locations, applications, channels, and departments.

Some parting thoughts

Strategic CFOs tell us they are trying to seize the opportunity “to be a business person versus a bean counting historically oriented CPA”. I believe a key element of this is seizing the opportunity to become the firm’s chief profitability officer. To do this well, CFOs need dependable data that can be sliced and diced by business dimensions. Armed with this information, CFOs can determine the most and least profitability, businesses, product lines, and customers. As well, they can come to the business table with the perspective to help guide their company’s success.

Related links
Solution Brief: The Intelligent Data Platform
Related Blogs
CFOs Discuss Their Technology Priorities
The CFO Viewpoint upon Data
How CFOs can change the conversation with their CIO?
New type of CFO represents a potent CIO ally
Competing on Analytics
The Business Case for Better Data Connectivity

Twitter: @MylesSuer

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Posted in Business Impact / Benefits, Business/IT Collaboration, CIO, Data Governance, Data Quality | Tagged , , , , , | Leave a comment

Once Again, Data Integration Proves Critical to Data Analytics

When it comes to cloud-based data analytics, a recent study by Ventana Research (as found in Loraine Lawson’s recent blog post) provides a few interesting data points.  The study reveals that 40 percent of respondents cited lowered costs as a top benefit, improved efficiency was a close second at 39 percent, and better communication and knowledge sharing also ranked highly at 34 percent.

Ventana Research also found that organizations cite a unique and more complex reason to avoid cloud analytics and BI.  Legacy integration work can be a major hindrance, particularly when BI tools are already integrated with other applications.  In other words, it’s the same old story:

You can’t make sense of data that you can’t see.

Data Integration Proves Critical to Data Analytics

Data Integration is Critical to Data Analytics

The ability to deal with existing legacy systems when moving to concepts such as big data or cloud-based analytics is critical to the success of any enterprise data analytics strategy.  However, most enterprises don’t focus on data integration as much as they should, and hope that they can solve the problems using ad-hoc approaches.

These approaches rarely work as well a they should, if at all.  Thus, any investment made in data analytics technology is often diminished because the BI tools or applications that leverage analytics can’t see all of the relevant data.  As a result, only part of the story is told by the available data, and those who leverage data analytics don’t rely on the information, and that means failure.

What’s frustrating to me about this issue is that the problem is easily solved.  Those in the enterprise charged with standing up data analytics should put a plan in place to integrate new and legacy systems.  As part of that plan, there should be a common understanding around business concepts/entities of a customer, sale, inventory, etc., and all of the data related to these concepts/entities must be visible to the data analytics engines and tools.  This requires a data integration strategy, and technology.

As enterprises embark on a new day of more advanced and valuable data analytics technology, largely built upon the cloud and big data, the data integration strategy should be systemic.  This means mapping a path for the data from the source legacy systems, to the views that the data analytics systems should include.  What’s more, this data should be in real operational time because data analytics loses value as the data becomes older and out-of-date.  We operate a in a real-time world now.

So, the work ahead requires planning to occur at both the conceptual and physical levels to define how data analytics will work for your enterprise.  This includes what you need to see, when you need to see it, and then mapping a path for the data back to the business-critical and, typically, legacy systems.  Data integration should be first and foremost when planning the strategy, technology, and deployments.

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8 Information Management Challenges for UDI Compliance

frustrated_man_computer

Is your compliance team manually reconciling data for UDI compliance reports?

“My team spends far too much time pulling together medical device data that’s scattered across different systems and reconciling it in spreadsheets to create compliance reports.” This quotation from a regulatory affairs leader at a medical device manufacturer highlights the impact of poorly managed medical device data on compliance reporting, such as the reports needed for the FDA’s Universal Device Identification (UDI) regulation. In fact, an overreliance on manual, time-consuming processes brings an increased risk of human error in UDI compliance reports.

If you are an information management leader working for a medical device manufacturer, and your compliance team needs quick and easy access to medical device data for UDI compliance reporting, I have five questions for you:

1) How many Class III and Class II devices do you have?
2) How many systems or reporting data stores contain data about these medical devices?
3) How much time do employees spend manually fixing data errors before the data can be used for reporting?
4) How do you plan to manage medical device data so the compliance team can quickly and easily produce accurate reports for UDI Compliance?
5) How do you plan to help the compliance team manage the multi-step submission process?

Watch this on-demand webinar "3 EIM Best Practices for UDI Compliance"

Watch this on-demand webinar “3 EIM Best Practices for UDI Compliance”

For some helpful advice from data management experts, watch this on-demand webinar “3 Enterprise Information Management (EIM) Best Practices for UDI Compliance.”

The deadline to submit the first UDI compliance report to the FDA for Class III devices is September 24, 2014. But, the medical device data needed to produce the report is typically scattered among different internal systems, such as Enterprise Resource Planning (ERP) e.g. SAP and JD Edwards, Product Lifecycle Management (PLM), Manufacturing Execution Systems (MES) and external 3rd party device identifiers.

The traditional approach for dealing with poorly managed data is the compliance team burns the midnight oil to bring together and then manually reconcile all the medical device data in a spreadsheet. And, they have to do this each and every time a compliance report is due. The good news is your compliance team doesn’t have to.

Many medical device manufacturers are are leveraging their existing data governance programs, supported by a combination of data integration, data quality and master data management (MDM) technology to eliminate the need for manual data reconciliation. They are centralizing their medical device data management, so they have a single source of trusted medical device data for UDI compliance reporting as well as other compliance and revenue generating initiatives.

Get UDI data management advice from data experts Kelle O'Neal, Managing Partner at First San Francisco Partners and Bryan Balding, MDM Specialist at Informatica
Get UDI data management advice from data experts Kelle O’Neal, Managing Partner at First San Francisco Partners and Bryan Balding, MDM Specialist at Informatica

During this this on-demand webinar, Kelle O’Neal, Managing Partner at First San Francisco Partners, covers the eight information management challenges for UDI compliance as well as best practices for medical device data management.

Bryan Balding, MDM Solution Specialist at Informatica, shows you how to apply these best practices with the Informatica UDI Compliance Solution.

You’ll learn how to automate the process of capturing, managing and sharing medical device data to make it quicker and easier to create the reports needed for UDI compliance on ongoing basis.

 

 

20 Questions & Answers about Complying with the FDA Requirement for Unique Device Identification (UDI)

20 Questions & Answers about Complying with the FDA Requirement
for Unique Device Identification (UDI)

Also, we just published a joint whitepaper with First San Francisco Partners, Information Management FAQ for UDI: 20 Questions & Answers about Complying with the FDA Requirement for Unique Device Identification (UDI). Get answers to questions such as:

What is needed to support an EIM strategy for UDI compliance?
What role does data governance play in UDI compliance?
What are the components of a successful data governance program?
Why should I centralize my business-critical medical device data?
What does the architecture of a UDI compliance solution look like?

I invite you to download the UDI compliance FAQ now and share your feedback in the comments section below.

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Posted in Data Governance, Data Integration, Data Quality, Enterprise Data Management, Life Sciences, Manufacturing, Master Data Management, Vertical | Tagged , , , , , , , , , , , , , , | Leave a comment

Reflections Of A Former Data Analyst (Part 2) – Changing The Game For Data Plumbing

 

Elephant cleansing

Cleaning. Sometimes is challenging!

In my last blog I promised I would report back my experience on using Informatica Data Quality, a software tool that helps automate the hectic, tedious data plumbing task, a task that routinely consumes more than 80% of the analyst time. Today, I am happy to share what I’ve learned in the past couple of months.

But first, let me confess something. The reason it took me so long to get here was that I was dreaded by trying the software.  Never a savvy computer programmer, I was convinced that I would not be technical enough to master the tool and it would turn into a lengthy learning experience. The mental barrier dragged me down for a couple of months and I finally bit the bullet and got my hands on the software. I am happy to report that my fear  was truly unnecessary –  It took me one half day to get a good handle on most features in the Analyst Tool, a component  of the Data Quality designed for analyst and business users,   then I spent 3 days trying to figure out how to maneuver the Developer Tool, another key piece of the Data Quality offering mostly used by – you guessed it, developers and technical users.  I have to admit that I am no master of the Developer Tool after 3 days of wrestling with it, but, I got the basics and more importantly, my hands-on interaction with the entire software helped me understand the logic behind the overall design, and see for myself  how analyst and business user can easily collaborate with their IT counterpart within our Data Quality environment.

To break it all down, first comes to Profiling. As analyst we understand too well the importance of profiling as it provides an anatomy of the raw data we collected. In many cases, it is a must have first step in data preparation (especially when our  raw data came from different places and can also carry different formats).  A heavy user of Excel, I used to rely on all the tricks available in the spreadsheet to gain visibility of my data. I would filter, sort, build pivot table, make charts to learn what’s in my raw data.  Depending on how many columns in my data set, it could take hours, sometimes days just to figure out whether the data I received was any good at all, and how good it was.

which one do you like better?

which one do you like better?

Switching to the Analyst Tool in Data Quality, learning my raw data becomes a task of a few clicks – maximum 6 if I am picky about how I want it to be done.  Basically I load my data, click on a couple of options, and let the software do the rest.  A few seconds later I am able to visualize the statistics of the data fields I choose to examine,  I can also measure the quality of the raw data by using Scorecard feature in the software. No more fiddling with spreadsheet and staring at busy rows and columns.  Take a look at the above screenshots and let me know your preference?

Once I decide that my raw data is adequate enough to use after the profiling, I still need to clean up the nonsense in it before performing any analysis work, otherwise  bad things can happen — we call it garbage in garbage out. Again, to clean and standardize my data, Excel came to rescue in the past.  I would play with different functions and learn new ones, write macro or simply do it by hand. It was tedious but worked if I worked on static data set. Problem however, was when I needed to incorporate new data sources in a different format, many of the previously built formula would break loose and become inapplicable. I would have to start all over again. Spreadsheet tricks simply don’t scale in those situation.

Rule Builder in Analyst Tool

Rule Builder in Analyst Tool

With Data Quality Analyst Tool, I can use the Rule Builder to create a set of logical rules in hierarchical manner based on my objectives,  and test those rules to see the immediate results. The nice thing is, those rules are not subject to data format, location, or size, so I can reuse them when the new data comes in.  Profiling can be done at any time so I can re-examine my data after applying the rules, as many times as I like. Once I am satisfied with the rules, they will be passed on to my peers in IT so they can create executable rules based on the logic I create and run them automatically in production. No more worrying about the difference in format, volume or other discrepancies in the data sets, all the complexity is taken care of by the software, and all I need to do is to build meaningful rules to transform the data to the appropriate condition so I can have good quality data to work with for my analysis.  Best part? I can do all of the above without hassling my IT – feeling empowered is awesome!

Changing The Game For Data Plumbing

Use the Right Tool for the Job

Use the right tool for the right job will improve our results, save us time, and make our jobs much more enjoyable. For me, no more Excel for data cleansing after trying our Data Quality software, because now I can get a more done in less time, and I am no longer stressed out by the lengthy process.

I encourage my analyst friends to try Informatica Data Quality, or at least the Analyst Tool in it.  If you are like me, feeling weary about the steep learning curve then fear no more. Besides, if Data Quality can cut down your data cleansing time by half (mind you our customers have reported higher numbers), how many more predictive models you can build, how much you will learn, and how much faster you can build your reports in Tableau, with more confidence?

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CSI: “Enter Location Here”

Last time I talked about how benchmark data can be used in IT and business use cases to illustrate the financial value of data management technologies.  This time, let’s look at additional use cases, and at how to philosophically interpret the findings.

ROI interpretation

We have all philosophies covered

So here are some additional areas of investigation for justifying a data quality based data management initiative:

  • Compliance or any audits data and report preparation and rebuttal  (FTE cost as above)
  • Excess insurance premiums on incorrect asset or party information
  • Excess tax payments due to incorrect asset configuration or location
  • Excess travel or idle time between jobs due to incorrect location information
  • Excess equipment downtime (not revenue generating) or MTTR due to incorrect asset profile or misaligned reference data not triggering timely repairs
  • Equipment location or ownership data incorrect splitting service cost or revenues incorrectly
  • Party relationship data not tied together creating duplicate contacts or less relevant offers and lower response rates
  • Lower than industry average cross-sell conversion ratio due to inability to match and link departmental customer records and underlying transactions and expose them to all POS channels
  • Lower than industry average customer retention rate due to lack of full client transactional profile across channels or product lines to improve service experience or apply discounts
  • Low annual supplier discounts due to incorrect or missing alternate product data or aggregated channel purchase data

I could go on forever, but allow me to touch on a sensitive topic – fines. Fines, or performance penalties by private or government entities, only make sense to bake into your analysis if they happen repeatedly in fairly predictable intervals and are “relatively” small per incidence.  They should be treated like M&A activity. Nobody will buy into cost savings in the gazillions if a transaction only happens once every ten years. That’s like building a business case for a lottery win or a life insurance payout with a sample size of a family.  Sure, if it happens you just made the case but will it happen…soon?

Use benchmarks and ranges wisely but don’t over-think the exercise either.  It will become paralysis by analysis.  If you want to make it super-scientific, hire an expensive consulting firm for a 3 month $250,000 to $500,000 engagement and have every staffer spend a few days with them away from their day job to make you feel 10% better about the numbers.  Was that worth half a million dollars just in 3rd party cost?  You be the judge.

In the end, you are trying to find out and position if a technology will fix a $50,000, $5 million or $50 million problem.  You are also trying to gauge where key areas of improvement are in terms of value and correlate the associated cost (higher value normally equals higher cost due to higher complexity) and risk.  After all, who wants to stand before a budget committee, prophesy massive savings in one area and then fail because it would have been smarter to start with something simpler and quicker win to build upon?

The secret sauce to avoiding this consulting expense and risk is a natural curiosity, willingness to do the legwork of finding industry benchmark data, knowing what goes into them (process versus data improvement capabilities) to avoid inappropriate extrapolation and using sensitivity analysis to hedge your bets.  Moreover, trust an (internal?) expert to indicate wider implications and trade-offs.  Most importantly, you have to be a communicator willing to talk to many folks on the business side and have criminal interrogation qualities, not unlike in your run-of-the-mill crime show.  Some folks just don’t want to talk, often because they have ulterior motives (protecting their legacy investment or process) or hiding skeletons in the closet (recent bad performance).  In this case, find more amenable people to quiz or pry the information out of these tough nuts, if you can.

CSI: "Enter Location Here"

CSI: “Enter Location Here”

Lastly; if you find ROI numbers, which appear astronomical at first, remember that leverage is a key factor.  If a technical capability touches one application (credit risk scoring engine), one process (quotation), one type of transaction (talent management self-service), a limited set of people (procurement), the ROI will be lower than a technology touching multiple of each of the aforementioned.  If your business model drives thousands of high-value (thousands of dollars) transactions versus ten twenty-million dollar ones or twenty-million one-dollar ones, your ROI will be higher.  After all, consider this; retail e-mail marketing campaigns average an ROI of 578% (softwareprojects.com) and this with really bad data.   Imagine what improved data can do just on that front.

I found massive differences between what improved asset data can deliver in a petrochemical or utility company versus product data in a fashion retailer or customer (loyalty) data in a hospitality chain.   The assertion of cum hoc ergo propter hoc is a key assumption how technology delivers financial value.  As long as the business folks agree or can fence in the relationship, you are on the right path.

What’s your best and worst job to justify someone giving you money to invest?  Share that story.

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Posted in Business Impact / Benefits, Business/IT Collaboration, Data Integration, Data Quality, Governance, Risk and Compliance, Master Data Management, Mergers and Acquisitions | Tagged , , , | Leave a comment

What’s In A Name?

Naming ConventionsSometimes, the choice of a name has unexpected consequences. Often these consequences aren’t fair. But they exist, nonetheless. For an example of this, consider the well-known study by the National Bureau of Economic Research study that compares the hiring prospects of candidates with identical resumes, but different names. During the study, titled a “Field Experiment on Labor Market Discrimination,” employers were found to be more likely to reply candidates with popular, traditionally Caucasian names than to candidates with either unique, eclectic names or with traditionally African-American names. Though these biases are clearly unfair to the candidates, they do illustrate a key point: One’s choice when naming something can come with perceptions that influence outcomes.

For an example from the IT world, consider my recent engagement at a regional retail bank. In this engagement, half of the meeting time was consumed by IT and business leaders debating how to label their Master Data Management (MDM) Initiative.  Consider these excerpts:

  • Should we even call it MDM? Answer: No. Why? Because nobody on the business side will understand what that means. Also, as we just implemented a Data Warehouse/Mart last year and we are in the middle of our new CRM roll-out, everybody in business and retail banking will assume their data is already mastered in both of these.  On a side note; telcos understand MDM as Mobile Device Management.
  • Should we call it “Enterprise Data Master’? Answer: No. Why? Because unless you roll out all data domains and all functionality (standardization, matching, governance, hierarchy management, etc.) to the whole enterprise, you cannot.  And doing so is a bad idea as it is with every IT project.  Boiling the ocean and going live with a big bang is high cost, high risk and given shifting organizational strategies and leadership, quick successes are needed to sustain the momentum.
  • Should we call it “Data Warehouse – Release 2”? Answer: No. Why? Because it is neither a data warehouse, nor a version 2 of one.  It is a backbone component required to manage a key organizational ingredient – data –in a way that it becomes useful to many use cases, processes, applications and people, not just analytics, although it is often the starting block.  Data warehouses have neither been conceived nor designed to facilitate data quality (they assume it is there already) nor are they designed for real time interactions.  Did anybody ask if ETL is “Pneumatic Tubes – Version 2”?
  • Should we call it “CRM Plus”? Answer: No. Why? Because it has never intended or designed to handle the transactional volume and attribution breadth of high volume use cases, which are driven by complex business processes. Also, if it were a CRM system, it would have a more intricate UI capability beyond comparatively simple data governance workflows and UIs.

Consider this; any data quality solution like MDM, makes any existing workflow or application better at what it does best: manage customer interactions, create orders, generate correct invoices, etc.  To quote a colleague “we are the BASF of software”.  Few people understand what a chemical looks like or does but it makes a plastic container sturdy, transparent, flexible and light.

I also explained hierarchy management in a similar way. Consider it the LinkedIn network of your company, which you can attach every interaction and transaction to.  I can see one view, people in my network see a different one and LinkedIn has probably the most comprehensive view but we are all looking at the same core data and structures ultimately.

So let’s call the “use” of your MDM “Mr. Clean”, aka Meister Proper, because it keeps everything clean.

While naming is definitely a critical point to consider given the expectations, fears and reservations that come with MDM and the underlying change management, it was hilarious to see how important it suddenly was.  However, it was puzzling to me (maybe a naïve perspective) why mostly recent IT hires had to categorize everything into new, unique functional boxes, while business and legacy IT people wanted to re-purpose existing boxes.  I guess, recent IT used their approach to showcase that they were familiar with new technologies and techniques, which was likely a reason for their employment.  Business leaders, often with the exception of highly accomplished and well regarded ones, as well as legacy IT leaders, needed to reassure continuity and no threat of disruption or change.  Moreover, they also needed to justify their prior software investments’ value proposition.

Aside from company financial performance and regulatory screw-ups, legions of careers will be decide if, how and how successful this initiative will be.

Naming a new car model for a 100,000 production run or a shampoo for worldwide sales could not face much more scrutiny.  Software vendors give their future releases internal names of cities like Atlanta or famous people like Socrates instead of descriptive terms like “Gamification User Interface Release” or “Unstructured Content Miner”. This may be a good avenue for banks and retailers to explore.  It would avoid the expectation pitfalls associated with names like “Customer Success Data Mart”, “Enterprise Data Factory”, “Data Aggregator” or “Central Property Repository”.  In reality, there will be many applications, which can claim bits and pieces of the same data, data volume or functionality.  Who will make the call on which one will be renamed or replaced to explain to the various consumers what happened to it and why.

You can surely name any customer facing app something more descriptive like “Payment Central” or “Customer Success Point” but the reason why you can do this is that the user will only have one or maybe two points to interface with the organization. Internal data consumers will interact many more repositories.  Similarly, I guess this is all the reason why I call my kids by their first name and strangers label them by their full name, “Junior”, “Butter Fingers” or “The Fast Runner”.

I would love to hear some other good reasons why naming conventions should be more scrutinized.  Maybe you have some guidance on what should and should not be done and the reasons for it?

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The Total Data Quality Movement is Long Overdue!

Total Data QualityTotal Quality Management, as it relates to products and services has it’s roots in the 1920s.  The 1960’s provided a huge boost with rise of guru’s such as Deming, Juran and Crosby.  Whilst each had their own contribution, common principles for TQM that emerged in this era remain in practice today:

  1. Management (C-level management) is ultimately responsible for quality
  2. Poor quality has a cost
  3. The earlier in the process you address quality, the lower the cost of correcting it
  4. Quality should be designed into the system

So for 70 years industry in general has understood the cost of poor quality, and how to avoid these costs.  So why is it that in 2014 I was party to a conversation that included the statement:

“I only came to the conference to see if you (Informatica) have solved the data quality problem.”

Ironically the TQM movement was only possible based on the analysis of data, but this is the one aspect that is widely ignored during TQM implementation.  So much for ‘Total’ Quality Management.

This person is not alone in their thoughts.  Many are waiting for the IT knight in shining armour, the latest and greatest data quality tools secured on their majestic steed, to ride in and save the day.  Data quality dragon slayed, cold drinks all round, job done.  This will not happen.  Put data quality in the context of total quality management principles to see why:  A single department cannot deliver data quality alone, regardless of the strength of their armoury.

I am not sure anyone would demand a guarantee of a high quality product from their machinery manufacturers.  Communications providers cannot deliver high quality customer services organisations through technology alone.  These suppliers have will have an influence on final product quality, but everyone understands equipment cannot deliver in isolation.  Good quality raw materials, staff that genuinely takes pride in their work and the correct incentives are key to producing high quality products and services.

So why is there an expectation that data quality can be solved by tools alone?

At a minimum senior management support is required to push other departments to change their behaviour and/or values.  So why aren’t senior management convinced that data quality is a problem worth their attention the way product & service quality is?

The fact that poor data quality has a high cost is reasonably well known via anecdotes.  However, cost has not been well quantified, and hence fails to grab the attention of senior management.  A 2005 paper by Richard Marsh[i] states:  “Research and reports by industry experts, including Gartner Group, PriceWaterhouseCoopers and The Data Warehousing Institute clearly identify a crisis in data quality management and a reluctance among senior decision makers to do enough about it.”  Little has changed since 2005.

However, we are living in a world where data generation, processing and consumption are increasing exponentially.  With all the hype and investment in data, we face the grim prospect of fully embracing an age of data-driven-everything founded on a very poor quality raw material.  Data quality is expected to be applied after generation, during the analytic phase.  How much will that cost us?  In order to function effectively, our new data-driven world must have high quality data running through every system and activity in an organization.

The Total Data Quality Movement is long overdue.

Only when every person in every organization understands the value of the data, do we have a chance of collectively solving the problem of poor data quality.  Data quality must be considered from data generation, through transactional processing and analysis right until the point of archiving.

Informatica DQ supports IT departments in automating data correction where possible, and highlighting poor data for further attention where automation is not possible.  MDM plays an important role in sustaining high quality data.  Informatica tools empower the business to share the responsibility for total data quality.

We are ready for Total Data Quality, but continue to await the Total Data Quality Movement to get off the ground.

(If you do not have time to waiting for TDQM to gain traction, we can help you measure the cost of poor quality data in your organization to win corporate buy-in now.)


[i] http://www.palgrave-journals.com/dbm/journal/v12/n2/pdf/3240247a.pdf

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In a Data First World, Knowledge Really Is Power!

Knowledge Really IS Power!

Knowledge Really IS Power!

I have two quick questions for you. First, can you name the top three factors that will increase your sales or boost your profit? And second, are you sure about that?

That second question is a killer because most people — no matter if they’re in marketing, sales or manufacturing — rely on incomplete, inaccurate or just plain wrong information. Regardless of industry, we’ve been fixated on historic transactions because that’s what our systems are designed to provide us.

“Moneyball: The Art of Winning an Unfair Game” gives a great example of what I mean. The book (not the movie) describes Billy Beane hiring MBAs to map out the factors that would win a baseball game. They discovered something completely unexpected: That getting more batters on base would tire out pitchers. It didn’t matter if batters had multi-base hits, and it didn’t even matter if they walked. What mattered was forcing pitchers to throw ball after ball as they faced an unrelenting string of batters. Beane stopped looking at RBIs, ERAs and even home runs, and started hiring batters who consistently reached first base. To me, the book illustrates that the most useful knowledge isn’t always what we’ve been programmed to depend on or what is delivered to us via one app or another.

For years, people across industries have turned to ERP, CRM and web analytics systems to forecast sales and acquire new customers. By their nature, such systems are transactional, forcing us to rely on history as the best predictor of the future. Sure it might be helpful for retailers to identify last year’s biggest customers, but that doesn’t tell them whose blogs, posts or Tweets influenced additional sales. Isn’t it time for all businesses, regardless of industry, to adopt a different point of view — one that we at Informatica call “Data-First”? Instead of relying solely on transactions, a data-first POV shines a light on interactions. It’s like having a high knowledge IQ about relationships and connections that matter.

A data-first POV changes everything. With it, companies can unleash the killer app, the killer sales organization and the killer marketing campaign. Imagine, for example, if a sales person meeting a new customer knew that person’s concerns, interests and business connections ahead of time? Couldn’t that knowledge — gleaned from Tweets, blogs, LinkedIn connections, online posts and transactional data — provide a window into the problems the prospect wants to solve?

That’s the premise of two startups I know about, and it illustrates how a data-first POV can fuel innovation for developers and their customers. Today, we’re awash in data-fueled things that are somehow attached to the Internet. Our cars, phones, thermostats and even our wristbands are generating and gleaning data in new and exciting ways. That’s knowledge begging to be put to good use. The winners will be the ones who figure out that knowledge truly is power, and wield that power to their advantage.

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Posted in Data Aggregation, Data Governance, Data Integration, Data Quality, Data Transformation | Tagged , , | Leave a comment

The King of Benchmarks Rules the Realm of Averages

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:

The King of Benchmarks Rules the Realm of Averages

Making the Math Work (courtesy of Scott Adams)

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 the sign says it - do it!

If the sign says it – do it!

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.

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Posted in Business Impact / Benefits, Business/IT Collaboration, Data Integration, Data Migration, Data Quality, Enterprise Data Management, Master Data Management | Tagged , , , | Leave a comment

Driving Third Wave Businesses: Ensuring Your Business Has The Right To Win

TofflerAs 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

information age

To me, this drives to three salient conclusions for information age businesses:

  1. Information needs to drive further down organizations because top decision makers do not have the background to respond at the pace of change.
  2. Information needs to be available faster which means that we need to reducing the processing time for structure and unstructured information sources.
  3. 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

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

siloedLots 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?

fixing

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

payoffs

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.

Related links
Solution Brief: The Intelligent Data Platform
Related Blogs

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