Taking a “Moneyball” approach to child welfare workforce woes

Could you imagine running a business with a 35-90% annual turnover rate? What if the average time to fill a position was anywhere between 3-12 months? This alone would cripple most organizations, but is a common reality in state and local child welfare agencies.

High turnover and vacancy rates are not a recent phenomenon in the child welfare industry. However, the numbers above demonstrate that not enough has been done to truly address this issue. Most of what is accepted as sourcing, recruiting, interviewing best practices today is largely based on conventional wisdom, which is proving ineffective.

Very similar to how baseball evaluated potential and current talent prior to “Moneyball”, child welfare often takes a subjective and flawed approach to addressing workforce woes. These approaches are built on ideas that are convenient, appealing and deeply assumed. It brings to mind a quote from Jim Pinkerton, “It’s human nature to stick with traditional beliefs, even after they outlast any conceivable utility”.

Common responses have included increases in salaries, decreases in minimum requirements (degrees and experience), more training, ineffective retention programs and additional funding requests for more positions. We’ve seen the outcomes of these efforts.

In a recent presentation at a national child welfare conference, I asked an audience of more than 100 child welfare professionals how many of them were aware of a jurisdiction where more positions actually fixed a systematic problem such as turnover and opiate abuse. The response? Crickets! The reality is that many jurisdictions which do enjoy funding for new positions continue to struggle with turnover and high vacancy rates. I believe that is because they are not currently looking at the root of the problem, nor are they leaning on data to do so.

Workforce analytics can help build a better team

Child welfare can truly tackle this issue, much like professional sports and industry, but it will take much more than talking about reform and innovation. It will require action and “disruptive innovation”.

Workforce analytics can provide insight into issues such as recruitment processes (time to hire) and success factors, retention concerns, as well as overall workforce metrics. HR systems data on demographics, work history, recruitment, selection, payroll, promotions, behavior and performance can be analyzed and surfaced through a visual dashboard that provides insights to better understand and solve the turnover issue.

If you can reduce unplanned turnover, cut the amount of time key positions are vacant and improve your success in hiring the “right” candidates, you can make a dramatic difference in the ability to impact children and families.

Sample child welfare HR overview dashboard showing employee and hiring process summaries

Sample child welfare HR overview dashboard showing employee and hiring process summaries. [Click to enlarge.]

Key areas that can be improved using data and analytics to inform appropriate policy, procedure, & practice include, but are not limited to:


  • Pinpoint the best candidates for a certain job. Identify and acquire top talent based on traits, experience, accomplishments and information often overlooked by traditional recruiting and assessment methods.
  • Challenge conventional wisdom as to what top talent looks like and where it comes from.
  • Identify the best ways to engage and attract desired candidates.


  • Reduce the amount of time needed to fill a vacancy
  • Identify causes (process & people) for delays.


  • Automate data reports on employee attrition, headcount and promotions.
  • Analyze characteristics of the most successful employees.
  • Make data-informed decisions on internal promotions.


  • Identify which employees are most likely to leave (voluntary & involuntary).
  • Uncover root causes of turnover
  • Identify actions to reduce risk (training, work environment, salary, team structure etc.)
  • Reveal factors that may increase the likelihood of an employee’s success in their role.
  • Identify retention activities that have a positive impact on current and future employees.

Similar to many child welfare agencies, the Oakland A’s were cash-strapped (relatively, of course) and had to find creative ways to be competitive. State and local child welfare agencies can also find new ways to attract and retain high-quality talent, and win in a “game” for more important than any World Series.


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From compliance to commitment: The power of student growth data

Instead of stepping back on using student growth data, let's build on what we've created. Photo by Flickr user breity.

Instead of stepping back on using student growth data, let's build on what we've created.
Photo by Flickr user breity.

As teachers, we lean into our experience. We trust our judgment about students and our instruction. We trade teaching stories with colleagues. And increasingly, we examine student growth data that illuminates our practice and occasionally suggests we refine our approach to individual students.

In the past decade, states and districts have leapt forward in their abilities to collect, analyze and use a wide range of data on what students know and how much progress they’ve made. Districts and schools have invested in data-driven cultures and infrastructures that put insights into the hands of educators who then put it to work for students.

However, the opportunity for teachers and administrators to see such growth information is far from universal, and the new federal education law – the Every Student Succeeds Act – does not require states to use student growth measures in teacher evaluation.  It is my hope that states, school and districts see the substantial value in providing growth data both for students and also for ourselves, as teachers.  Data helps us shine the light on important information.  To borrow from Maya Angelou, it helps us to know better and thus, do better.

As states and districts work to implement positive changes under ESSA, I hope they will move from compliance with district or school mandates, to commitment to using student growth data to improve teaching and learning.

Teachers make dozens of decisions every day to help students learn. Growth data illuminates the practices that actually accelerate student progress.

Christopher Lopez, Principal at Martin Luther King Jr. Elementary School in Lancaster, Pennsylvania, equips his teachers with insights into their students. “Knowing that each student enters the educational system with unique circumstances, growth data allows teachers to meet them where they are, while setting realistic and attainable goals to close achievement gaps.”

Ethan Lenker, Superintendent of Pitt County Schools in North Carolina, has been faced with teacher skepticism.

“I had to spend a great deal of time convincing teachers that using data was not about playing ‘gotcha games,’” Dr. Lenker says. “The fear is diminishing, and we are really focusing on this important aspect of teaching and learning and its relationship to student achievement.”

States have the power to use data to highlight successes and identify areas where schools need to do better.

In Tennessee, educators have had access to growth data for two decades. State Commissioner Candice McQueen notes both the reflective and predictive capabilities.  “Objective student growth data has helped shine a light on equity needs and our historically underserved populations.  Measuring student growth also helps us understand if our students are on track to be ready for postsecondary opportunities.”

The passage of ESSA offers educators the opportunity to transform public education on their own terms. In the move from compliance to commitment, let’s not allow this policy change to dial back progress in our school systems.

Commissioner McQueen asserts, “We will continue to base decisions on as much objective evidence and research as possible to support teachers, school, and districts in the work they do to improve student achievement and provide meaningful postsecondary choice to almost one million students across the state.”

How will you show your commitment to improving student outcomes?



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How can we help other foster care youth soar like Simone Biles?

Measuring student growth of foster care children can help them soar. Pic courtesy of Flickr user Ally Middleton

Measuring student growth of youth in foster care can help them soar.
Pic courtesy of Flickr user Ally Middleton

Foster youth present a unique challenge to educators. Probably the most famous former foster care child in the world right now is Simone Biles, already one of the more formidable and successful athletes in the history of American sports.  As a big fan of women’s gymnastics, Biles has thrilled me with her accomplishments. The beauty coupled with the strength that she and her competitors display is simply awe-inspiring.

In addition to her success in Rio, I find her life story equally stirring.  Simone Biles was born to an absent father and drug-addicted mother.  She spent time in foster care before being adopted by her grandparents.

According to the U.S. Department of Health and Human Services, about 400,000 children are in foster care in the United States. These children, as well as other highly mobile students such as homeless students and military-connected students, require additional attention to ensure their needs are met by our educational system.

Mobility can have an adverse impact on learning so states, districts, and schools need strategies keeping these students on a path to success. The Every Student Succeed Act (ESSA), requires states, for the first time, to report separately on academic outcomes for homeless students, foster youth, and military-connected students. These new requirements are an important step in ensuring that the needs of these vulnerable students are met.  Data will be a critical component in serving these students.

EdWeek has published a series of articles on the specific needs and vulnerabilities of highly-mobile students. As part of that series, one article examines the challenges of measuring the academic growth of those students, including high-mobility, missing test scores and small group sizes.  Simone Biles is a success story in more ways than one.  She has beat all of the odds and reached tremendous success.  Let’s help all students meet their full potential by using every resource available to meet them where they are.

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Measuring benefits of a state government center of analytics

Government center of analytics ROI comes in many forms

Government center of analytics ROI comes in many forms

The fourth and final post in the series about a state government center of analytics concentrates on the big question – what value does analytics bring to business? (Links to previous entries are at the conclusion of this post.)

While this “show me the money” question can be answered in many different ways, there is always interest in tallying the value of an analytic solution versus the cost to develop and implement that solution.  Strong return on investment can help ensure continued support and funding to sustain current solutions and to generate support for future analytic development efforts.

So what is a successful analytic outcome?

Is it saving dollars by reducing the number of participants in a government program, or is it better matching of programs to the needs of those participants?  Is it reducing juvenile recidivism by 10%, or demonstrating that outreach programs led to 5% fewer juveniles entering the justice system in the first place?

Is it greater efficiencies in application processing, improved public safety, better use of public facilities, or the ability to analyze and address disease outbreaks more quickly?

Analytics can address so many different problems – but the value depends on how the business community defines success.

Tangible Business Benefits

To ensure tangible benefits can be measured, a project must be able to document baseline metrics and demonstrate changes over time.  Tangible benefits may result in actual dollar savings, or other efficiencies that can be converted to dollar savings.

Care should be used when estimating and reporting tangible benefits as they do not always result in dollars that can be returned to the budget and spent on other government initiatives.  Dollars identified, in fraud detection for example, may require time consuming investigative activities and litigation to recoup.  In other cases, the recouped money is designated to return to a federal program rather than being retained by the state.  In the case of time efficiencies, the dollars saved may not represent positions to be eliminated, but instead allow critical resources to shift to more meaningful  activities.  While the monetary value of the benefit may not result in hard dollars – the impact on the business is still positive.

Intangible Business Benefits

Intangible, or soft benefits, are more subjective and difficult to quantify.  Soft benefits may include things like improved public safety, timely compliance with federal regulations, enhanced reputation, or more engaging customer interaction.

What is the value of meeting federal reporting guidelines on time?  How do you determine that a criminal event was prevented or a life was saved?  And what is the value of that life?  How do we calculate the benefit of a citizen having a positive interaction with a government website?

While these benefits can be harder to measure, intangible benefits have value and should be documented as part of any analytics project reporting.

Enterprise Program Benefits

In addition to the benefits from each analytic solution, an enterprise program can provide value to government as well.  Enterprise data governance ensures quality, reliable and timely data is available to support a variety of business decisions.  Common technologies build analytics knowledge and expertise across the enterprise.  Repeatable and reusable data sources and analytic components, along with project management best practices enable more efficient development cycles and time to value for new analytic solutions. And an enterprise program helps government advance its data management and analytics maturity.

In an enterprise Center of Analytics, partnership and collaboration is key to finding the value of analytic solutions.  Data alone is of little value.  Analysis without reliable, quality data cannot provide the insight needed for key business decisions.  And an analytic solution without an active and engaged user community to leverage the analysis cannot bring value and results to the business.  The Center of Analytics can help government find the right path to capitalize on data and analytics to improve government outcomes.

Check out "Hey, government of [insert state], where's your center of analytics?", "4 keys to building a state government enterprise analytics system" and "State government center of analytics goes nowhere without user engagement", if you missed them.

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Smart Cities experts: Collaboration is key

Government, academia and the private sector all have a role in Smart Cities.

Government, academia and the private sector all have a role in Smart Cities.

Cities must work with companies, universities, other cities and organizations to truly realize the Smart Cities vision. This was a consistent message at last week’s Smart Cities Innovation Summit, where leaders from more than 200 cities met with technology and service providers and academics to talk about new innovations that will improve the lives of citizens across the globe.

In conjunction with the conference, the National Institute of Standards and Technology (NIST) hosted its Global Cities Team Challenge (GCTC) Expo.  Throughout the events, one message rang out loud and clear: cities that collaborate will be more successful.

“It’s not what a Smart City is; it’s what a Smart City does.” - Dr. Sokwoo Rhee, Associate Director of Cyber-Physical Systems Program, NIST

One of the best ways cities can collaborate is to create Smart Cities projects that are replicable. Dr. Rhee encouraged cities to share their successes with one another to promote technological advancements.  As cities learn how to adopt technologies that benefit their citizens, their experience and knowledge can help other cities quickly adopt similar solutions.

“Collaboration is the ‘new competition’.” - Former Governor of Maryland, Martin O’Malley

Collaborations with the private sector were strongly encouraged. And not just between governments and companies, but between companies themselves.  The Mayor of Austin, Steve Adler, told the audience that a “Smart City is one that recognizes that they cannot act alone and that they need to work with others to approach technology.”

The presentation of nearly a hundred innovative projects underscored O’Malley’s and Adler’s messages.  Each is a joint effort of companies and local government partners. The exciting projects ranged from suits for emergency workers that garner biometric information to tracking devices that protect victims of domestic abuse. Throughout the conference, companies expressed excitement over sharing what they are accomplishing and looked for opportunities to partner with other companies to advance their initiatives.

Most companies recognize the value of specializing in what they do best and teaming up with companies that specialize in other technologies.  As a result, many Smart City solutions are the compilation of dove-tailed technologies delivered by a team of “best in class” companies.

“A Smart City thinks about how everyone plays into a solution.” - Mark Dowd, USDOT Assistant Secretary for Research and Technology

Not only should we work together to create solutions, but we should consider how the solutions will affect citizens and develop relationships between stakeholders.  This is a refreshing perspective as we put the excitement of new technologies aside and remember that the purpose of employing them is to better the lives of our citizens and improve our local economies.

“People live in cities. But, we live also in the world.  We are obligated to share what we have learned with the rest of the world.” - Jack Mikkers, Mayor of Veldhoven, The Netherlands

Every project presented specifically addressed a real challenge that a local government is facing and provided solutions that would have an impact on the people of that community.  As we continue to develop advanced technologies, let us remember to collaborate with other cities, companies, universities, organizations, and even countries, to improve lives and better our world.



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Tackling 4 common concerns about analytics for child well-being

Analytics can make a true difference in child well-being efforts. Flickr image by ann_jutatip

Analytics can make a true difference in child well-being efforts.
Flickr image by ann_jutatip

In my last blog post, I introduced common concerns I’ve heard about predictive analytics in child well-being efforts. In this post, I want to address those concerns and reassure leaders and advocates that predictive analytics can be a tremendous boon to our ability to help kids and ease the burden on caseworkers.

They concerns I hear most are about:

  • False Positives
  • Racial Bias/Disparity
  • Equation vs. Comprehensive Process
  • Current Tools are Sufficient

Concern:  Child well-being predictive analytic solutions may result in a high number of false positives, giving workers added work.

This concern is misguided. Sophisticated analytics, like SAS for Child Safety, actually has identified less than 5% of overall cases as being at the highest level of risk of maltreatment and/or fatality.  Current validated actuarial risk and needs assessment have routinely identified from 25-30% of cases as being highest risk. This increases the potential amount of false positives up to 500%.

Child safety models are built on a multitude of risk factors, many of which vary in intensity (not just Yes/No binary values). These risk factors are intuitive. The literature and common sense would agree on the peril a child is exposed to given extreme values of these risk factors, such as:

  • Scores of prior maltreatment allegations by child’s primary caregivers
  • Multiple victimization events in caretaker’s childhood
  • Low maternal age

To be in the highest risk segments, a child needs to have extreme values in a multitude of risk factors (not just one).  In such an environment, risk of general harm to a child is also elevated, not just risk of fatality.

Fatality modeling is more narrowly focused. For each fatality there may be dozens or more near misses (injuries but not fatalities). However, data for serious near misses is not readily available. As terrible as it may be, a child who ends up in the ICU due to abuse is considered a false positive for a fatality risk model.

We have to remember that risk to child well-being is ongoing. In our research at SAS, we have observed children in extreme risk segments at the end of a study period that eventually become fatalities. That said, risk can also decrease over time as, for instance, a child’s age increases or there are changes in household composition.

Concern:  Predictive analytics may attribute to racial bias and disparity within communities.

Racial disparity is not apparent in fatality risk.  Literature and SAS’s own modeling in multiple jurisdictions has found no significant difference in maltreatment and/or fatality risk for black versus white households.   Of some interest was the fact that Hispanic households had a slightly lower fatality risk.

The fact is, race is vastly eclipsed by other risk factors like prior maltreatment allegations, making racial factors of little value in risk models.

Concern: Analytics is "just" a mathematical equation, no a comprehensive process 

Analytics actually IS a comprehensive process that helps generate a “golden record” of an individual. The child well-being models that support these efforts are based on historical data multiple factors and outcomes, such as fatalities, maltreatment, permanency, homelessness, etc. More accurate and complete data is modeled, allowing for workers to be more confident and proactive.

Collections of various factors determine risk, not individual facts or flags. This eliminates the one-size-fits-all approach to case response (e.g. children under 5 are high risk and require a full investigation), which is not proven out by the risk model.  A high risk score means there is a multitude of simultaneous risk factors present.  The risk score brings this to the attention of the case worker to help inform decisions, coupled with their professional judgement.

Concern: Current actuarial tools are sufficient, so why use analytics?

Case workers are also analytic models (biological not mathematical) and in general, not very good ones.  This is not because they aren’t dedicated, intelligent professionals, of course. However, judgement or consensus decision making is shown in the literature to underperform formal actuarial methods.

This is due to a number of factors, including high caseworker turnover rates and the associated variances in caseworker experience. Caseworkers can introduce bias in the scoring process when using actuarial methods like Structured Decision Making. In more extreme situations, there are examples of caseworkers manipulating actuarial tools to force certain actions like a home removal.

All of this could be helped by better information, but there’s a problem there, too. Caseworkers too often are provided with incorrect and/or overwhelming data. A combination of poor data quality and an inability to sift through what’s relevant and not amid an avalanche of data hinders decision-making.

Operational analytic models work across the risk spectrum and can also assist in screening and triaging cases so they can be routed based on available data.  In addition, data from multiple agencies can better inform the model and resulting decisions. Additional data is especially important in creating more accurate risk assessments in situations with limited report histories for a child.

The debate over analytics, actuarial tools and basic human judgement will continue, and it should. We should all work towards finding the balance of approaches that gives us the best chance to help kids. That said, I hope I’ve alleviated some of the more common concerns and look forward to continuing the discussion in the comments section.


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Addressing concerns of predictive analytics for child well-being

As state and local government leaders and community advocates explore how predictive analytics can improve child well-being outcomes, many questions and potential concerns surface.

It is critical to understand that government youth services agencies across the United States have used actuarial Risk & Need Assessment tools for many years. Many of these were developed using predictive analytics to identify “static” risk factors, such as low birth weight and mother was previously in foster care, that contribute to risk of maltreatment.

While these tools are a good starting point, there face implementation problems such as accuracy requirements, worker bias, amount of time to complete, etc.   With an operational analytic approach, analytics is continually assessing and reassessing data, making the system smarter. Ongoing assessment of risk and needs allows for both static and dynamic factors to be used when calculating risk of maltreatment.  This supports increased accuracy, real time risk scoring, limited worker bias, etc.

Below is a comparison/contrast of actuarial tools vs. and operational analytic solution such as SAS for Child Safety:


I worked in child well-being for 22 years, and have met with countless leaders and community advocates to discuss advance analytic approaches and their applicability to human services. Several concerns have been raised consistently that can be addressed with data driven and research based responses.  These include:

  • False Positives
  • Racial Bias/Disparity
  • Equation vs. Comprehensive Process
  • Current Tools are Sufficient

In my next posts, I will dive deeper into these concerns as well as findings from my peers in SAS Solutions on Demand, who are engaged in pioneering work on child welfare analytics. I will explain why leaders and child well-being advocates can feel confident in predictive analytics, and its ability to help kids and ease the burden on caseworkers.

Do you have concerns beyond these, or heard of others? Please share in the comments.

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Celebrate (?!) Tax Day

Embrace the inevitability of Tax Day! Image by Flickr user Paul Stumpr

Embrace the inevitability of Tax Day!
Image by Flickr user Paul Stumpr

Happy Tax Day, America!

Today marks our annual ritual of filing tax returns in the United States.  And complaining about taxes.  And cursing the IRS (even though it's misguided to shoot the messenger, in my opinion).

Think you know a lot about taxes?  Let's travel back in time to 1913, when the current version of the US income tax code was first enacted.  Here are four fun tax facts to ponder:

  1. The first US individual income tax return was 4 pages in length... including instructions.  Don't believe me?  Check it out!
  2. Tax brackets have ranged from a low of 0.375% (1929) to a high of 94% (1944/45).  The highest "low" bracket?  It was 23% during World War II (1944/45).  The lowest "high" bracket?  It was 7% from 1913 through 1915.  (For argument's sake, we'll omit the 0% bracket for very low income taxpayers.)
  3. Think the tax code has too many deductions?  Deductions have been part of the US tax code from Day 1.  Here are the "Original 8" deductions from the original US income tax law (summarized here for brevity):
    • Business expenses
    • Interest paid on "indebtedness"
    • "All national, state, county, school, and municipal taxes paid..."
    • Losses "incurred in trade or arising from fires, storms, or shipwreck"
    • "Debts... ascertained to be worthless and charged off within the year"
    • Depreciation
    • Dividends
    • Withholding amounts
  4. Since 1913, the number of returns filed with the IRS has grown by 4,120%!  A mere 357,598 returns were filed in 1914 (for tax year 1913).  Now, during peak filing season, the IRS processes more returns than that in a single day and receives almost 150 million individual income tax returns each year.

It's pretty clear that the present-day US tax code -- and how it is administered -- is vastly different than what Congress enacted in 1913.  (BONUS FACT! Tax Day was originally March 1st.) Times change...

The challenges tax administrators face today are daunting.  Globalization of commerce.  The rise of technology and data analytics.  A change in attitudes about civic responsibility. The good news is that there are a lot of smart people who think about how to help tax administrators solve these challenges.  Like who?

So, on this day of angst and financial stress, be sure to check out what these experts have to say about our modern Tax Day.  They have ideas that solve problems that William Henry Osborn (IRS Commissioner in 1913) could have never imagined would exist.

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State government center of analytics goes nowhere without user engagement

"I find your lack of faith in a center of analytics disturbing." Note: This is not the way to engage users. Image by Flickr user Ripster55

"I find your lack of faith in a center of analytics disturbing." Note: This is not the way to engage users.
Image by Flickr user Ripster55

In this third post about a government center of analytics, the focus is on creating an environment that enables successful implementation, and perhaps even more importantly, successful adoption of new analytic solutions. (Check out "Hey, government of [insert state], where's your center of analytics?" and "4 keys to building a state government enterprise analytics system", if you missed them.)

While analytic solutions can bring significant benefit to business decision makers, those benefits are only realized if the solutions is actually used to impact business decisions.  New analytic solutions represent change.  And change can create both anticipation and trepidation.

Questions about how analytics will impact job security, create more work, reveal information that may be less than positive, and other concerns can impact willingness to embrace new technology.  By recognizing these challenges, a center of analytics team can facilitate more successful implementations.

Engage the business users

It’s important to understand the business need and how access and insight to enterprise data can improve an organization decisions and service of citizens. But remember that an agency leader’s, and a front-line worker’s, idea of what is needed may differ.  Acknowledging these different perspectives helps ensure that the right people are involved early and often so the analytics will meet everyone’s needs. Here are some tips to engage business users.

  • Avoid developing analytics for the sake of analytics. Ensure there is measurable benefit for the user.
  • Keep it simple. Fix the critical problems first to show value and then expand to more advanced analytic capabilities.
  • Allow the user to “see and feel” how the analytic solution will work through tool demonstrations and building prototypes.
  • Build an advisory team of end users to ensure the final outcome works well for the user community.

And keep in mind that no analytic solution can replace the knowledge and expertise of the business user.  Analytics cannot make a decision about how to act on a business problem – but it can help equip the business user with the right information to make the decision.

Learn a new language

One of the best phrases I’ve heard was shared by the Oregon Youth Authority when discussing their analytics approach to support juvenile justice reform – “learn a new language”.  The point of the message was that business users of an analytic solution and developers of an analytic solution each speak different languages based on prior experiences and knowledge.  When striving to build a new solution, finding a common new language for mutual communication is critical to the adoption and integration of a new solution into business processes.

If the users of the solution find it challenging or frustrating to use, if they haven’t been involved in learning the new language of the solution, they will return back to what they’ve always known and the solution may become shelfware.  Regular, iterative engagement, training, and education can help institutionalize the new language, increasing the chances of the analytics becoming embedded in business process and changing business outcomes.

Operationalize the result

A focus on change management can help organizations understand how analytics will help make current processes and searches for information data more efficient.  Who wouldn’t want to be freed up from tedious administrative tasks to focus on more challenging and meaningful activities?

But that line of thinking can change slowly. Training and adoption efforts should be a planned and iterative activity.  Find the champions and innovators in an organization – those people who like a new opportunity.  Tap into their enthusiasm to encourage others.  Pilot the solution to work out the kinks before deploying to a more expansive user community.  These activities can help identify, mitigate and resolve inhibitors to usage of the new solution.

Learn, refine, repeat

Finally, recognize that developing and using analytic solutions is a learning process.  Once the user community can use their analysis, they begin to understand what else they might be able to do with the data.  Models are refined, new processes developed and additional data sources added.  Analytics allows us to be proactive, creative and better informed.

Watch for the final post in this series about finding the value in analytics!

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Tax returns, identity theft and hackers – Oh my!

Welcome to Tax Week, identity thieves!  Photo by Flickr user Aranami

Welcome to Tax Week, identity thieves!
Photo by Flickr user Aranami

Monday, April 18th is Tax Day, aka, National Identity Theft Day. OK, that part’s not true, but as millions of taxpayers go online to file taxes, it may as well be. The majority of taxpaying citizens file online, using services such as the popular TurboTax, H & R Block, and TaxSlayer . Even the IRS now offers its own free online tax filing service, Free File.  While online filing provides a convenient and more expedited means to submit tax documentation, it also introduces challenges akin to a massive clearance special on the Dark Web. With all that personally identifiable information (PII) flying around the web, you (almost) can’t blame criminal enterprises. They’re kids in a candy store… They just can’t help themselves.

In all seriousness, identity theft and fraud are real problems that far too many of us have faced in recent years, thanks to the “internet of everything”.  Never before have we enjoyed the ability to recon a vacation property via Google Earth, log into our bank account to check our balances or pay bills, “chat” with colleagues over instant message, purchase that miniature horse pet door, or binge watch another season of Homeland – right from that one spot on the couch you haven’t moved from…. for days.  And as convenient as all that may be, the very same technology that allows us to avoid human contact for weeks on end, also provides a very attractive vehicle for criminal activity.  The same benefits we enjoy while not cyber stalking an ex on Facebook, cyber criminals and hackers enjoy while “breaking into” financial institutions, the Department of Defense, retailers and even the IRS.  And this time of year, it’s like Cyber Christmas.

From across the world, or the basement next door, bad actors could be submitting fraudulent tax returns with the information stolen from tax preparing software websites or the IRS itself.  The data that can be obtained from tax documents is the cybercrime “holy grail.”  Far more valuable than a person’s bank account info, or a credit card stolen from a retailer’s point of sale, tax data exposes so much of one’s personal life, including family members and their PII and employment information. With these golden nuggets, a motivated bad guy can steal an identity by applying for new credit lines and causing a whole mess of liability issues for the unlucky victim to unravel.

While I have never personally endured such an ordeal I have heard from many that it’s so challenging to recover from that the punishment for actually committing a petty crime is a seemingly better outcome. Considering this, I am surprised to still see audits that cite continuing security vulnerabilities in e-filing systems, additional data breaches and even unresolved vulnerabilities in software months after detection.

While there is evidence government agencies are facing decreasing budgets, while cyber criminals continue to become more advanced, responsible parties seem to be outraged at such “lax security”. But t the fact of the matter is – without adequate priorities, funding and oversight, we cannot expect our sensitive information to protect itself.

Right now, as tens of millions of taxpayers transmit their PII over the web, criminals are planning and executing to commit fraud.  We know that tax data is highly valuable, and identity theft is debilitating for a victim, yet the ability to verify true identities is still difficult.  As the 2015 tax dollars are reconciled, it is my hope that all the “responsible parties” take notice of the risk citizens face with their tax information, and take the necessary steps (priorities, planning, funding, execution and oversight) to ensure the best possible safeguards in protecting it.


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