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Problem-Solving Courts

The scope of criminal court research and evaluation has grown with the advent of problem-solving courts. Examples of problem-solving courts include drug courts, domestic violence courts, reentry courts, and veterans treatment courts.

The Problem-Solving Court Model

Problem-solving courts differ from traditional courts in that they focus on one type of offense or type of person committing the crime.

An interdisciplinary team, led by a judge (or parole authority), works collaboratively to achieve two goals:

• Case management to expedite case processing and reduce caseload and time to disposition, thus increasing trial capacity for more serious crimes.
• Therapeutic jurisprudence to reduce criminal offending through therapeutic and interdisciplinary approaches that address substance use disorders and other underlying issues without jeopardizing public safety and due process.

The most common problem-solving courts are drug courts, but several other types of programs apply similar approaches to address violent and repeat offending, and returns to incarceration. [Note: Repeat offending is often referred to as "recidivism" in criminal justice research.]

Learn more about:

• Drug courts
• Domestic violence courts

Other NIJ projects in this area include:

• " Identifying Those Who Served: Modeling Potential Participant Identification in Veterans Treatment Courts ," and article in the inaugural issue of Drug Court Review , published by the National Drug Court Resource Center.
• The final report or executive summary as submitted to the National Institute of Justice.
• NIJ’s completed Evaluation of Second Chance Act Adult Reentry Courts that examines program processes, impacts, and costs.
• Past evaluations of two community court programs, see  A Community Court Grows in Brooklyn: A Comprehensive Evaluation of the Red Hook Community Justice Center, (Executive Summary) (pdf, 13 pages) , and  Dispensing Justice Locally: The Impact, Costs, and Benefits of the Midtown Community Court (pdf, 361 pages) .

Cite this Article

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In This Article Expand or collapse the "in this article" section Problem-Solving Courts

Introduction, general overviews.

• Anthologies
• Reference Resources
• Transforming Behavior
• Public Defenders
• Therapeutic Jurisprudence and Restorative Justice
• Drug Courts
• Mental Health Courts
• Reentry Courts
• Domestic Violence Courts
• Family Courts
• Community Courts
• Practical Efficacy
• Race and Class Issues
• Social and Legal Critiques
• Challenging the Court-Centered Model
• The Problem of Net Widening
• Proposals for Reform
• Extension to Foreign Jurisdictions

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• Communicating Scientific Findings in the Courtroom
• Community-Based Justice Systems
• Community-Based Substance Use Prevention
• Crime Control Policy
• Plea Bargaining
• Prosecution and Courts
• Sentencing Policy
• The Juvenile Justice System
• Therapeutic Jurisprudence

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Problem-Solving Courts by Eric J. Miller LAST REVIEWED: 14 April 2011 LAST MODIFIED: 14 April 2011 DOI: 10.1093/obo/9780195396607-0073

Problem-solving courts are a recent and increasingly widespread alternative to traditional models of case management in criminal and civil courts. Defying simple definition, such courts encompass a loosely related group of practice areas and styles. Courts range from those addressing criminal justice issues, such as drug courts, mental health courts, reentry courts, domestic violence courts, and juvenile courts, to those less directly connected with traditional criminal justice issues, including family courts, homelessness courts, and community courts, to name just a few. Most courts, however, share some distinctive common features: channeling offenders away from traditional forms of legal regulation or punishment, relying on a more or less lengthy program of supervision and intervention that utilizes the informal or institutional authority of the judge, and a robust toleration of relapse backed by a graduated series of sanctions directed at altering the participants’ problematic conduct. These courts work to stream participants out of the traditional legal system either at the front end, prior to judgment being entered, or at the back end, as a consequence of entry of judgment, but prior to sentencing or other case disposition. Many, but not all, of these courts subscribe to the practice of either therapeutic or restorative justice (or both).

The major texts listed here are mostly book-length treatments and articles that covering issues common to the problem-solving courts in general by focusing on discrete court styles. Nolan 2001 ; Hora, et al. 1999 ; and Mackinem and Higgins 2008 discuss drug courts, whereas Berman, et al. 2005 ; Casey and Rottman 2005 ; Thompson 2002 ; and Winick 2003 are principally interested in the neighborhood or quality-of-life courts. Furthermore, the authors provide variable depth of treatment, often determined by the type of analysis. Berman, et al. 2005 ; Hora, et al. 1999 ; and Winick 2003 have all played an active role in developing various aspects of problem-solving court practice: they tend to focus on descriptions of court operation and practical impact. Articles written by law professors, social scientists, or anthropologists, such as Thompson 2002 , Mackinem and Higgins 2008 , and Fagan and Malkin 2003 , tend to place problem-solving courts in a more theoretically oriented style of analysis, bringing to bear core legal values, or sociological or cultural critique.

Berman, Greg, and John Feinblatt, with Sarah Glazer. 2005. Good courts: The case for problem-solving justice . New York: New Press.

Broad and accessible overview of problem-solving courts, and in particular those addressing quality-of-life issues, against the background of therapeutic jurisprudence and restorative justice. Suitable for undergraduate and graduate students.

Casey, Pamela M., and David B. Rottman. 2005. Problem-solving courts: Models and trends . Justice System Journal 26.1: 35–56.

Simple and effective overview of the key elements of different styles of problem-solving courts. Suitable for all levels of study

Fagan, Jeffrey, and Victoria Malkin. 2003. Theorizing community justice through community courts . Fordham Urban Law Review 30.3: 897–954.

Seminal examination of the manner in which community courts use the problem-solving method to generate public legitimacy for low-level criminal courts. Suitable for undergraduate and graduate students.

Hora, Peggy Fulton, William G. Schma, John T. A. Rosenthal. 1999. Therapeutic jurisprudence and the drug-treatment court movement: Revolutionizing the criminal justice system’s response to drug abuse and crime in America . Notre Dame Law Review 74.2: 439–538.

One of the essential works on the drug court movement and the use of therapeutic justice in the courtroom. Suitable for undergraduates and graduate students.

Mackinem, Mitchell B., and Paul Higgins. 2008. Drug court: Constructing the moral identity of drug offenders . Springfield, IL: C. C. Thomas.

A thorough and informative study of all aspects of drug-court operation, paying particular attention to the perspective of drug court participants. Suitable for undergraduates and graduate students.

Nolan, James L., Jr. 2001. Reinventing justice: The American drug court movement . Princeton Studies in Cultural Sociology. Princeton, NJ: Princeton Univ. Press.

The most important single work on drug courts, and a seminal study of the problem-solving movement from a sociological perspective. Suitable for undergraduate and graduate students.

Thompson, Anthony C. 2002. Courting disorder: Some thoughts on community courts. Washington University Journal of Law and Policy 10:63–100.

Discussing the emergence of the community court movement and the features it shares with other forms of problem-solving courts. Suitable for undergraduate and graduate students.

Winick, Bruce J. 2003. Therapeutic jurisprudence and problem solving courts . Fordham Urban Law Journal 30.3: 1055–1103.

Seminal overview of problem-solving courts from the perspective of therapeutic jurisprudence, written by one of the founders of the therapeutic justice movement. Suitable for undergraduate and graduate students.

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Problem-Solving Courts

• First Online: 11 January 2023

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• Lacey Schaefer   ORCID: orcid.org/0000-0002-2981-2542 3 &
• Caitlyn Egan 4  

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This chapter considers the catalyst for developing specialist problem-solving courts across Australia. It charts their emergence and assesses their evolution from tentative beginnings to the critically important role they play within the contemporary criminal justice system. Use of the term ‘problem-solving’ is, in itself, somewhat controversial, as these courts might be better considered as ‘problem-oriented’ to reflect that they cannot solve the causes of criminal behaviour. Australia has observed an ‘Americanisation’ of these specialty courts: this somewhat confused identity creates challenges and warrants change. It describes the potentially coercive nature of participation in problem-solving courts and the contentious sentencing practices that undergird them. There are multiple complexities and therefore challenges inherent in the contemporary operationalisation of problem-solving courts, including equity of access; resourcing issues; and case co-ordination hurdles. In order to ‘solve problems’ related to offending, problems must be better defined, access increased and solutions better resourced.

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Legislation

Drug Court Act 1998 (NSW) No 150.

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Lacey Schaefer

Caitlyn Egan

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Practitioner Perspective: A Reflection on Problem-Solving Courts in Australia

• Elizabeth Daniels 

Queensland Magistrates Court, Brisbane, QLD, Australia

Elizabeth Daniels

I started my legal career in 2011 as a young general practice lawyer in rural Queensland, spending much of my time representing clients in Magistrates Courts for a variety of criminal proceedings. During that period, I did not devote much thought to the purpose of the justice system, nor did I readily recognise the system’s shortcomings when it came to interacting with those experiencing or exposed to domestic and family violence (DFV).

To me, courts were a place for justice, punishment and accountability for someone’s actions according to the law. Even with my narrow view of the function of the justice system, I still recall questionable practices, particularly in the domestic and family violence (DFV) jurisdiction, which did not serve the needs of those seeking protection. I regularly observed lawyers representing perpetrators enter courthouse saferooms and demand victims withdraw applications for protection orders or make submissions to the court that a domestic violence offence was ‘not overly serious’ and punishment ‘should be at the lower end of the sentencing regime’.

Throughout the second decade of the twenty-first century, we have seen significant government reform and a spotlight on solving the ‘problem’ of DFV, including the delivery of the ground-breaking report of the Queensland Special Taskforce on Domestic and Family Violence (2015) Not now, not ever: Putting an end to domestic and family violence in Queensland (NNNE Report) in February 2015. I am fortunate to say, through the introduction of specialist DFV courts, I have played an active role in one of the more significant justice system reforms in Queensland’s history.

Background and Challenges

In response to the recommendations from the NNNE Report, a trial of the Southport Specialist Domestic and Family Violence Court (SDFVC) commenced in September 2015 and a report evaluating this court was released in 2022 (ARTD, 2022). The NNNE Report implored the Queensland Government to reform the justice system to ensure it better protected victims/survivors (and their children), achieved fair and protective outcomes and made perpetrators of violence accountable for their behaviour. Many reported to the Taskforce that the justice system (courts and police) only further victimised or marginalised victims. From inception, the implementation of a specialist court and the key outcomes it was to achieve had overarching support from government and non-government agencies alike; however, factors such as day-to-day operations, how we would achieve reform, and what would be the ‘measures of success’ remained key challenges.

From the outset, the SDFVC had a clear mandate: to create a justice system response where the safety of victims/survivors was paramount and perpetrator accountability was a key objective. The specialist court model differed from traditional courts as it became a place of engagement for people attending court and provided not only a legal response but encouraged ‘wrap-around’ DFV support for those who attended. The model required all stakeholders (government and non-government/legal and social work) to work together in a way and to a magnitude which was unprecedented. The concepts of integration, collaboration and co-ordination would become the cornerstones of the court’s operation.

A major challenge was ensuring that the model maintained the separation of powers of the court, preserved the functions of individual stakeholder roles (prosecutors, lawyers, DFV support services) and pursued reform. Despite the collaborative spirit being evidenced from the outset, it did not prevent issues arising which placed significant pressures on all to reflect and understand how their organisations internal operations, purpose and traditional functions may be contributing to the ongoing generation of barriers and marginalising or re-victimising those seeking protection/safety. This reflective practice was particularly challenging for government departments with deeply entrenched organisational cultures, which at the time were reported as not consistent with a ‘best practice’ response to DFV or the specialist court approach.

Some of the more significant challenges for practitioners involved in the implementation of SDFVCs have included:

an ‘open door policy’ and broad eligibility criteria for accessing the specialist court, leading to substantial increases in case numbers, workloads and file complexity (which contributed to staff burnout, placed pressure on resources, and impacted the ongoing sustainability of the model);

integrated methods of working for government and non-government agencies, including the need to proactively share information about parties attending the court to ensure the most appropriate orders were made (including the granting of protection orders) 1 ;

a lack of a common risk screening and assessment tool or any uniform terminology 2 ;

complexity and confusion, particularly for victims, in navigating the legal processes (combination/intersection of both civil and criminal proceedings in Queensland) and limited support available to assist; and

limited availability of specialist DFV support services to assist persons attending court (and for legal practitioners to refer clients to). The services available would eventually increase in functionality/scope and assist victims in drafting and filing protection order applications, conduct risk assessments and generate safety plans (including securing emergency accommodation), receive referrals to men’s support services and assist entry into behaviour change programmes. These support activities were identified by stakeholders as key engagement opportunities which if conducted at court and in a timely fashion, could assist in promoting the protection and safety of victims, increase engagement and potentially achieve perpetrator accountability.

Reforms and Solutions

In February 2017, Griffith University released its Evaluation of the Specialist DFV Court Trial in Southport , making several interim findings and key recommendations (Bond et al., 2017). The evaluation found the Southport SDFVC had made significant inroads toward achieving its desired objectives. Fundamentally the court had taken steps to increase engagement with parties and to create a safe place for victims to attend and seek protection, all the while providing ‘wrap-around’ support for both victims and perpetrators. Recommendations were made highlighting some of the areas for continued improvement, perhaps most significantly the need for increased perpetrator accountability and access to men’s behaviour change programmes, as well as consideration as to how the model might work in other locations in Queensland.

As a practitioner working across the model, I attribute the positive outcomes and the ‘specialist’ nature of the court to two key initiatives adopted by Southport (and replicated at subsequent specialist court locations). First, the implementation of the Operational Working Group (OWG)—a weekly stakeholder meeting with representatives from each agency and the dedicated DFV magistrates to openly discuss issues, challenges, failures or successes of how the model was operating and developing. 3 This was one of the key ‘problem-solving’ elements of the model. Second, the commitment to ‘continuous improvement’ and ‘innovation’—this was despite differences in opinion, ever changing court operations and ongoing pressures on stakeholders’ resources including funding, staffing, workloads and fluctuations in government agenda/reform momentum. The commitment and collaborative spirit displayed by those involved in the implementation, development and ongoing sustainability of SDFVCs is what has made it a true privilege to be a practitioner involved.

Sustainability of Problem-Solving Courts

Without the delivery of the second evaluation of the Southport SDFVC at the time of writing this reflection, it is difficult to comment on the sustainability of the model from an evidence-based perspective. As a practitioner involved in inception and ongoing implementation, some of the challenges for sustainability include:

ensuring models are properly resourced and are not ‘person-based’ or rely upon goodwill to function;

the ongoing need for clarity about the model and its core elements—as the SDFVC developed at a rapid pace and in an organic fashion, the model continued to evolve making it challenging to define and sustain over a longer period and across multiple locations;

the need for the model to reflect diversity, be accessible to people from all cultural backgrounds and diverse groups, and to be able to translate notions of ‘best practice DFV’ to courts across the state of Queensland (including regional areas/First Nations communities); and

clarity around the concept of ‘success’, particularly in relation to the goal of perpetrator accountability.

Upon reflection of my involvement in the implementation of specialist DFV courts in Queensland, it has been encouraging to see the justice system proactively and creatively adapt in its response to NNNE. From my perspective, it is important to continue reflecting on the model, both internally and externally through independent evaluations and reviews to improve. Building the OWG as a key function/component of the SDFVC was integral to the success of the model and in my view could be adapted to other problem-solving courts.

In my experience, the questionable practices observed during the start of my career are far less likely to be observed in the SDFVC—if they were, the OWG would certainly have something to say. Despite the significant reform to date, it remains imperative the justice system continues its journey to ensure courts are a place of safety for victims seeking protection and perpetrator accountability remains at the forefront of the response. It is these key objectives which must be achieved before the long-term objective of eliminating domestic and family violence can be realised.

This was in comparison with the traditional ‘siloed’ approach by agencies only submitting to the court ‘what they knew’. At the time of commencement of the SDFVCs, there were no uniform processes or platforms for sharing of information between agencies. The amendments to the Domestic & Family Violence Protection Act 2012 (Qld) introducing Part 5A regarding increased information sharing did not come into effect until 30 May 2017.

In 2017, the Queensland Government introduced the Common Risk and Safety Framework (CRASF). The framework was developed for use by government and non-government community service agencies. It articulates a shared understanding, language and common approach to recognising, assessing and responding to DFV risk and safety action planning, including common minimum standards and approaches for in an attempt to adopt a more uniformed approach.

Note that the OWG continues to date, albeit with less frequent meetings, but still as a key part of the model.

ARTD. (2022). The Southport Specialist Domestic and Family Violence Court: Process Evaluation 2017–2020 . Department of Justice and Attorney-General. https://www.courts.qld.gov.au/__data/assets/pdf_file/0010/722674/southport-specialist-dfv-process-evaluation-2017-2020.pdf

Queensland Special Taskforce on Domestic and Family Violence. (2015). Not now, not ever: Putting an end to domestic and family violence in Queensland . Report provided to the Premier.

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Schaefer, L., Egan, C. (2022). Problem-Solving Courts. In: Camilleri, M., Harkness, A. (eds) Australian Courts. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-19063-6_9

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PROBLEM SOLVING Definition & Legal Meaning

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Problem-solving | Definition

Problem-solving refers to the process of identifying, analyzing, and resolving issues that juvenile offenders encounter, fostering better decision-making.

What is Problem-solving?

Definition and importance.

Problem-solving is the process of identifying challenges, analyzing their causes, and developing effective solutions. In the context of juvenile justice, problem-solving is crucial for helping young offenders address their issues, make better decisions, and avoid future legal troubles. Effective problem-solving skills empower juveniles to navigate life’s challenges constructively.

The Problem-solving Process

The problem-solving process involves several steps:

• Identifying the Problem: The first step is to clearly define the problem. This involves understanding the nature of the issue, its impact, and its root causes. For juvenile offenders, problems can range from behavioral issues to environmental influences.
• Analyzing the Problem: Once the problem is identified, the next step is to analyze it. This involves gathering information, understanding the context, and identifying factors that contribute to the problem. For example, if a juvenile is struggling in school, factors like learning difficulties, family issues, or peer pressure may need to be considered.
• Generating Solutions: After analyzing the problem, the next step is to brainstorm possible solutions. This involves thinking creatively and considering various approaches to address the issue. For instance, solutions for a juvenile struggling in school might include tutoring, counseling, or changing schools.
• Evaluating and Selecting Solutions: Not all solutions will be effective, so it’s important to evaluate them. This involves considering the pros and cons of each option and selecting the best one based on feasibility, effectiveness, and resources available.
• Implementing the Solution: Once a solution is selected, it needs to be implemented. This involves putting the plan into action and ensuring that all necessary steps are followed. For a juvenile, this might mean attending counseling sessions regularly or following a new study schedule.
• Reviewing and Reflecting: After implementing the solution, it’s important to review the outcome. This involves assessing whether the problem has been resolved and reflecting on what worked and what didn’t. This step helps juveniles learn from their experiences and improve their problem-solving skills.

Problem-solving in the Juvenile Justice System

Problem-solving is a key component of rehabilitation programs in the juvenile justice system. It helps juveniles develop critical thinking and decision-making skills. Here’s how it is applied:

• Behavioral Programs: Many rehabilitation programs include problem-solving training as part of behavioral therapy. Juveniles learn how to identify and manage their emotions, set goals, and solve problems constructively.
• Educational Programs: Schools within juvenile detention centers often include problem-solving as part of the curriculum. This helps juveniles improve their academic performance and cope with school-related issues.
• Counseling and Therapy: Counselors and therapists work with juveniles to address personal issues such as substance abuse, family problems, and peer pressure. Problem-solving techniques are used to help juveniles develop coping strategies and make positive changes.

Benefits of Problem-solving Skills

Developing strong problem-solving skills offers several benefits for juvenile offenders:

• Improved Decision-making: Juveniles learn to make better choices by considering the consequences of their actions and evaluating different options.
• Enhanced Coping Skills: Effective problem-solving helps juveniles cope with stress, anxiety, and other emotional challenges.
• Increased Confidence: Successfully solving problems boosts self-esteem and confidence, encouraging juveniles to tackle future challenges.
• Reduced Recidivism: Juveniles with strong problem-solving skills are less likely to reoffend because they can navigate life’s challenges more effectively.

Challenges in Teaching Problem-solving

While problem-solving skills are essential, teaching them can be challenging:

• Varied Backgrounds: Juveniles come from diverse backgrounds with different levels of support and education. Tailoring problem-solving training to meet individual needs can be difficult.
• Resistance to Change: Some juveniles may resist learning new ways of thinking and behaving, especially if they have a history of negative experiences with authority figures.
• Resource Limitations: Limited resources and high caseloads can restrict the amount of individualized attention probation officers and counselors can provide.

Problem-solving is a vital skill for juvenile offenders, helping them address issues, make better decisions, and avoid future legal problems. Through structured training and support, the juvenile justice system aims to equip young offenders with the tools they need to navigate life’s challenges constructively. By fostering effective problem-solving skills, we can help juveniles build a foundation for a successful and law-abiding future.

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Arbitration vs Mediation: The Definition of Mediation as a Problem Solving Process

The definition of mediation as a dispute resolution process and how to use mediation to manage conflict.

By Lawrence Susskind — on October 5th, 2023 / Consortium Schools & Other Boston Area Universities , Mediation

The definition of mediation is often as contextual as the conflict it attempts to resolve. Mediation is often thought of as a last step to adjudicate disputes .

In this article, professor Lawrence Susskind spells out the hidden advantages of using mediation early in the process to solve problems and reach voluntary compliance agreements. He reveals the three requirements necessary for problem-solving mediation using a practical negotiation case study.

The Organization for Economic Cooperation and Development (OECD) holds multinational corporations to appropriately high standards of corporate social responsibility. OECD member states include thirty of the major economies of the world.

Back in 2006, they adopted guidelines regarding human rights, environmental protection, the rights of workers and child protection. In 2016, they were in the throes of a ten year review. Every member country appointed an NCP — a National Contact Point — to investigate claims that multinational corporations headquartered in their country, or their subsidiaries wherever they might be located, had violated the guidelines. The NCPs investigated as best they could (often with very limited staff and budget).

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How Alternative Dispute Resolution (ADR) Resolves Disputes and Creates Value for Each Side

The assumption is that being called out by a national government would push multinationals to correct whatever guideline infractions they or their subsidiaries may have committed. Unfortunately, it was been hard for the NCPs to complete many of the needed investigations, particularly those filed by unions or NGOs in far off corners of the world. On some occasions, NCPs did not find sufficient evidence that the guidelines had been violated, but there were clearly circumstances that needed attention. At a recent meeting of all the NCPs and some of their constituent organizations (including their Trade Union Advisory Group, their Business and Industry Advisory Group, and OECDWatch) the NCPs were reminded that their goal should be to rectify inappropriate practices, not just determine whether the guidelines had been violated. More generally, the NCPs were urged to step back from their adjudicatory (or investigatory) efforts and build their problem-solving capabilities.

Definition of Mediation as a Problem Solving Process

In particular, they were urged to take their mediation mandate seriously.

I am very supportive of a “problem-solving” view of mediation. In too many situations, mediation is viewed as the last step in adjudication (i.e. when impasse has been reached), rather than as the first step in a collaborative effort to head off a problem or work out a creative solution.

When a complaint is filed, an NCP must determine whether the charges should be taken seriously. It sometimes does this by asking its national embassy to “make inquiries” about the reputation of the company against whom a complaint has been filed. Then, it might follow up with a call to the company and ask for “its version” of the story. In short, the NCP tries to determine whether the company has, in fact, violated the OECD corporate social responsibility guidelines. They proceed this way because their primary goal is to determine the legitimacy of the claims that are brought. If, however, the NCP’s goal were to correct inappropriate practices or implement appropriate remedies, it might, instead, select a qualified mediator — located in the place where the infraction presumably occurred — to meet informally with the relevant parties and see what might be worked out. The more informal the interaction, the less likely the parties are to overstate their claims or react defensively. If such problem-solving fails, the NCP can always revert to its investigatory role.

If you were a company accused of violating OECD guidelines, wouldn’t you prefer to meet privately with a neutral party (who would keep what you said confidential) than to have to defend yourself in a public way as an official investigation gets underway? From the standpoint of preserving your corporate image, mediation is certainly preferable. If you were a trade union or an environmental NGO concerned about the actions of a company in your area, wouldn’t you prefer to have a professional mediator bring everyone together to respond to your concerns than to wait a year or longer while an invisible agency (often in another part of the world) determines whether OECD guidelines have been violated and then writes a report?Adjudication in the absence of enforcement (and that is the situation in globally) won’t guarantee change. Mediation leading to voluntary agreements will almost always guarantee compliance with whatever has been worked out.

The Definition of Mediation as a Problem Solving Process

Mediation as problem-solving requires three things:

(1) a willingness on the part of all the relevant stakeholders to work together to resolve the problem or deal with the situation;

(2) the availability of a trusted “neutral” with sufficient knowledge and skill to manage difficult conversations ; and

(3) an agreement on procedural ground rules (i.e., confidentiality, timetable, agenda, good faith effort, etc.). OECD and its NCPs are seriously considering emphasizing problem-solving mediation in the years ahead.

Read More on Professor Susskind’s Blog

Leave comment below and tell us how you approach mediation using these concepts?

Lawrence Susskind, Ford professor of Urban and Environmental Planning, The Massachusetts Institute of Technology; author of  Built to Win ; co-author of  Breaking Robert’s Rules  and  Breaking the Impasse

Originally published in 2010.

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I like that you pointed out how mediation leading to voluntary agreements would almost always guarantee compliance with whatever has been worked out. I was watching a very interesting show on the TV earlier and it showed how mediation works. It looked very useful, and I heard there are even establishments now that offer professional mediation solutions.

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problem-solving

Definition of problem-solving

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What is Problem Solving? (Steps, Techniques, Examples)

By Status.net Editorial Team on May 7, 2023 — 5 minutes to read

What Is Problem Solving?

Definition and importance.

Problem solving is the process of finding solutions to obstacles or challenges you encounter in your life or work. It is a crucial skill that allows you to tackle complex situations, adapt to changes, and overcome difficulties with ease. Mastering this ability will contribute to both your personal and professional growth, leading to more successful outcomes and better decision-making.

Problem-Solving Steps

The problem-solving process typically includes the following steps:

• Identify the issue : Recognize the problem that needs to be solved.
• Analyze the situation : Examine the issue in depth, gather all relevant information, and consider any limitations or constraints that may be present.
• Generate potential solutions : Brainstorm a list of possible solutions to the issue, without immediately judging or evaluating them.
• Evaluate options : Weigh the pros and cons of each potential solution, considering factors such as feasibility, effectiveness, and potential risks.
• Select the best solution : Choose the option that best addresses the problem and aligns with your objectives.
• Implement the solution : Put the selected solution into action and monitor the results to ensure it resolves the issue.
• Review and learn : Reflect on the problem-solving process, identify any improvements or adjustments that can be made, and apply these learnings to future situations.

Defining the Problem

To start tackling a problem, first, identify and understand it. Analyzing the issue thoroughly helps to clarify its scope and nature. Ask questions to gather information and consider the problem from various angles. Some strategies to define the problem include:

• Brainstorming with others
• Asking the 5 Ws and 1 H (Who, What, When, Where, Why, and How)
• Analyzing cause and effect
• Creating a problem statement

Generating Solutions

Once the problem is clearly understood, brainstorm possible solutions. Think creatively and keep an open mind, as well as considering lessons from past experiences. Consider:

• Creating a list of potential ideas to solve the problem
• Grouping and categorizing similar solutions
• Prioritizing potential solutions based on feasibility, cost, and resources required
• Involving others to share diverse opinions and inputs

Evaluating and Selecting Solutions

Evaluate each potential solution, weighing its pros and cons. To facilitate decision-making, use techniques such as:

• SWOT analysis (Strengths, Weaknesses, Opportunities, Threats)
• Decision-making matrices
• Pros and cons lists
• Risk assessments

After evaluating, choose the most suitable solution based on effectiveness, cost, and time constraints.

Implementing and Monitoring the Solution

Implement the chosen solution and monitor its progress. Key actions include:

• Communicating the solution to relevant parties
• Setting timelines and milestones
• Assigning tasks and responsibilities
• Monitoring the solution and making adjustments as necessary
• Evaluating the effectiveness of the solution after implementation

Utilize feedback from stakeholders and consider potential improvements. Remember that problem-solving is an ongoing process that can always be refined and enhanced.

Problem-Solving Techniques

During each step, you may find it helpful to utilize various problem-solving techniques, such as:

• Brainstorming : A free-flowing, open-minded session where ideas are generated and listed without judgment, to encourage creativity and innovative thinking.
• Root cause analysis : A method that explores the underlying causes of a problem to find the most effective solution rather than addressing superficial symptoms.
• SWOT analysis : A tool used to evaluate the strengths, weaknesses, opportunities, and threats related to a problem or decision, providing a comprehensive view of the situation.
• Mind mapping : A visual technique that uses diagrams to organize and connect ideas, helping to identify patterns, relationships, and possible solutions.

Brainstorming

When facing a problem, start by conducting a brainstorming session. Gather your team and encourage an open discussion where everyone contributes ideas, no matter how outlandish they may seem. This helps you:

• Generate a diverse range of solutions
• Encourage all team members to participate
• Foster creative thinking

When brainstorming, remember to:

• Reserve judgment until the session is over
• Encourage wild ideas
• Combine and improve upon ideas

Root Cause Analysis

For effective problem-solving, identifying the root cause of the issue at hand is crucial. Try these methods:

• 5 Whys : Ask “why” five times to get to the underlying cause.
• Fishbone Diagram : Create a diagram representing the problem and break it down into categories of potential causes.
• Pareto Analysis : Determine the few most significant causes underlying the majority of problems.

SWOT Analysis

SWOT analysis helps you examine the Strengths, Weaknesses, Opportunities, and Threats related to your problem. To perform a SWOT analysis:

• List your problem’s strengths, such as relevant resources or strong partnerships.
• Identify its weaknesses, such as knowledge gaps or limited resources.
• Explore opportunities, like trends or new technologies, that could help solve the problem.
• Recognize potential threats, like competition or regulatory barriers.

SWOT analysis aids in understanding the internal and external factors affecting the problem, which can help guide your solution.

Mind Mapping

A mind map is a visual representation of your problem and potential solutions. It enables you to organize information in a structured and intuitive manner. To create a mind map:

• Write the problem in the center of a blank page.
• Draw branches from the central problem to related sub-problems or contributing factors.
• Add more branches to represent potential solutions or further ideas.

Mind mapping allows you to visually see connections between ideas and promotes creativity in problem-solving.

Examples of Problem Solving in Various Contexts

In the business world, you might encounter problems related to finances, operations, or communication. Applying problem-solving skills in these situations could look like:

• Identifying areas of improvement in your company’s financial performance and implementing cost-saving measures
• Resolving internal conflicts among team members by listening and understanding different perspectives, then proposing and negotiating solutions
• Streamlining a process for better productivity by removing redundancies, automating tasks, or re-allocating resources

In educational contexts, problem-solving can be seen in various aspects, such as:

• Addressing a gap in students’ understanding by employing diverse teaching methods to cater to different learning styles
• Developing a strategy for successful time management to balance academic responsibilities and extracurricular activities
• Seeking resources and support to provide equal opportunities for learners with special needs or disabilities

Everyday life is full of challenges that require problem-solving skills. Some examples include:

• Overcoming a personal obstacle, such as improving your fitness level, by establishing achievable goals, measuring progress, and adjusting your approach accordingly
• Navigating a new environment or city by researching your surroundings, asking for directions, or using technology like GPS to guide you
• Dealing with a sudden change, like a change in your work schedule, by assessing the situation, identifying potential impacts, and adapting your plans to accommodate the change.
• How to Resolve Employee Conflict at Work [Steps, Tips, Examples]
• How to Write Inspiring Core Values? 5 Steps with Examples
• 30 Employee Feedback Examples (Positive & Negative)

What is AI (artificial intelligence)?

Humans and machines: a match made in productivity  heaven. Our species wouldn’t have gotten very far without our mechanized workhorses. From the wheel that revolutionized agriculture to the screw that held together increasingly complex construction projects to the robot-enabled assembly lines of today, machines have made life as we know it possible. And yet, despite their seemingly endless utility, humans have long feared machines—more specifically, the possibility that machines might someday acquire human intelligence  and strike out on their own.

Get to know and directly engage with senior McKinsey experts on AI

Sven Blumberg is a senior partner in McKinsey’s Düsseldorf office; Michael Chui is a partner at the McKinsey Global Institute and is based in the Bay Area office, where Lareina Yee is a senior partner; Kia Javanmardian is a senior partner in the Chicago office, where Alex Singla , the global leader of QuantumBlack, AI by McKinsey, is also a senior partner; Kate Smaje and Alex Sukharevsky are senior partners in the London office.

But we tend to view the possibility of sentient machines with fascination as well as fear. This curiosity has helped turn science fiction into actual science. Twentieth-century theoreticians, like computer scientist and mathematician Alan Turing, envisioned a future where machines could perform functions faster than humans. The work of Turing and others soon made this a reality. Personal calculators became widely available in the 1970s, and by 2016, the US census showed that 89 percent of American households had a computer. Machines— smart machines at that—are now just an ordinary part of our lives and culture.

Those smart machines are also getting faster and more complex. Some computers have now crossed the exascale threshold, meaning they can perform as many calculations in a single second as an individual could in 31,688,765,000 years . And beyond computation, which machines have long been faster at than we have, computers and other devices are now acquiring skills and perception that were once unique to humans and a few other species.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

AI is a machine’s ability to perform the cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem-solving, and even exercising creativity. You’ve probably interacted with AI even if you don’t realize it—voice assistants like Siri and Alexa are founded on AI technology, as are some customer service chatbots that pop up to help you navigate websites.

Applied AI —simply, artificial intelligence applied to real-world problems—has serious implications for the business world. By using artificial intelligence, companies have the potential to make business more efficient and profitable. But ultimately, the value of AI isn’t in the systems themselves. Rather, it’s in how companies use these systems to assist humans—and their ability to explain to shareholders and the public what these systems do—in a way that builds trust and confidence.

For more about AI, its history, its future, and how to apply it in business, read on.

Learn more about QuantumBlack, AI by McKinsey .

Introducing McKinsey Explainers : Direct answers to complex questions

What is machine learning.

Machine learning is a form of artificial intelligence that can adapt to a wide range of inputs, including large sets of historical data, synthesized data, or human inputs. (Some machine learning algorithms are specialized in training themselves to detect patterns; this is called deep learning. See Exhibit 1.) These algorithms can detect patterns and learn how to make predictions and recommendations by processing data, rather than by receiving explicit programming instruction. Some algorithms can also adapt in response to new data and experiences to improve over time.

The volume and complexity of data that is now being generated, too vast for humans to process and apply efficiently, has increased the potential of machine learning, as well as the need for it. In the years since its widespread deployment, which began in the 1970s, machine learning has had an impact on a number of industries, including achievements in medical-imaging analysis  and high-resolution weather forecasting.

The volume and complexity of data that is now being generated, too vast for humans to process and apply efficiently, has increased the potential of machine learning, as well as the need for it.

What is deep learning?

Deep learning is a more advanced version of machine learning that is particularly adept at processing a wider range of data resources (text as well as unstructured data including images), requires even less human intervention, and can often produce more accurate results than traditional machine learning. Deep learning uses neural networks—based on the ways neurons interact in the human brain —to ingest data and process it through multiple neuron layers that recognize increasingly complex features of the data. For example, an early layer might recognize something as being in a specific shape; building on this knowledge, a later layer might be able to identify the shape as a stop sign. Similar to machine learning, deep learning uses iteration to self-correct and improve its prediction capabilities. For example, once it “learns” what a stop sign looks like, it can recognize a stop sign in a new image.

What is generative AI?

Case study: vistra and the martin lake power plant.

Vistra is a large power producer in the United States, operating plants in 12 states with a capacity to power nearly 20 million homes. Vistra has committed to achieving net-zero emissions by 2050. In support of this goal, as well as to improve overall efficiency, QuantumBlack, AI by McKinsey worked with Vistra to build and deploy an AI-powered heat rate optimizer (HRO) at one of its plants.

“Heat rate” is a measure of the thermal efficiency of the plant; in other words, it’s the amount of fuel required to produce each unit of electricity. To reach the optimal heat rate, plant operators continuously monitor and tune hundreds of variables, such as steam temperatures, pressures, oxygen levels, and fan speeds.

Vistra and a McKinsey team, including data scientists and machine learning engineers, built a multilayered neural network model. The model combed through two years’ worth of data at the plant and learned which combination of factors would attain the most efficient heat rate at any point in time. When the models were accurate to 99 percent or higher and run through a rigorous set of real-world tests, the team converted them into an AI-powered engine that generates recommendations every 30 minutes for operators to improve the plant’s heat rate efficiency. One seasoned operations manager at the company’s plant in Odessa, Texas, said, “There are things that took me 20 years to learn about these power plants. This model learned them in an afternoon.”

Overall, the AI-powered HRO helped Vistra achieve the following:

• approximately 1.6 million metric tons of carbon abated annually
• 67 power generators optimized
• $60 million saved in about a year

Read more about the Vistra story here .

Generative AI (gen AI) is an AI model that generates content in response to a prompt. It’s clear that generative AI tools like ChatGPT and DALL-E (a tool for AI-generated art) have the potential to change how a range of jobs  are performed. Much is still unknown about gen AI’s potential, but there are some questions we can answer—like how gen AI models are built, what kinds of problems they are best suited to solve, and how they fit into the broader category of AI and machine learning.

For more on generative AI and how it stands to affect business and society, check out our Explainer “ What is generative AI? ”

What is the history of AI?

The term “artificial intelligence” was coined in 1956  by computer scientist John McCarthy for a workshop at Dartmouth. But he wasn’t the first to write about the concepts we now describe as AI. Alan Turing introduced the concept of the “ imitation game ” in a 1950 paper. That’s the test of a machine’s ability to exhibit intelligent behavior, now known as the “Turing test.” He believed researchers should focus on areas that don’t require too much sensing and action, things like games and language translation. Research communities dedicated to concepts like computer vision, natural language understanding, and neural networks are, in many cases, several decades old.

MIT physicist Rodney Brooks shared details on the four previous stages of AI:

Symbolic AI (1956). Symbolic AI is also known as classical AI, or even GOFAI (good old-fashioned AI). The key concept here is the use of symbols and logical reasoning to solve problems. For example, we know a German shepherd is a dog , which is a mammal; all mammals are warm-blooded; therefore, a German shepherd should be warm-blooded.

The main problem with symbolic AI is that humans still need to manually encode their knowledge of the world into the symbolic AI system, rather than allowing it to observe and encode relationships on its own. As a result, symbolic AI systems struggle with situations involving real-world complexity. They also lack the ability to learn from large amounts of data.

Symbolic AI was the dominant paradigm of AI research until the late 1980s.

Neural networks (1954, 1969, 1986, 2012). Neural networks are the technology behind the recent explosive growth of gen AI. Loosely modeling the ways neurons interact in the human brain , neural networks ingest data and process it through multiple iterations that learn increasingly complex features of the data. The neural network can then make determinations about the data, learn whether a determination is correct, and use what it has learned to make determinations about new data. For example, once it “learns” what an object looks like, it can recognize the object in a new image.

Neural networks were first proposed in 1943 in an academic paper by neurophysiologist Warren McCulloch and logician Walter Pitts. Decades later, in 1969, two MIT researchers mathematically demonstrated that neural networks could perform only very basic tasks. In 1986, there was another reversal, when computer scientist and cognitive psychologist Geoffrey Hinton and colleagues solved the neural network problem presented by the MIT researchers. In the 1990s, computer scientist Yann LeCun made major advancements in neural networks’ use in computer vision, while Jürgen Schmidhuber advanced the application of recurrent neural networks as used in language processing.

In 2012, Hinton and two of his students highlighted the power of deep learning. They applied Hinton’s algorithm to neural networks with many more layers than was typical, sparking a new focus on deep neural networks. These have been the main AI approaches of recent years.

Traditional robotics (1968). During the first few decades of AI, researchers built robots to advance research. Some robots were mobile, moving around on wheels, while others were fixed, with articulated arms. Robots used the earliest attempts at computer vision to identify and navigate through their environments or to understand the geometry of objects and maneuver them. This could include moving around blocks of various shapes and colors. Most of these robots, just like the ones that have been used in factories for decades, rely on highly controlled environments with thoroughly scripted behaviors that they perform repeatedly. They have not contributed significantly to the advancement of AI itself.

But traditional robotics did have significant impact in one area, through a process called “simultaneous localization and mapping” (SLAM). SLAM algorithms helped contribute to self-driving cars and are used in consumer products like vacuum cleaning robots and quadcopter drones. Today, this work has evolved into behavior-based robotics, also referred to as haptic technology because it responds to human touch.

• Behavior-based robotics (1985). In the real world, there aren’t always clear instructions for navigation, decision making, or problem-solving. Insects, researchers observed, navigate very well (and are evolutionarily very successful) with few neurons. Behavior-based robotics researchers took inspiration from this, looking for ways robots could solve problems with partial knowledge and conflicting instructions. These behavior-based robots are embedded with neural networks.

Learn more about  QuantumBlack, AI by McKinsey .

What is artificial general intelligence?

The term “artificial general intelligence” (AGI) was coined to describe AI systems that possess capabilities comparable to those of a human . In theory, AGI could someday replicate human-like cognitive abilities including reasoning, problem-solving, perception, learning, and language comprehension. But let’s not get ahead of ourselves: the key word here is “someday.” Most researchers and academics believe we are decades away from realizing AGI; some even predict we won’t see AGI this century, or ever. Rodney Brooks, an MIT roboticist and cofounder of iRobot, doesn’t believe AGI will arrive until the year 2300 .

The timing of AGI’s emergence may be uncertain. But when it does emerge—and it likely will—it’s going to be a very big deal, in every aspect of our lives. Executives should begin working to understand the path to machines achieving human-level intelligence now and making the transition to a more automated world.

For more on AGI, including the four previous attempts at AGI, read our Explainer .

What is narrow AI?

Narrow AI is the application of AI techniques to a specific and well-defined problem, such as chatbots like ChatGPT, algorithms that spot fraud in credit card transactions, and natural-language-processing engines that quickly process thousands of legal documents. Most current AI applications fall into the category of narrow AI. AGI is, by contrast, AI that’s intelligent enough to perform a broad range of tasks.

How is the use of AI expanding?

AI is a big story for all kinds of businesses, but some companies are clearly moving ahead of the pack . Our state of AI in 2022 survey showed that adoption of AI models has more than doubled since 2017—and investment has increased apace. What’s more, the specific areas in which companies see value from AI have evolved, from manufacturing and risk to the following:

• marketing and sales
• product and service development
• strategy and corporate finance

One group of companies is pulling ahead of its competitors. Leaders of these organizations consistently make larger investments in AI, level up their practices to scale faster, and hire and upskill the best AI talent. More specifically, they link AI strategy to business outcomes and “ industrialize ” AI operations by designing modular data architecture that can quickly accommodate new applications.

What are the limitations of AI models? How can these potentially be overcome?

We have yet to see the longtail effect of gen AI models. This means there are some inherent risks involved in using them—both known and unknown.

The outputs gen AI models produce may often sound extremely convincing. This is by design. But sometimes the information they generate is just plain wrong. Worse, sometimes it’s biased (because it’s built on the gender, racial, and other biases of the internet and society more generally).

It can also be manipulated to enable unethical or criminal activity. Since gen AI models burst onto the scene, organizations have become aware of users trying to “jailbreak” the models—that means trying to get them to break their own rules and deliver biased, harmful, misleading, or even illegal content. Gen AI organizations are responding to this threat in two ways: for one thing, they’re collecting feedback from users on inappropriate content. They’re also combing through their databases, identifying prompts that led to inappropriate content, and training the model against these types of generations.

But awareness and even action don’t guarantee that harmful content won’t slip the dragnet. Organizations that rely on gen AI models should be aware of the reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content.

These risks can be mitigated, however, in a few ways. “Whenever you use a model,” says McKinsey partner Marie El Hoyek, “you need to be able to counter biases  and instruct it not to use inappropriate or flawed sources, or things you don’t trust.” How? For one thing, it’s crucial to carefully select the initial data used to train these models to avoid including toxic or biased content. Next, rather than employing an off-the-shelf gen AI model, organizations could consider using smaller, specialized models. Organizations with more resources could also customize a general model based on their own data to fit their needs and minimize biases.

It’s also important to keep a human in the loop (that is, to make sure a real human checks the output of a gen AI model before it is published or used) and avoid using gen AI models for critical decisions, such as those involving significant resources or human welfare.

It can’t be emphasized enough that this is a new field. The landscape of risks and opportunities is likely to continue to change rapidly in the coming years. As gen AI becomes increasingly incorporated into business, society, and our personal lives, we can also expect a new regulatory climate to take shape. As organizations experiment—and create value—with these tools, leaders will do well to keep a finger on the pulse of regulation and risk.

What is the AI Bill of Rights?

The Blueprint for an AI Bill of Rights, prepared by the US government in 2022, provides a framework for how government, technology companies, and citizens can collectively ensure more accountable AI. As AI has become more ubiquitous, concerns have surfaced  about a potential lack of transparency surrounding the functioning of gen AI systems, the data used to train them, issues of bias and fairness, potential intellectual property infringements, privacy violations, and more. The Blueprint comprises five principles that the White House says should “guide the design, use, and deployment of automated systems to protect [users] in the age of artificial intelligence.” They are as follows:

• The right to safe and effective systems. Systems should undergo predeployment testing, risk identification and mitigation, and ongoing monitoring to demonstrate that they are adhering to their intended use.
• Protections against discrimination by algorithms. Algorithmic discrimination is when automated systems contribute to unjustified different treatment of people based on their race, color, ethnicity, sex, religion, age, and more.
• Protections against abusive data practices, via built-in safeguards. Users should also have agency over how their data is used.
• The right to know that an automated system is being used, and a clear explanation of how and why it contributes to outcomes that affect the user.
• The right to opt out, and access to a human who can quickly consider and fix problems.

At present, more than 60 countries or blocs have national strategies governing the responsible use of AI (Exhibit 2). These include Brazil, China, the European Union, Singapore, South Korea, and the United States. The approaches taken vary from guidelines-based approaches, such as the Blueprint for an AI Bill of Rights in the United States, to comprehensive AI regulations that align with existing data protection and cybersecurity regulations, such as the EU’s AI Act, due in 2024.

There are also collaborative efforts between countries to set out standards for AI use. The US–EU Trade and Technology Council is working toward greater alignment between Europe and the United States. The Global Partnership on Artificial Intelligence, formed in 2020, has 29 members including Brazil, Canada, Japan, the United States, and several European countries.

Even though AI regulations are still being developed, organizations should act now to avoid legal, reputational, organizational, and financial risks. In an environment of public concern, a misstep could be costly. Here are four no-regrets, preemptive actions organizations can implement today:

• Transparency. Create an inventory of models, classifying them in accordance with regulation, and record all usage across the organization that is clear to those inside and outside the organization.
• Governance. Implement a governance structure for AI and gen AI that ensures sufficient oversight, authority, and accountability both within the organization and with third parties and regulators.
• Data management. Proper data management includes awareness of data sources, data classification, data quality and lineage, intellectual property, and privacy management.
• Model management. Organizations should establish principles and guardrails for AI development and use them to ensure all AI models uphold fairness and bias controls.
• Cybersecurity and technology management. Establish strong cybersecurity and technology to ensure a secure environment where unauthorized access or misuse is prevented.
• Individual rights. Make users aware when they are interacting with an AI system, and provide clear instructions for use.

How can organizations scale up their AI efforts from ad hoc projects to full integration?

Most organizations are dipping a toe into the AI pool—not cannonballing. Slow progress toward widespread adoption is likely due to cultural and organizational barriers. But leaders who effectively break down these barriers will be best placed to capture the opportunities of the AI era. And—crucially—companies that can’t take full advantage of AI are already being sidelined by those that can, in industries like auto manufacturing and financial services.

To scale up AI, organizations can make three major shifts :

• Move from siloed work to interdisciplinary collaboration. AI projects shouldn’t be limited to discrete pockets of organizations. Rather, AI has the biggest impact when it’s employed by cross-functional teams with a mix of skills and perspectives, enabling AI to address broad business priorities.
• Empower frontline data-based decision making . AI has the potential to enable faster, better decisions at all levels of an organization. But for this to work, people at all levels need to trust the algorithms’ suggestions and feel empowered to make decisions. (Equally, people should be able to override the algorithm or make suggestions for improvement when necessary.)
• Adopt and bolster an agile mindset. The agile test-and-learn mindset will help reframe mistakes as sources of discovery, allaying the fear of failure and speeding up development.

Learn more about QuantumBlack, AI by McKinsey , and check out AI-related job opportunities if you’re interested in working at McKinsey.

Articles referenced:

• “ As gen AI advances, regulators—and risk functions—rush to keep pace ,” December 21, 2023, Andreas Kremer, Angela Luget , Daniel Mikkelsen , Henning Soller , Malin Strandell-Jansson, and Sheila Zingg
• “ What is generative AI? ,” January 19, 2023
• “ Tech highlights from 2022—in eight charts ,” December 22, 2022
• “ Generative AI is here: How tools like ChatGPT could change your business ,” December 20, 2022, Michael Chui , Roger Roberts , and Lareina Yee  
• “ The state of AI in 2022—and a half decade in review ,” December 6, 2022, Michael Chui , Bryce Hall , Helen Mayhew , Alex Singla , and Alex Sukharevsky  
• “ Why businesses need explainable AI—and how to deliver it ,” September 29, 2022, Liz Grennan , Andreas Kremer, Alex Singla , and Peter Zipparo
• “ Why digital trust truly matters ,” September 12, 2022, Jim Boehm , Liz Grennan , Alex Singla , and Kate Smaje
• “ McKinsey Technology Trends Outlook 2023 ,” July 20, 2023, Michael Chui , Mena Issler, Roger Roberts , and Lareina Yee  
• “ An AI power play: Fueling the next wave of innovation in the energy sector ,” May 12, 2022, Barry Boswell, Sean Buckley, Ben Elliott, Matias Melero , and Micah Smith  
• “ Scaling AI like a tech native: The CEO’s role ,” October 13, 2021, Jacomo Corbo, David Harvey, Nicolas Hohn, Kia Javanmardian , and Nayur Khan
• “ What the draft European Union AI regulations mean for business ,” August 10, 2021, Misha Benjamin, Kevin Buehler , Rachel Dooley, and Peter Zipparo
• “ Winning with AI is a state of mind ,” April 30, 2021, Thomas Meakin , Jeremy Palmer, Valentina Sartori , and Jamie Vickers
• “ Breaking through data-architecture gridlock to scale AI ,” January 26, 2021, Sven Blumberg , Jorge Machado , Henning Soller , and Asin Tavakoli  
• “ An executive’s guide to AI ,” November 17, 2020, Michael Chui , Brian McCarthy, and Vishnu Kamalnath
• “ Executive’s guide to developing AI at scale ,” October 28, 2020, Nayur Khan , Brian McCarthy, and Adi Pradhan
• “ An executive primer on artificial general intelligence ,” April 29, 2020, Federico Berruti , Pieter Nel, and Rob Whiteman
• “ The analytics academy: Bridging the gap between human and artificial intelligence ,” McKinsey Quarterly , September 25, 2019, Solly Brown, Darshit Gandhi, Louise Herring , and Ankur Puri  

This article was updated in April 2024; it was originally published in April 2023.

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Spending Too Much Time on Emails? This Pillsbury IP Partner Developed an AI Tool to Solve the Problem

Josh Tucker, a Pillsbury Winthrop Shaw Pittman IP partner in Austin, had one goal: "I want to be able to yell at my computer and make it do what I want."

June 07, 2024 at 01:47 PM

4 minute read

Artificial Intelligence

Brenda Sapino Jeffreys

Senior reporter

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Thank you for sharing, what you need to know.

• Pillsbury Winthrop Shaw Pittman IP partner Josh Tucker developed a tool he calls Winthrop to help him answer some emails quickly.
• An engineer whose practice focuses on software-related patent applications, Tucker used open-source software to create Winthrop, which he uses on his personal computer with no connection to the firm's network. .
• Tucker hopes the firm will eventually make the tool available to other lawyers at the firm.

Intrigued by the possibilities that AI could make his job as a lawyer more efficient, Pillsbury Winthrop Shaw Pittman ‘s Josh Tucker, an intellectual property partner in Austin, built an AI tool he named Winthrop to speed up the time-consuming responsibility of email.

Tucker developed the tool using open source software, and he has been using it for the last three months to help him save time responding to some emails.

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What Is Big Data?

Sherry Tiao | Senior Manager, AI & Analytics, Oracle | March 11, 2024

In This Article

Big Data Defined

The three “vs” of big data, the value—and truth—of big data, the history of big data, big data use cases, big data challenges, how big data works, big data best practices.

What exactly is big data?

The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three “Vs.”

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.

Two more Vs have emerged over the past few years: value and veracity . Data has intrinsic value. But it’s of no use until that value is discovered. Equally important: How truthful is your data—and how much can you rely on it?

Today, big data has become capital. Think of some of the world’s biggest tech companies. A large part of the value they offer comes from their data, which they’re constantly analyzing to produce more efficiency and develop new products.

Recent technological breakthroughs have exponentially reduced the cost of data storage and compute, making it easier and less expensive to store more data than ever before. With an increased volume of big data now cheaper and more accessible, you can make more accurate and precise business decisions.

Finding value in big data isn’t only about analyzing it (which is a whole other benefit). It’s an entire discovery process that requires insightful analysts, business users, and executives who ask the right questions, recognize patterns, make informed assumptions, and predict behavior.

But how did we get here?

Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and ‘70s when the world of data was just getting started with the first data centers and the development of the relational database.

Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services. Hadoop (an open source framework created specifically to store and analyze big data sets) was developed that same year. NoSQL also began to gain popularity during this time.

The development of open source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they make big data easier to work with and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users are still generating huge amounts of data—but it’s not just humans who are doing it.

With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data.

While big data has come far, its usefulness is only just beginning. Cloud computing has expanded big data possibilities even further. The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data. And graph databases are becoming increasingly important as well, with their ability to display massive amounts of data in a way that makes analytics fast and comprehensive.

Discover the Insights in Your Data

• Who are the criminals passing dirty money around and committing financial services fraud?
• Who has been in contact with an infected person and needs to go into quarantine?
• How can feature engineering for data science be made simpler and more efficient?

Click below to access the 17 Use Cases for Graph Databases and Graph Analytics ebook.

Big Data Benefits

• Big data makes it possible for you to gain more complete answers because you have more information.
• More complete answers mean more confidence in the data—which means a completely different approach to tackling problems.

Big data can help you address a range of business activities, including customer experience and analytics. Here are just a few.

Download your free ebook to learn about:

• New ways you can use your data
• Ways the competition could be innovating
• Benefits and challenges of different use cases

While big data holds a lot of promise, it is not without its challenges.

First, big data is…big. Although new technologies have been developed for data storage, data volumes are doubling in size about every two years. Organizations still struggle to keep pace with their data and find ways to effectively store it.

But it’s not enough to just store the data. Data must be used to be valuable and that depends on curation. Clean data, or data that’s relevant to the client and organized in a way that enables meaningful analysis, requires a lot of work. Data scientists spend 50 to 80 percent of their time curating and preparing data before it can actually be used.

Finally, big data technology is changing at a rapid pace. A few years ago, Apache Hadoop was the popular technology used to handle big data. Then Apache Spark was introduced in 2014. Today, a combination of the two frameworks appears to be the best approach. Keeping up with big data technology is an ongoing challenge.

Discover more big data resources:

Big data gives you new insights that open up new opportunities and business models. Getting started involves three key actions:

1.  Integrate Big data brings together data from many disparate sources and applications. Traditional data integration mechanisms, such as extract, transform, and load (ETL) generally aren’t up to the task. It requires new strategies and technologies to analyze big data sets at terabyte, or even petabyte, scale.

During integration, you need to bring in the data, process it, and make sure it’s formatted and available in a form that your business analysts can get started with.

2.  Manage Big data requires storage. Your storage solution can be in the cloud, on premises, or both. You can store your data in any form you want and bring your desired processing requirements and necessary process engines to those data sets on an on-demand basis. Many people choose their storage solution according to where their data is currently residing. The cloud is gradually gaining popularity because it supports your current compute requirements and enables you to spin up resources as needed.

3.  Analyze Your investment in big data pays off when you analyze and act on your data. Get new clarity with a visual analysis of your varied data sets. Explore the data further to make new discoveries. Share your findings with others. Build data models with machine learning and artificial intelligence. Put your data to work.

To help you on your big data journey, we’ve put together some key best practices for you to keep in mind. Here are our guidelines for building a successful big data foundation.

Learn More About Big Data at Oracle

• Try a free big data workshop
• Infographic: How to Build Effective Data Lakes