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The Team That Wasn't (HBR Case Study and Commentary)

By: Suzy Wetlaufer, Jon R. Katzenbach, J. Richard Hackman, Genevieve Segol, Paul P. Baard, Ed Musselwhite, Kathleen Hurson, Michael Garber

Eric Holt had one responsibility as FireArt's director of strategy: to put together a team of people from each division and create and implement a comprehensive plan for the company's strategic…

• Length: 11 page(s)
• Publication Date: Nov 1, 1994
• Discipline: Organizational Behavior
• Product #: 94612-PDF-ENG

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Eric Holt had one responsibility as FireArt's director of strategy: to put together a team of people from each division and create and implement a comprehensive plan for the company's strategic realignment within six months. It seemed like an exciting, rewarding challenge. Unfortunately, the team got off on the wrong foot from its first meeting. Randy Louderback, FireArt's charismatic and extremely talented director of sales and marketing, seemed intent on sabotaging the group's efforts. Anxiously awaiting the start of the team's fourth meeting, Eric was determined to address Randy's behavior openly in the group. But before he could, Randy provoked a confrontation, and the meeting ended abruptly. What should Eric do now? Is Randy the team's only problem? In 94612 AND 94612Z, Jon R. Katzenbach, J. Richard Hackman, Genevieve Segol, Paul P. Baard, Ed Musselwhite, Kathleen Hurson, and Michael Garber offer advice in this fictional study.

THIS CASE STUDY INCLUDES BOTH THE CASE AND THE COMMENTARY. FOR TEACHING PURPOSES, THE REPRINT IS ALSO AVAILABLE IN TWO OTHER VERSIONS: CASE STUDY ONLY, REPRINT 94612X, AND COMMENTARY ONLY, REPRINT 94612Z.

Nov 1, 1994

Discipline:

Organizational Behavior

Harvard Business Review Digital Article

94612-PDF-ENG

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The Team That Wasn't (HBR Case Study)

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Publication Date: November 01, 1994

Eric Holt had one responsibility as FireArt's director of strategy: to put together a team of people from each division and create and implement a comprehensive plan for the company's strategic realignment within six months. It seemed like an exciting, rewarding challenge. Unfortunately, the team got off on the wrong foot from its first meeting. Randy Louderback, FireArt's charismatic and extremely talented director of sales and marketing, seemed intent on sabotaging the group's efforts. Anxiously awaiting the start of the team's fourth meeting, Eric was determined to address Randy's behavior openly in the group. But before he could, Randy provoked a confrontation, and the meeting ended abruptly. What should Eric do now? Is Randy the team's only problem? In 94612 and 94612Z, Jon R. Katzenbach, J. Richard Hackman, Genevieve Segol, Paul P. Baard, Ed Musselwhite, Kathleen Hurson, and Michael Garber offer advice on this fictional case study. For teaching purposes, this is the case-only version of the HBR case study. The commentary-only version is reprint 94612Z. The complete case study and commentary is reprint 94612.

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The Team That Wasn’t Harvard Case Solution & Analysis

Home >> Harvard Case Study Analysis Solutions >> The Team That Wasn’t

ANSWER TO QUESTION # 1

Upon analysing the case comprehensively, following are the problems that are detected in the team of Fire Art.

Lack of Cohesion

There is a lack of cohesion among the members of the team made in Fire Art. There is no synergy among the team members which further leads to the inefficiency of performance of the team. This iscaused because Eric lacks in making appropriate efforts in order to create a culture of group working

Lack of Motivation

The motivational level of the team members is very low. This has caused due to several issues faced by the team. This is further decreasing the overall efficiency performance of the team to a considerable extent.

Lack of Mutual understanding

There is no mutual understanding among the members of the team. This is because Eric has not used the team building model effectively, which implies a process of building an effective team in four stages.

The first stage known as “FORMING” implies that the team members should develop a mutual understanding in order to work efficiently and effectively. Thus, lack in mutual understanding would decrease the overall performance of the team.

Difference in Contextual Backgrounds

Eric selected the team members of different contextual backgrounds. This has increased the overall conflicts among the team members as each members in the team has different cultural and ethical values, which gives rise to conflicts among the team members and subsequently decreases the overall performance of the team.

Superiority complexes

The team members selected by Eric are of various levels of hierarchy such as Randy pertains from the top level of hierarchy, whereas, Ray pertains to lower middle level of hierarchy. Thus, this creates superiority complexes in lower level employees, which further effects negatively on the overall performance of the team work.

Personal Biasness:

After comprehensively analysing the case, it seems that Randy is only considered about gaining only his personal advantage, rather than working as a team. This further creates a negative impact on the efficiency of team work

Excessive Criticism on other’s opinion

Randy often criticizeson others’ opinions; this decreases the motivational level of the member providing opinion. Thus, this decreases the willingness of participation among the team members, which further reduces the efficiency of the performance of the team.

Lack of conflict resolution techniques:

Eric is unable to use conflict resolution techniques efficiently and effectively in order to resolve the rising conflicts among the team members. This has caused frustration among the team members as conflicts remain unresolved.

This also decreases the overall motivational level among the members of the team which further leads to team work inefficiency.

Unable to meet deadlines

Eric has not properly set the priorities of the tasks allocated to the team. In addition to this, Eric has not made and communicated the time schedule of the tasks to the team members. This decreases the ability of the team to complete their tasks before deadlines, efficiently and effectively

Lack of rewards

There is no rewarding structure in the team, neither monetary nor non-monetary, this has decreased the overall motivational level of team members, which has further decreased the overall level of commitment and dedication among the team members. This further poses a negative effect on the overall efficiency of the team work.

Lack of creativity

There is a lack in creativity among the team members, as they are unable to generate fresh ideas as well as they are also unable to exploit new and unidentified opportunities. This further decreases the overall efficiency of the team work.

Lack of team learning

The team members in the team are unable to gain advantage as well as they are also unable to learn from each other.Since the most senior member in the team, Randy, is only busy in leg pulling of other team members and does not share his skills and expertise with other team members, therefore, the other team members are unable to get advantage and learn from his skills and expertise.

Vague roles:

Eric has not appropriately assigned the respective roles to the team members, which has created role ambiguity among the team members. This has further enhanced the level of frustration among the team members and has subsequently reduced the overall efficiency of work performed by team members. The Team That Wasn’t Casen Solution

Difficulty in making decisions:

Eric is unable to formulate effective decisions as well as other members of the team also find it difficult in making effective decisions. Lack in collaboration and coordination among the team members becomes a hurdle in making strategic decisions efficiently and effectively.

Thus, this decreases the overall motivational level among the team members as well as this also creates frustration among the team members. As a result, this also decreases the overall efficiency of the team effort......................

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How police pieced together the case against Samuel Woodward in the killing of Blaze Bernstein

I t was six days into the massive, nationally-watched search for a missing 19-year-old last seen at a small Lake Forest park, and something just wasn’t making sense to the lead homicide investigator who had been assigned to assist the Orange County Sheriff Department’s missing person team.

Cell phone records outlining the movement of Blaze Bernstein — the missing young man — and Samuel Woodward — the former classmate who authorities for days had already suspected of being involved in the disappearance — were showing both their phones in the same area of Borrego Park during the early morning hours, at which point the signal for Bernstein’s phone signal abruptly disappeared but Woodward’s continued to move.

Dozens of search and rescuers, volunteers, family members and deputies had spent more than a week going over the park, including the area where Bernstein’s cell phone signal appeared to cease. But Sgt. Dylan Jantzen and several other members of the homicide team decided to head out to check the area again.

The warmer weather that aided the early days of the search was gone, replaced by the time the investigators began scouring the bushes on the outskirts of the park with a winter chill and pouring rain. As Jantzen peered into a pipe in an unlocked drainage area he heard a colleague urgently cry out his name.

“Just the way he said it, I knew,” Jantzen recalled during recent courtroom testimony. “I turned around and walked over to him and I saw what I saw.”

Hours of heavy downpour had unveiled portions of Bernstein’s body in a makeshift grave among the bushes. A tragic end to a missing persons case that drew interest around the country immediately turned into a full-scale homicide investigation which, more than five years later, has led to the ongoing murder trial of Woodward .

Testimony from investigators — which has made up much of the prosecution case — has provided the most detailed picture yet as to how a frantic search for the missing Woodward turned into the highest profile Orange County murder case in recent memory .

Bernstein, a pre-med student at the University of Pennsylvania who was visiting his parent’s home for winter break — left the family residence late on Jan. 2, 2018 and never returned . Through Bernstein’s social media accounts, his parents learned he had met up with Woodward, a former classmate at the Orange County School of the Arts.

In phone calls with the parents and a deputy taking a missing persons report, Woodward said he drove Bernstein to the park, where he claimed Bernstein got out of his parked car to go meet a third person whose identity Woodward didn’t know. Woodward at one point described standing by bathrooms next to the parking lot and watching as Bernstein walked to a trail or pathway on the other end of the park.

Bernstein’s parents — who lived near Borrego Park and knew it well – didn’t believe that what Woodward was telling them made any sense. Law enforcement would soon have their own suspicions.

Members of the sheriff’s department search and rescue team gathered in the parking of a Hobby Lobby near the park. Joseph Saddler, a former reserve deputy and veteran of hundreds of searches, described teams spreading out inside the park and within the nearby — and far larger — Whiting Ranch trail system.

The search teams went over trails, looked through drainage areas, and did line searches in heavy vegetation. ATV riders assisted with the trail searches, while helicopters and drones kept an eye out overhead. As the search dragged on for days, the hope for finding Bernstein alive waned.

“On (January 4) I was told I was searching for a missing person, by (January 6) I came to the conclusion that I may be searching for someone who is deceased,” Saddler said.

From the beginning, it didn’t seem like a normal missing person case, Capt. Jack Ackerman said.

“It seemed like it would be unusual for someone to go missing at night in the dark and cold with no resources,” the captain testified.

Investigators were juggling a flood of tips from the general public and social media, including hundreds of tips submitted to a public phone line.

“We were getting inundated by the public, by businesses, by social media tips,” Jantzen said. “We were getting information from Blaze’s friends, people who didn’t even live in the state of California, from the Bernstein family, from the family attorney. It was a lot.”

Investigators were also spending their own time out at Borrego Park. Jantzen described running into Woodward during one visit, an encounter that left him even more suspicious.

Woodward had newly cut and dyed hair, and was keeping his hands in the pocket of a hooded sweatshirt, despite the relatively warm weather, the investigator said.

“When he did show his hands they were bandaged — multiple bandaids on each hand and you could see the blood on the bandaids,” Jantzen said. “And his fingernails were completely caked with dirt.”

As the search for Bernstein continued, investigators set up 24-hour surveillance of Woodward and his vehicle, a rented Nissan Rogue.

At one point, a sheriff’s surveillance team watched as Woodward walked out to his vehicle, which was parked on a street near his parent’s Newport Beach home, and began cleaning the car. Video taken by a member of the surveillance team showed Woodward walking around the car with a spray bottle and rag in his hands, methodically cleaning the car doors and back bumper.

One night during the surveillance, Woodward walked to his car, got a large bag or some type of item from the back of the vehicle and walked it over to the side of his parent’s home, Sgt. Travis Arburua testified.

With Bernstein’s phone not active, the investigators reached out to his cell phone provider for information. Those records — and the tracking it allowed investigators to create — were what spurred Jantzen and the other investigators to return to the park on January 9 , six days after the search began.

Capt. Ackerman was walking on a steep embankment near the fence of the adjacent elementary school when he saw some raised ground and a fallen tree branch. He walked over and moved the branch aside.

“At that point I saw a left hip and left leg area of a human body,” Ackerman testified, adding that he backed away, called his partner over and waited for the crime lab and coroner’s office officials to arrive.

Hours later, a team of investigators carried out a search warrant at the Woodward home. As the family members sat on a couch in their living room, a dozen or so investigators went through the residence.

Sgt. Matthew Parrish, who helped oversee the search, recalled the moment when one of his colleagues opened up a desk drawer and found what appeared to be a suspicious knife with a stain on it.

“Do you think it has blood on it?” Parrish recalled the investigator asking.

A sleeping bag, which also appeared to have blood on it, was found on the side of the home and also collected. Woodward himself was taken to the headquarters of the sheriff homicide unit in Santa Ana, then allowed to go home, albeit still under surveillance.

Testing was fast-tracked through the Orange County District Attorney’s Office rapid DNA unit. Jantzen, the lead investigator, juggled phone calls from the lead prosecutor and his surveillance unit, and as soon as the DNA results were returned Woodward was formally arrested .

“He was pulled over — he was driving with his mother — and was taken into custody,” the sergeant said.

Blood on the knife was matched to Bernstein, while blood found in Woodward’s vehicle was matched to both him and Bernstein , according to testimony.

Looking over a notebook of Woodward’s, Jantzen spotted a swastika, his first hint of Woodward’s connection to the Atomwaffen Division, a racially motivated, violent extremist group . Craig Goldsmith, a now-retired sheriff forensic examiner, described finding thousands of anti-semitic and homophobic images on Woodward’s phone and other electronics.

That material led prosecutors to add a hate crime enhancement to the first-degree murder charge that Woodward was facing. Woodward’s attorney has acknowledged he killed Bernstein, but denied the hate crime allegation, which if proven by a jury would result in a much longer prison sentence.

Pointing to the electronic evidence, the prosecution has accused Woodward of keeping a “hate diary” in which he described — in explicit detail — matching up with gay men on dating websites and then “ghosting” or scaring them. Deputy District Attorney Jennifer Walker has directly told jurors that Woodward killed Bernstein because Bernstein was gay.

Related links

• Ex-classmate testifies to receiving ‘sexually charged’ messages from accused killer Samuel Woodward
• Samuel Woodward killed former classmate Blaze Bernstein, but it was not a hate crime, defense lawyer says
• Blaze Bernstein’s disappearance was ‘beginning of hell,’ father testifies in murder trial
• Samuel Woodward, accused of killing Blaze Bernstein, to be evaluated by mental health experts
• Blaze Bernstein’s killing, 1 year later: How his death has impacted Orange County and beyond

The defense has countered by painting a more nuanced portrait of Woodward as a young man on the autism spectrum who was confused by his own sexuality and drawn into an extremist group by members who targeted outcasts for recruitment.

At times seemingly flirtatious online messages between Woodward and Bernstein — which Bernstein allegedly shared with other friends despite telling Woodward he would keep them a secret — have been shown to the jury.

After a break this week, jurors will return for testimony next week, with the prosecution nearing the end of their case and the defense preparing to begin to call their witnesses. The trial is expected to last until late June.

©2024 MediaNews Group, Inc. Visit ocregister.com. Distributed by Tribune Content Agency, LLC.

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Los Angeles Galaxy | Swanson: Galaxy fans got what they want – a…

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Los Angeles Galaxy

Los angeles galaxy | swanson: galaxy fans got what they want – a team worth cheering, the galaxy's successful supporters strike is case study in what's possible when teams do right by their fans.

Toxic at times. Demanding, typically. Experts, of course.

Certifiable, so you could call them to the stand to testify, bring them on as a second and third and 1,000th opinion to diagnose a team’s problems. Because they always know best.

They could tell you more than the people on the team, the people whose literal jobs it is to run the team. People who are paid big bucks to do it, who are privy to the private, inner-workings, the deepest, diveiest data and who, nonetheless, are usually doing it wrong.

Right that you fans are ridiculous? Or right that you, the people most voluntarily invested in an organization, tend to have the right idea?

Well, take the Galaxy as a case-study.

The MLS club’s fans woke up happy Sunday, their Galaxy in third place in the Western Conference standings and two points behind first-place Real Salt Lake after playing that team to a dramatic 2-2 draw the night before, a result that kept the home team – now 5-2-5, 20 points – undefeated at Dignity Health Sports Park.

A year ago, it would’ve been hard to find fans less happy with their side.

The Galaxy’s fans weren’t aggrieved because of how their team started the first dozen matches last season – 2-7-3, 9 points – it was seasons of pent-up frustration that boiled over into an outright revolt.

It was the meager total of two playoff appearances since 2017. The churn of coaches. The misguided name-over-game player signings and ill-afforded, off-the-field missteps that drew sanctions from MLS. The lack of direction off the pitch. The lack of defense and offense on it.

The perceived lack of respect.

And, though no one in the Galaxy’s galaxy would want to admit it, the unavoidable specter of success from L.A.’s other MLS football club up the freeway at BMO stadium, where in its sixth year, the Black & Gold was playing to defend its first MLS Cup, the cherry on a long and rapidly growing list of accomplishments.

It all pained the Galaxy’s devotees so much that hundreds of them felt they had no choice but to take a stand – outside of the stadium.

They brought their fervor for their favorite team to a picket line, boycotting its first seven home matches last season and re-entering the stadium only after Chris Klein, the team’s president who was in his 11th season with the club and who had just been re-signed to a multi-year deal, was ousted . (T​echnical director Jovan Kirovski would be excused in the offseason.)

Klein Out Protest by LA Galaxy Supporter Groups/Fans #LAGalaxy #mls #barras #ultras #hinchas #kleinout pic.twitter.com/X8xXGfDQoX — Ultras Barras Usa (@UltrasBarrasUsa) February 5, 2023

And so they got what they wanted, these futbol fanatics. They really did.

New ideas, fresh legs, renewed hope.

Will Kuntz was elevated to the club’s top front office decision-maker in December and signed Joseph Paintsil from the Belgian Pro League and Gabriel Pec from Brazil’s Série A as designated players, pairing them up with premier playmaker Riqui Puig. The Galaxy also added defender Miki Yamane to help and brought aboard former LAFC goalkeeper John McCarthy.

And now, look: The Galaxy’s 23 goals rank second in the MLS (behind Lionel Messi’s Miami FC, with an eye-popping 35). And, despite having faced a series of tough opponents and playing only five of its first 12 matches at home, the Galaxy is near the top of the table, in such a good place that fans are starting to dream really big again.

“What’s this team’s ceiling?” I asked again and again Saturday before the Galaxy and Real Salt Lake clashed, and again and again I got responses like Manuel Moreno’s: “Win a championship,” the 36-year-old from Lynwood said. “I think we deserve it. The faithful ones deserve it.”

Though, yes, the team remains a work in progress. Coach Greg Vanney didn’t hesitate to say so Saturday, explaining what transpired to require a two-goal second-half comeback: “Our team is still maturing and growing up together.”

But that’s precisely what the people on the picket line were clamoring for; they wanted to root for a club that was trending upward instead of fading into obscurity.

“Yeah, our voices were heard, they were definitely heard,” said Ivan Osorio, a 23-year-old supporter from L.A., who was among those who boycotted matches last season. “It was every game that we had at home – we wanted to come, but it was for the team that we couldn’t. We wanted better.

“Now, you see the big improvement; last year we were in the bottom, now we’re fighting for first. It’s a way big improvement. The club did what they needed to do.”

Osorio feels validated, of course. But so too does Jose Juarez, who was waiting Saturday to enter what would again be a sold-out stadium. He was queued up in Legends Plaza, near where I watched an impassioned protest of peeved supporters beat their drum a year earlier.

“I did support the boycott, but I also supported my team. It was very conflicting,” said Juarez, who decided last season it was best to continue to attend games, usually with his son, now-9-year-old Geronimo. “Now it’s like a payoff to a fan like me, in my mind. Because I supported them, I feel rewarded.”

That’s another thing about fans, they don’t necessarily agree.

So, wait. If fans aren’t all on the same page, maybe it’s just as well that they’re not calling all the shots?

12 hours ago lol – sports! https://t.co/sF1Je0vVQ1 pic.twitter.com/F9N5tLRSvR — Mirjam Swanson (@MirjamSwanson) May 12, 2024

Maybe it’s for the better that the fast-fingered folks who were weighing in Saturday morning on the Reddit thread: “Should LA get rid of Miguel berry?” aren’t pulling the player personnel levers in real time?

Because 12 hours after that online discussion – with many attendees starting to stream out of the stadium, too many of those fans giving up on a team that hadn’t yet given up – it was Berry who came through at the death.

Berry, a forward who, in his first season with the Galaxy, has taken just three shots in 12 matches, whose toe-poke off a Puig pass found its way past RSL’s goalkeeper Zac MacMath with seconds to spare. Berry, who was the hero after Paintsil was pulled and left the pitch.

But you know how fans can be: Impatient. Demanding. Smarter-than-thou. Happy to be right.

Also, though: Optimistic. Faithful. Proud. And sometimes, happy to be wrong too.

This is why you don’t leave an @LAGalaxy game early!! @ACBrigade @LARiotSquad @GalaxyPodcast pic.twitter.com/68slrTnuOQ — – (@thekevinryder) May 12, 2024

What’s the first word you were thinking when Miguel Berry equalized at the death? 👀 pic.twitter.com/pYkZL24YTY — Galaxy Fan Talk (@LAGalaxyFanTalk) May 12, 2024

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Will Trump Testify At Hush Money Trial? Here’s Why Some Lawyers Think It’s Unlikely.

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Former President Donald Trump’s lawyers are expected to start presenting their defense in his criminal trial next week after prosecutors rest their case, but it still remains to be seen whether the ex-president will take the stand and testify himself—though Trump has wavered on the issue, and legal experts largely believe it would be an unwise move.

Former President Donald Trump returns to the courtroom after a short break during his hush money ... [+] trial on May 14 in New York City.

The defense is expected to start presenting its case next week, after prosecutors said Tuesday that ex-Trump attorney Michael Cohen , who will take the stand for a third day on Thursday, will be their final witness.

Trump’s attorneys aren’t expected to call many witnesses—if any—with attorney Todd Blanche telling the judge Tuesday they have one expert witness they may call, but that’s the only one they have set as of now.

Blanche said Tuesday it’s still unclear if Trump will testify, answering “no” when Judge Juan Merchan asked if the attorney had any “indication” of whether Trump would testify or if any “determination” had been made on the issue.

Trump publicly committed to testifying when the trial first got underway, telling reporters, “All I can do is tell the truth. And the truth is that there’s no case” just before the trial began.

Trump became more noncommittal in an April 26 interview with Newsmax, where he said only that he would testify “if it’s necessary”—and the ex-president has not committed to testifying since, though he did falsely claim a gag order against him barred him from taking the stand, which Merchan swiftly clarified was not the case.

Trump spokesperson Steven Cheung has not yet responded to a request for comment.

Get Forbes Breaking News Text Alerts: We’re launching text message alerts so you'll always know the biggest stories shaping the day’s headlines. Text “Alerts” to (201) 335-0739 or sign up here .

It’s common for defendants not to testify in criminal trials, with many defense attorneys believing the risk of a defendant harming their own case outweighs the benefits. When the trial began last month, Merchan reminded jurors Trump has a right not to testify, and if he chooses not to take the witness stand, they can’t hold it against him.

Should Trump Testify?

While it’s still up in the air whether Trump will testify, legal experts suggest doing so would hurt the ex-president’s case. “It would be suicide for” Trump to testify, left-leaning attorney Norm Eisen said on CNN Tuesday, arguing there’s “no way” his lawyers would allow him to take the stand. Trump’s former attorney Tim Parlatore said the same on CNN Tuesday, telling Kaitlan Collins that he “personally would suggest that he probably should not” testify. Both Eisen and Parlatore suggested doing so would hurt Trump’s case, with Parlatore arguing it would “significantly increase” Trump’s chances of conviction because “if the jury disbelieves him on anything, however small, that’s something they’re gonna hold against him and be much more likely to convict.” If Trump is convicted, Eisen suggested taking the stand could also lead to a more severe punishment, arguing that if Merchan believes Trump may have lied under oath, “it virtually ensures a sentence of incarceration.” While legal experts suggest Trump’s lawyers are near certain to prefer their client stay off the stand, however, they also note the ex-president has a history of not listening to his attorneys.

What To Watch For

Any decision on whether Trump will take the stand is likely to be made at the last minute, legal experts have noted, with Parlatore saying the decision will be made “down to the wire” based on whether it’s “worth taking the risk,” and former federal prosecutor Joyce Vance noting in April it’s “unlikely” Trump’s lawyers will decide “until the moment is close at hand.” If Trump’s lawyers don’t take very long to present their defense—whether or not Trump testifies—it’s possible the case could go to the jury as soon as next week. The prosecution is likely to rest its case Thursday or on Monday—the court will be off on Friday for Trump’s son Barron Trump’s graduation—depending on how long Cohen’s testimony runs.

Surprising Fact

While this case marks Trump’s first criminal trial, the ex-president has recently taken the stand at several of his recent civil trials, testifying about defamation allegations brought against him by writer E. Jean Carroll and the fraud allegations brought against him and his company. Neither testimony appeared to help his case, as he was found liable in both cases and ordered to pay $88.3 million and $454.2 million, respectively. In his order finding Trump and his co-defendants liable in the fraud case , Judge Arthur Engoron argued Trump “severely compromised his credibility” when testifying, noting the ex-president “rarely responded to the questions asked, and he frequently interjected long, irrelevant speeches on issues far beyond the scope of the trial.”

Key Background

Trump faces 34 felony charges of falsifying business records in his Manhattan trial, which is one of four criminal cases that’s been brought against the ex-president. The charges stem from a $130,000 payment Cohen made to adult film star Stormy Daniels in the days before the 2016 election in order to cover up her allegations of having an affair with Trump. Trump then allegedly reimbursed Cohen for the payment—paying him $420,000 after adding in other expenses and enough money to cover taxes—which were paid through a series of reimbursement checks throughout 2017. Prosecutors allege those reimbursements were handled through the Trump Organization and falsely labeled as being for legal services, which Trump has denied, as his lawyers have claimed the payments were correctly labeled and tried to distance Trump from the reimbursement scheme. Trump has pleaded not guilty to the charges against him—as well as in his other three cases—decrying the case as a politically motivated “witch hunt” designed to hurt his campaign. The trial, which has been ongoing since mid-April, has included multiple witnesses tying Trump to the hush money scheme, with Cohen directly testifying that Trump approved the Daniels payment and was involved with the reimbursement scheme. As the criminal defendant, Trump has been required to be present in the courtroom every day of the trial—though media reports suggest he has regularly dozed off during the proceedings.

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Published: 5 April 2024 Contributors: Tim Mucci, Cole Stryker

Big data analytics refers to the systematic processing and analysis of large amounts of data and complex data sets, known as big data, to extract valuable insights. Big data analytics allows for the uncovering of trends, patterns and correlations in large amounts of raw data to help analysts make data-informed decisions. This process allows organizations to leverage the exponentially growing data generated from diverse sources, including internet-of-things (IoT) sensors, social media, financial transactions and smart devices to derive actionable intelligence through advanced analytic techniques.

In the early 2000s, advances in software and hardware capabilities made it possible for organizations to collect and handle large amounts of unstructured data. With this explosion of useful data, open-source communities developed big data frameworks to store and process this data. These frameworks are used for distributed storage and processing of large data sets across a network of computers. Along with additional tools and libraries, big data frameworks can be used for:

• Predictive modeling by incorporating artificial intelligence (AI) and statistical algorithms
• Statistical analysis for in-depth data exploration and to uncover hidden patterns
• What-if analysis to simulate different scenarios and explore potential outcomes
• Processing diverse data sets, including structured, semi-structured and unstructured data from various sources.

Four main data analysis methods  – descriptive, diagnostic, predictive and prescriptive  – are used to uncover insights and patterns within an organization's data. These methods facilitate a deeper understanding of market trends, customer preferences and other important business metrics.

IBM named a Leader in the 2024 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions.

Structured vs unstructured data

What is data management?

The main difference between big data analytics and traditional data analytics is the type of data handled and the tools used to analyze it. Traditional analytics deals with structured data, typically stored in relational databases . This type of database helps ensure that data is well-organized and easy for a computer to understand. Traditional data analytics relies on statistical methods and tools like structured query language (SQL) for querying databases.

Big data analytics involves massive amounts of data in various formats, including structured, semi-structured and unstructured data. The complexity of this data requires more sophisticated analysis techniques. Big data analytics employs advanced techniques like machine learning and data mining to extract information from complex data sets. It often requires distributed processing systems like Hadoop to manage the sheer volume of data.

These are the four methods of data analysis at work within big data:

The "what happened" stage of data analysis. Here, the focus is on summarizing and describing past data to understand its basic characteristics.

The “why it happened” stage. By delving deep into the data, diagnostic analysis identifies the root patterns and trends observed in descriptive analytics.

The “what will happen” stage. It uses historical data, statistical modeling and machine learning to forecast trends.

Describes the “what to do” stage, which goes beyond prediction to provide recommendations for optimizing future actions based on insights derived from all previous.

The following dimensions highlight the core challenges and opportunities inherent in big data analytics.

The sheer volume of data generated today, from social media feeds, IoT devices, transaction records and more, presents a significant challenge. Traditional data storage and processing solutions are often inadequate to handle this scale efficiently. Big data technologies and cloud-based storage solutions enable organizations to store and manage these vast data sets cost-effectively, protecting valuable data from being discarded due to storage limitations.

Data is being produced at unprecedented speeds, from real-time social media updates to high-frequency stock trading records. The velocity at which data flows into organizations requires robust processing capabilities to capture, process and deliver accurate analysis in near real-time. Stream processing frameworks and in-memory data processing are designed to handle these rapid data streams and balance supply with demand.

Today's data comes in many formats, from structured to numeric data in traditional databases to unstructured text, video and images from diverse sources like social media and video surveillance. This variety demans flexible data management systems to handle and integrate disparate data types for comprehensive analysis. NoSQL databases , data lakes and schema -on-read technologies provide the necessary flexibility to accommodate the diverse nature of big data.

Data reliability and accuracy are critical, as decisions based on inaccurate or incomplete data can lead to negative outcomes. Veracity refers to the data's trustworthiness, encompassing data quality, noise and anomaly detection issues. Techniques and tools for data cleaning, validation and verification are integral to ensuring the integrity of big data, enabling organizations to make better decisions based on reliable information.

Big data analytics aims to extract actionable insights that offer tangible value. This involves turning vast data sets into meaningful information that can inform strategic decisions, uncover new opportunities and drive innovation. Advanced analytics, machine learning and AI are key to unlocking the value contained within big data, transforming raw data into strategic assets.

Data professionals, analysts, scientists and statisticians prepare and process data in a data lakehouse, which combines the performance of a data lakehouse with the flexibility of a data lake to clean data and ensure its quality. The process of turning raw data into valuable insights encompasses several key stages:

• Collect data: The first step involves gathering data, which can be a mix of structured and unstructured forms from myriad sources like cloud, mobile applications and IoT sensors. This step is where organizations adapt their data collection strategies and integrate data from varied sources into central repositories like a data lake, which can automatically assign metadata for better manageability and accessibility.
• Process data: After being collected, data must be systematically organized, extracted, transformed and then loaded into a storage system to ensure accurate analytical outcomes. Processing involves converting raw data into a format that is usable for analysis, which might involve aggregating data from different sources, converting data types or organizing data into structure formats. Given the exponential growth of available data, this stage can be challenging. Processing strategies may vary between batch processing, which handles large data volumes over extended periods and stream processing, which deals with smaller real-time data batches.
• Clean data: Regardless of size, data must be cleaned to ensure quality and relevance. Cleaning data involves formatting it correctly, removing duplicates and eliminating irrelevant entries. Clean data prevents the corruption of output and safeguard’s reliability and accuracy.
• Analyze data: Advanced analytics, such as data mining, predictive analytics, machine learning and deep learning, are employed to sift through the processed and cleaned data. These methods allow users to discover patterns, relationships and trends within the data, providing a solid foundation for informed decision-making.

Under the Analyze umbrella, there are potentially many technologies at work, including data mining, which is used to identify patterns and relationships within large data sets; predictive analytics, which forecasts future trends and opportunities; and deep learning , which mimics human learning patterns to uncover more abstract ideas.

Deep learning uses an artificial neural network with multiple layers to model complex patterns in data. Unlike traditional machine learning algorithms, deep learning learns from images, sound and text without manual help. For big data analytics, this powerful capability means the volume and complexity of data is not an issue.

Natural language processing (NLP) models allow machines to understand, interpret and generate human language. Within big data analytics, NLP extracts insights from massive unstructured text data generated across an organization and beyond.

Structured Data

Structured data refers to highly organized information that is easily searchable and typically stored in relational databases or spreadsheets. It adheres to a rigid schema, meaning each data element is clearly defined and accessible in a fixed field within a record or file. Examples of structured data include:

• Customer names and addresses in a customer relationship management (CRM) system
• Transactional data in financial records, such as sales figures and account balances
• Employee data in human resources databases, including job titles and salaries

Structured data's main advantage is its simplicity for entry, search and analysis, often using straightforward database queries like SQL. However, the rapidly expanding universe of big data means that structured data represents a relatively small portion of the total data available to organizations.

Unstructured Data

Unstructured data lacks a pre-defined data model, making it more difficult to collect, process and analyze. It comprises the majority of data generated today, and includes formats such as:

• Textual content from documents, emails and social media posts
• Multimedia content, including images, audio files and videos
• Data from IoT devices, which can include a mix of sensor data, log files and time-series data

The primary challenge with unstructured data is its complexity and lack of uniformity, requiring more sophisticated methods for indexing, searching and analyzing. NLP, machine learning and advanced analytics platforms are often employed to extract meaningful insights from unstructured data.

Semi-structured data

Semi-structured data occupies the middle ground between structured and unstructured data. While it does not reside in a relational database, it contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Examples include:

• JSON (JavaScript Object Notation) and XML (eXtensible Markup Language) files, which are commonly used for web data interchange
• Email, where the data has a standardized format (e.g., headers, subject, body) but the content within each section is unstructured
• NoSQL databases, can store and manage semi-structured data more efficiently than traditional relational databases

Semi-structured data is more flexible than structured data but easier to analyze than unstructured data, providing a balance that is particularly useful in web applications and data integration tasks.

Ensuring data quality and integrity, integrating disparate data sources, protecting data privacy and security and finding the right talent to analyze and interpret data can present challenges to organizations looking to leverage their extensive data volumes. What follows are the benefits organizations can realize once they see success with big data analytics:

Real-time intelligence

One of the standout advantages of big data analytics is the capacity to provide real-time intelligence. Organizations can analyze vast amounts of data as it is generated from myriad sources and in various formats. Real-time insight allows businesses to make quick decisions, respond to market changes instantaneously and identify and act on opportunities as they arise.

Better-informed decisions

With big data analytics, organizations can uncover previously hidden trends, patterns and correlations. A deeper understanding equips leaders and decision-makers with the information needed to strategize effectively, enhancing business decision-making in supply chain management, e-commerce, operations and overall strategic direction.  

Cost savings

Big data analytics drives cost savings by identifying business process efficiencies and optimizations. Organizations can pinpoint wasteful expenditures by analyzing large datasets, streamlining operations and enhancing productivity. Moreover, predictive analytics can forecast future trends, allowing companies to allocate resources more efficiently and avoid costly missteps.

Better customer engagement

Understanding customer needs, behaviors and sentiments is crucial for successful engagement and big data analytics provides the tools to achieve this understanding. Companies gain insights into consumer preferences and tailor their marketing strategies by analyzing customer data.

Optimized risk management strategies

Big data analytics enhances an organization's ability to manage risk by providing the tools to identify, assess and address threats in real time. Predictive analytics can foresee potential dangers before they materialize, allowing companies to devise preemptive strategies.

As organizations across industries seek to leverage data to drive decision-making, improve operational efficiencies and enhance customer experiences, the demand for skilled professionals in big data analytics has surged. Here are some prominent career paths that utilize big data analytics:

Data scientist

Data scientists analyze complex digital data to assist businesses in making decisions. Using their data science training and advanced analytics technologies, including machine learning and predictive modeling, they uncover hidden insights in data.

Data analyst

Data analysts turn data into information and information into insights. They use statistical techniques to analyze and extract meaningful trends from data sets, often to inform business strategy and decisions.

Data engineer

Data engineers prepare, process and manage big data infrastructure and tools. They also develop, maintain, test and evaluate data solutions within organizations, often working with massive datasets to assist in analytics projects.

Machine learning engineer

Machine learning engineers focus on designing and implementing machine learning applications. They develop sophisticated algorithms that learn from and make predictions on data.

Business intelligence analyst

Business intelligence (BI) analysts help businesses make data-driven decisions by analyzing data to produce actionable insights. They often use BI tools to convert data into easy-to-understand reports and visualizations for business stakeholders.

Data visualization specialist

These specialists focus on the visual representation of data. They create data visualizations that help end users understand the significance of data by placing it in a visual context.

Data architect

Data architects design, create, deploy and manage an organization's data architecture. They define how data is stored, consumed, integrated and managed by different data entities and IT systems.

IBM and Cloudera have partnered to create an industry-leading, enterprise-grade big data framework distribution plus a variety of cloud services and products — all designed to achieve faster analytics at scale.

IBM Db2 Database on IBM Cloud Pak for Data combines a proven, AI-infused, enterprise-ready data management system with an integrated data and AI platform built on the security-rich, scalable Red Hat OpenShift foundation.

IBM Big Replicate is an enterprise-class data replication software platform that keeps data consistent in a distributed environment, on-premises and in the hybrid cloud, including SQL and NoSQL databases.

A data warehouse is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence and machine learning.

Business intelligence gives organizations the ability to get answers they can understand. Instead of using best guesses, they can base decisions on what their business data is telling them — whether it relates to production, supply chain, customers or market trends.

Cloud computing is the on-demand access of physical or virtual servers, data storage, networking capabilities, application development tools, software, AI analytic tools and more—over the internet with pay-per-use pricing. The cloud computing model offers customers flexibility and scalability compared to traditional infrastructure.

Purpose-built data-driven architecture helps support business intelligence across the organization. IBM analytics solutions allow organizations to simplify raw data access, provide end-to-end data management and empower business users with AI-driven self-service analytics to predict outcomes.