Read the attached article titled as How to Make Better Decisions with Less Data by Tanya Menon and Leigh Thompson, published in Harvard Business Review, and answer the following Questions:

1. Summarize the article and explain the main issues discussed in the article. (In 500-600 words)

Please note the word count and don't copy from other answers on chegg.

Article

STRATEGIC THINKING

How to Make Better

Decisions with Less Data

by Tanya Menon and Leigh Thompson

NOVEMBER 07, 2016

Maria, an executive in financial services, stared at another calendar invite in Outlook that would

surely kill three hours of her day. Whenever a tough problem presented itself, her bosss knee-jerk

response was, Collect more data! Maria appreciated her bosss analytical approach, but as the

surveys, reports, and stats began to pile up, it was clear that the team was stuck in analysis paralysis.

And despite the many meetings, task forces, brainstorming sessions, and workshops created to solve

any given issue, the team tended to offer the same solutions often ones that were recycled from

prior problems.

As part of our research for our book, Stop Spending, Start Managing, we asked 83 executives how

much they estimated that their companies wasted on relentless analytics on a daily basis. They

reported a whopping $7,731 per day $2,822,117 per year! Yet despite all of the data available, people

often struggle to convert it into effective solutions to problems. Instead, they fall prey to what Jim

March and his co-authors describe as garbage can decision making: a process whereby actors,

problems, and possible solutions swirl about in a metaphorical garbage can and people end up

agreeing on whatever solution rises to the top. The problem isnt lack of data inside the garbage can;

the vast amount of data means managers struggle to prioritize whats important. In the end, they end

up applying arbitrary data toward new problems, reaching a subpar solution.

To curb garbage-can decision making, managers and their teams should think more carefully about

the information they need to solve a problem and think more strategically about how to apply it to

their decision making and actions. We recommend the data DIET approach, which provides four

steps of intentional thought to help convert data into knowledge and wisdom.

Step 1: Define

When teams and individuals think about a problem, they likely jump right into suggesting possible

solutions. Its the basis of many brainstorming sessions. But while the prospect of problem solving

sounds positive, people tend to fixate on familiar approaches rather than stepping back

to understand the contours of the problem.

Start with a problem-finding mindset, where you loosen the definitions around the problem and

allow people to see it from different angles, thereby exposing hidden assumptions and revealing new

questions before the hunt for data begins. With your team, think of critical questions about the

problem in order to fully understand its complexity: How do you understand the problem? What are

its causes? What assumptions does your team have? Alternately, write about the problem (without

proposing solutions) from different perspectives the customer, the supplier, and the competitor,

for example to see the situation in new ways.

Once you have a better view of the problem, you can move forward with a disciplined data search.

Avoid decision-making delays by holding data requests accountable to if-then statements. Ask

yourself a simple question: If I collect the data, then how would my decision change? If the data

wont change your decision, you dont need to track down the additional information.

Step 2: Integrate

Once youve defined the problem and the data you need, you must use that information effectively.

In the example above, Maria felt frustrated because as the team collected more and more pieces of

the jigsaw puzzle, they werent investing the same amount of time to see how the pieces t together.

Their subconscious beliefs or assumptions about problems guided their behaviour, causing them to

follow the same tired routine time and time again: collect data, hold meetings, create strategy

moving forward. But this is garbage-can decision making. In order to keep the pieces from coming

together in an arbitrary fashion, you need to look at the data differently.

Integration lets you analyze how your problem and data t together, which then lets you break down

your hidden assumptions. With your team, create a KJ diagram (named after author Kawakita Jiro) to

sort facts into causal relationships. Write the facts on notecards and then sort them into piles based

on observable relationships for example, an increase in clients after a successful initiative, a drop

in sales caused by a delayed project, or any other data points that may indicate correlated items or

causal relationships. In doing this, you can create a visual model of the patterns that emerge and

make connections in the data.

Step 3: Explore

At this point in the process, you may have some initial ideas or solutions based on your KJ diagrams.

Nows the time to develop them. To facilitate collaborative exploration, one of our favorite exercises

(often used in art schools) is what we call the passing game. Assign distinct ideas to each team

member and give each individual five minutes to develop it by drawing or writing in silence. Then

have them pass their work to a teammate, who continues drafting the idea while they take over

a teammates creation.

Discuss the collaborative output. Teammates recognize how it feels to give up ownership of an idea

and how it feels to both edit and be edited; they also recognize their implicit assumptions about

collaboration. The new perspective forces them to confront directions that they didnt choose or

never would have considered. Indeed, you can add multiple sequential passes (like a telephone

game) to demonstrate the ideas unpredictable evolution as three or four teammates play with the

initial ideas. After allowing people this space for exploration, discuss the directions that are most

fruitful.

Step 4: Test

The last dimension requires team members to use their powers of critical thinking to consider

feasibility and correct for overreach. Design tests to see if your plan forward will work. Under which

types of situations will the solution fail? Select a few critical tests and run them. While people often

over-collect data that supports their priors, people under-collect discofirming data. By running even

a single test that fights confirmation biases, you can see what you need to see, even if you dont want to.

The solution to garbage-can decisions isnt cutting out data entirely. Thinking strategically about

your data needs pushes you to do more with less widening, deepening, integrating, extending, and testing the data you do have to convert it into knowledge and wisdom. In practicing the mental

exercises above with your team, you can curb your appetite for data while getting better at digesting

the data you have.

Tanya Menon (menon.53@osu.edu) is an associate professor of Management and Human Resources at the Ohio State

Universitys Fisher College of Business. She is the co-author of Stop Spending, Start Managing: Strategies to Transform

Wasteful Habits (Harvard Business Review Press, 2016).

Leigh Thompson is the J. Jay Gerber Professor of Dispute Resolution and Organizations at the Kellogg School of

Management. She is the author of Creative Conspiracy: The New Rules of Breakthrough Collaboration (HBR Press, 2013)

and co-author of Stop Spending, Start Managing: Strategies to Transform Wasteful Habits (HBR Press, 2016).