What's the Best Approach to Data Analytics? Pl go through this HBR article very carefully and

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What's the Best Approach to Data Analytics? Pl go through this HBR article very carefully and

by Tom O'Toole

MARCH 02, 2020

JOE DANIEL PRICE/GETTY IMAGES

In practicing data analytics for more than 30 years, and leading, advising, interviewing and teaching

executives in many industries on data analytics for five years, I've observed that their approaches

generally fall into one of five scenarios: two that typically fail, two that sometimes work partially, and

one that has emerged as best. Let's take a look at each:

1. We're here to help do you have any problems to solve?

This scenario often starts with the CEO (sometimes prompted by the board) deciding to hire a data

scientist and establish a data analytics group. The data team sets out in the organization with great

however, don't have a practical understanding of the business, and the business leaders don't know

what, exactly, the data analysts are supposed to do or how to use them. As a senior executive of a

very large enterprise said, "Our CEO hired a data scientist who reports to me, but I'm not sure what

he does or what to do with him." As the business leaders and the data scientists try to gure out how

to relate, not much business value is created.

2. Boil the ocean.

Well-intended enthusiasm for putting data science to use can lead to overly ambitious aspirations to

impact the entire company at once. The reality, however, particularly in large companies, is there are

too many legacy data systems, too many practical issues, and too few people on the data science

team to produce a significant business lift across the whole company in short order. Business results

typically fall well short of high expectations. As an executive of a multinational European

manufacturing company observed, "We've been at this for three years, and spent millions of euros,

but we don't have much to show for it." In the end, not much business value is actually realized.

3. Let a thousand flowers bloom.

The third scenario has promise: C-level leaders direct that data analytics should be adopted

throughout the company. The practical use is left to the discretion of each business unit leader or

function head. While data analytics gets grounded in and kept close to the business, much depends

on if and how individual business heads choose to use it. Some embrace it and achieve significant

results; others aren't sure what to do or else avoid it. "Data analytics" often becomes just enhanced

business reporting. Databases, systems and tools proliferate. With fragmented efforts, it is difficult to

scale the resultant activities and determine how much business value is being created.

4. Three years and $10 million from now, it's going to be great.

This rational approach is undertaken with all the right intentions that data analytics can create

business value, but require commitment, investment, and time. The problem is this approach

typically results more in process than business outcomes. Often it involves a series of workshops,

committees, and meetings that drag on without much to show for it. Multiyear investments are

difficult to sustain without any business results in the face of competing budget demands and

changing business conditions. A large industrial company, for example, has been planning,

developing, and discussing their data analytics initiatives for years, but executives wonder where the

effort is headed and when it will show business value. Despite a promising start, too much time

passes without business results, and support wanes.

5. Start with high-leverage business problems.

Finally, the approach that works best: Identify a small number of "high-leverage" business problems

that are tightly defined, promptly addressable, and will produce evident business value, and then

focus on those to show business results. The specific business problem drives the team to identify the

data needed and analytics to be used. Quick wins demonstrate business value. For example, a

company that operates medical imaging clinics saw a high-leverage problem in patient "no shows."

and technicians. Reducing "no shows" directly improves the bottom line. There's no substitute for

business results to build credibility for data analytics and sustain commitment.

Best Practices for Data Analytics

As we look across these scenarios, best practices become clear, including:

Data science can't happen in a silo. It must be tightly integrated into the business organization,

operations and processes.

There needs to be joint prioritization. Business leaders and data scientists should jointly decide

which business problems to focus on. If there is any question about priority, the final call should go

the business heads.

Leaders need to be conversant in data science. Business leaders don't need in-depth expertise in

data science, but they require a basic, working understanding. Being conversant enables business

leaders to work effectively with their data science teams.

You may need to accept "inconvenient outcomes." Data inevitably creates transparency and

reveals business insights that can be unexpected, uncomfortable, and unwelcome. Data analytics

will unearth inefficiencies and misconceptions that complicate leadership and disrupt

conventional thinking. Business leaders who crush or ignore answers they don't like will rapidly

undercut the value of data analytics.

By observing the different approaches taken by a wide range of companies, we can see what works

and what doesn't to connect data analytics to creating real business value. Because if your data

analytics isn't adding real value to the business, it's not going to be successful or sustainable.

Question is------------------------------------------------------------------------------------------------------------

Pl do some additional research that will attempt to validate or disprove the concepts presented in the HBR Digital Article

A key to your success will be the thoroughness you applied to understanding and documenting the original article. Restates the premise of the HBR article and then reports on what you found in the business literature to validate or disprove it. At the very least, your response should indicate how businesses differed from the approach given in the HBR article.Your sources should include at least four peer-reviewed journal (e.g., Harvard Business Review, Sloan Management Review, etc.) articles as well as additional references from other sources, e.g., Forbes, Business Week, the Economist, Wall Street Journal, etc. your reference list must include at least one citation for each reference.

Your findings, with details, that substantiate or reject the theme of the original article..pl comment on this in words..pl don't make short cut as I need the answer for very very big...Megaproject .So pl take your own time and give your best.

Your great efforts are highly appreciable.

Thanks!