What makes a great product manager (PM): a perfect combination of skills like business acumen, market orientation, technical skill, and soft ones… the usual suspects.

Unfortunately (or fortunately, depending on your position), just as our management thinking is becoming outdated and requires reform, we also need to update our view of this ultimate management role.

We need to understand and re-define this role from a more meaningful, tasteful, and human-centered perspective so that we’re able to leverage its full capacity and avoid costly mistakes.

Machine learning is now seen as a quick fix for solving all problems, but not always.

Most people reading this are hopefully familiar with machine learning and the relevant algorithms used to classify or predict outcomes based on data. Nevertheless, it is crucial to understand that machine learning is not always the answer to all problems. Given the value of machine learning, it can be hard to accept that sometimes it is not the best solution to a problem. In this section, I intend to shift the reader’s attention that there are times when machine learning is the right solution, and times when it is not.

We all know, Machine learning, a subset of artificial intelligence…

Post Credit — Credit: Laura Wentze & Sara Brown

The days of businesses thinking whether they must bounce on the information analytics bandwagon, or having a single data analyst on staff, are gone. For firms today, the focus has grown to become to build the right team to completely harness all that statistics has to offer.

As companies add to their data teams, analytics jobs are increasingly popular — “data scientist” “data engineer” and “Business Intelligence engineer” were in the top 10 of LinkedIn’s 2020 Emerging Jobs Report, determined by earnings potential, job satisfaction, and a number of job openings.

Companies are expected to continue to add and grow…

Gone are the days when raw information was once all unfold in excel archives and ancient databases within the organization’s context. With the upward thrust of the technology, we have facts in social media and cloud-based services that themselves generate heaps of data. It is challenging to get a general view of how the enterprises are performing barring having to look for reports in special places. …

DAX Syntax

Dax Syntax is like writing functions in Excel or writing a statement in SQL. The same parts appear every single time.

First, we have a name for a particular measure that we’re providing.

In this case, it’s total sales. Remember, no spaces. And we have an operator.

In this case, and in most cases, the equal sign, followed by the name of the DAX function.

Now, remember that sum is a really short word. You might think, well that really wasn’t all that helpful, but some of the functions, so to speak, are more complex, longer names, and more keystrokes…

Data modeling: The basics

Data modeling is the process of taking your organization’s data and creating a model that can be used then for reporting and forecasting by the business. Sounds simple, but not necessarily.

Business Intelligence vs. Business Analytics — What’s HOT and What’s NOT?

Today’s business problem solutions have business intelligence and Business Analytics. They both have become an integral part of how are answered. Both involve data gathering, modeling, and insight gathering and are used interchangeably. However, they are quite different from each other, particularly the scope of the problem each address.

The Business Analytics and business intelligence has revolutionized the computer and software field to an extent that this has created a rift and a question in today's world on what to do and what not.

Let's talk about a degree in these, A degree gives one the entry path or recognition…

As a startup, does the sales and distribution strategy bring lasting success or the product?

According to award-winning Vancouver-based angel investor Boris Wertz, there are two opinions:

“On one end of the spectrum, many startups think that great products sell themselves, while the other camp argues that it’s the channel and monetization that define a company’s success,” writes Wertz. “The simple answer to the question is you need both.”

A recent study shows that over 17 percent of startups fail because of unsatisfactory products, and the top reason for startups failing is creating a product no one needs.

But don’t…

Hariom Singh

💡 Innovative & creative by heart — I am an intersection of Product, Analytics, Strategy & Management. 🎩 An advocate of a design approach focused on helping

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