How to Achieve Product Development Success via the MVP

data analytics

(This is the first in a series of three posts examining the startup journey from MVP to full-scale product, with real-life examples).

One of the main reasons so many tech startups fail is because they neglect to check whether there is a need for their “brilliant” service or product in the first place.

As many as 42% of startups fail because they hadn’t verified that there was a market need for their product. They may spend months or even years developing a product, which turns out to be of no relevance to anyone. The only way to avoid this is by testing your product in front of real users as quickly as possible before forking out a lot of money on product development.

Many successful companies – including legendary startups such as Airbnb, Dropbox, Facebook and Amazon – have avoided this pitfall by adopting what is known as the Minimum Viable Product (MVP) approach.

The lean startup methodology has become a powerful, if not pivotal, concept for product development, particularly in the IT world.

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Why Data Science is Not Just Glorified Data Analytics

Big data and how businesses can learn from it is one of the key elements of business intelligence and has led to an explosion in the need for the right skills to help enterprises comb through large datasets and identify actionable insights.

In this context, terms such as data analytics and data science are often used interchangeably, leaving companies uncertain as to which functions and tools they need to get the best results from their data. For example, data science is often dismissed as glorified data analytics, which can’t be further from the truth. You’ll also find many articles entitled “data science vs data analytics”, which don’t make sense as the one can’t replace the other, and neither one is more important. It is essential to understand that although the two fields are very much interconnected and there is definitely some overlap, they are unique and each approach brings a very different value to your business.

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Enterprise Data Warehouses EDW: The Future of Business Intelligence

 

By now, there can be little doubt that all enterprises can benefit from data-driven insights to allow owners and managers to unlock the organization’s full potential.
These days a company’s data is one of its key resources and the insights derived from data, also known as business intelligence or BI, lead to a better understanding of customers, improved competitiveness, and the ability to better navigate market and industry fluctuations.

Business intelligence, BI data, or business analytics can be defined as a process for analyzing data to discover insights that help business leaders make better decisions. One of the top five business intelligence trends of 2021, highlighted in a survey of over 2000 professionals, identified the need for organizations to manage their own data and to make good use of it as a top priority. What’s evident from this survey is that organizations nowadays want to go beyond the collection of as much data as possible, to actually using the data at their disposal intelligently to boost business. Continue reading

3 Ways your Business Can Get Ready for Digital Transformation

Digital transformation or digitization has become part of the everyday business lexicon across all industries and should be the topmost priority for any business that wants to remain relevant in 2021 and beyond.

According to the World Economic Forum, successful digital transformation will be the difference between success and failure for businesses in this coming era: Companies now face an urgent choice − go digital or go bust!

Technological developments, growing demand from digitally aware consumers, increased competition from digital providers, and enhanced accessibility and connectivity are only a few of the reasons why digital transformation now has to be a no-brainer for business owners across industries.

Recent findings indicate that 45% of companies that have embraced digital transformation reported a positive business impact as a result and also reported higher net revenue growth. Digital-first companies are also 64% more likely to achieve their business goals than their peers.

The outbreak of the coronavirus further accelerated the need for companies to become digitalized and many business owners have responded to the dramatic disruption by embracing digital transformation.

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How to Get Started With Sentiment Analysis

Understanding the customer experience and your brand’s reputation are some of the major benefits to be derived from using big data, making it one of the most valuable resources to tap into for all businesses in the 21st century.

One way to start mining the masses of data available at your fingertips is through sentiment analysis. For example, by analyzing thousands of product reviews, you could discover how customers feel about your pricing plan or customer service levels.

Monitoring brand sentiment on social media in real-time, as well as over time, could lead to the detection of disgruntled customers,enabling you to course-correct and react earlier. Continue reading

Hybrid Technology Teams in a Global Marketplace

The globalization of the world’s economy, coupled with the ever-increasing competition for global talent and an increase in remote workers has led to one notable shift amongst companies — expanding recruitment efforts beyond the location of their physical headquarters. Companies are turning to global talent for a host of reasons, most notably as a catalyst to expanding into new markets and remaining competitive in recruiting talent.

Even before our “new normal”, flexible work arrangements were already in high demand among the workforce and new applicants. Now in the post-COVID era, many companies are operating, out of necessity, in a remote work environment. While some have returned to the office, many companies, including major multinationals like Mondelez, Morgan Stanley and Google, are embracing a digital model that allows their teams to work from home across the world. For many companies, working remotely is now the new normal, as they don’t plan to return to a physical office or are significantly reducing office space. Continue reading

How Data Visualization Will Help Your Business

With the rise of big data, more companies are collecting and storing vast amounts of information about their business, revenue, and customers. However, while the adoption of big data has accelerated significantly in recent years, many companies are struggling to extract meaningful information from the abundance of data at their fingertips. Others are unable to take full advantage of their data due to cumbersome dashboards that are difficult to use and laborious, manual data retrieval methods.

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Structured vs. Unstructured Data: 3 Key Differences

In today’s digital economy, data is a company’s biggest asset. Though data comes in many forms, identifying whether structured or unstructured data will meet your business’s needs is of the utmost importance, and ultimately determines which method of analysis to use.

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What is Sentiment Analysis and How Does It Work?

Sentiment analysis is a machine learning technique that employs text analysis algorithms, natural language processing (NLP), and statistics to analyze customer sentiment — classifying opinions into positive, negative, or neutral categories. Understanding a client’s reactions on an emotional level is vital for unearthing the deepest insights required to perfect the customer experience. For this reason, companies are in a race to understand their customers — what they’re saying, how they’re saying it, and what they mean.

An estimated 80% of the world’s data is unstructured, the majority of which is unstructured text such as customer reviews, feedback forms, surveys, social media data, and the like. This data is hard to analyze, understand, and sort through, making the process time-consuming and expensive. Sentiment analysis, also known as opinion mining or emotion AI, uses NLP to understand the context of data, and instill structure into it via tagging and categorizing.

By analyzing unstructured customer feedback at scale, such as customer reviews and social media conversations, businesses are better able to listen to their customers and make informed decisions relating to their products and services.

While there are various types of sentiment analysis approaches, those with the highest level of adoption include fine-grained sentiment analysis, emotion detection, aspect-based sentiment analysis, and intent analysis.

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What Does the Future of Digital Marketing Have in Store?

Embracing digital transformation within your organization will be vital to your business’s success in the near to long-term future. Whether you’re already immersed in the digital economy, or transitioning towards fully embracing what it has to offer, there’s no question that it will continue to change rapidly. Digital marketing is evolving at a similar pace, and trends suggest a drastically different landscape in the coming years. Fortunately, businesses that embrace digital transformation will be able to harness the power of data and in turn improve customer experience and product innovation, while adding more tools to their marketing toolbelt.

Below we discuss what we can expect from digital marketing in the future — these trends will drastically change the industry while allowing businesses to make the most of their data on the path to digital transformation.

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