Tag: data analytics challenges

All About Data Science and Its Growing Importance for Businesses

With the world entering the era of big data, the need for its storage has also increased. This was a cause of concern for businesses until 2010 and their focus was centered around building solutions and frameworks to store the huge data volumes. Today Hadoop and other frameworks have successfully solved the issue of storage and the focus of businesses have shifted to the processing of this data- Data Science is the secret sauce here. The future of artificial intelligence lies in Data Science. Therefore, it is very crucial to understand what is data science and how can it add premium value to your business.

Are you wondering how to identify order and patterns in data? Data science may hold the key to success. Get in touch with our data scientists to know how identifying patterns, as well as spotting trends, can help the bottom line of your business. 

What is Data Science?

Data Science is a blend of various algorithms, tools, and machine learning principles that help unveil hidden patterns from the raw data. Data Science is a more forward-looking approach that analyzes the past or current data and predicts the future outcome that further helps in making informed decisions. It has the potential to answer open-ended questions such as “what” and “how” scenarios. Data scientists need to master the full spectrum of the data science life cycle in order to uncover useful intelligence for their organizations. There is a need for a certain level of flexibility and understanding to maximize outcomes at each phase of the data science process.

Data science is an interdisciplinary field that deals with systems and processes, that are used to extract insights or knowledge from huge amounts of data. Data extracted can be structured or unstructured. Data science process is a continuation of data analysis fields that includes predictive analysis, statistics, and data mining. Data science visualization is a vast field that uses a lot of techniques and theories that are a part of other fields like mathematics, information science, chemometrics, statics, and computer science. Some of the methods used in data science include:

  • Probability models
  • Machine learning
  • Signal processing
  • Data mining
  • Statistical learning
  • Database
  • Data engineering
  • Visualization
  • Pattern recognition and learning
  • Uncertainty modeling
  • Computer programming

With the ongoing technological advancements, many aspects of data science are gaining immense importance, especially big data. Data science is not just limited to big data, which in itself is a vast field because big data solutions are more focused on pre-processing and organizing the data rather than analyzing it.

Want to know how data-driven techniques and data analytics can help you solve business-related problems. Request for more information here.

What is the difference between data science and machine learning? 

Difference #1: Data science tools create insights from data dealing with all real-world complexities. This comprises tasks like extracting data and understanding requirement etc. Whereas, machine learning classifies or predicts outcome accurately for new data point by learning patterns from historical data using mathematical models.

Difference #2: In data analytics, most of the input data is generated as human consumable data which is to be analyzed or read by humans like images or tabular data whereas input data for machine learning are transformed specifically for algorithms used.

Difference #3: Data analytics and science require domain expertise, strong SQL skills, ETL and data profiling skills whereas machine learning requires a strong understanding of mathematics, data wrangling with SQL model specific visualization.

Quantzig’s analytics solutions offer clients with the right big data technologies, tools, capabilities, and frameworks to handle the variety, volume, and velocity of data to help organizations make informed business decisions while ensuring information security and privacy. Request a free proposal to know more about our portfolio of services.

What are the benefits of data science?

Benefit #1: Helps to understand the customers efficiently

Data science tools help brands to understand and analyze their customers in a much empowered and enhanced manner. Customers are the base and soul of any brand and have a very important role to play in the success and failure of any brand. With the help of data visualization, brands can connect with their customers in a personalized manner. Furthermore, this can ensure better brand engagement and power.

Benefit #2: Improves the brand’s engagement with the customers

Data science is gaining huge attention because it enables brands to communicate their story in an engaging and effective manner. When companies and brands use this data in a comprehensive way, they can share their story more effectively with their target audience, thereby creating better brand engagement. After all, nothing better than human emotions can connect with consumers more effectively.

Benefit #3: Assists in complex problem solving

Data science is a field that is constantly evolving and growing. With so many data science tools being developed, almost every day, big data is assisting organizations and brands to solve complex problems in human resource, IT, and resource management in a strategic manner. This means better use of both material and non-material resources.

Benefit #4: Findings of data science is applicable to every industry

This is one of the most important aspects of data science that its results and findings are applicable to any sector such as healthcare, travel, and education. Understanding the true and appropriate implications of data science can help sectors in analyzing and addressing their challenges in an effective fashion.

Benefit #5: Aid in mitigating challenges of demand and sustainable future

Data analyticsfocuses on the use of general methods without modifying its application, irrespective of the domain. This approach is quite different from traditional statistics that generally tend to focus on offering solutions specific to particular domains or sectors. Today, data analytics and science has far-reaching implications. With the aid of speech recognition, machine translation data science is helping almost every industry to mitigate challenges of demand as well as paving way for the sustainable future.

Data Analytics Services are Crucial to Your Business. Learn Why and How!

Without data analytics services, business leaders are blind, wandering onto the web like a deer on a freeway.”

To have a good data management strategy into place, underpinning all business processes, is an apparent requirement for all business professionals. But it is tougher than you would think to get the data management process right. Almost every business has a need for data-driven insights today, but many are not yet realizing that ambition. Consequently, data management tends to get disintegrated and driven by multiple stakeholders. This results in high degree of disparate and inaccurate data for organizations and they face a number of challenges in maintaining it. Additionally, with the sheer volume of data, it has become very difficult for organizations to aggregate, manage, and create value from data. Therefore, businesses need to identify a more sophisticated approach to data visualization and the importance of good quality data. Right from building better relationships with customers to overcoming external and internal data visualization challenges, there is a need to revamp and evolve enterprise data management practices.

This is where data analytics services can help. If you are looking for ways to transform raw numbers into targeted solutions by gathering and analyzing day-to-day data within your company, it’s high time that you leverage data analytics services. Also, digital analytics can help you unveil hidden correlations by segmenting your market and customizing your products or services with the help of proper data frameworks. With data analytics services, a huge amount of unstructured data become concise and comprehensive data that are helpful in modeling and predicting your market opportunities. Whether you are in the healthcare, media and entertainment, insurance or chemical industry, data analytics services have become an essential requisite for survival. With that being said, let’s take a look at what is data analytics below and how it can help your business reach the pinnacle of growth.

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Top 3 Benefits of Data Analytics That Can Take Wearable Technology to the Next Level

From fitness and activity trackers to “smart clothing” and smartwatches, the possibilities are practically limitless within the industry for wearable technology. And this growth is further enhanced by the promise of high monetary rewards. But design and data collection are only a small component of the potential that wearables offer to both companies and consumers. Taking wearable technology to the next level of usefulness requires companies to build in additional analysis features that increase engagement levels, improves the usefulness of the wearable technology, and provides an unparalleled experience to the consumer. In other words, the benefits of data analytics can help companies to effectively determine what the wearable technology is actually doing for their consumers. But the question that arises here is, what are the benefits of data analytics and how can it help wearable technology to become even more popular. This article has answers to this question. Here, we have discussed in detail some of the significant benefits of data analytics that have potential to take wearable technology to the next level in 2019.

Benefits of Data Analytics

Detailed insights from collected data

One of the most crucial benefits of data analytics is that it can turn collected data into the foundation needed for actionable insights, and in doing so provides additional company and consumer benefits. For example, a sleep tracking device might collect data on how and when a consumer is sleeping. But without the analysis of the collected data, the device only helps in identifying the hours that a consumer sleeps or fails to reach a REM cycle. Does this knowledge help consumers in any way? The answer is simple, no! It is in this context that the real benefits of data analytics are realized.

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Personalized services based on habits 

By leveraging the benefits of data analytics, wearable devices can provide personalized offers to consumers. By analyzing the data captured by wearable technologies, companies can create marketing offers that are customized to each consumer. For example, a step-tracker can reveal that a consumer takes the most steps around lunch time. By leveraging this information viaRequest Proposal personalized offers, companies can encourage the consumer to take more steps throughout the day. 

Improved employee health and productivity

One of the key benefits of data analytics is their ability to improve the health and productivity of employees. Wearable technology can collect the data needed for a company to analyze the productive hours of the day of their employees. Additionally, the devices can also monitor employees’ health, so that the company can mitigate risks associated with lack of sleep, high levels of stress, and other health symptoms that contribute to an ineffective and unhappy workforce.


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3 Common Mistakes to Avoid in Big Data Analytics

Today, big data analytics is one of the most crucial processes for any business, big or small. For data scientists, it acts as a pair of glasses that helps them see the actual reality of a business’ performance, beyond scattered numbers in graphs. A proper, solid, and reliable analysis allows you to make fact-based and rational decisions, but if mistaken, advanced data analytics can lead you astray and you might end suffering a huge loss. So, it can be safely presumed that it is not enough to have good quality data unless you use the datasets efficiently. However, there are many hurdles that businesses might encounter along the way. While implementing a new strategy to strengthen your business with advanced data analytics, mistakes can prevent you from realizing its complete potential. So, in this article, we have summed up some of the common blunders businesses should avoid while developing a big data analytics strategy.

Mistakes to Avoid in Big Data Analytics

Being rigid in processes and products

If you are rigid with your process and product, you are committing a big mistake. You should begin your project in a way that is both strategic in vision and agile in execution. Therefore, you need to pick technologies that are open and expandable. For example, you must avoid vendor lock-in by using open source tools. For obtaining optimum results from advanced data analytics, it is important to foster a culture that fosters failing fast and learning from mistakes. You must avoid letting egos drive your project and understand that if your team experiments on ten things, eight of them might not work. You should get people on board in your data project team who can thrive in this sort of DevOps style of work.

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Neglecting security and governance at the beginning

Today, security and governance are extremely important, as privacy is one of the major concerns in almost every industry. Businesses still tend to start big data analytics projects as pilots, with just a handful of people working on them, and without security and governance baked in. This is a huge mistake when it comes to big data analytics. You must get compliance, governance, and security conversations started on the very first day of the project. You must carefullRequest Proposaly choose the right governance strategies, as well as the right technology of governance.

Relying on the same KPI’s always

As things are constantly changing and your business is subjected to constant dynamics, so you must learn to adapt to the ever-changing environment. This is how you can prosper. So, try not to hold on to the old performance indicators that are used to measure your success in the past. You need to use newer and more suitable tools to make advanced data analytics tools reflect the current performance of your business and identify what really drives your business forward.

Quantzig’s Advantage

Being a leader in offering big data analytics services, Quantzig helps businesses to manage, store, and integrate huge datasets. Also, we help businesses to gain predictive insights that facilitate proactive business decisions and pre-emptive planning. Additionally, Quantzig promises to deliver best-in-class frameworks for multi-dimensional data aggregation and utilizes visualization-based data discovery tools for insight generation.


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Ask Me Anything: 3 Answers to Data Analytics Challenges in the Retail Industry

The retail industry is one of the oldest industries ever known to humanity. The functioning of the retail industry has changed very little over time. However, the pace of development and adoption of innovation is increasing rapidly at present times due to the advent of data analytics and automation. Big data has changed the retail landscape by making operations efficient and improving the forecasting accuracy; thus, boosting the overall profitability.

What Strategies and Models to Choose While Making Pricing Decisions?Free demo

One of the most challenging tasks in the retail industry is pricing. Price it too high, and you may lose a customer, price it too low, and it hurts your margins. Then, there is a host of other pricing problems including, pricing decisions across different channels, geographies, pricing benchmarks, and markups. Quantzig can help you identify the right pricing model for your business by factoring in various factors such as demand, inventory levels, and activities of the competitors in the retail industry. We can also help you implement predictive data analytics solutions that will help you implement a flexible pricing solution that automatically adjusts all factors affecting pricing and suggests the optimal price points for all SKUs.

How to Forecast Market Trends to Know What Products to Keep in Stock?

One of the most troubling tasks in fashion retail is to predict the trends for the upcoming season. However, predicting what products will be trending in the future is a problem faced by all sorts of retailers looking to make a name for themselves in the retail industry. We at Quantzig, help retailers choose appropriate tools to gain a better understanding of the current platforms trending in the retail sector. As consumers are increasingly revealing their purchasing habits online, our data analytics solutions can help retailers gain access to purchase history, social media accounts, consumer demand, and market trends affecting the retail industry.

What Demand Forecasting Tools to Use and How to Maintain Optimal Stock Levels?

Most of the products in the retail industry are seasonal, and demand forecasting is usually done on a historical basis. But the method is outdated and won’t be sufficient in an age where demand is highly fluctuating, trends are rapidly changing, and consumer preferences are dynamic. With the help of Quantzig, you don’t have to worry about complexities of demand forecasting regarding inventory positioning, long tail items, stock replenishment, and seasonal trends.

Quantzig’s data analytics solutions can help players in the retail industry understand the data regarding demand, trends and seasonality, lost sales, suppressions, and promotions.

Why Quantzig?

 

Today, managers have access to a large stream of data, and decision-making on the basis of gut-feeling, the rule of thumb, and guesswork are largely eliminated due to the advent of data analytics.

“Without data analytics, companies are blind and deaf, wandering out onto the web like a deer on a freeway,” said a leading data analytics expert from Quantzig.

For more than 14 years, we have assisted our clients across the globe with end-to-end data management and analytics services to leverage their data for prudent decision making. Our firm has worked with 120+ clients, including 55+ Fortune 500 companies. At Quantzig, we firmly believe that the capabilities to harness maximum insights from the influx of continuous information around us is what will drive any organization’s competitive readiness and success. Our objective is to bring together the best combination of analysts and consultants to complement our clients with a shared need to discover and build those capabilities, and drive continuous business excellence.

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