Tag: applications of big data in marketing

3d rendering of industrial workspace showing storytellingon computer screen. All screen graphics are made up.

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.

IR30

5 Ways Big Data and Analytics is Revolutionizing the Food Industry

The food industry is one of the most profitable sectors across the globe. However, with the increase in global competition, companies in the food industry have started facing numerous challenges such as reducing supply chain waste, optimizing supply chain efficiency, boosting sustainable growth, implementing green policies, and finding a competitive advantage. To deal with such challenges, businesses in the food industry need to focus on leveraging big data and analytics to keep close tabs on new supply chain trends and competitors’ market progress.

Inefficient supply chain leads to low-profit margins and more wastage. Furthermore, if the supply chain is not optimized, it can result in the inability to adapt quickly to a problem and can increase operational costs. Therefore, players in the food industry must leverage the benefits of technology like big data and analytics to gain better insights into the ability to track and monitor supply chain activities in real time.

At Quantzig, we understand that to ensure the efficiency and performance of your supply chains, from sourcing to manufacturing to delivery, optimization is required. And to help companies in food industry thrive in the competitive landscape, our team of experts has highlighted five ways in which big data and analytics can help companies in the food industry to become market leaders. 

(more…)

3

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.

bannerqz

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.


Want to leverage Quantzig’s analytics solution and BI visualization services for strategic decision making?

Contact US

IR34

3 Ways Advanced Data Analytics Can Drive the Next Wave of Growth for Transportation and Logistics Companies

Today, transportation and logistics (T&L) companies have embraced advanced data analytics in their operations since it allows them to run sophisticated networks globally. But unfortunately, the investments made in advanced data analytics is not satisfactory. Now, it is time for that to change if the companies in the T&L sector really want their sales performance to grow. The commercial analytics capabilities of this sector lag average performance, which is primarily because of the reason that sales forces in this sector rely heavily on outdated processes and lack proper insights into preferences of their customers and growth opportunities. Here rises the need for big data analytics for T&L companies. They already have enough data and can utilize big data analytics extensively to earn desired outcomes. With years of experience in offering data analytics solutions to businesses, we have noted that companies in this sector who embrace advanced data analytics can generate an additional 2-5% percent return on their investment. In this article, we have summed up three things that T&L companies need to do to realize the true potential of advanced data analytics:

(more…)

IR39

Top 3 Applications of Big Data in Marketing That You Should Know About

Today, the amount of data that is being generated is increasing exponentially. This exponential increase in the data offers both a challenge and an opportunity for marketers. The question of “how to harness and process huge data in lesser time and more innovative ways to deliver detailed insights and enhanced business performance?” can be answered with the aid of big data. Applications of big data in marketing are changing the marketing landscape. With rapid advancements in technology, applications of big data in marketing have become very rampant. Big data coupled with artificial learning helps the marketers to understand the market, modify strategies, and improve decision making in a much better way. Applications of big data in marketing give sufficient insights to explore and analyze market trends, customers’ behavior, aContact USnd competitor’s strategies. So, now let’s see what the common applications of big data in marketing are, which would help marketers to make wiser and better decisions:

(more…)