by Dr. Jane Fitzgerald | Jun 17, 2023 | Business, Data Science
The Truth About Data Scientists.
Data science is the latest buzzword in the business sector. Everyone, from business professionals to beginners, is talking about it. Data science is one of the most popular and promising career paths for skilled and up-to-date professionals.
So, what the heck is data science in business?
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Traditionally, data science is researching, collecting/acquiring, and analyzing a vast volume of data, including data mining and data programming skills. Data scientists are skilled professionals that organize and analyze a vast amount of data for businesses/organizations.
Since the dawn of the 21st century, data science has evolved far beyond its traditional threshold. Rapid changes in modern business management with regards to the birth of the internet motivates organizations to uncover more useful information, stay competitive, and ensure customer satisfaction. Organizations must learn how to improve functions and optimize processes through data science. The only way data-scientists can make this possible is by mastering every stage, making up the data science life-cycle.
Five Stages of the Data Science Life-Cycle
Data scientists seeking to deliver results in an insightful, understandable, and compelling way must be efficiently skilled in every stage.
Technically, there are five stages in the life cycle, and these are:
Stage #1. Capture
Under this stage, data scientists must be vast and efficient in data acquisition/collection, professional in data entry, skilled in signal reception, and data extraction.
Stage #2. Maintain
Skill sets required for this stage include data warehousing, good knowledge of data cleansing, effective data staging, excellent data processing skills, and data architecture.
Stage #3. Process
At this stage, the data scientist initiates data mining, and then proceed to cluster/classification. The last two-know-how skills required in this stage are data modeling and data summarization.
Stage #4. Analyze
This phase requires extreme exploratory/confirmatory data analytic skills, and expertise in predictive analysis, solid regression skills, text mining, and superb qualitative analysis.
Stage #5. Communicate
Communication is the last stage of the life cycle. The skill sets the data scientist is expected to have here are;
a. Data reporting (vital to organizations in meeting their overall goals and objectives),
b. Data visualization (a roadmap for businesses module implementation), and lastly,
c. Business intelligence and decision making for an overall business drive.
How Does Data Science Impact a Business?
Businesses use data science to improve product quality and day-to-day operations diagnostically.
Data scientists with high-level technical skills help businesses identify vital questions and collect
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data from multiple data sources. Next, they organize the information obtained, and translate the extracted results into actionable solutions, and communicate the same to management for positive business decisions.
These positive skills account for why data scientists have become prominent and sought after in all industries. Their ability to build and analyze algorithms with strong programming knowledge, quantitative skills in linear algebra and statistics, plus excellent communication skills makes them the knight in shining armor for any organization.
The vitality of data scientists in modern business practice cannot be overemphasized.
Data science and data scientists are a crucial aspect of every business setup in the 21st century. They do not just help businesses achieve their operational, strategic, and financial goals, but also help optimize acquisition, growth, and retention in customer success through the information it provides.
However, poorly utilized data science provokes huge financial loss in an organization.
Conclusion
Data scientists have the power to shape how organizations conduct business in line with customers’ changing needs and the rapid development of technology. There is a growing need for data scientists in the business sector. The job is ranked as the best profession in the United States for three consecutive years—2016-2018—and still growing. Also, the demand for experts in data science has increased by 28% in 2020, and from all indications, there is a sign of it slowing down soon.
by Anika D | Dec 19, 2022 | Data Science
The demand for data management has been on the rise lately. Interestingly, these demands are effectively transformed into data center demands worldwide. Keeping a tap on the latest innovations will help companies stay ahead of the competition.
This competitive edge will also help develop brand awareness and brand loyalty. So, here are the top trends of data center demand that will decide its future in 2023.
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Artificial Intelligence – We have seen how innovative AI programs and applications help optimize a data center’s demand performance. Experts expect to maintain this momentum in 2023 as well.
They also expect improved predictive analytics to identify potential problems and address them instantly. Companies expect transparency in AI applications to get a clear picture of the data’s health and quality.
Edge Computing – Edge computing has been around for a while lately. It uses the current processing powers of systems for resolving actions and requests. The companies expect edge computing to be more flexible in 2023.
It should be able to reduce inactivity and boost the performance of IoT devices. Enterprises like American Tower and Amazon are also planning to invest in this technology in the coming time. Apart from this, many enterprises consider investing in a different promising edge.
Server Virtualization – Server virtualization is a leading data center demand technology. It allows enterprises to incorporate different users on a single server. It also helps service providers and data centers in segmenting servers. This technology also boosts efficiency in processes by ensuring the optimum utilization of available resources.
Automation – Companies adopted remote automated management processes during the pandemic. Data center automation is also expected to play a vital role in 2023. Companies wish to boost their productivity, efficiency, and profitability with the help of this technology. Moreover, the absence of human intervention will also enable 100% data accuracy.
Hybrid Cloud – Hybrid cloud segment is booming at an impressive rate of 5.1% CAGR. To get the best of both the world, public and private cloud, the maximum number of companies are switching to hybrid cloud deployments.
Hybrid cloud offers brilliant data security control options to companies. With this advanced technology, companies expect to manage critical data and resources more efficiently in 2023. They want to ease transferring data to public servers in the coming times.
Intelligent Monitoring – The global data center demand market is predicted to touch the $209.8 billion mark by the end of 2032. It means that this technology will play a vital role in strengthening industries across the globe.
Intelligent monitoring is another trend expected to rise in 2023. Companies can use this technology to monitor and access complex data through easy-to-use automated applications. Companies wish to use intelligent monitoring to prevent roadblocks and predict their requirements effectively.
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Summing Up!
So, these are the top expectations from enterprise data center demand in 2023. Companies who want to make the most of it should know the top enterprise technology trends in 2023. It will help them become a consumer-centered brand in the long run.
by Anika D | Dec 17, 2022 | Data Science
Big data has emerged as a boon for enterprises. It helps companies use data more effectively. It also leads to better data churning, higher operational efficiency, and enhanced flexibility. With optimum data utilization, companies can offer more customer-focused products and services.
Staying abreast with the latest in the industry is crucial to make the most of this technology. It is what this post is about. Stay with us as we outline the top trends that will rule in 2023.
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Digital Transformation – There were 9.7 billion devices connected to the internet in 2020. By the time we reach 2030, this number is expected to cross 29.4 billion. It means that data generated by all these devices will play a crucial role in developing the world.
AI and ML will also play an equally important role in digital technology. Companies should hire experts to know the seven types of artificial intelligence and their uses. It will help in extracting inferences from the huge amount of data. In the coming years, we will find more advanced versions of data transformation.
Cloud Technology – Do you know that over 70% of companies have migrated their workloads to cloud technology? It proves that cloud adoption will rise in the coming years. Experts call it a sustainable solution for IT and business structures as they constantly evolve.
However, engineers should work on reducing the complexity of this technology. It should be simplified enough to help beginners instantly collect, analyze, and understand data. It will also help employees ensure 100% safety and security of the data stored on their systems.
Data as a Service (DaaS) – According to the latest survey report, the DaaS sector is expected to cross the $13.45 Billion mark this year. It indicates that enterprises can double their chances of leading the race by keeping an eye on the top enterprise technology trends to watch for in 2023. It is high time enterprises consider investing in this high-end technology soon.
Agile and Composed Analytics – Composed and agile analytics models make innovation attainable for organizations. It helps brands to grow digitally and differentiate themselves from the rest of the lot. Composable and edge data analytics try to provide a user-friendly, seamless, and flexible experience.
It utilizes various data analytics, ML, and AI solutions. It will also increase productivity, promote cooperation, and enhance agility in business operations. This domain of this field is also expected to grow significantly in 2023.
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Summing Up!
So, these are the top trends to look forward to in 2023. Many companies have realized that big data technology is crucial to their future in the wake of cut-throat competition. Further, the quantity of data and the need for its analysis will also increase with the technology.
To stay ahead in the game, companies should adopt the latest trends. Enterprises can also consult industry experts to learn the nuances of these technologies. It will help them establish their dominance in the market in less time.
by Anika D | Nov 26, 2022 | Data Science
Data management demands are on the rise worldwide. These demands are swiftly getting converted into data center demands across the globe. Therefore, keeping an eye on the latest innovations around data centers is crucial. Here are the top ten technology trends that will dominate the data center design domain in the coming years.
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Top Data Center Design Trends
Server Virtualization – This technology allows service providers and data centers to house several users on a single server by segmenting servers. It also encourages scalability as the entire workload is dispersed across multiple servers. Server virtualization also facilitates efficiency by ensuring the optimum utilization of resources.
Edge Computing – It is a data center architecture. It utilizes the current processing powers of devices for resolving requests and actions. It brings down latency and enhances the overall performance of IoT devices. Many big players like Amazon Web services and American Tower are also planning to go for edge computing by next year. They are also planning to invest in various applications of edge computing.
Hybrid Cloud – To gain the best of private and public cloud, more and more businesses are shifting towards hybrid cloud deployments. It offers excellent security and control to companies. With this technology, service providers can keep and handle critical resources and data on private servers and transfer them to public ones. This segment is growing at a rate of 5.1% CAGR.
Artificial Intelligence – Advanced AI programs help optimize a data center’s performance. With the help of predictive analytics, AI identifies areas of concern and addresses them with different energy usage and processes. It also gives businesses a clear picture of the data center components’ health. It is a great way to replace and repair components that need special attention.
Automation – Pandemic has sped up the adoption of remote management and automated processes worldwide. Data center automation will play a significant role in the future. It will boost efficiency and productivity with minimal involvement of human resources. Moreover, it will also give employees more time to focus on their core tasks.
Intelligent Monitoring – It is another trend to look for in 2023. Enterprises have started using automated, intelligent, and live monitoring to access data through simple mobile applications. This real-time information helps companies predict their requirements and roadblocks effectively.
Green Data Center – What is a green data center? Well, a data center with minimal environmental influence and maximum energy efficiency is called a green data center. Its stakeholders and investors are equally committed to promoting sustainable solutions that support climate resilience. This starts a new trend where we can expect companies to invest in renewable methods and resources that reduce power consumption and emissions.
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Summing Up!
So, it was all about data center design. Its global market is set to reach $209.8 billion by the end of 2032. Data centers will play a crucial role in empowering industries around the world. Therefore, it is crucial for companies to stay abreast with the top enterprise technology trends in 2023. After all, all this is going to shape the future of the world.
by Anika D | Oct 29, 2022 | Data Science
As more and more enterprises are turning to the cloud to advance their infrastructure and reduce their workloads, DaaS, or Data as a Service, is becoming a popular solution for data management, integration, analytics, and storage.
By incorporating this service, enterprises can enhance their data agility, boost the integrity and reliability of their data, and decrease time-to-insight. It is what this blog post is going to be about. Stay with us as we outline all the essentials you need to know about this revolutionary concept.
What is DaaS?
DaaS is an advanced data management strategy that leverages cloud services to store, integrate, process, and analyze data services through a network connection. It outsources a major portion of its data integration, storage, and processing operations to the cloud. All these qualities make DaaS one of the most popular data management tools so far.
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Advantages of Data as a Service
According to the latest survey report, the desktop visualization sector is expected to cross the $13.45 Billion mark this year. Moreover, companies can double their chances of leading the race by keeping an eye on the top enterprise technology trends to watch for in 2023. It is high time enterprises consider investing in these high-end technologies at the earliest. Here are the top benefits of DaaS for enterprises:
- Improved flexibility – It is more flexible and scalable than other on-premises alternatives, as more resources can be allotted to cloud applications instantaneously.
- Better functionality – Cloud offers a robust infrastructure that is less likely to fail. It makes DaaS less vulnerable to disruptions and downtime.
- Cost-effective – Companies can effectively optimize their processing costs and data management expenses with its solution.
- Smaller manpower requirement – Managing this service is an easy task. Companies need not hire in-house data setup and management staff to manage it. Experienced DaaS providers can effectively handle this task for enterprises.
- Automated maintenance – The services and tools on DaaS platforms are managed and updated automatically by the DaaS provider. It eliminates the need for the customers to manage all the tools themselves.
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Challenges of Data as a Service
DaaS comes with a range of challenges for enterprises. Let us have a look at them:
- DaaS can pose a security risk for the business. However, they can reduce the risk by installing a digital immune system in the organization. They can hire experts to understand how the new digital immune system is helping enterprises.
- DaaS also needs an additional compliance setup.
- Transferring a high volume of data on DaaS is also a time-consuming affair.
Bottomline
Gartner forecasted that the number of DaaS users will increase by 150% in the span of 3 years – 2020-2023. With the workforce model developing to adopt the hybrid working system, flexible schedules, and contract and temporary employees, it has become a must-have for enterprises to meet their business needs in this new culture. And we can expect to touch and even cross the 150% estimate soon.
by Anika D | Sep 27, 2022 | Data Science
Big data is a major component in the tech industry, thanks to the actionable results businesses obtain. However, such data sets require understanding and proper tools to uncover the right information. To better understand big data, data science, and data analytics, both fields have gone from large academia rather than becoming an integral business intelligence element.
However, it isn’t easy to differentiate between data analytics and data science. Though they are interconnected, they give different results and follow different approaches. If you want to study your business data, it is important to grasp what they bring to the table and how both are unique. To optimize your big data analytics, examine the main differences between data science and data analytics.
What is Data Science?
Data science is a versatile field focused on determining actionable insights from large raw and structured data sets. This field primarily directs on unearthing answers to what you don’t know. Data science professionals use different techniques to get answers, incorporating computer science, statistics, predictive analytics, and machine learning to determine through massive datasets.
The main goals of data scientists are to ask queries and find potential avenues of study with minimum concern about specific concerns. Experts accomplish this by forecasting possible trends and exploring disconnected data sources.
What is the Meaning of Data Analytics?
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Data analytics concentrates on performing statistical analysis on existing datasets. Analysts focus on making processes to store, process, and organize data to uncover actionable results for current problems. More simply, the data analytics field directs toward solving problems to which we don’t know the answers. More importantly, it produces results that can lead to immediate improvements.
Difference Between Data Science and Data Analytics
The following are some main differences between data science and data analytics:
The basic distinctions in both add to their fundamental objectives. Analytics is inclined towards understanding the business-oriented data to obtain insights that you can turn into action. Whereas data science is tuned toward finding answers to suitable business queries that were not addressed before.
Data analysis focuses on answering questions for improved decision-making in businesses. It uses available data for unearthing actionable results. On the other hand, data science focuses on finding new questions that have come up till now and their answers too.
Data analytics focuses on putting historical data into context, whereas data science emphasizes machine learning and predictive modeling. It is a multidisciplinary process covering algorithms alike. Both data analytics and science need data tapping through diverse methods. Data scientists usually deal with raw data and use a fusion of mathematical, statistical, and machine-learning methods for processing and interpreting data to get results.
They tailor advanced methods for data modeling with models, ML-based algorithms, prototypes, and custom analysis. Data analysts study datasets for picking trends and conclusions and collect large volumes of data for organizing and analyzing the same. They recognize relevant patterns and present findings through suitable visualization, like graphs and charts. Data Science uses C++, Python, Java, and Perl as common languages, but data analytics needs knowledge of R and Python.
Another difference between data science and data analytics is their skill requirements. Here are some skills needed for data analytics:
- Excel, SQL database
- Intermediate statistics
- BI tools such as Power BI for reporting purposes
- A Hadoop-based analysis is used to get conclusions from unstructured data
- Skills in databases, statistics, modeling, and predictive analytics are beneficial for aspiring data analysts
Skills needed for data science include:
- Mathematics
- Advanced Statistics
- Predictive modeling
- Machine learning
- In-depth knowledge of programming
- Data Visualization with QlikView, D3.js, Tableau, and other tools
- SQL, NoSQL databases such as MongoDB and Cassandra
- Programming languages like R, Scala, and Python
Differences Between Data Science and Data Analytics Using an Example
Data analytics focuses mainly on intermediate statistics and excellence in problem-solving skills. Here are some the examples of using data analytics:
- Improving the medical care quality through electronic health record systems and digitized healthcare system
- Stopping cybercriminals in their tracks by strengthening the detection of threats
- Developing reasonable products warranties
Data science mainly focuses on machine learning, advanced statistics, mathematics, programming, and predictive modeling. Here are some examples of channeling data science in business expansion:
- In the manufacturing field, by forecasting the product demand
- To optimize the supply chain in the field of logistics
Which Career is Right for you?
Data analysts and data scientists both job titles are deceptively similar, given the differences in responsibilities, educational requirements, or career trajectory. However, once you consider factors like your personal interests, background, and expected salary, you can decide the right career for you and move on to your path to success.
