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How does data science power business?

Data science

Today’s companies find a treasure trove of data. You may have read or heard it before, in the last two years more than 90% of the existing data in the world has been generated. They represent oceans of information that grow daily and need to be exploited, and that brings data science to the forefront.

In this article, we will look at how data science encompasses a set of analytical tools that turn data into valuable information in support of informed decisions. How does it do this? Let us see:

  1. What do we mean when we say data science?
  2. Why is it so important to consider using data science?
  3. What are the benefits that data science will bring to our company?
  4. How do companies make use of data science?
  5. What difficulties can we encounter in the use of data science?
  6. How are data science teams formed?
  7. What are the differences between business intelligence vs. data science?
  8. How do companies rely on data science?
  9. Why should my company invest in data science?

1. What do we mean when we say data science? 

data science

Data science refers to a subset or set of applications of artificial intelligence that convert data into valuable information for decision making. Data science brings together a set of analytical tools from statistics, numerical analysis, predictive analytics, and other scientific methods to extract value from data captured. The internet, mobile devices, IoT objects, intelligent sensors, and various other sources of processable data.

The ultimate goal of data statistics involves providing valuable information for business decision-making, strategic planning, research, new developments, academia, and other uses. Data science incorporates several disciplines. For example, data engineering, data preparation, data mining, predictive analytics, machine learning (ML), and data visualization.

As well as statistics, mathematics, software development and programming. It takes place primarily by trained data scientists, although lower-level data analysts may also participate. In addition, many organizations now rely in part on citizen data scientists, a group that can include business intelligence (BI) professionals, business analysts, data-savvy business users, data engineers, and other workers who do not have a formal data science background.

2. Why is it so important to consider using data science?

Data science plays an important role in virtually every aspect of business operations and strategy. For example, it provides customer insights that help companies create stronger marketing campaigns and targeted advertising to increase product sales. It helps manage financial risks, detect fraudulent transactions and prevent equipment breakdowns in manufacturing plants and other industrial environments.

Helps block cyber-attacks and other security threats to IT systems. From an operating perspective, data statistics efforts can optimize the operation of supply chains, product inventories, distribution networks, and client service. At a more fundamental level, they point the way to greater efficiency and cost reduction.

Data statistics also enables companies to create business plans and strategies that rely on informed analysis of customer behavior, market trends, and competition. Without it, companies can miss opportunities and make poor decisions. Data science also plays a vital role in areas beyond normal business operations. In the healthcare sector, its uses include disease diagnosis, image analysis, treatment planning, and medical research.

Academic institutes use data science to track student achievement and enhance their marketability to future students. Sports teams analyze player performance and plan game strategies through data statistics. Government agencies and public policy organizations become heavy users as well.

3. What are the benefits that data science will bring to our company?

Overall, one of the biggest benefits of data science involves empowering and facilitating better decision-making. Organizations that invest in it can include quantifiable data-driven evidence in their business decisions. Ideally, these data-driven decisions will lead to stronger business performance, cost and process savings, and smoother business workflows.

The specific business benefits of data statistics vary by company and industry. In customer-focused companies, for example, data statistics assists in identifying and refining target audiences. Marketing and sales departments can mine customer data to improve conversion rates and create personalized marketing campaigns and promotional offers that yield higher sales.

In other cases, benefits include reduced fraud, more effective risk management, more profitable financial trading, increased manufacturing uptime, improved supply chain performance, stronger cybersecurity protections, and improved patient outcomes. Generating data statistics also enables a real-time analysis of it.

The analyses, reports, and information provided by data science enable corporations to identify new business opportunities and better focus their marketing and sales programs, among other significant contributions.

4. How do companies make use of data science?

The benefits of investing in data science become tangible regardless of the sector or industry where it works, and the way companies make use of it varies according to the area. For example, customer service-oriented businesses use data statistics to refine their target audience and enhance the pre-and post-sales experience. Other examples of the use and contribution of data statistics include: 

  • Marketing: extracting data on consumer behavior and perception to improve conversion rates from leads to loyal customers.
  • Advertising: efficient promotional campaigns, better targeting of marketing resources (personalized campaigns).
  • Finance: detect fraud in financial services by recognizing suspicious and atypical behavior; effective risk management.
  • Manufacturing: equipment failure prediction, inventory optimization, production planning, and work centers.
  • Logistics: optimization of supply chain performance, intelligent dispatching. Predictive analysis of traffic patterns, breakdowns, and weather conditions.
  • Health: improved disease diagnosis, and preventive health.

More specifically in the IT sector, companies and cloud computing service providers use data science to predict service scaling, budget the supply and consumption of their SaaS, PaaS, and IaaS services on demand as well as optimize migrations and workloads.  In cybersecurity, research shows the use of data statistics applied to the operation of intelligent systems, such as SIEM and SOAR platforms.

5. What difficulties can we encounter in the use of data science?

data science

Data science is inherently challenging due to the advanced nature of the analytics involved. The sheer amount of data typically analyzed adds to the complexity and increases the time it takes to complete projects. In addition, data scientists often work with big data sets that may contain a variety of structured, unstructured, and semi-structured data, further complicating the analysis process.

A major challenge is to remove biases in data sets and analytical tools. That includes problems with the underlying data itself and those that data scientists unconsciously build into algorithms and predictive models. Such biases can skew analytics results if not identified and addressed, generating faulty findings that lead to poor business decisions.

Locating the appropriate data to analyze becomes another challenge. In a report published in January 2020, Gartner analyst Afraz Jaffri and four of his colleagues at the consultancy also cited choosing the right tools, managing analytic model implementations, quantifying business value, and maintaining models as major hurdles.

6. How are data science teams formed?

Numerous companies have established a separate team, or multiple teams, to handle data statistics activities. As technology writer Mary K. Pratt in an article on how to set up a data science team, there is more to an effective team than the data scientists themselves.

Typically, the team is led by a data science director, data statistics manager, or lead data scientist. Who needs to report to the chief data officer, chief analytics officer, or vice president of analytics. The chief data scientist is another management position that has emerged in some organizations.

Some data science teams are centralized at the enterprise level, while others are decentralized to individual business units or have a hybrid structure that combines these two approaches. It may also include the following positions:

  • Data engineer.
  • Data analyst.
  • Machine learning engineer.
  • Data visualization developer.
  • Translator of data.
  • Data architect.

6.1 What do data scientists do and what skills do they need?

The primary role of data scientists is to analyze data, often in large quantities, to find useful information that can be shared with corporate executives, business managers, and workers, as well as government officials, physicians, researchers, and many others.

Data scientists also create AI tools and technologies for implementation in various applications. In both cases, they collect data, develop analytical models, and then train, test, and run the models against the data.

As a result, data scientists must possess a combination of data preparation, data mining, predictive modeling, machine learning, statistical analysis, and mathematical skills, as well as experience with algorithms and coding, for example, programming knowledge in Python, R, and SQL languages. Many are also tasked with creating data visualizations, dashboards, and reports to illustrate the results of analytics

In addition to those technical skills, data scientists require a softer skill set, including business knowledge, curiosity, and critical thinking. Another important skill is the ability to present data insights and explain their significance in a way that is easy for business users to understand. That includes data storytelling capabilities to combine data visualizations and narrative text into a prepared presentation.

7. What are the differences between business intelligence vs. data science?

Like data statistics, basic business intelligence and reporting are intended to help guide operational decision-making and strategic planning. But BI is primarily focused on descriptive analysis: what happened or is happening now that an organization should be responding to or addressing?

BI analysts and self-service BI users work primarily with structured transactional data that is extracted from operational systems, cleansed and transformed for consistency, and loaded into a data warehouse or data mart for analysis. Business performance, trend, and process monitoring represent a common use case for BI.

Data statistics involves more advanced applications of analytics. In addition to descriptive analytics, it includes predictive analytics, which anticipates behavior and future events, as well as prescriptive analytics, which attempts to determine the best path of action to address the problem being analyzed.

Unstructured or semi-structured data types-for example, log files, sensor data, and text-are common in data science applications, along with structured data. In addition, data scientists often want access to raw data before it has been cleaned and consolidated so that they can analyze the entire data set or filter and prepare it for specific analytical uses.

8. How do companies rely on data science?

Data science allows streaming services to track and analyze what users watch, which helps determine what new TV shows and movies they produce. Data-driven algorithms are also used to create personalized recommendations based on a user’s viewing history.

Financial services. Banks and credit card companies mine and analyze data to detect fraudulent transactions, manage financial risks in loans and lines of credit, and evaluate customer portfolios to identify opportunities for additional sales.

Healthcare. Hospitals and other healthcare providers use machine learning models and additional data science components to automate X-ray analysis and help doctors diagnose diseases and plan treatments based on previous patient outcomes.

Manufacturing. Uses of data statistics at manufacturers include optimizing supply chain management and distribution, as well as predictive maintenance to detect potential equipment failures in plants before they occur.

9. Why should my company invest in data science?

With all of the above, here on Connect Tech, we can reflect on why IT companies and businesses should invest in data science. Simply put, data science-based platforms represent a massive success story that drives enterprise revenue to another level.

We talk about a global market that increases at an average annual rate of 39.2%. Becoming a business that bases its business strategies and technologies on data statistics offers benefits in both operations and results.

Interested in learning more about the advantages of data science for your company? We would like to be your main source of information on this and many other topics. If you would like to contact us, just call us at +971 43 316 688, or if you prefer, leave us a message at contact@connectech.dev. We look forward to hearing from you!

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