Data science - What is it and why you are missing out
Have you also come across the phrases ‘’big data’’, ‘’data analytics’’, and ‘’data science’’ and wondered what the heck they mean and if you should even bother to find out?
As it turns out, these phrases sound foreign, but they are easy to understand.
And they represent something fascinating: Vast piles of data that hide massively useful information and the tools to extract meaning and insights.
When talking about big data, how big is big? It is estimated that over 2.5 quintillion bytes (2.5 e+9 GB) of the data is created every day, and it’s growing every second. A large percentage of that data represents information that companies gather about customers, employees, and competitors.
What is data science?
Data science uses various disciplines, including statistical analysis, machine learning, algorithms, programming, and mathematics to discover patterns in large volumes of data and gain actionable business insights from them. These insights can be invaluable to businesses, potentially leading to far-reaching business decisions.
Where does data analytics come in?
Data analytics is a component of data science. It’s the science of analyzing raw data in order to draw conclusions about that information. Structured, unstructured, and semi-structured data are used for analyzes.
It’s data analytics that reveals patterns hidden in data like the proverbial needle in a haystack. Data science uses the outcome of data analytics to solve problems, to adjust business practices, and improve overall efficiency.
It’s easy to see the difference between data analytics and data science. Data analytics uses data to understand how things are currently or were in the past; data science uses the same data to understand what might be in the future.
Enter the data scientist. How to describe a data scientist. Well, data science turns the adage “Jack of all trades, and master of none’’ on its head. Data scientists are, in fact, masters of many trades.
Data scientists are highly-skilled and qualified individuals who combine their knowledge of computer science, mathematics, modeling, statistics, analytics, machine learning as well as sound business sense to help organizations find answers to many vexing business questions.
How data science benefits your business
Data science can extract real meaning from large volumes of data and leverage the knowledge gained to benefit organizations.
The discipline brings an analytical approach based on numbers, facts, and statistics that can help companies come to novel conclusions that lead to market-beating products and services.
Investing in data science can add value to your business in critical areas.
1. Better decision making
According to a survey by Deloitte, 49% of respondents who use analytics in their enterprises say that analytics leads to better decision-making.
Data scientists use predictive analytic tools to gain insights into data and put that information in the hands of the company’s executive team to guide their decision making. Through predictive models, data science can also help businesses to simulate various scenarios and predict the outcome of certain decisions.
The data scientist plays a curial role in clearly communicating the data insights and what it can mean for the company. A data scientist must be able to communicate the findings clearly and concisely to people who don’t understand his work and don’t have the time to analyze all the data. They just want to know what it means.
The most effective data scientists are also trained in business and can explain the business implications of their findings.
While data analytics is playing an increasingly large role in corporate decision-making in general, it is becoming even more important in driving business strategy. Almost two thirds (62%) of respondents said that analytics played an important role. About 17% of respondents reported that corporate strategy and competitive advantage are “heavily dependent” upon the company’s analytical capabilities,
2. Improved product development
Analytics is crucial to product development. Without analytics, product development teams would not know how well or if their products will succeed in the marketplace.
Data analytics can verify product concepts, giving product development teams the information they need to test and adjust the product. It can guide the product design process so no time is wasted.
Decisions based on years of experience or simply intuition now take a back seat to data analytics as a basis for decision making since data analytics makes decisions more objective and reliable.
Data analytics can give the development team insights into user experience. They can find out why their product is popular or not, and how consumers are using it. These insights can lead to products and services that consumers need, not what development teams think they need.
3. Optimize your hiring process to hire the best talent
In today’s corporate environment, a data-driven approach to recruitment is essential if you want to hire the best talent. Companies like Google, Cisco, and Deloitte have proven that recruitment analytics consistently leads to the best hires.
Analytics in recruitment holds many benefits:
- It gives you an objective perspective on the effectiveness of your recruitment efforts.
- It automatically keeps track of possible top hires for you, so you can contact them when the time comes.
- It helps you to develop a pool of top candidates and hires.
- It can show you where your processes work and don’t work so you can improve them.
- It enables recruitment personnel to distinguish between candidates that are the real deal and will deliver high-quality work and those candidates that merely look good but are not likely to deliver.
- It can predict your future staffing needs, what positions might go vacant in the future so you can pre-empt the situation and take timely action.
4. Creating a smarter team
Data science can come up with insights that can significantly enhance employee effectiveness. The insights gained through data analytics can be incorporated into digital reports and documentation and put at the disposal of employees who can study it and refer to it when needed.
In this way, a company can create a well-informed and smart team that is aware of all new data-driven insights and how to use them to benefit the company.
5. Target your audience more effectively
Every interaction a customer makes with a brand can be used to understand why the customer purchased a specific product or service. Data science can leverage a whole host of data points to understand consumer behavior including, past purchases, social media interactions, website visits, and email marketing responses.
Data analytics can use all this information to generate insights that can help you to target your audience more effectively because you have discovered which of your company offerings they use, how they use it, and why.
Data analytics can also find correlations between different metrics, which can lead to the development of innovative products and services that fulfill the needs of your target audience.
6. Enjoy a competitive advantage
Information collected from across the organization and industry gives insights into where improvements are needed, what’s happening in sales and marketing, products or services that are doing well or floundering, and more. It can also show where there are product and service gaps in the market.
All this information can be used by the company to increase collaboration and productivity within the organization and improve decision-making. All of this translates into better results and ultimately outperforming the competition.
Demand for data science skills is booming
Businesses are waking up to the benefits that data science holds for them. In an effort to find cost-effective and innovative advantages from data science, more enterprises are employing data scientists.
A report by IBM predicted that by 2020 the number of positions for data and analytics talent in the United States will increase by 364,000 openings, to 2,720,000. Job openings for data scientists and similar advanced analytical roles were predicted to reach 61,799.
Experts agree that the only way to stay ahead of the competition will be for companies to invest in analytical capabilities and to integrate analytics into decisions and processes. Already, organizations of all sizes are leveraging data to better understand and improve their operations, financial models, customer relationships, supply chain, workforce, business opportunities, and competitive standing.
The Deloitte report concludes that while data may not be replacing common sense and gut instinct as a way to come to a decision, it is becoming an irreplaceable strategic tool for corporations.
Datahunter and Apption are experts in helping organizations better utilize their data, embed it into their systems for better decisions, and protect their data security, quality, and privacy protections.
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