It has been rightly stated by many that the sexiest and most attractive job of the 21st century is “Data Scientist”. However, have you ever wondered why this job title such a sought after position in today’s market? The short and simple answer is that over the last decade there has been a huge explosion of data which has to be analyzed and examined by skilled professionals in order to make sense. Only then you can figure out how this data could be useful and relevant to a business organization.
Data Science and Business Intelligence
Keep in mind that today’s business enterprises, both small and large alike, cannot afford to overlook data science as it has quickly become a vital aspect of a variety of different industries, such as finance, healthcare, agriculture, risk management, marketing optimization, marketing analytics, fraud detection, and public policy among others.
The rates of data creation and consumption have been studied, predicted and hyped over for years now. According to estimates, almost 90% of the total data available to us today was created in just the last two years. As a race, we create more than 2.6 quintillion bytes of data on an everyday basis.
With this exponential growth in the quantity of data, an industry which is bound to thrive is the data science and data analytics industry. This is why Dan Vesset, group VP of Information Management and Analytics at the IDC, predicted that international revenues for data science and business analytics would increase to over $203 billion in 2020 from $130.1 billion in 2016, with a CAGR of about 11.7%.
Data Science and Analytics Definition
Data science is a technical interdisciplinary field. The field utilizes scientific methods, algorithms, processes, and systems. The purpose is to extract insights and knowledge from data in a variety of forms, both unstructured and structured. The process is quite similar to data mining in some aspects.
Data science is an “idea to unify data analysis, statistics, machine learning, and other related methods” to ” better understand, analyze and evaluate actual phenomena” with the help of data. Data science employs theories and techniques drawn from many different fields. These fields lie within the context of statistics, mathematics, information science, as well as computer science.
With each passing day, it’s becoming evident that there is a pressing need to unlock and harness the potential of big data. This step can help you gain business insights. And this requires processing and analysis of huge data sets. This is where data scientists step into action.
Investment In Data Science Can Pay Off Well
A new research study from Forrester Research, cosponsored by Data Science, a leading provider of a top-notch data science platform, concluded that organizations leveraging “insight-driven practices” which incorporate the valuable results derived from data science research are almost twice as likely to become market leaders in their particular industries. Also, Forrester calls these organizations “Insights Leaders.”
On the other hand, it calls their counterparts Insights Laggards. These include firms that are unable to make effective and efficient use of data science research and developments.
And, as you can imagine, it is great being an Insights Leader. For instance, eighty percent of these Insights Leaders reported sales growth which exceeded five percent. And that is not all. Almost half (46 percent) claim that their company’s growth and success is exceeding the expectations of the shareholders.
If you need more convincing, then here are some key reasons your business should consider investing in data science.
Data Science Business Opportunity
Without data science, you will miss out on a lot of new opportunities. Each day new data is available via freely available and constantly changing datasets. These include Google Analytics, Google Trends, and Google Search Console. In addition, there are several paid tools for data analytics available as well. The paid sources include Moz tools, SEMRush, and many more.
Practically one of the key barriers when it comes to big data is allocating the expertise and time needed to stay on top of data and data trends. Practically, achieving this is nearly impossible without topnotch expertise. Moreover, organizations have to prioritize data science within the business culture.
Data Science Business Benefits
Keep in mind that the kind of actionable and valuable insights which data science could bring to your organization and business include:
- Data-driven prescribed initiatives and actions based on changing and new information sets
- Repeat marketing actions and campaigns tied to past performance
- Topical opportunities that you can act on now
- Process driven actions which are not feasible for human completion
- Seasonal trends and nuances to plan for
- Re-combining and analysis of data sets to gather new insights without having to do the manual data work
Data Science and Marketing Analytics
Technology can make it simpler to break down marketing successes as well as failures to the finest details. You can easily figure out what is working, what can be improved, and perhaps most importantly, what isn’t working.
Thanks to data science, what was once a wild game of trial and error primarily based on our gut feelings has now become a precision, well defined and targeted process. In the past, all businesses could do in order to gauge and evaluate the success of their marketing campaigns was determine whether sales or revenue moved up or down following an advertisement.
Today, on the other hand, through proper data analytics, you could easily and quickly determine your return on investment for almost everything. This includes data about individual advertisements to your aggregate marketing efforts, say. Moreover, you can uncover the data analysis for the last five or 10 years, which is great.
Investing in Data Science and Analytics
This is another important reason to invest in data science. About 75% of companies are either investing or planning to invest in big data and data science in the next 2 years. In addition, Forbes reported that almost 83% of companies are now prioritizing data initiatives as critical or of high importance.
Therefore, as your business’ competitors invest heavily in data science and analytics to become more data-driven, your organization should respond likewise if you are to survive in today’s competitive marketplace. Keep in mind that soon, data science and analytics would become a competitive necessity and not just a competitive advantage.
Data Science for Sales
Keep in mind that information derived or generated from analytics and data science could help you see and assess past general assumptions. Moreover, you can find and study the detailed facts and information. This learning would enable you to laser target your sales or marketing strategy.
Equipped with the right information, you will be able to develop campaigns, which build on past successes and improve returns. Analytics and data science will quickly and easily show you key things, such as:
- Who is purchasing your products
- Which marketing initiatives led to more purchases
- Where consumers are purchasing your products
- Customer demographics
- How your customers were able to find you
- Repeat customer statistics
Note that this information enables you to go from simply and arbitrarily marketing your products to young men. Instead, you can target men between, say, 21 and 35, who live in large cities, earn a higher than average salary and have an interest in technology. Knowing and understanding who your best and most loyal customers are will have a considerable impact on your company’s bottom line.
Data Science Process Improvement
Many people tend to equate data science and data analytics with data visualization, data warehousing, and predictive models. A majority of people do not realize that investments in data science and analytics could help identify different ways to optimize and improve key business processes. A perfect example of this is a bank or credit union’s customer loan origination process. The consumer usually completes an application, often online, and their application goes through numerous processes. Keep in mind that processors, underwriters, and funders view the application while it moves through the standard application pipeline.
How Data Science can Improve Business Efficiency
Are you able to track when these handoff points occurred? And do you really know where consumers are abandoning their applications and why? Just imagine how it can help improve and streamline the lending process if you were in a position to capture all this valuable information.
Keep in mind that understanding, appreciating and resolving your consumer pain points would help improve customer experience and satisfaction. If you understand touch points and handoffs throughout your application process, your staff will enable much better tracking and communicate timely to cut down the application timeline.
Data Science Business Strategy
When individuals, or even complete departments, are not concentrating on those activities, which align with your company’s overall business strategy, the result could be devastating. That said, if you establish key performance indicators as well as metrics to measure and evaluate success, everyone will stay on the same page.
As a result, activity will stay in line with your company’s overall strategy while outcomes will be easier to predict:
Data Science and Efficiency
In most companies, executives veer away from daily operations and express surprise at the fact that many of your company reports undergo manual preparation. As you can imagine, this process is time-consuming, often requiring more time than you think. Remember that manual report preparation is usually in Excel, which can open up considerable opportunities for various errors in formulas as well as other fat-fingered mistakes.
If your analysts end up spending 40% or more of their precious time manipulating data and preparing reports, how much time do they have for analyzing and assessing that data and these reports? It is worth mentioning that these reporting inefficiencies could cost your company at least tens of thousands of dollars on a yearly basis.
Data Mining for Data Science
Reading through several resumes all day long is a daily chore in most recruiters’ life. However, that is changing considerably because of data science. With the great amount of information that is available on talent, via social media, job search websites and corporate databases, data science experts can easily work their way through these data points to find those candidates who best fit your organization’s needs.
By carefully mining the huge volume of data, which is already available, processing (in-house) for applications and resumes, and even sophisticated and data-driven aptitude tests, as well as games, data science could help the recruitment team make more accurate and speedier selections.