“There were 5 Exabyte of information created between the dawn of civilization through 2003, but that much information is now created every 2 days.” -Eric Schmidt, ex-CEO of Google.
Organizations are utilizing this information through information researchers, i.e. data professionals who discover growth opportunities for organizations from vast databases by revealing information like patterns, correlations, market trends, and client preferences.
Domains across diver industries praise data science for the business insights it uncovers. The utilization of data online has increased and prompted a phase where all our fundamental exercises are completed on the web -from ordering food and shopping to business and client details. Data Science is the field that can enable organizations to reveal important business data like understanding the market and the competition and set them on track.
Think of it like this: You are a first-time Netflix user, and after signing in successfully, you are presented with a list of recommended movies, TV-shows, documentaries, etc. How does Netflix know what you would like to watch? This is where data science comes into the picture. So, let’s dive into it after tackling the basics.
Data Science – what is it all about?
Data Science is a progressively forward-looking methodology. It is an exploratory path that focuses on analyzing the past or current information. This analysis has enabled it to foresee future results with the approach of making educated choices. Data Science addresses the open-ended inquiries concerning the “what”, “how” and “why” of information. It is a process that includes statistics, visualization, deep learning, and machine learning.
Data Science is the understanding of where the data is being collected from, what it shows, and how it can be turned into something valuable. It identifies patterns from huge piles of structured and unstructured data for a business. It utilizes logical strategies, procedures, calculations, and frameworks to separate information from data. Using this data to make real choices is a crucial practice for any business.
Let’s have a look at the life cycle of data science:
1. Obtaining and understanding data
Before starting with a project, it is essential to understand its basic requirements, priorities, and budget. Other specifications, including required resources, technology, and data for the project, need to be taken under consideration as well.
2. Processing data
Data is never clean. Therefore, the next step after obtaining the data is to extract useful and vital information out of it. Here’s how you can do that:
- Data cleaning: Revising conflicting information by rounding out missing data quality and subtract out the noisy data.
- Data transformation: It involves standardization, transformation, and assembling of data through the ETL method (Extract, Transform, and Load method).
- Data reduction: Using different methodologies to reduce the size of data by removing the outliers but keeping the outcome consistent.
- Data integration: Settling the conflicts in data and taking care of any redundancies.
3. Modeling and planning
After understanding and cleaning of data, authentic data is selected by reducing the dimensions to the features required for modelling. Next, you need to determine the relationship between the variables of the selected data and set a base for the algorithm.
4. Interpretation of data
After modeling the data, it is interpreted by data scientists who then discover ways to use that data to gain important insights. Through predictive and prescriptive analysis the findings are kept business driven to show actionable insights and then present final reports, codes, and briefings. This benefits by exploring how we can repeat or get a positive response and be saved from a negative one.
5. Communicating Results
Technical skills aren’t the only requirement here, as your findings will be presented to people with less technical knowledge. Your data must be presented in such a way that the audience can understand it entirely.
6. Decision Making
In this phase, business decisions are made based on the latest findings and whether more information is needed or not.
How can Data Science help your business grow?
A systematized scientific approach that makes decisions supported with data, numbers, facts, statistics and multiple algorithms, can provide reasonable and logical solutions. Data science is a strategic process that is beneficial for any business model. It not only helps in the decision-making process but also makes it more efficient.
A few years ago, RR Donnelly, a marketing communication company, opened a logistics division to ship print materials to consumers and businesses. The general operation was pretty much aligned however, variables such as weather, geography, drivers and political climates were adding extra cost to the services. The solution that RR Donnelly found was derived from machine learning and analytics. This concept helped in predicting transport rates for a week’s period and attained a 99% accuracy. “The project paid for itself in under a year, and we’re still seeing growth in that business related to freights,” Ken O’Brien, CIO says.
Source:- Google Image
Here are 7 ways you can use data science to grow your business:
1. Utilizing historical data
Historical data can guarantee that you connect with the right clients. You can inspect your customers’ past behaviour and fabricate predictive models to figure out their future actions.
You can utilize historical data to deploy better decisions and actions. You can understand and estimate the outcome of the decision made by the unit by studying the steps taken in the past. Similarly, you can utilize your historical data to figure out which web structure best serves your clients and also, determine the items that you can prescribe to certain clients as well.
2. Establishing new openings
Data scientists, while analyzing the organization’s current systems and processes, look for ways to develop a more significant and systematic process. They prepare additional methods and algorithms aiming towards improving the currently deprived value from the data. This can drive advancement and permit new product/service improvement and help you discover new opportunities for your organization.
3. Better leadership with perceptible proofs
A data scientist assists the management by maximizing the staff’s analytical skills. He/She gathers the data and provides it to the employees, allowing businesses to make a keen and sharp team. Employees can use the data whenever necessary and drive more conversions with the experience they’ve earned. This can help organizations reach to conclusions that are substantiated by quantitative arguments, thereby increasing the opportunity of getting ideal and more consistent results.
4. Cautiously characterize your objective market
Every organization collects customer data that can help them learn about their audience and understand their behaviour. This will allow you to comprehend with the essential needs and changes the customer is looking for and alter your business growth according to your audiences’ convenience.
Organizations can use other data sets in correlation with the customer data sets to find different combinations that work for their business. For example: which age group is attracted towards a particular product and then release promotions and offers targeting that age group.
5. Making your product more relevant
As discussed previously, data science with historical data can help compare your products with its competitors. This way you can stay one step ahead of them and better understand your audience’s needs. Data combined with analytics helps businesses stay competitive and understand the market trends and change. This helps organizations deliver products before the demand begins or increases.
6. Recruiting the right talent
Data science enables businesses to identify candidates that are likely to drop out, this can save the cost of training a new employee. With all the data collected on social media, job hunting sites and corporate databases, businesses can use data science strategies to look for the most suitable candidate. This could help companies choose an applicant that will match their office culture rather than hiring someone who excels in academia only.. Working in such a manner can help companies choose the right candidate.
7. Helps in creating a Data-Driven system
With data science coming into the picture, it has replaced taking high-end business risks as it helps in making well-informed decisions. Creating a data-driven environment helps the company move forward in a more systematic way. Furthermore, it also helps them formulate a logical and informed decision-making process.
It is not just for the data science team but also for the organization as a whole to really follow data strategies. Once the staff understands the service capabilities, they can focus on the business challenges with the effective use of data systems and data-driven insights.
Executing data science procedures all through your business helps in improving and enhancing leadership, recruitment, preparation, advertisement, and that’s just the beginning. Data enquiring can prompt settling on well-educated choices that ensure your organization’s development. Setting aside the effort to utilize data science and find the proof behind your execution is an instrument that each business should, for the most part, deem important.