Data, data, data — it’s all we hear about. We hear about how much is created, how it’s critically important, and that businesses are leveraging it to reach their goals and increase their revenues. But how? Yes, much of it is technology based, but if there is an astronomical amount of data being generated every day, how are businesses supposed to keep up?
Well, with data science.
Data science uses a combination of various tools, algorithms, formulas, and machine learning principles to draw hidden patterns from raw data. These patterns can then be used to gain a better understanding of a variety of factors and influence decision making. Data science does more than just crunch numbers — it reveals the “why” behind your data.
Data science is the key to making information actionable by using massive volumes of data to predict behaviors and infer meaning from correlating data in a meaningful way. From finding the best customers and charging the right prices to allocating costs accurately and minimizing work-in-progress and inventory, data science is helping businesses maximize innovation.
Data science tools and technologies have come a long way, but no developments were more important than the improvement of the Internet of Things (IoT), artificial intelligence, and machine learning. Working together, these technologies are what enable data scientists to quickly and accurately make these groundbreaking determinations that influence countless business decisions. IoT processing provides the data; data scientists develop machine learning and AI algorithms to understand the data and gain new insights; then, businesses deploy cutting-edge platforms to continuously monitor, evaluate and ultimately act on data, thereby adding value to many business processes and outcomes.
Starting your journey with data science can feel like an overwhelming undertaking, but when strategized and prepared correctly, it doesn’t have to be. Data science initiatives can deliver significant value to enterprises when the right opportunities are identified and new talents and technology are explored.
To help you get started, here is a breakdown of how to use data science and its corresponding tools to unlock your business’s potential and get the most out of your most valuable asset.
How to Use Data Science in Your Business
Data science can positively impact many business functions, both customer-facing and internally. And while the benefits and potential uses of data science are vast, here are some of the primary ways organizations have used data science in their operations, and the solutions they are using to get results.
- Quantifiable & Data-Driven Decision Making
This is arguably the biggest reason many businesses utilize data science applications, and its usually also the biggest benefit. When organizations can organize, make sense of, and leverage their data, they can make more accurate predictions, forecasts, and plans for all areas of their operations. Using data science tools, businesses can determine what elements they need to focus on to reach their most important targets and can then implement the most effective plans to reach them. One relatively new but exciting feature of this technology is the ability to analyze streaming data through time series analysis, giving businesses real-time feedback that they can act on.
- Better Understanding of Customer Intent
Organizations can now use data science tools to more effectively and accurately understand customer intent and their data, thanks in large part to what is known as natural language processing. Otherwise known as NLP, natural language processing utilizes AI to read, write, understand, and ultimately extract meaning from human language to make decisions. This is a major advancement for artificial intelligence and is changing the game for businesses and data scientists. Using NLP, they have expanded capabilities such as topic modeling, name entity recognition, and sentiment detection, all of which can help them more effectively utilize their data and understand their customers.
Recruiting and retaining quality and skilled employees is a struggle for many businesses, regardless of industry. NLP is also making a difference here, by automating aspects of the recruiting process to help organizations find better candidates, faster. Using unique algorithms, data science can “read” resumes and decide whether or not a candidate is worth pursuing. It can even select resumes based on specific character and personality traits, which enables businesses to get very specific about the type of person they are looking to hire.
- Opportunity Identification
Another capability of data science tools and analytics is opportunity identification. Using historical and forecasted market data, businesses can identify geographic areas to target to penetrate for sales and marketing initiatives with greater accuracy. Data can inform new market decisions and make predictions as to whether a new venture is likely to be cost effective. This will ultimately help organizations determine what is worth the investment and whether they can expect to see a return.
Industry Opportunities with Data Science
Data science has far-reaching impacts on businesses across a variety of industries. To give you a better understanding of how different organizations are leveraging the benefits of data science, here are some use case examples from four major industries.
Data Science in Healthcare
The promise of big data in the health industry has never been more attainable. From performing predictive analytics during diagnosis to transforming treatment and improving patient outcomes, data science has never been bigger than it is right now in healthcare. In fact, McKinsey estimated that big data initiatives in the US healthcare system “could account for $300 billion to $450 billion in reduced healthcare spending or 12 to 17 percent of the $2.6 trillion baselines in US healthcare costs.”
Here are some examples of data science-driven healthcare innovations:
- Genomics and Precision Medicine: Using machine learning programs to analyze data from single cell sequencing, biomarkers, and genetics, providers have been able to develop and innovate more effective drugs at a faster rate.
- Clinical and Claims Data: Providers are improving patient outcomes and cost effectiveness by leveraging and analyzing markers from readmission predictions, efficacy and comparative analytics, prescription adherence, and market access analytics.
- EHR Data Usability: Using NLP, health care providers can read and act on electronic health records faster and more effectively. NLP can analyze, scan, and organize EHR interfaces into sections, making it easier for clinicians to find buried data and make diagnoses they might have otherwise missed.
Data Science in Manufacturing
Manufacturing in today’s digital world is all about staying one step ahead. From predictive process control and supply chain forecasting to predictive maintenance and image classification, leveraging data is the best way for modern manufacturers to keep up with industry competition.
Here are some examples of how data science is being applied in manufacturing:
- Supply Chain Forecasting: By utilizing supply chain data, such as transaction data, demographics, purchasing history, and trends, retailers can reduce inventory cost and maximize supply-chain optimization.
- Predictive Maintenance: No manufacturer can afford unexpected downtime, but with data science they can reduce failure rates and forecast maintenance using risk sensor stream data.
Data Science in Retail
For retailers in the digital age, data is everything and everywhere — shoppers and customers generate 2.5 quintillion bytes of data each day. From personalized offers to optimizing merchandise and inventory to next-generation store design, leveraging data to innovate is the status quo.
Some examples of the benefits of data science for retailers include:
- Real-Time Pricing Optimization: Retailers can use data science to optimize pricing based on consumer engagement, including historical sales data, price scheduling, and segment-level price changes.
- Data-Driven Stock, Inventory, and Ordering: Retailers can improve inventory optimization based on data from demand plans, forecasts, sales history, trends, and even local events/weather patterns.
- Trend Forecasting: Using NLP, data scientist can “scrape” online content to discover trending topics and likeability associated with those topics. Through topic modeling, retailers can then rank trends by likeability and use this data to inform purchasing of new inventory.
Data Science in Financial Services
From financial modeling to detecting risk and fraud to performing customer and credit analytics, leveraging data to innovate is a mainstay of the financial services market. To keep up with industry standards and competitors, many firms are turning to data science and machine learning. Data science helps firms extract knowledge from their data so that they can make fact-based business decisions, all while protecting sensitive customer information.
Some examples of data science and machine learning applications in financial services include:
- Credit Analytics: Firms can analyze risk and compliance management data, including customer relationship management system data, credit, risk, merchant records, products, and services.
- Customer Analytics: Transcription of calls between customers and financial service employees can be analyzed through sentiment detection to provide the organization with insight regarding which customer service team members has the most positive and/or negative interactions with customers.
Getting Started with Data Science
The good news is that leveraging data science in your business is just a couple of steps away. The most important thing any business can do to prepare to implement data science initiatives is to organize their data. Unstructured data is a problem for 95% of businesses and prevents them from making more informed decisions across their operations. The first step is to gather as much data as possible — the more, the better — which will help prepare the data for data analytics and data science solutions.
Once your data is compiled, it’s time to hand it over to a trusted team of experts, who can move the data to a cloud format, where it can be analyzed and ultimately leveraged by your organization. At Hitachi Solutions, we have been helping businesses from a variety of industries put their data to work for them. Our accessible and effective solutions can be tailored to any operation and any goal. If you’re ready to make the most of your data, contact us — we’re ready to get started.