What Data Science Is

Data Science turns raw data into accurate predictions, so you can make high-impact decisions that lead to competitive advantage. And with our lives increasingly driven by data, using it has become a focal point for organizations of all types.

Traditional business intelligence is nothing new, but it only tells you what a problem is, how it happens, how often and where. Data Science reveals the source of a problem and what the future holds. And that’s kind of like holding the winning lottery ticket.

The raw material of Data Science is Big Data. Defined by the seven V’s, this includes volume, variety, velocity, value, variability, veracity, and visualization.

Visualization, which is what Data Science is most known for, involves the use of sophisticated graphs such as pie charts, bar graphs, time charts, line graphs, histograms, scatter plots and others. These can be integrated to analyze various data sets, giving you fresh and valuable insights, even in real-time. For example, you could know the implications of real-time customer purchases.

The type of analysis that can reveal the future, is known as Predictive Analytics. And with Google and Amazon routinely using it, everyone is not far behind. It’s no secret then, that the future of Data Science is now.

Why Data Science Is Important

Ninety percent of the world’s data was created in only the last two years, and the digital revolution is happening as you read this. The amount of data being generated daily is a staggering amount. 2.5 quintillion bytes! That’s enough pennies to cover the surface of the earth twice a day.

The US alone creates 2,657,700 gigabytes of Internet data every minute. For every minute you spend reading this article, YouTubers will have watched 4.14 million videos.

Further, Netflix members viewed 69,444 hours of shows, 154,200 Skype calls were made, 45,787 Uber trips were taken, Twitter sent 456,000 tweets, Instagram added 46,740 photos, there were 3.6 million Google searches, 600 new Wikipedia page edits were made, and 103,447,520 spam emails were sent.

While data like this can reveal business insights, most companies are completely unprepared for the challenge. If they were, they’d have tools for solving their most complex business problems. For example:

  • Predicting future demand, with predictive analytic
  • Recommending buyer options with recommendation engines
  • Optimizing marketing campaigns with tactical optimization
  • Detecting fraud, with automated decision engines
  • Understanding customers better, with nuanced learning

Where Data Science is Used

In some industries, Data Science is hot. In others, it’s very hot. For example, some of these include Healthcare, Finance, Retail/e-commerce, Non-profit, Logistics and People Management. And here are some of the data-driven challenges industries face and how Data Science can help solve them.

Healthcare Challenges Data Science Solves

Most would agree that the healthcare industry is overpriced and inefficient. For example, many of the treatments people receive are based on inaccurate diagnosis, and of little help. Data Science can help physicians make better treatment decisions and recommend more effective preventative care. In the process, this will significantly reduce healthcare costs.

  • Claims review prioritization
  • Medicare/Medicaid fraud
  • Medical resources allocation
  • Alerting and diagnostics from real-time patient data
  • Prescription compliance
  • Physician attrition
  • Survival analysis
  • Medication (dosage) effectiveness
  • Readmission risk

Financial Industry Challenges Data Science Solves

Explosive amounts of data available to the financial sector. And this enables companies to offer credit online with less risk, such as loans for start-up entrepreneurs. Further, companies can create a baseline for spending patterns, and identify when something abnormal happens to prevent fraud.

  • Credit card fraud
  • Credit risk
  • Treasury or currency risk
  • Fraud detection
  • Accounts Payable Recovery
  • Anti-money laundering
  • Debt collection

How Data Science is Revolutionizing Ecommerce and Retail

For both retail stories and Ecommerce, Data Science helps to understand customer better. This makes it easier to gauge creditworthiness. Further, it facilitates more accurate pricing which gives customers access to lower prices, and companies benefit from increased sales.

  • Pricing
  • Best Offer Analysis
  • Merchandizing
  • Inventory Management (how many units)
  • Warranty Analytics
  • Market Basket Analysis
  • Cannibalization Analysis
  • Location of new stores
  • Product layout in stores
  • Shrinkage analytics

Data Science for Social Good

Nonprofit organizations need resources to achieve their missions. But to attract donors, they must prove their worth by showing their work is getting results. Data science can help non-profits acquire funds and do more good. In addition, it will help them make better informed decisions.

  • Optimize fundraising
  • Identify and target groups
  • Discover relationships
  • Develop incentives
  • Measure performance of activities
  • Optimize relief efforts
  • Tackle issues related to education, healthcare, public safety and the environment
  • Allocate funds appropriately

Data Science for Logistics

Logistics is an ongoing challenge with many variables to contend with. These include shifting demand, human error, traffic, fuel costs, and changes in the weather. Predictive logistic analytics can be applied to all these, enabling companies to experience less mechanical downtime, more efficient routes, happier customers and higher stock prices.

  • Managing demand forecasting
  • Order picking from existing stocks
  • Replenishment procurements to keep stock levels adequate
  • Packaging for efficient delivery
  • Routing of packages to avoid choke points

Data Science for Management

Finding the right people is the key to growing your business. And your success depends as much on your people as it does on your product. While it’s an ongoing challenge to recruit, hire, train, and manage them, you need the right people poised to take the right action at the right time. And Data Science can help. Let our Machine Learning Management Consultants help you with the strategy.

  • Resume screening
  • Employee churn
  • Training recommendation
  • Talent management
  • Tracking work hours
  • Detecting employee theft
  • Call center/call routing
  • Call center message optimization
  • Volume forecasting
  • Staff rostering

Data Science Steps

Before you can get started Data Science, you must ask some key questions. What problems should we investigate? How will the data be collected? Which analytic tools will we use for data analysis? Here are six steps involved in a Data Science project:

Step #1. Define the Problem

The greater part of solving a problem is defining it. Then you must translate your questions about the data into something actionable.

  • Who are the customers?
  • Why do they buy?
  • How do we predict if they’ll buy?
  • What’s our return-on-investment for increasing sales?

Step #2. Collect raw data

Here you must determine what data you have, what you need and how you’re going to get it.

  • Surveys
  • Experiments for gathering qualitative or quantitative data
  • Gathering data in tables with records and types of data.


Step #3. Clean the data

Your data may be structured, but it can still be messy. And the quality of your output depends the quality of your input.

  • Eliminate common errors
  • Watch for invalid entries
  • Check for date range errors
  • Data registered from before sales started

Step #4: Examine the data

This step where you search for the best ideas to test. And then decide what you think will turn into insights.

  • Prioritize your questions
  • Look for interesting patterns
  • Consider irregularities
  • Find commonalities
  • Trace patterns for deeper analysis

Step #5: Perform In-depth Analysis

Now you’re ready to apply your knowledge of statistics, math, and technology to crunch the data and reveal some insights.

  • For example, create a predictive model that compares one group of customer data with a benchmark.
  • Or, learn which marketing channels are more likely to appeal to certain groups

Step #6: Visualize the Data

Start communicating your data:

  • Find a compelling story idea
  • Craft a story structure
  • Tie your data in with the story
  • Reveal the insights of the data
  • Plan a narrative that shows how the problem is solved
  • Motivate people to action

Core Competencies

Our data engineers and data scientists have core competencies in data normalization, data matching, attribution, and prediction.


  • Competency at all levels of data infrastructure – from foundational database design to cutting edge predictive analytics.
  • Development and execution on crucial data governance and design decisions.
  •  The ability to ensure data integrity and to implement best-practice quality assurance.
  • Data infrastructure with a focus on yielding high-level and in-depth insights into operational performance for both transactional analytical systems.


  • Cataloging of cross-sectional details about your customers, products, and metrics.  
  • Analysis of conversion KPIs and outcome prediction utilizing advanced machine learning techniques.
  • Analysis and visual interpretation of “customer conversion stories” to assist key decision-makers and strategic development.


  • Discovery and measurement of new cohort populations.
  • Delivery of key insights from operational metrics and KPIs.
  • Crafting of targeted and personalized marketing messages and reach untapped marketing segments.
  • Identification of customers sub-groups, demographic cohorts, and consumer behavioral traits.
  • Strategic analysis and development of core business insights – churn and customer retention analysis and customer profiles.


  • Analysis of trends and seasonality – analyze existing patterns to uncover ongoing trends and to predict future trends.
  • Intervention analysis – determine whether manipulation of certain attributes will yield desirable outcomes.


  • Financial risk and fraud analysis/forecasting.
  • Digital marketing and advertisements:
    • Lead scoring
    • Content targeting
    • Optimization of marketing campaigns
  • Website traffic prediction – planning for peak hours and resource allotments.
  • Geographic and demographic sales forecasting – consumer segments targeting.


  • General, content-based textual classification.
  • Sentiment Analysis – value from unstructured, textual data in customer forums or from social media.
  • Topical keyword tagging – Automatic generation and application of content-based tags for articles and blogs.

Machine Learning for Data Science

Machine learning has an elevated status in Data Science. And this is because of the key role it plays in enabling machines to learn from the past and predict the future. By running the data through algorithms and applying statistical analysis, Data Scientists can predict an output value within an acceptable range. These techniques fall into three areas:

  • Supervised. A human gives the computer specific examples or feedback on the correctness of a decision.
  • Unsupervised. The computer learns on its own by uncovering hidden patterns without explicit direction.
  • A software program optimizes the behavior, but without specific directions or feedback on less than perfect results.

How Data Science is Transforming Sales & Marketing

One of the greatest opportunities for leveraging Data Science is sales & marketing. For example, Google trends uses data science to reveal worldwide rates of searches showing you what’s trending for an industry or product.

And it helps your sales & marketing team describe customers in greater detail for better product availability, pricing, supply, and logistics. Further, advertisements can be positioned for better visibility, lead scoring helps close more sales, and customer lifetime value calculations can help direct marketing strategy, especially for early stage businesses. And Salesforce.com makes reaching these goals easier than ever.

What is salesforce.com software used for?

Salesforce.com is the leading Customer Relationship Management software solution delivered through the web. Originally created to help companies manage phone calls, emails, meetings and social media, it now offers a wide range of business solutions. For example, it has artificial intelligence built-in.

The Benefits of a Salesforce AI Strategy

A salesforce artificial intelligence strategy includes sales, service, marketing, and commerce. AI tools for service can help cross-sell and upsell at the right time, and their marketing tools deliver the best content, the commerce tools offer personalized recommendations. And it’s all driven by Salesforce Einstein.

How salesforce Einstein Works to Deliver AI

Einstein is the Data Science component of Salesforce.com. It lets you discover insights, predict outcomes, and help customers get what they want. Predictive Lead Scoring reveals what leads will convert, Forecasting helps predict future value, like reaching quota, and Recommendations help online shoppers get more bang for their buck.

Data Science for Business from RTS Labs

RTS Labs helps clients through every step of the Data Science development process. First, we help you define your most pressing problem or greatest opportunity. Next, we help you collect, clean and examine your raw data. Finally, we help you perform in-depth analysis and visualize your data for better decision-making.

If you’re already using Salesforce.com, you must have realized that buying the system is the easy part. Now it needs to be customized to your needs, with end-user training for a high adoption rate. Not only do we provide Data Science Consulting and Machine Learning development, we offer Custom Software Development, Analytics Development, and Salesforce development, all In-house.

Request a consultation now

Our Technology Expertise

We use proven technologies available on the market and continue to learn new ones

  • Kubernetes
  • hive