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As enterprises increasingly turn to digital transformation, data science is becoming front and center to the discussion surrounding data analytics.
“Data science is the cutting edge of data analytics,” write Dr. Jay Boisseau and Dr. Lucas Wilson at CIO. “It’s a process of testing, evaluating and experimenting to create new data analytics techniques and new ways to apply them.” Not only that, but it is becoming central to enterprise operations. “Strong enterprise data cultures should include data scientists who continually strive to increase capabilities while working to enable the larger enterprise staff to use mature, proven analytics tools,” the researchers conclude.
With that mindset at the forefront, RTS Labs is dedicated to integrating the scientific approach that data science encourages to all the projects we undertake. And we want to enable you to do the same. In this brief guide to data science, you’ll learn:
You can learn more about what RTS Labs is doing with data science for enterprises on our data science consulting page. Otherwise, let’s jump in.
The form and function of data science run together: 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 data science 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, along with a few details. In contrast, data Science reveals the source of a problem and can make relatively accurate predictions for where and when the same problem could crop up in the future. It’s like holding the winning lottery ticket.
Behind data science is raw data — the material that feeds everything. Big data is typically defined by “the seven V’s”: volume, variety, velocity, value, variability, veracity, and visualization. These answer how much data is available, how fast it can be accessed, where it comes from, how it can be used and more.
Most of the “v’s” have to do with with the form of data. But two parts (visualization and value) have more to do with the function of data for the enterprise. Visualization graphs 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. This is where enterprises get the value of big data from.
Ninety percent of the world’s data was created in only the last two years — we’re in the middle of the digital revolution. The amount of data being generated daily is staggering: 2.5 quintillion bytes! If the bytes were converted to pennies, it would be enough to cover 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. WhatsApp hit 100 million calls per day a long time ago.
You get the picture. We’re dealing with a lot of data these days. And enterprises can harness that data for growth.
While data can reveal business insights, most companies are completely unprepared for the challenge. With the right preparation, data tools can solve complex business problems:
Data science and DevOps can work beautifully together. Both place an emphasis on closing the feedback loop between teams to achieve continuous improvement across systems, processes and outcomes. And both can make for lightweight operations, particularly when run on Docker within containers.
Source: Syed Sadat Nazrul on Medium
Obviously DevOps and data science each have their own focus as well: data science projects focus on filtering, extracting and formatting data for more effective use, while DevOps projects focus on bridging technology operations and development for better quality assurance and outcomes. Even in their differences, one can see the similarity: when done well, both DevOps and data science projects both depend on and improve the entire enterprise.
Because of this dependency and impact, projects work best when DevOps and data science teams work together. “DevOps involves infrastructure provisioning, configuration management, continuous integration and deployment, testing and monitoring,” writes Janakiram MSV at Forbes.
As organizations prepare to take full advantage of the data available to them (whether in the form of data analytics or machine learning), operators will increasingly work with both developers and data scientists (and data engineers). “Data engineering, a niche domain that deals with complex pipelines that transform the data, demands close collaboration of data science teams with DevOps,” Janakiram concludes.
Data scientists use huge amounts of information to find and predict problems, along with insights into and correlations between nodes. The intel they gather can drastically improve DevOps’ efforts. In turn, DevOps can help data science teams develop a containerized approach to their data projects.
Right now, integrating data science and DevOps at the enterprise level will position organizations ahead of the curve. Soon, informing DevOps projects with data and data projects with a DevOps approach could very well become the norm. Why not position yourself with the experts?
RTS Labs works within a host of industries to bring DevOps and data science expertise into enterprise operations. Here are some of the data-driven challenges industries face and how data science can help solve them.
While it’s not true across the board, many organizations in the healthcare industry struggle with efficiency. Data Science can help physicians make better treatment decisions and recommend more effective preventative care — not to mention more accurate and faster billing.
Uses for data science in healthcare include:
There are incredible amounts of data available to the financial sector. This enables companies to offer credit online with less risk, such as loans for start-up entrepreneurs. Using data, companies can create a baseline for spending patterns, and identify when something abnormal happens to prevent fraud.
These are just some of the applications of data science within the financial industry:
Data science helps both retail stores and eCommerce brands better understand their customers. This makes it easier to gauge creditworthiness, facilitate accurate pricing, upsell and make personalized suggestions.
Consumer brands are using data science to improve:
Nonprofit organizations need resources to achieve their mission. But to attract donors, they must prove their worth by showing the result of their work. Data science can help nonprofits acquire funds and do more good — all with better informed decisions.
Logistics management requires an accurate view of constantly changing variables: shifting demand, human error, traffic, fuel costs, and changes in the weather, to name a few. Logistics managers can apply predictive to all of these challenges, enabling companies to experience less mechanical downtime and more efficient routes.
Uses in logistics include:
Finding the right people is the key to growing your business. While it’s an ongoing challenge to recruit, hire, train, and manage them, data science can help. Data science and data analytics can improve:
Taking on a data science project is not a laissez-faire undertaking. You should identify the problems you are trying to solve, define data gathering methods and analytical tools and more. These are the six steps we recommend for any data science project — along with critical questions to ask, steps to take and tools to use.
The first step to solving a problem is defining it. Then you must translate your questions about the data into something actionable. For example, in retail you should ask:
Here you must determine what data you have, what you need and how you’re going to get it. You can use:
Your data may be structured, but it can still be messy. And the quality of your output depends the quality of your input. The steps include:
This step where you search for the best ideas to test. And then decide what you think will turn into insights. This stage has a few steps of its own:
Now you’re ready crunch the data and reveal some insights. For example, in retail you can create a predictive model that compares one group of customer data with a benchmark. Alternatively, learn which marketing channels are more likely to appeal to certain groups based on behavioral data that you’ve gathered.
This is where you need the right tools in place.
Start communicating your data to staff, customers and stakeholders. Visualizing the data can help you:
Any technology partner that you work with — as well as their data engineers and data scientists — should have core competencies in data normalization, data matching, attribution, and prediction.
Which area you focus on depends on both the enterprise problem you are trying to solve and the industry that you work in. Data normalization, for example, may be most important to an enterprise undergoing legacy modernization efforts, while a predictive modeling tool may be best for a new, digital native eCommerce brand.
Data science can improve nearly all of your business processes, from existing Salesforce projects to new enterprise applications built with DevOps. RTS Labs will help you realize all of its potential.
There’s no question data science is transforming modern business. The only question is how will you harness its power to make better high-impact decisions, build a better business with a competitive advantage?