Information Wealth in Big Data
By Mitch Pudil / January 11, 20213 min read
Big data is necessary for achieving key insights that lead to maximizing competitive advantage and ROIs. Companies can have all the information in the world, but without a team of data scientists, they can easily miss blindspots that can cost millions in revenue. In other words, you can have all the bricks in the world, but without an architect, tools and labor know-how, you won’t have a well-built house.
Most modern corporations have more data than they know what to do with. The Forbes Technology Council compiled a list of the most common mistakes businesses make when dealing with data, such as analysis paralysis and letting data sit in a silo. It’s crucial for companies to avoid these pitfalls on their path to digital transformation.
That’s where data science comes in. It’s the data scientist’s job to gain actionable insights from all those gigabytes of information. When strategies are data-driven, they bear powerful results.
Netflix uses big data analytics for targeted advertising, collecting search and watch history from its millions of subscribers to gain insights on what keeps viewers hooked. The streaming platform’s recommendation system is powered by machine learning (ML) and sophisticated algorithms, sending personalized suggestions of what viewers should watch next. This data-driven personal touch amps up customer experience, keeping viewers subscribing month after month.
PepsiCo also takes a data-driven analysis approach to business. The global drink brand analyzes metrics collected from its warehouse and point of sale inventories to forecast production and shipment needs, ensuring retailers consistently have the correct products in the correct amount.
Big data can help businesses increase ROI, drive customer retention and mitigate risk factors. Unlocking the power of that data starts with the Discovery Phase, Codazen’s unique approach to analyzing our clients’ data and putting it to work. Here are the five steps our data scientists use, from nailing down the initial problem to building out machine learning models.
Step 1: Understand the Problem
The first step of big data analytics is understanding the context of the problem that needs solving, with a focus on the motivation. Even the biggest, most well-funded corporations struggle with understanding what data they already have, what they still need and how to leverage it to achieve their goals. To find answers, they need data scientists to ask the right questions.
Codazen Data Scientist, Neal Munson, explains the importance of understanding the business directive in order to find real insights specific to the data project. According to Neal:
We may find a lot of correlations from the data we get, but the key is to sort out which ones are useful. For example, data from a restaurant could say that food is cooked before it’s served, but that information probably isn’t new and wouldn’t provide additional value. Discovering that particular tables at the restaurant tend to produce higher paying customers could be game-changing.
Identifying in detail the needs and uses of each project is integral to maximizing the benefits of big data.
Step 2: Access the Data
Once the goal of the project is figured out, the next step is to look at the information. Connecting to your company’s database or using APIs are two common ways of obtaining usable metrics.
Maryam Farboodi, assistant professor of finance at the MIT Sloan School of Management, and her colleagues calculated the different availability of data in small and large companies, looking at how data interacted with the growth of processing power. Farboodi says that since large companies produce more data than smaller companies, this abundance in data “accelerates advances in processing speeds and computing and helps investors view these large companies as a less risky bet.”
But what happens if a client has access to only one type of data when other types are needed? Data scientists can access the missing information by looking for open sources on the internet. If a zoo wants metrics on the weather to figure out how to keep its animals comfortable but it hasn’t collected any weather data, scientists can acquire what they need from open platforms online. These open datasets are an incredible resource of additional information.
Once a company has collected the required data, our scientists can confidently assess how projects can benefit from artificial intelligence.
Step 3: Identify the Value of AI
Although artificial intelligence is a powerful tool, data scientists need to first understand how to go about solving the problem without AI before they try to automate the solution or create an ML model.
Let’s say you’re trying to classify dog breeds. You have a video of different dogs and you want to know which breeds are in the video. As a human, you can watch the video and figure out the breeds visually from your limited knowledge of dogs. An ML model, however, can be trained to recognize hundreds of different dog breeds from what they look like and potentially other aspects of the dogs such as their bark. It can do so much quicker and with much more accuracy than a human who’s visually identifying the breeds.
In the vast majority of cases, the question isn’t whether an ML model should be used to solve the problem, but the best way to have an ML model fit into a solution. Determining how AI can use information from the data is the key to delivering the desired results.
Step 4: Clean the Data
The next step is to transform the data into a useful format for ML models. Making sure data is homogeneous and clean can be time-consuming. According to a survey by data science platform, Anaconda, 45% of a data scientist’s time is spent loading and cleaning data before they can use it to develop models and visualizations.
If videos are being used, they must be transformed into sets of images, which must again be transformed into matrices of numbers. To classify dog breeds by bark, each bark needs to be broken down by volume and pitch with a scale for every millisecond. This preprocessed data is then fed into an ML model.
Raw data can also be cleaned by combining it with other raw data using feature extraction. For example, the number of customers at a store can be combined with the sum of money that store made to figure out per capita profits. Combining different variables into features reduces the amount of data that needs to be processed, turning it into a format that can easily be used by ML models.
Step 5: Create the Model
This step in the Discovery Phase includes clustering data points together, creating algorithms used in predictions and building specific processes for turning data into desired insights or results. It’s crucial to determine the kind of ML model that’s helpful for a project.
For example, unsupervised algorithms can be used to find patterns or draw inferences from unlabeled data. Supervised learning, on the other hand, can be used in training models to predict or classify variables such as height, price or number of customers. Reinforcement learning is used to attain a complex objective or maximize a specific dimension over many steps.
Amazon uses ML tools built on data from product descriptions and customer feedback to identify the best packaging with the least waste, reducing the use of boxes from 69% to 42%. This reduction not only saves the company money, but helps the environment.
Another example is with the medical industry: Aarthi Janakiraman, Technical Insights Research Manager at Frost & Sullivan, a research and consulting firm, says, “Integrating AI and ML methods into drug discovery pipelines would cut down cost and time, and increase the efficiency of the entire research and development (R&D) process.”
Choosing the right ML model maximizes ROI. Our data scientists use pre-trained models available online or build their own from scratch.
Step 6: Refine the Solution
The Discovery Phase doesn’t end after the ML model is built. In order for the model to remain accurate, it needs to be continuously improved, given new data and retrained. Iteration is key to unlocking the power of information.
Adjusting the model appropriately produces more accurate results. Since data can change over time, predictions offered by static machine learning tools begin to degrade as time goes on, becoming less accurate and, therefore, less useful.
Companies can overcome this by continuously updating and retraining their ML models with new data and identifying and implementing new features. Our data scientists can provide these sustainable solutions to keep your data working for you.
Codazen Solutions: Data Discovery
Information is only useful with the right understanding. Our in-house data science team analyzes your data and builds machine learning models to provide effective solutions for your business.
From retail and banking to the auto industry and the medical field, companies can benefit from discoveries in big data to accelerate their timelines. But, to truly make use of it, organizations need to have a data science-driven strategy that aligns with business goals.
Codazen’s data scientists know how to leverage data to increase revenues and customer satisfaction while reducing costs and risk. We specialize in data collection methods and partner with clients to propel their digital transformation.
Contact us to learn more about how our unique Discovery Phase can help you harness the power of data.