What five questions guide the analytics process?
A viable predictive model that yields valuable outcomes requires a methodical team approach to goal-setting, data integrity and model development, deployment and validation.
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Published: 15 Dec 2021 There are many ways to conceptualize the predictive analytics process cycle. Execution will vary according to organization, domain and industry. In many cases, the overall process lifecycle may be embedded across one or more applications, services or algorithms. In the most straightforward cases, the only connection to the predictive process is providing feedback to a service maintained by someone else. For example, predictive analytics is baked into fraud engines and spam filters. Marking an email as spam or a transaction as fraud provides feedback to a predictive process that someone else maintains. At the other extreme, a more mature predictive analytics process includes three integrated cycles around data acquisition, data science and model deployment that feed into each other. Gartner's MLOps framework, for example, includes complementary processes around development, model release and model deployment that overlap and work together. What are the applications of predictive analytics?"Predictive analytics has broad use cases across healthcare, retail, sales and marketing, and a plethora of other sectors and industries," said Elif Tutuk, vice president of innovation and design at SaaS software provider Qlik. Predictive analysis can help forecast inventory levels, make customer recommendations, prioritize leads and improve healthcare. This article is part of What is predictive analytics? An enterprise guide
"One of the primary ways in which these [predictive] models can be used practically is by analyzing customer shopping behavior," reported Bill Szybillo, business intelligence manager at ERP software provider VAI. "In an age of rapid demand and supply chain shortages, this is critical for businesses hoping to keep shelves stocked and customers in the aisles or customers on a website. However, it's not easy to just develop these forecasts at the snap of a finger, as this often requires ongoing data collection for months and often years in some instances." Predictive analytics is "a universal technology," Tutuk added, "but the challenge comes from the tools and the data itself." That's why understanding the process cycle is helpful. If the wrong tools are used or if inaccurate or outdated data is included, the predictive outcomes will be negatively impacted. Businesses must practice due diligence when selecting data partners and ensuring their data is accurate and not siloed or otherwise limited in any way. Various data services make it easier to start with vetted external data that can shine a light on other factors leading to rapidly changing trends. Many enterprises are combining internal data records with external sources to glean insights. "[A] major trend in predictive analytics," Szybillo said, "is the ability to help manufacturers determine future inventory levels not only through past usage, but also using external data sources with their internal analytics data, such as weather patterns, changes in demand, insights into the supply chain and more." What are the steps in the predictive analytics process?Five key phases in the predictive analytics process cycle require various types of expertise: Define the requirements, explore the data, develop the model, deploy the model and validate the results. Although each of these steps may be driven by one particular expertise, each step of the process should be considered a team effort. Statisticians, for instance, can help business users make informed decisions. Data scientists can help business analysts select better data sets. Data engineers can work with data scientists to create models that are easier to deploy. Although various business applications, analytics toolkits and cloud services may automate many of these processes, understanding the entire process can help locate process bottlenecks and improve accuracy. Following is a detailed view of the predictive analytics process cycle and the experts influencing each step. 1. Define the requirementsBusiness user or subject matter expert 2. Explore the dataStatistician or data analyst 3. Develop the modelData scientist 4. Deploy the modelData engineer 5. Validate the resultsBusiness user and data scientist Augmenting the predictive processThe predictive analytics process cycle has traditionally been complicated, time-consuming and arduous. Better tools, management processes and cloud services are helping to improve the process. "Many steps in this process are now automated or augmented," said Carlie Idoine, research director for business analytics at Gartner. New tools can augment data preparation, model building and deployment. XOps characterizes the various capabilities involved in the predictive analytics process cycle, including DataOps, ModelOps, AIOps, MLOps and Platform Ops. "In the past," Idoine explained, "finding the right data and bringing it together usually took most of the time in building a model. Now augmented data preparation can automate much of that process." Augmented model building tools can help data scientists determine which features or combinations of variables lead to the best predictive outcomes. Augmented model deployment tools allow data scientists to push models onto an infrastructure created by data engineering teams. Although these augmented and automated tools make it easier for business users to drive most of the process, Idoine cautioned against eliminating the need for experts to oversee the process. "Automation," she said, "does not take experts out of the loop but makes the process more efficient, and this allows different types of users to use the tools to take advantage of predictive analytics." What are the 5 steps in data analytics?In this post we'll explain five steps to get you started with data analysis.. STEP 1: DEFINE QUESTIONS & GOALS.. STEP 2: COLLECT DATA.. STEP 3: DATA WRANGLING.. STEP 4: DETERMINE ANALYSIS.. STEP 5: INTERPRET RESULTS.. What are the first 3 5 questions you would ask from client on any analytics assignment?In digital analytics, it's all about asking the right questions.. Is this data accurate? Can we trust it? ... . What's missing? Do we have the full picture? ... . Is this data meaningful? ... . What can we measure and analyze to get more meaningful results? ... . Have I done proper QA?. What are key analytical questions?To sum it up, here are the most important data questions to ask:. What exactly do you want to find out?. What standard KPIs will you use that can help?. Where will your data come from?. How can you ensure data quality?. Which statistical analysis techniques do you want to apply?. What are the key steps in the analytics process?6 Steps in the Business Analytics Process. Step 1: Identifying the Problem. The first step of the process is identifying the business problem. ... . Step 2: Exploring Data. ... . Step 3: Analysis. ... . Step 4: Prediction and Optimization. ... . Step 5: Making a Decision and Evaluating the Outcome. ... . Step 6: Optimizing and Updating.. |