Many of today’s businesses feel like they’re drowning in data but starving for insights. Sound familiar? You collect customer information, sales figures, production logs, marketing metrics – yet still struggle to turn that data into decisions that move the needle. That’s where the data science lifecycle comes into play. It’s not just for big companies with massive analytics teams. When applied thoughtfully, even small and mid-size businesses can use it to create better predictions and drive real outcomes.

Here, you’ll walk through how to apply each stage of the data science lifecycle, how to make it practical for your business, and how modern platforms streamline the process so you don’t have to become a data scientist overnight.

• Define the Problem

Data science doesn’t begin with the data, but rather with a problem you want to solve. The first stage of the lifecycle is problem definition. You need to ask: What decision are you trying to improve? What outcome do you want? What business processes need to change?

Maybe you want to reduce customer churn, optimize delivery routes, predict equipment failure, or increase high-value customer purchases. Whatever it is, you must define it clearly. Ambiguous goals lead to vague results.

For example, instead of saying “we want more sales,” define “we want to increase repeat purchases by 20 percent within 12 months from our top 25 percent of customers.” 

• Collect and Clean the Data

Once you have your question, the next stage is data collection and preparation. You’ll need to gather data that’s relevant to your question – customer history, transaction logs, equipment hours, social engagement…whatever applies.

Allocate time and resources to clean the data, if needed. Standardize entries, handle missing values, remove duplicates, and ensure your data is properly labeled and structured. This stage lays the foundation. If your data is unreliable, the models and insights built on top of it will be shaky. So take your time here.

• Explore and Visualize

With clean data in hand, you move into exploratory data analysis (EDA). This is where you interact with the data to discover patterns, trends, anomalies, and relationships. It’s not about making decisions yet – it’s about understanding what the data is telling you.

You might ask:

• Which customers are most likely to churn?
• What repair incidents frequently precede equipment breakdown?
• Are there seasonal patterns in your sales?
• What cohorts of users behave differently?

Visualization tools help a lot in this stage. You might map customer segments, chart equipment failures, or link marketing campaigns to ROI. The insights you uncover here shape the next phase.

• Model and Predict

Now you advance into modeling. Based on the patterns you identified, you build predictive models. These might forecast customer churn probability, equipment failure dates, sales volumes, or other outcomes relevant to your problem.

This is where data science often feels intimidating, but modern platforms are making this far more accessible. Advanced tools now allow you to build and validate models with less manual coding, and focus instead on interpreting results and making decisions.

• Prescribe and Act

Here’s where the magic happens. Predictive insights are valuable, but only when they trigger action. This stage – sometimes called prescriptive analytics – transforms forecasts into decisions, workflows, and changes that move your business forward.

For example, if your model predicts that a fleet vehicle has a 70 percent chance of requiring maintenance in the next 30 days, the prescriptive step is: “Schedule maintenance now before breakdown.” Or if a customer is likely to churn, the action might be: “Offer them a personalized discount or onboarding call.”

• Monitor and Improve

The final stage of the lifecycle is often underappreciated: monitoring and maintenance. Even the best models degrade over time because business conditions change, data evolves, and new patterns emerge. You need to track how your predictions perform and continuously refine your approach. This stage keeps your analytics alive and relevant.

Real-World Example: Fleet Maintenance

Let’s bring this to life with a concrete example. Imagine you manage a delivery fleet. You’re spending a lot on maintenance, facing unplanned breakdowns, and struggling to allocate vehicles efficiently. Here’s how the data science lifecycle helps:

• Problem Definition: Reduce unplanned maintenance costs by 30 percent over the next year.

• Data Collection: Gather data on vehicle hours, miles driven, past repair records, driver logs, fuel usage.

• Exploratory Analysis: Visualize patterns – certain vehicles break down more under specific conditions, locations, or driver behaviors.

• Modeling: Build a model to predict which vehicles are at highest risk of needing repair in the next 30 days.

• Prescriptive Action: Using fleet maintenance software, schedule preventive maintenance for high-risk vehicles, replace tires proactively, and rotate drivers based on risk patterns. By tracking standard repair times and comparing performance against them, you create a culture of accountability and motivate staff to complete repairs more efficiently.

• Monitor and Improve: Track breakdowns and maintenance costs. Measure whether the model’s predictions align with real outcomes. Tune variables, update thresholds, and adjust scheduling logic.

In this scenario, you move from reactive maintenance – “fix something when it breaks” – to proactive maintenance: “fix it before it breaks.” That shift saves money, improves uptime, and builds accountability across your fleet team.

Your Path Forward

You don’t need to hire a 50-person analytics team to start. Every business can begin with one clear question and one clean data set. Start small and add complexity only when you’re ready. This is your best path forward for sustained results!

The post How to Use the Data Science Lifecycle in Your Business appeared first on Green Prophet.

Today, we probably hear the word ‘digital’ at least once a day. From digital technologies to digital media, digital marketing, and digital transformation, it’s safe to say that everyone wants to go digital. 

While the rise of the Internet of Things and smartphones significantly contributed to this move, the recent Covid-19 outbreak accelerated the need for digital transformation like never before. 

As business owners and organisations scrambled to update their legacy technology and keep their services running, one point was painfully obvious — they just weren’t prepared to digitalise their offerings. 

And so, to overcome this hurdle, today, businesses across the globe are investing significant resources and capital to drive rapid digital transformation, because ‘’the future of business is digital and automated’’ (The Scalers, 5 Digital transformation Trends For 2021 and Beyond).

However, much of these efforts have produced disappointing results. In fact, recent research conducted by McKinsey showed that only 16% of organisations reported their digital transformation as successful while the others were unable to improve performance in the long haul. 

And so, in this piece, we present some actionable steps that you can implement to digitally transform your business. From rethinking the tools and technologies you use to redesigning processes, we’ve covered it all. 

1. Creating the right digital strategy 

Sometimes, digital transformation fails because companies pay too much attention to technology and don’t focus enough on creating the right digital strategy. The reality is that technology is only one part of the equation, and digital transformation requires a holistic approach. 

The first step in your digital transformation journey is to create a clear and coherent transformation strategy through which you hope to digitise your offerings. This begins with formulating a digital vision at all levels, from the senior management to the last employees, middle managers, and external collaborators. 

The ideal digital strategy involves rethinking the business model and maintaining a culture of constant adaptation of products and services to keep up with the ever-changing market trends. By doing this, you can target what you’re expecting to change, what outcomes you can expect, and then deliberately design for that. It also includes changing the internal structure in all functional areas of the business, such as HR, finance, operations, marketing, and communication. 

2. Datafying your processes

Datafication is the process of transforming social action into quantified data, in turn, making a business data-driven. It starts by collecting new data from various sources using IoT devices, creating detailed customer profiles, and making your workplace, processes, and products smart. This process allows all the previously invisible aspects of your organisation traceable so that they can be observed, analysed, and optimised. 

Today, the numerous technologies that we have at our disposal make this process easier and cheaper. Using low-cost sensors, cloud computing, and connected devices, you can capture data consistently across different products, processes, and workplaces. And apart from offline customer touchpoints, data can also be collected through online or social media interactions. 

When you start datafying your business, the best way to do it is to start small, with simple processes or products that are relatively easy to datafy. Once those are done, you can focus on more complex processes. 

3. Analysing data

Once you’ve datafied all your processes and stored them in a distributed manner, the next step is to analyse the data using advanced analytics. In some cases, the data collected can prove to be the factor that gives businesses a competitive advantage. And thus, it is essential to use descriptive and predictive analysis to help understand the environment and context of the organisation. 

Use the data collected to create comprehensive analytical models using different data mining techniques. While decision-makers and stakeholders will primarily be involved in this process, it’s a good practice to share the insights with more members of your organisation. This leads to empowerment, which will result in a shift in power and culture. Also, in data-driven businesses, the real decision-makers are employees who either face the customer on a day-to-day basis or are directly involved in creating the service/product. And so, involving those employees can prove to be beneficial. 

4. Automating your organisation

Once you’ve analysed your data, the final step is to automate your business processes, augmenting all your customer touchpoints with smart technologies. In addition, you can also better understand the overview of collected and used data. And this is where AI can be invaluable. The end goal of the digital transformation process is to generate reliable and unbiased AI that results in increased productivity and efficiency. 

Once you have successfully automated your processes, it will directly affect the future of work in your business. AI will be more used to augment workers than to substitute your employees. 

To wrap it up

To digitally transform your organisation, you should datafy your processes, collect and distribute data, analyse it using various data mining techniques, and use artificially intelligent algorithms to automate your business processes. This will transform your business into one that is ready for the data-driven future ahead. 

The post How to Digitally Transform Your Business appeared first on Green Prophet.

Artificial sweeteners, promoted as aids to weight loss and diabetes prevention, could actually speed up the development of glucose intolerance and metabolic disease like diabetes; and they do it in a surprising way: by changing the composition and function of the gut microbiota – the substantial population of bacteria residing in our intestines.

These findings, the results of experiments in mice and humans, were published today in Nature.

Among other things, says Dr. Eran Elinav of the Weizmann Institute’s Immunology Department, who led this research together with Prof. Eran Segal of Computer Science and Applied Mathematics Department, the widespread use of artificial sweeteners in drinks and food may be contributing to the obesity and diabetes epidemic that is sweeping much of the world.

For years researchers have been puzzling over the fact that non-caloric artificial sweeteners do not seem to assist in weight loss, and some studies have suggested they may even have an opposite effect.

Graduate student Jotham Suez in Elinav’s lab, who led the study, collaborated with graduate students Tal Korem and David Zeevi in Segal’s lab and Gili Zilberman-Shapira in Elinav’s lab in discovering that artificial sweeteners, even though they do not contain sugar, nonetheless have a direct effect on the body’s ability to utilize glucose. Glucose intolerance – generally thought to occur when the body cannot cope with large amounts of sugar in the diet – is the first step on the path to metabolic syndrome and adult-onset diabetes.

The scientists gave mice water laced with the three most commonly used artificial sweeteners – in the equivalent amounts to those permitted by the FDA. These mice developed glucose intolerance, as compared to mice that drank water, or even sugar water. Repeating the experiment with different types of mice and different doses of the sweeteners produced the same results – these substances were somehow inducing glucose intolerance.

Next, the researchers investigated a hypothesis that the gut microbiota are involved in this phenomenon. They thought the bacteria might do this by reacting to new substances like artificial sweeteners, which the body itself may not recognize as “food.” Indeed, artificial sweeteners are not absorbed in the gastrointestinal tract, but in passing through they encounter trillions of the bacteria in the gut microbiota.

The researchers treated mice with antibiotics to eradicate many of their gut bacteria; this resulted in a full reversal of the artificial sweeteners’ effects on glucose metabolism. Next, they transferred the microbiota from mice that consumed artificial sweeteners to ‘germ-free’ mice – resulting in a complete transmission of the glucose intolerance into the recipient mice.

This, in itself, was conclusive proof that changes to the gut bacteria are directly responsible for the harmful effects to their host’s metabolism. The group even found that incubating the microbiota outside the body, together with artificial sweeteners, was sufficient to induce glucose intolerance in the sterile mice. A detailed characterization of the microbiota in these mice revealed profound changes to their bacterial populations, including new microbial functions that are known to infer a propensity to obesity, diabetes and complications of these problems in both mice and humans.

Does the human microbiome function in the same way? Elinav and Segal had a means to test this as well. As a first step, they looked at data collected from their Personalized Nutrition Project (www.personalnutrition.org), the largest human trial to date to look at the connection between nutrition and microbiota. Here, they uncovered a significant association between self-reported consumption of artificial sweeteners, personal configurations of gut bacteria and the propensity for glucose intolerance.

They next conducted a controlled experiment, asking a group of volunteers who did not generally eat or drink artificially sweetened foods to consume them for a week and then undergo tests of their glucose levels as well as their gut microbiota compositions.

The findings showed that many – but not all – of the volunteers had begun to develop glucose intolerance after just one week of artificial sweetener consumption. The composition of their gut microbiota explained the difference: The researchers discovered two different populations of human gut bacteria – one that induced glucose intolerance when exposed to the sweeteners, the second that had no effect either way.

Elinav believes that certain bacteria in the guts of those who developed glucose intolerance reacted to the chemical sweeteners by secreting substances that then provoked an inflammatory response similar to sugar overdose, promoting changes in the body’s ability to utilize sugar.

Segal says: “The results of our experiments highlight the importance of personalized medicine and nutrition to our overall health. We believe that an integrated analysis of individualized ‘big data’ from our genome, microbiome and dietary habits could transform our ability to understand how foods and nutritional supplements affect a person’s health and risk of disease.”

Elinav adds: “Our relationship with our own individual mix of gut bacteria is a huge factor in determining how the food we eat affects us. Especially intriguing is the link between use of artificial sweeteners – through the bacteria in our guts – to a tendency to develop the very disorders they were designed to prevent; this calls for reassessment of today’s massive, unsupervised consumption of these substances.”

Image of artificial sweetener from Shutterstock

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