Development Agile

Why Data Science Project Management is crucial to securing the future of your business

In today’s business world, there is an increasing demand for artificial intelligence (AI)-driven solutions. From manufacturing, to retail, to healthcare, to finance, and in many other industries, artificial intelligence is rapidly transforming how organizations operate. However, these approaches are hampered by the application of inappropriate project management methods such as known approaches from software development (e.g. the agile method Scrum) and the associated expectations.

Over the past several decades, agile project management has become the standard approach to developing and delivering software iteratively throughout the life of the project. At each delivery, the software is updated to reflect feedback, mostly commonly from customers.

Unfortunately, this methodology is not optimal in terms of data science projects. The agile methods are driven by time and expectation, and this can not be conveniently applied in the data science projects exposed to probabilities and the volume and velocity of big data. With the growing demand for the integration of artificial intelligence into a variety of business processes, it is imperative to use more specialized project management approaches. Let’s look at two models that quickly replace the traditional agile approach.


CRISP-DM stands for “cross-industry standard process for data mining,” and was developed in the 1990s as a method for mining data with the goal of generating business insights. It offers six stages for using data science to solve business problems:

  1. Understand the business, its challenges, and its data needs
  2. Understand the data, including the data types and the information they contain
  3. Prepare the data so that a model can process and analyze them
  4. Create a model that transforms the raw data into actionable insights
  5. Evaluate the model for accuracy
  6. Test the model against new data

This approach to project management seems fairly straightforward, but may be even a little rudimentary for today’s complex data sets.

Growing out of CRISP-DM, ASUM-DM was developed by IBM to provide a more “complete implementation lifecycle.” The “analytics solutions unified method [for data mining]” takes the steps involved in CRISP-DM a bit further, offering additional and sometimes parallel cycles that incorporates more testing and versioning, similar to the agile model.

The Big Advantage of Data Science

Agile project management is great for its ability to test, gather feedback, and make adjustments as needed. But in today’s data science world, it simply doesn’t scale. Data science, however, can take into account far more diverse data sets, analyze them to produce actionable insights. In other words, having a huge database of customer interactions isn’t enough. It’s also often necessary to look at data produced by processes as well as data from e.g. external sources or other real time data, both of which would not fit into an agile project management process. It is increasingly important for businesses to take a bigger view that includes the context involved in customers’ actions.

Is it time to future-proof your business?

In today’s hyper-connected world, data is the new lifeblood for businesses, which need every advantage they can get. Artificial intelligence, when applied to a specific process or business problem, can create new efficiencies that businesses can leverage in order to ensure both long-term sustainability and profitability. But in order to arrive at this point, businesses need to throw-out older models for problem solving and look to more refined and complex models such as those offered in data science.

If you’re ready to find ways to future-proof your business contact us. Data can help you identify areas where artificial intelligence can have the biggest impact on your business and develop a custom solution to overcome even the most difficult challenges.

Best regards,
Florian Horn

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