The Ideal Data Science Strategy Toolkit/data-science-insights/the-ideal-data-science-strategy-toolkit

The Ideal Data Science Strategy Toolkit

The Ideal Data Science Strategy Toolkit

Data is ubiquitous and acquiring data is not a challenging task anymore. To make data useful though, is something most often companies fail to come up with.
However, a major problem most often businesses face today is not how to gather more data, but how to use the data they already own. Another big question is, how are you planning to use data science if you’re looking to extract actionable insights from your data? Many organizations have now realized that their business success will depend on data capabilities. Not to mention, many companies across different sectors are looking to adopt the data-driven culture. Companies that rely on data expect a better financial performance, says a Harvard Business Review study. This is why companies need to formulate a data science strategy.
To answer this question in-depth, let us first define the type of goals and identify sources your organization needs to focus on.
Before developing a strategy as to how you need to use your data, you need to start by identifying larger goals. For instance, what the company wants to achieve? Every business’s end goal is to generate more leads or more revenue. This is why you need to have a tangible number or a clearly defined goal. And the key to achieving this is by ensuring you articulate your goals first, then later look at your data.

Developing a data science strategy toolkit

Once you’ve figured out the larger goals, it is time to build the data science strategy. Leaders in data science should start creating a model and framework that orchestrates business opportunities. They are:

Data-driven goals

Data offers multiple competitive advantages such as increased efficiencies, customer insights, time-saving, better targeting, personalization of our marketing efforts, and more. Therefore, you need to ask yourself, what are the measurable goals you’re looking to achieve and what data you should use. For instance, you could probably use data from your past campaigns and compare the user’s statistics to achieve a certain percentage of engagement growth in the upcoming months.

Ecosystem or infrastructure

The next step should be to identify your project ecosystem or infrastructure – the type of technology stack you’re going to use, do you want your data science team to build everything in-house or would it be more efficient to purchase an off the shelf product, how and where will you store your data? How do you wish to retrieve the actionable insights? Do you want it in the form of a report, or you’re looking for a dashboard system for you to re-use later?
Every answer to all these questions depends on your measurable goals.

The right AI process

This step involves the type of methods you wish to implement. How particular are you regarding transparency? Do you prefer using an explainable but less effective scripted solution or a black box solution? This decision will influence whether you’ll be able to use the right AI process or you’ll still be sticking to using the statistical inference.
Scripted solutions take a long time to code, but you can understand every element. Whereas AI solutions are tougher to explain. However, this is likely to change since the growth in explainable AI is expanding.


The organization needs to determine who will be responsible to deliver the end goal? If you’re looking for a better technical solution, you will require a better data scientist. Even marketers having good knowledge of the product can help steer these developers and data scientists toward the right direction. At times, it can be challenging to determine all the details or even impossible in certain cases. More importantly, if your solution relies only on automation and not statistics, figuring out the details can be difficult. At this stage, you need to ensure you have the right data science strategy in place.
However, if your budget permits, you can still hire an external expert who can figure out all the difficulties and give you a fresh approach to solve your problems.

In a nutshell

Data science is a new force driving businesses today. Every industry requires data to improve its business performance and provide better products for its customers. Building the right strategy are key to the success of an organization’s business.

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