Data Science is a powerful weapon for businesses to leverage to make better decisions, elevate operational efficiency, improve customer satisfaction, and increase revenue. However, according to an Accenture survey , only 32% of business leaders affirm that they have been able to extract value from the data at their disposal.

Adopting data science initiatives that deliver business outcomes requires a strategic approach, a commitment to data-driven decision-making, and expert advice along the way to adoption. Here are some steps to consider when implementing data science initiatives in your organization:

1. Define What Outcome You Are Looking For

The first step is to define what outcomes you are looking for. This means understanding what success looks like, how it will be measured, and who will be responsible for measuring it. It also means defining the timeframe in which to achieve that success.

For example, if you want to increase traffic on your website by 25% over the next six months, this could be broken down into several specific goals:

  • Increase the number of visitors per month by 10%.
  • Increase the number of repeat visitors by 2%.
  • Increase the average time spent on site per visit by 30 seconds.

The desired outcomes will drive the data strategies required to bring them about.

2. Document the Processes to be Automated and Measure Automation’s Impact

Data science is, at its core, about defining processes to deriving insights from data. To ensure that you are realizing the business outcomes you want from your data science initiatives, it is crucial to document the processes that need to be automated to derive those insights. This will help ensure that you’re reaping the benefits of a data-driven approach while making sure that you can measure the impact of your automation efforts.

If you don’t have the right processes documented, this is an excellent opportunity to start documenting them. If you already have documentation in place but haven’t been able to track how valid they been so far, it’s time for an audit. Take a look at what processes are being transformed and see if there are any areas where improvement can be made, or new opportunities can be identified.

3. Foster a Data-Driven Culture

A data-driven culture entails adopting a data-driven mindset and using technology that supports the development of this mindset while democratizing data science initiatives.
To that end, two key elements of such culture include:

  • Investing in data infrastructure: Ensure that you have the right tools and technologies to support your data science initiatives. This may include data storage and management systems, data analytics and visualization tools, ML- and AI-powered platforms, etc.
  • Implementing data science initiatives: Once you have a clear plan and the required infrastructure in place, you can begin implementing your data science initiatives. This may involve developing predictive models, creating dashboards and reports, or implementing algorithms to automate data-driven processes.

4. Adopt an Agile Approach to Data Science

This is an age where enterprises need speed as well as agility. The process that can be swiftly modified to start delivering outcomes quickly in tune with the changing conditions is more valuable than one that takes a while to turn around. This sets the tone for agile organizations where data scientists are expected to deliver results in short cycles (usually 2-4 weeks) with continuous feedback from stakeholders. This means that data scientists need to be able to move quickly and respond effectively to change.

Moreover, in an agile environment, teams work in sprints to focus on one problem and deliver a solution. This approach allows faster delivery of results which is vital for businesses trying to keep up with the evolving market conditions.

5. Use a Center of Excellence Framework

The Center of Excellence framework is a systematic way to standardize data science practices across teams, create an environment for collaboration, and bring forth the best practices.

A centralized team can help set data science standards and ensure that everyone follows them. This is particularly useful if the company has multiple teams leveraging data. In addition to setting standards, the centralized team can help share information about what works well and what doesn’t.

6. Empower Business Stakeholders

Data science teams and business stakeholders should work together to ensure effective business decision making with a focus on data-driven insights.

Data scientists have access to a wealth of information but might not always interpret the data in the most meaningful way for their business stakeholders. There is often a possibility of a disconnect between what the data shows and how it’s interpreted. This can lead to miscommunication between the two groups and confusion around priorities, resulting in missed opportunities and poor decisions.

To avoid this scenario, both parties need to work together on defining how and when data will be used in decision-making processes. This not only equips business stakeholders with the information and insights they need to make strategic decisions but also helps them articulate their needs in a way that can be translated into actionable insights.

7. Monitor and Evaluate

Regularly monitor and evaluate the results of your data science initiatives to ensure that they are delivering the desired business outcomes. This also helps you identify any challenges or obstacles in the process so that adjustments can be made as needed.

How Can Ellicium Help?

We’ve seen how data science initiatives can be central to the success of any business. However, without a cohesive adoption strategy , data science initiatives might never reach to fruition. The problem is compounded by the fact that implementing such a strategy and establishing a robust infrastructure to extract value from data is an extremely complex and intricate process and one that requires a lot of time and resources.

This is where Ellicium can help. As a part of our Data Science as a Service offering, we empower businesses by:

 

  • Developing custom data science use cases
  • Identifying the right data to utilize
  • Developing datasets to support the use cases
  • Creating algorithms to transform data into actionable insights
  • Developing on-premises or cloud infrastructures to drive data science initiatives

These services ensue substantial value to businesses, especially in terms of monetizing their data. All in all, we help you design and implement a strategy that delivers business outcomes – increased revenue, improved customer service and relationships, and reduced operational costs.

For more information, connect with experts at Ellicium today.