It’s Time to Take Data Science to the Next Level

[RichardHatheway]

How to build a repeatable process for delivering nonstop data-driven insights

Data science refers to using data analytics, including artificial intelligence (AI) and machine learning (ML), to deliver insights that are buried in mountains of data. These advanced analytics applications can help you differentiate your business with intelligence that fuels business innovation, highlights new and better ways to serve customers, and uncovers efficiencies to drive down costs.

However, there is often a disconnect between the amount of data available and your organization’s ability to operationalize the applications that derive value from it. Moving models from discovery to production is still a last-mile challenge for many organizations. Consider the fact that 98% of IT leaders know that better access to data-driven insights will lead to higher profitability, better customer experiences, and increased revenue growth, while only 14% report having a process that consistently delivers successful projects.[1]

For many organizations, this disconnect is creating a widening gap in deriving insights from data. But this gap also represents an opportunity. If you can successfully industrialize data insights—by creating a defined, repeatable, and scalable process for operationalizing analytics applications—you can close the execution gap and spark exponential growth in analytical capacity and capability.

Industrializing data science evolves the process of operationalizing advanced analytics applications and moving from a one-off, high-touch, specialized endeavor to an assembly line, process-driven approach in much the same way Henry Ford revolutionized car manufacturing more than 100 years ago. When Ford industrialized his manufacturing process using specialized workers and automated conveyor belts, the time to build a car went from more than 12 hours to just over 1.5 hours. This automation and process innovation paved the way for mass production and lowered costs so more people could afford his automobiles.[2] It also changed manufacturing forever, along with the very fabric of our society.

Bringing the same industrialization mindset to data science democratizes data insights and transforms your business, putting you leaps and bounds ahead of the competition. Once up and running, your innovation factory will give you:

• Faster time to value

Provision development, test, and production environments in minutes and instantly onboard new data scientists with their preferred tools and languages without creating siloed development environments.

• Improved productivity

Data scientists can spend their valuable time building models and analyzing results rather than waiting for infrastructure, tools, and data.

• Reduced risk

Governance and security are baked into every step of the process. Automated, scalable, and repeatable processes are based on best practices to enhance success rates.

However, getting from one side of the data-to-insight gap to the other requires overcoming a number of challenges, as well as building best practices and repeatable, scalable processes throughout the data value chain. How do you get from where you are now to the other side of the data divide as directly as possible? Read the new ebook to take a deep dive into the challenges, success factors, and best practices for building your innovation factory.

Learn more:

• Build an innovation factory (ebook)
• Build your innovation factory (infographic)

[1] Forrester study commissioned by HPE, “Operationalize Machine Learning,” June 2020.

[2] History, “Ford’s assembly line starts rolling,” accessed September 2022.

Hewlett Packard Enterprise

HPE Ezmeral on LinkedIn | @HPE_Ezmeral on Twitter

@HPE_DevCom on Twitter