3 arrows

Get a Free AWS Exam retake using promo code DOUBLESHOT


Four Steps to Improve Business Efficiency with Data Science

Anuradha Nandan | Monday, June 29, 2020

Data analytics can help your organization achieve new levels of performance and efficiency. Still, it’s hard to know where to start — especially if you don’t have a well-defined strategy or team focused on data science, which is now the underlying factor of top business...

Four Steps to Improve Business Efficiency with Data Science

Data analytics can help your organization achieve new levels of performance and efficiency. Still, it’s hard to know where to start — especially if you don’t have a well-defined strategy or team focused on data science, which is now the underlying factor of top business initiatives, including improving customer experiences and operational efficiency. However, IT leaders tasked with building the right team quickly realize that there’s stiff competition when it comes to recruiting. Thirty-five percent of IT leaders anticipate difficulty in finding qualified data science and analytics candidates throughout 2020 and beyond.

By starting with a clear vision and following a few prescriptive steps, you can both simplify and accelerate your journey toward becoming a data-driven enterprise. With the right roadmap and training in place, you can create exciting new career paths within your company, build a highly capable team from your pool of existing employees and meet your data goals with a competent and capable data science team.

Step 1: Start With a Clear Vision for Data Science

Data can tell you almost anything about your company, allowing you to make strategic decisions based on facts and proven methodologies — if you have people who know how to get the most out of your data. But before you can build and train your team, you’ll want to have a clear understanding of your goals and intent. What leading technology brand does your organization use and does it include AWS, IBM, VMware, SAP, Oracle, or others? 

Not only will a clear vision help inform your decision making, but it can also help you sell the business value of analytics to management and your board of directors. You can start crafting your vision and mapping your path toward implementation by answering questions such as:

  • What do you hope to achieve through data analytics?
  • What types of data do you already have? Where is it located and how does it support your goals?
  • How does your data science vision support your corporate vision, strategy and roadmap?
  • Does your current company culture support data-driven decision making? What can you do to improve the awareness and adoption of your data strategy?
  • Who are your current data champions and how can they help propel your vision forward?
  • What skills and tools does your data science team currently have?

Step 2: Identify Your Skills Gaps

Data science is a broad, umbrella term. While your data science team may have professionals who carry the title of “data scientist,” you’ll also employ data analysts, data engineers and database administrators. You may also want to enlist data platform specialistsbusiness intelligence experts and software developers who specialize in big data artificial intelligence and machine learning. Many data visionary companies have appointed a Chief Data Officer who can advocate the priority, vision and scope of your data science practices across all branches of the enterprise.

All these distinct roles require individual skill sets that complement one another to make up a fully productive data science organization. With a clear vision in mind, you can better analyze your existing team to see which skills you already have in house. From there, you can identify skills gaps and develop a plan for recruitment, hiring, onboarding and ongoing training and enablement of your data science team.

Step 3: Develop Enticing Career Paths to Attract and Retain Top Talent

Even if you’re starting small with a dedicated team working toward a proof of concept, you’re laying the groundwork for an expansive and exciting new branch of your organization. Part of a successful data strategy includes setting defined career progression paths for those who are a part of your data organization from the start and those who you attract as you grow.

Developing enticing career paths starts with understanding the professional interests of your team members and pairing those interests with job titles. Data science is a multifaceted discipline that requires a range of skill sets, from being able visualize the big picture to drilling down into the more granular details. You’ll have both visionary programmers who aren’t afraid to experiment and statisticians who rely on rigorous and repeatable processes. Each data science role that makes up your organizational structure requires a unique mindset, specific talents and certain core capabilities.

Within each of your data science roles, (including data scientist, analyst, engineer, developer, etc.), you’ll also want to determine the characteristics and responsibilities for entry-level, mid-range and expert titles. Your career paths should also allow for lateral movement to accommodate changing interests and growing awareness in your cross-functional teams. Ultimately, you’re creating career paths that minimize or eliminate your skills gaps and allow each team member to progress in their careers, continue learning and lend greater value to your organization.

Step 4: Empower Your Team Through Training

In many ways, data science is still catching up with established disciplines like computer science, mathematics and strategic business planning. This evolution means continual new discovery and fundamental changes to data science strategies, tools and platforms. Planned, ongoing and compulsory training for your data science team will help keep your business ahead of industry changes and enable you to adapt to new and better ways of gaining insight from your data.

By partnering with a leader in technology training, you can develop a program that educates your teams in support of your strategic goals and is in line with your company’s career progression paths. Look for a training partner that offers a wide variety of individual courses and certification paths for data science fundamentals, along with courses that cover the tools and platforms you plan to use. Also, your provider should offer various training delivery methods to fit your team’s schedule and needs, including instructor-led in-person and online courses, self-paced training, on-demand training and both group and individual options. Removing barriers to success is critical, so make sure your teams receive the training they need, right when they need it.

Download the ExitCertified whitepaper, “How to Ensure Maximum ROI on IT Initiatives with the Right Learning Partner" to discover how a learning partner can support you in all stages of planning, implementing, and enabling high-quality training that aligns business and IT initiatives. 

 ExitCertified offers expert courses and vendor-approved training across a breadth of data science topics, tools, platforms and disciplines. With ExitCertified, you can develop a corporate training program that helps you close your skill gaps and start reaching your data goals. Whether you choose instructor-led group training in person or through the iMVP® online platform, 24/7 on-demand training or self-paced learning through learning subscriptions, your employees will benefit from expert curriculum developed by leaders in the data science field.

Talk to us today about how the right corporate training program can help you empower your team to deliver game-changing insights from data. 

Contact Us 1-800-803-3948
Contact Us
FAQ Get immediate answers to our most frequently asked qestions. View FAQs arrow_forward