The Data Science Landscape - dummies

The Data Science Landscape

By Lillian Pierson

Organizations and their leaders are still grappling with how to best use big data and data science. Most of them know that advanced analytics is positioned to bring a tremendous competitive edge to their organization, but very few have any idea about the options that are available or the exact benefits that data science can deliver.

Here, you are introduced to the major data science solution alternatives and the benefits that a data science implementation can deliver.

Exploring data science solution-alternatives

When looking to implement data science across an organization, or even just across a department, three main approaches are available: You can build an in-house data science team, outsource the work to external data scientists, or use a cloud-based solution that can deliver the power of data analytics to professionals who have only a modest level of data literacy.

Building your own in-house team

Here are three options for building an in-house data science team:

  • Train existing employees. This is a lower-cost alternative. If you want to equip your organization with the power of data science and analytics,then data science training can transform existing staff into data-skilled, highly specialized subject-matter experts for your in-houseteam.

  • Train existing employees and hire some experts. Another good option is to train existing employees to do high-level data science tasks, and also bring on a few new hires to fulfill your more advanced data science problem-solving and strategy requirements.

  • Hire experts. Some organizations try to fill their requirements by hiring advanced data scientists or fresh graduates with degrees in data science. The problem with this approach is that there aren’t enough of these people to go around, and if you do find someone who’s willing to come onboard, he or she is going to have very high salary requirements.

    In addition to the math, statistics, and coding requirements, data scientists must also have a high level of subject matter expertise in the specific field where they’re working. That’s why it’s extraordinarily difficult to find these individuals. Until universities make data-literacy an integral part of every educational program, finding highly specialized and skilled data scientists to satisfy organizational requirements will be nearly impossible.

Outsourcing requirements to private data science consultants

Many organizations prefer to outsource their data science and analytics requirements to an outside expert. There are two general routes: Outsource for the development of a comprehensive data science strategy that serves your entire organization, or outsource for piecemeal, individual data science solutions to specific problems that arise, or have arisen, within your organization.

Outsourcing for comprehensive data science strategy development

If you want to build an advanced data science implementation for your organization, you can hire a private consultant to help you with a comprehensive strategy development.

This type of service is likely going to cost you, but you can receive tremendously valuable insights in return. A strategist will know about the options available to meet your requirements, as well as the benefits and drawbacks of each. With strategy in-hand and an on-call expert available to help you, you can much more easily navigate the task of building an internal team.

Outsourcing for data science solutions to specific problems

If you’re not prepared for the rather involved process of comprehensive strategy design and implementation, you have the option to contract smaller portions of work out to a private data science consultant. This spot-treatment approach could still deliver the benefits of data science without requiring you to reorganize the structure and financials of your entire organization.

Leveraging cloud-based platform solutions

Some have seen the explosion of big data and data science coming from a long way off. Although it’s still new to most, professionals and organizations in the know have been working fast and furious to prepare. A few organizations have expended great effort and expense to develop data science solutions that are accessible to all.

Cloud applications such as IBM’s Watson Analytics offers users code-free, automated data services — from cleanup and statistical modeling to analysis and data visualization. Although you still need to understand the statistical, mathematical, and substantive relevance of the data insights, applications such as Watson Analytics can deliver some powerful results without requiring users to know how to write code or scripts.

If you decide to use cloud-based platform solutions to help your organization reach its data science objectives, remember that you’ll still need in-house staff who are trained and skilled to design, run, and interpret the quantitative results from these platforms. The platform will not do away with the need for in-house training and data science expertise — it will merely augment your organization so that it can more readily achieve its objectives.

Identifying the obvious wins

Through this book, I hope to show you the power of data science and how you can use that power to more quickly reach your personal and professional goals. No matter the sector in which you work, acquiring data science skills can transform you into a more marketable professional. The following is just a small list of benefits that data science and analytics deliver across key industry sectors:

  • Benefits for corporations, small and medium-sized enterprises (SMEs), and e-commerce businesses: Production-costs optimization, sales maximization, marketing ROI increases, staff-productivity optimization, customer-churn reduction, customer lifetime-value increases, inventory requirements and sales predictions, pricing-model optimization, fraud detection, and logistics improvements

  • Benefits for governments: Business-process and staff-productivity optimization, management decision-support enhancements, finance and budget forecasting, expenditure tracking and optimization, and fraud detection

  • Benefits for academia: Resource-allocation improvements, student performance management improvements, drop-out reductions, business-process optimization, finance and budget forecasting, and recruitment ROI increases