How Data Science is Driving Digital Transformation Now - part 2 Tips to Manage Data Science Projects

In last part we gone through various aspects on how Data Science is driving the Digital and business transformations, need of data science and people to manage it like data scientists

Let’s take a look at some good tips that will help us better deal with our Data science initiatives.

Technology and Business Strategy

Enterprises gain certain advantages in implementing modern, innovative technology. However, technology implementation and usage must reflect the strategic vision of the business. It becomes evident that companies must have a clear awareness of what they are trying to achieve with technology to ensure a positive outcome.

Skillful Staff

It is obvious that entering into this new digitalisation era will bring us new jobs and the digital transformation requires completely new skill sets. Therefore, companies should put their efforts into training existing staff members to master these skills and/or recruiting new people with different competencies. Both methodologies are important because as you bring new initiatives to drive business, you cannot keep replacing the current lot.

Intelligent Approach

We need to try to sharpen our approach by focusing our intellect on further innovation and data-driven decision making that can give us a potent edge.  We must develop predictive models based on new business intelligence that requires the right combination of human and artificial intelligence.

Data Operating Platform

Data processing and storage should be good and preferably cloud-based. It is recognised that businesses see an improvement in performance and security after switching to the cloud. Earlier I use to host my analytics in private cloud, however over the period, as data grow and more intelligent ecosystems we need to plug in like AI, moved to cloud native. Data Science teams, as well as Enterprises that had chosen a cloud-based platform, don’t need to spend time on administration for the infrastructure and instead can benefit from effortlessly-scalable computational resources.

Applications

Data science covers a very wide field and therefore its applications are countless. Various sectors such as New media (now i am working to bring this to traditional media as well) banking, transportation, e-commerce, healthcare, and many others are using data science to improve their products and services. By choosing the right platform, we can make our data science projects as effective as possible. For example, a platform that gives data scientists the tools to build scalable projects that can handle massive datasets, put them far ahead of their competitors.

Security Considerations

Given the challenges and risks, we need to make some considerations concerning cybersecurity and data protection that remain the primary concerns of CISOs when businesses decide to move to the cloud.

For a CISO, information security and privacy are of paramount importance, though from the point of view of a data scientist, security means automating version control so that you never risk forgetting to commit. Indeed, the ability to manage data versioning alongside code versioning ensures the science is always reproducible.

My advice to fellow CIO's and others in ICT is, take security as part of design stage rather than plugin later. very important aspect for any Digital initiatives you take in.

Summary

Any organization can be innovative, fast to deliver, and engaged with each new venture. Driving implementation at a rapid pace is increasingly accessible as long as businesses embrace and benefit from the opportunities of the digital age. With all the possibilities, tools, and empowering, innovative technologies coming out every day, organisations need to sharpen their business opportunities based on the right data-driven business models.

Data-driven decision making is more effective and realistic as the decisions are based on actual information and not assumptions. One of the important aspects of data visualisation is that it does not just take into consideration past data, but also anticipates the future based on various holistic factors.

For this reason, data science must be a fundamental component of any digital transformation effort.