Why We’re Seeing an Uptick in Data Science Architecting

From analyzing data-driven behavior to transforming the retail experience and leveraging powerful cloud computing tools, data science will take the lead for many organizations, empowering them to stay competitive, says Dustin Milberg, field CTO cloud services at InterVision.

As part of the disruption that the COVID-19 pandemic has brought on the world, many businesses are seeking new approaches to survive, corner their competition, and empower their futures. Given the information age that we live in, where data plays a major role in monetization, it comes as no surprise that so many organizations are finding new ways to use data. Organizations have landed on ways to improve their collection and use of datasets to increase revenue and their positions in the market. The continual proliferation of data and its sources are growing exponentially, making an already substantial requirement even more laborious.

Investing in a Strategic Service Provider

There is no shortage of chatter about specific data technologies like artificial intelligence, automation, machine learning, and data lakes, which all fall under the broader category of data science. From examining data-driven behavior paths to transform the future of travel to leveraging data analytics to enable customized streaming of OTT programming, investments will pivot to data science to stay competitive. For IT groups unable to invest directly, look for an increase in strategic service provider  (SSP) partnerships to extract the most value from data-science initiatives.

There are many benefits to your business collaborating with a strategic service providerOpens a new window . IT teams are facing the demand to reduce their company’s technical debt and create innovative ways to leverage data and drive growth while facing the threats of cybercriminals, natural disasters, and more. SSPs are able to help overworked and understaffed IT teams by delivering IT solutions and services that achieve both short-term wins and capabilities to deliver long-term strategies. With SSPs, IT teams can find success early on and find new ways to work efficiently and effectively as their company builds for the future.

If you choose to work with a strategic service provider, there are a few important things you should consider. First of all, does the provider align well with your organization’s culture? Then, you should ensure the provider has a strategy for your success, along with measurable key performance indicators. Following that, your organization needs to check if the provider has a deep bench of expertise in data science areas with adjacent services. Lastly, you should determine if the provider understands the consumption model and how to optimize cloud computing workloads. If you and your strategic service provider are on the same page for these four things, then it’s a smart investment.

Learn More: How Modern Data Centers Can Leverage Object Migration Software

Using Data for the Future

Organizing and keeping track of all datasets has increasingly become a challenge for businesses, which has given rise to new solutions for data storage, such as data warehouses and data lakes. The objective of these varying solutions is to prevent data silos where information has sprawled into hard-to-reach locations. It must be readily accessible yet secure. On top of this, analyzing that data for useful information once it enters those storage solutions is another challenge. Here, having strict tagging policies will go a long way in making your datasets actionable, even after they’ve been archived. 

Over the past year, businesses have learned the importance of preparing for the unexpected. With everyone working remote and many facing financial challenges during the pandemic, organizations have seen the need for connecting data and planning proactively, which will result in becoming more digital resilient. This digital resilience will occur due to businesses choosing the right work managementOpens a new window approach for them, allowing their leaders to have visibility on anything and everything that goes on across the organization.

Data science also requires the right talent at the helm to make it successful: data scientists. These individuals perform the difficult work of tailoring analytics tools, analyzing the datasets to ensure your machine learning engine and predictive analytics are running correctly, and making sense of the results afterward. This skillset is hard to find. While your organization may not intend to build a center of excellence around data science, it is a clear core competency of the right strategic service provider. Yet, another reason why engaging with a third-party expert is appealing. 

Data science is a part of the tech industry that is on the rise and in high demand. It’s become so essential for IT professionals, making it the number-one jobOpens a new window in America for four straight years, according to Glassdoor. In the world of data science, Python is dominating; a Kaggle survey reports that 93% of data scientistsOpens a new window use Python as the language for any of their projects.

As you look to the future and assess your best path forward, keep in mind that data science will undoubtedly play an important role in the competitiveness of a modern business. What steps do you need to take to architect and iterate for data science solutions? If you’re not ready to use data science currently, how could your data collection practices improve now so that when the time comes, it’s not a siloed or sprawled nightmare? The answers to these questions will help enable your future stance. 

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